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International Journal
of
Learning, Teaching
And
Educational Research
p-ISSN:
1694-2493
e-ISSN:
1694-2116
IJLTER.ORG
Vol.21 No.6
International Journal of Learning, Teaching and Educational Research
(IJLTER)
Vol. 21, No. 6 (June 2022)
Print version: 1694-2493
Online version: 1694-2116
IJLTER
International Journal of Learning, Teaching and Educational Research (IJLTER)
Vol. 21, No. 6
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Foreword
We are very happy to publish this issue of the International Journal of
Learning, Teaching and Educational Research.
The International Journal of Learning, Teaching and Educational
Research is a peer-reviewed open-access journal committed to
publishing high-quality articles in the field of education. Submissions
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We are grateful to the editor-in-chief, members of the Editorial Board
and the reviewers for accepting only high quality articles in this issue.
We seize this opportunity to thank them for their great collaboration.
The Editorial Board is composed of renowned people from across the
world. Each paper is reviewed by at least two blind reviewers.
We will endeavour to ensure the reputation and quality of this journal
with this issue.
Editors of the June 2022 Issue
VOLUME 21 NUMBER 6 June 2022
Table of Contents
Effectiveness of Virtual Laboratories in Teaching and Learning Biology: A Review of Literature .............................1
Celine Byukusenge, Florien Nsanganwimana, Albert Paulo Tarmo
Mindset and Levels of Conceptual Understanding in the Problem-Solving of Preservice Mathematics Teachers in
an Online Learning Environment.......................................................................................................................................18
Ma Luisa Mariano-Dolesh, Leila M. Collantes, Edwin D. Ibanez, Jupeth T. Pentang
Exploring the Teacher Professional Development Activities: Perspectives of Nigerian High School Teachers ......34
Oluwatoyin Ayodele Ajani
Mental Health and Wellbeing of Secondary School Teachers in Malaysia ...................................................................50
Kee Pau, Aslina Binti Ahmad, Hsin-Ya Tang, Ahmad Jazimin Bin Jusoh, Asma Perveen, Kong Kwoi Tat
Structure, Activities and Teacher Development in the Philippine Science Teachers’ Community of Practice ........ 71
Rhea F. Confesor, Rosario M. Belmi
Physics Course Content of University Physics Education Programme as Reference to Content Distribution of
JUPEB and WAEC Syllabi.................................................................................................................................................... 90
Olalekan T. Badmus, Abiodun A. Bada, Loyiso C. Jita
Rasch Validation of Instrument Measuring Gen-Z Science, Technology, Engineering, and Mathematics (STEM)
Application in Teaching during the Pandemic ............................................................................................................... 104
Hilman Qudratuddarsi, Riyan Hidayat, Raja Lailatul Zuraida binti Raja Maamor Shah, Nurihan Nasir, Muh Khairul
Wajedi Imami, Rusdi bin Mat Nor
The Level of Sports Participation and Academic Success among Malaysian Student-Athletes............................... 122
Jorrye Jakiwa, Siti Azilah Atan, Mohd Syrinaz Azli, Shahrulfadly Rustam, Norhafizah Hamzah, Aizuddin Amri Zainuddin
‘Publish or Perish’: a Transformation of Professional Value in Creating Literate Academics in the 21st Century138
Asep Kurnia Jayadinata, Kama Abdul Hakam, Tatang Muhtar, Tedi Supriyadi, J. Julia
E-learning Outcomes during the COVID-19 Pandemic.................................................................................................160
Sang Tang My, Hung Nguyen Tien, Ha Tang My, Thang Le Quoc
Saudi Teachers’ Attitudes towards using Online Learning for Young Children during the Covid-19 Pandemic 178
Ahlam A. Alghamdi
A Survey of Teachers’ Perceptions of a Learning Portfolio in Lesotho Classrooms .................................................. 194
Julia Mathabo Chere-Masopha
Purposeful Collaboration through Professional Learning Communities: Teacher Educators’ Challenges............ 210
Carolina Botha, Carisma Nel
Trends of Educational Technology (EdTech): Students’ Perceptions of Technology to Improve the Quality of
Islamic Higher Education in Indonesia............................................................................................................................ 226
Susanto ., Evi Muafiah, Ayu Desrani, Apri Wardana Ritonga, Arif Rahman Hakim
High School Students’ Mathematics Anxiety: Discouragement, Abuse, Fear, and Dilemma Induced through
Adults’ Verbal Behaviour .................................................................................................................................................. 247
Boj Bahadur Budhathoki, Bed Raj Acharya, Shashidhar Belbase, Mukunda Prakash Kshetree, Bishnu Khanal, Ram Krishna
Panthi
Entrepreneurship Education in Ghana: A Case Study of Teachers’ Experiences....................................................... 270
R J (Nico) Botha, M Obeng-Koranteng
Enhancing Students’ Attitudes in Learning 3-Dimension Geometry using GeoGebra............................................. 286
Marie Sagesse Uwurukundo, Jean Francois Maniraho, Michael Tusiime
Pre-Service Teachers' Perspectives towards the Use of GammaTutor in Teaching Physical Sciences in South
African Secondary Schools ................................................................................................................................................ 304
Sakyiwaa Boateng, Jogymol Kalariparampil Alex, Folake Modupe Adelabu, Thamsanqa Sihele, Vuyokazi Momoti
Continuing Professional Development of the Teacher Education Faculty among Philippine State Universities and
Colleges................................................................................................................................................................................ 324
Ninez B. Tulo, Jiyoung Lee
1
©Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License (CC BY-NC-ND 4.0).
International Journal of Learning, Teaching and Educational Research
Vol. 21, No. 6, pp. 1-17, June 2022
https://doi.org/10.26803/ijlter.21.6.1
Received Mar 3, 2022; Revised May 22, 2022; Accepted Jun 22, 2022
Effectiveness of Virtual Laboratories in Teaching
and Learning Biology: A Review of Literature
Céline Byukusenge
African Centre of Excellence for Innovative Teaching and Learning Mathematics
and Science (ACEITLMS), University of Rwanda College of Education (URCE),
Kayonza, Rwamagana, Rwanda
Florien Nsanganwimana
University of Rwanda College of Education (URCE), Kayonza,
Rwamagana, Rwanda
Albert Paulo Tarmo
Educational Psychology and Curriculum Studies, School of Education,
University of Dar es Salaam, Dar es Salaam, Tanzania
Abstract. Scholars have debated whether virtual laboratories are
educationally effective tools and if they should be continuously
developed. In this paper, we comprehensively review literature about the
effectiveness of virtual labs in teaching and learning biology to identify
the topics often taught and the linked learning outcomes. We used Google
Scholar, ERIC, and Web of Science electronic databases to access journal
articles and conference proceeding papers. Through a systematic
analysis, we obtained 26 articles solely related to virtual lab use in biology
education. The overall findings from the reviewed literature indicated
that virtual laboratories are often used on topics that seem abstract. These
include cell and molecular biology topics, followed by microbiology,
genetics, and other practical topics such as dissection and biotechnology.
This review study revealed that virtual labs are effective as they improve
students’ conceptual understanding, laboratory or practical skills, and
motivation and attitudes towards biology. We recommend the use of
virtual labs in teaching as a means of actively involving students in safer
and more cost-effective scientific inquiry.
Keywords: Biology topics; computer simulations; learning outcomes;
virtual laboratories/labs
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1. Introduction
1.1 Background
Information and communication technology is increasingly penetrating almost all
domains of human life, including education. In addition, with the current global
trend of achieving twenty-first century learning skills, where digital literacy is one
of the core goals, there is an increasing, understandable desire to bring more
educational technologies into the classroom (Dakhi et al., 2020; Smetana & Bell,
2012; Tarbutton, 2018). Globally, researchers and practitioners agree that
educational technology can transform the learning process by providing teachers
and students with access to relevant resources when integrated into teaching.
However, to be successful, educational technology should enhance the
achievement of learning objectives (Griffin, 2003), because effective technology
should enable students to achieve critical thinking by creating a shift from
memorizing factual knowledge to understanding principles and applications.
Like any other science subject, the teaching of biology inevitably requires
laboratory exercises as a part of the practical skills acquisition process (Borgerding
et al., 2013). Indeed, most biology topics heavily rely on practical activities,
especially in laboratories (Cavanagh et al., 2005; Çimer, 2012; Vijapurkar et al.,
2014). In addition, research has shown that laboratory activities can potentially
develop students’ intellectual abilities, such as critical thinking, scientific inquiry,
and practical skills. For instance, Hofstein and Mamlok-Naaman (2007) revealed
that science cannot be significant to students without practical experiences in the
school laboratory. When students have no access to laboratory activities and
experiences, they often meet with difficulties in the learning of biology, especially
in molecular biology topics (Boulay et al., 2010; Öztap et al., 2003; Sammet &
Dreesmann, 2017; Tibell & Rundgren, 2009).
Literature has shown that technology can provide students with laboratory
experience and enhance learning (Keller & Keller, 2005). However, the question
to be asked is which kind of technology can provide students with authentic
scientific practice and help them move from memorization to a deeper
understanding of concepts and applications. Research has shown that using
inquiry-based and learner-centered technologies that allow students to
manipulate and observe scientific phenomena (Flick & Bell, 2000; Sivin et al., 2000)
bring about a deeper understanding of concepts and applications. Virtual
laboratories, commonly called virtual labs, meet the criteria in this context.
Virtual lab technologies were proposed by the National Science Foundation’s
(NSF) task force to upgrade the state of STEM education as a dynamic response
to the sustainable preparation of the population for complex global challenges in
the twenty-first century (Borgman et al., 2008). Researchers have shown that
virtual labs could help make science concepts in general and biology in particular
more concrete (Olympiou et al., 2013) and meaningful for students without
requiring complex and costly equipment (Elangovan & Ismail, 2014; Makransky
et al., 2019; Marbach-Ad et al., 2008).
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Several pedagogical advantages have been highlighted regarding virtual lab use
in education. For instance, by using virtual labs, teachers can easily explain
complex theoretical concepts through a visual and immersive experience that can
make it simpler for students to understand the subject (Smetana & Bell, 2012).
With virtual labs, students try various experiments in risk–free environments
without fear of damaging equipment. In addition, students can conduct the same
experiment multiple times to ensure an understanding of the concept. Virtual labs
allow teachers to capture students’ attention and ensure their engagement and
motivation (Babateen, 2011). Furthermore, virtual labs help students to learn at
their own pace as they can prepare and perform laboratory experiments at any
time and place. With virtual lab technology, teachers and students can explore
topics that would otherwise be unworkable in conventional classes (Smetana &
Bell, 2012).
Radhamani et al. (2014) and Pearson and Kudzai (2015) emphasized the need for
virtual labs in teaching biology, especially in developing countries. They argued
that, generally, science education in developing countries faces many limitations.
These include shortage of laboratory equipment and reagents, space and time
constraints, insufficient laboratory protocol, inadequate technical support, and
safety, among other limitations. According to Radhamani et al. (2014), virtual labs
are asset tools to mitigate the challenges of insufficient laboratory equipment
needed in teaching biology topics such as biotechnology. This is despite some
drawbacks of virtual labs, such as students not being able to feel, smell, or touch
as in a physical laboratory.
While physical laboratories are absent or not fully equipped in many schools due
to the high costs of their equipment and maintenance, virtual labs have been
affirmed to lessen financial constraints related to laboratory equipment, space,
and maintenance (Fisher et al., 2012). These potential advantages have triggered
research interest, and a good number of empirical studies have been conducted
about the effectiveness of virtual laboratories (Breakey et al., 2008; Dyrberg et al.,
2017; Muhamad et al., 2010, 2012; Pope et al., 2017; Radhamani et al., 2014; Ray et
al., 2012; Triola & Holloway, 2011).
Along this vein, several review studies on the effect of virtual laboratories in
teaching sciences have been carried out (Brinson, 2015; De Jong et al., 2013; Ma &
Nickerson, 2006; Smetana & Bell, 2012; Udin et al., 2020). However, most reviews
only included laboratory practices of many other disciplines, such as physics,
chemistry, and engineering, with few review studies about the effectiveness of
virtual laboratories in teaching and learning biology (Udin et al., 2020). There is a
need to know for which topics of biology virtual labs are more useful and what
outcomes are brought about by virtual labs in the teaching and learning of
biology. Therefore, we assume that this study will shed light on the effectiveness
of virtual labs and in which preferred topics teachers are called to use the virtual
labs. This relates especially to those biology topics which seem difficult to be
taught by teachers and those which are too hard to understand for students
because they are too abstract. The following specific questions guide this literature
review:
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1. In which topics of biology are virtual laboratories the most useful?
2. What learning outcomes are best achieved using virtual laboratories in
biology?
1.2 Theoretical Context
The use of virtual laboratories in teaching and learning is based on David Kolb’s
(1984) experiential learning theory, which is rooted in the constructivist approach
and John Dewey’s work (Ouyang & Stanley, 2014). Around 1938, Dewey showed
that no learning happens without practice and the active involvement of students.
Kolb advocated and applied Dewey’s concept of “learning by doing”, believing
that learning occurs through cognitive and experiential learning (Kolb & Kolb,
2005). The core of experiential learning theory is the individual learner’s
participation and experiences (Ouyang & Stanley, 2014). The application of virtual
labs in teaching ensures students’ active learning (Evans et al., 2004). The use of
virtual labs allows learners to experiment with immediate feedback and
interactivity (Dyrberg et al., 2017; Tan & Waugh, 2013). Thus, virtual labs help
students to learn by doing and to become more engaged in their studies
(Gallagher et al., 2005; Marchevsky et al., 2003).
2. Methodology
We applied preferred reporting items for systematic reviews and meta-analyses
(PRISMA) principles and guidelines in our review (Moher et al., 2009). PRISMA
guidelines assist researchers in conducting transparent and comprehensive
systematic review reporting. These guidelines help researchers define research
strategies, eligibility criteria, the selection process, and the data collection process.
2.1. Literature Search
We used an open federated search in this review study to find relevant articles
from trusted databases. This type of search involves searching various electronic
databases for information relevant to the review study. We used certain keywords
to search and retrieve articles related to our study. These included “biology
laboratory”, “virtual laboratory in teaching biology”, “virtual labs and biology
topics”, “biology education and virtual laboratory”, “virtual and physical
laboratory”, “virtual lab and real lab”, and “effectiveness of virtual labs in biology
education”. We used trusted electronic databases such as Google Scholar, ERIC,
and Web of Science to access reliable articles and conference proceedings.
2.2 Inclusion and Exclusion Criteria
Using a systematic selection process and the elimination of duplicates, the first
stage of searching yielded 161 papers. Manual filtering was applied based on how
an article is relevant to our study. In selecting the relevant articles for inclusion in
the review, we screened the titles and abstracts of all recorded articles. We used
several inclusion and exclusion criteria to filter irrelevant articles (Table 1).
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Table 1: Inclusion and exclusion criteria used to select relevant studies
Inclusion criteria Exclusion criteria
Empirical studies in peer-reviewed
journals, and conference proceedings
Reviews in non-peer-reviewed journals
Virtual labs used for biology education - Virtual lab development procedures,
design, or architecture
- Virtual labs used for medical biology
Articles published in English Articles that are not in English
The screening of titles and abstracts yielded 38 publications. The publications
were further subjected to screening by checking their full-text content. The articles
that focused only on biology virtual lab development procedures, design, or
architecture without any relation to teaching biology were excluded. In this
regard, 12 publications were filtered out. Eventually, we gathered 26 studies
relevant to our review study, and each study was recorded to categorize
information for further analysis (see Table 2 and Figure 1). The PRISMA diagram
in Figure 1 shows the selection process. The obtained articles are dated from 2002
to 2019
Figure 1: PRISMA diagram of the selection process of the reviewed studies
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3. Results and Discussion
3.1. The Use of Virtual Laboratories in Teaching Biology Topics
In response to the first research question, we present in Table 2 the biology topics
in which virtual laboratories are most commonly used for effective teaching. We
also present the related learning outcomes that are most commonly enhanced by
the use of virtual labs.
Table 2. Biology topics in which virtual labs are used and related learning outcomes
SN Study Biology topic Measured learning outcome
1 Akhigbe and
Ogufere (2019)
Genetics Student attitudes and academic
achievement in genetics
2 Akpan and
Strayer (2010)
Frog dissection Actual dissection practices and
attitudes towards dissection
3 Breakey et al.
(2008)
Genetics Understanding of experimental
genetics procedures
4 Collier et al.
(2012)
Histology Content mastery and time
management
5 Diwakar et al.
(2011)
Biotechnology (No learning outcomes were
identified)
6 Dyrberg et al.
(2017)
Microbiology and
pharmaceutical
toxicology
Enhanced student positive attitudes,
motivation, and self-efficacy
7 Elangovan and
Ismail (2014)
Cell division Student conceptual understanding
of cell division
8 Flowers (2011) Various topics, most
of which are related to
cell and molecular
biology (DNA, cell
structure, enzyme-
controlled reaction,
cell reproduction)
Student perceptions of biology
9 Havlícková et
al. (2018)
Dissection Student motivation
10 Huppert et al.
(2002)
Microbiology Student science process skills and
academic achievement
11 Ismail et al.
(2016)
Microbiology
(dissolving pathogenic
bacteria)
Enhancing student scientific literacy
12 Kiboss et al.
(2006)
Cell division Conceptual understanding and
perceptions
13 Makransky et
al. (2016)
Microbiology Knowledge transfer and practical
skills
14 Makransky et
al. (2019)
Microbiology Student knowledge, motivation, and
self-efficacy in microbiology
15 Marbach et al.
(2008)
Molecular biology Enhanced student achievement
16 Meir et al.
(2005)
Introductory biology
(osmosis and
diffusion)
Student understanding of how these
processes work at a molecular level
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17 Muhamad et al.
(2012)
Cell division Student understanding of cell
division, specifically applications of
mitosis in cloning
18 Oser and Fraser
(2015)
Genetics Student perception of the learning
environment, attitudes towards the
topic, and achievement
19 Pope et al.
(2017)
Evolution Student understanding of natural
selection concepts
20 Radhamani et
al. (2014)
Biotechnology Enhanced student achievement
21 Shelden et al.
(2019)
Cell division Understanding of cell division
phases
22 Stuckey-Mickell
and Stuckey-
Danner (2007)
Introductory biology Enhanced student perceptions
23 Tan and Waugh
(2013)
Molecular biology Student conceptual understanding
and attitudes in molecular biology
24 Toth et al.
(2009)
DNA and gel
electrophoresis
Student understanding and
laboratory skills
25 White et al.
(2007)
Genetics Conceptual understanding
26 Whitworth et
al. (2018)
Enzyme kinetics Conceptual understanding
Table 2 displays the topics in which virtual labs were used and the learning
outcomes that were attained as a result of their use. The reviewed articles are
dated from 2002 to 2019. We did not find literature for the years 2020 to 2022. In
the reviewed studies, virtual labs were used to teach genetics, dissection,
microbiology, cell division, osmosis, DNA and gel electrophoresis, enzyme
kinetics, biotechnology, evolution, histology, and introduction to biology. Virtual
labs were used most frequently in teaching microbiology and cell division.
Moreover, some of the learning outcomes that were attained using virtual labs
included conceptual understanding, knowledge transfer, practical skills
acquisition, and enhanced positive attitudes, motivation, and self-efficacy among
students. The topics and learning outcomes are further described in the following
sections, respectively.
3.2. Topics in Which Virtual Labs are the Most Useful
We analyzed the reviewed studies to identify which biology topics were most
taught using virtual labs. Figure 2 shows the different topics that were facilitated
using virtual labs.
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Figure 2. Biology topics in which virtual labs were used as per the reviewed studies
It is not by coincidence that the identified topics in Figure 2 employ virtual
laboratories. The listed topics are perceived by both teachers and students to be
difficult, abstract, and daunting due to their complexity, difficulty to visualize,
and not being practicable in normal physical school laboratories. For instance,
before conducting their study on developing and implementing a scenario-based
biology virtual lab, Muhamad et al. (2012) carried out a preliminary investigation
of a survey type involving 72 students and 10 high school teachers. Their
investigation aimed to identify the biology topic that was most difficult to teach
and learn and to focus on developing a virtual lab for it. Their preliminary study
findings indicated cell division as the most difficult topic for both teachers and
students (Muhamad et al., 2010).
Tan and Waugh (2013) undertook research employing virtual reality simulations
in teaching and learning molecular biology in Singapore high schools. Teachers
claimed that the topic of molecular biology was challenging and difficult to teach.
They also indicated different complaints by students about teaching materials
used by their teachers, such as diagrams and 2D presentations, which do not
enable them to see DNA and protein molecules. Tan and Waugh (2013) argued
that before studying molecular biology by use of virtual reality simulations, it was
difficult for students to relate the structure and molecular interactions for cell
functioning. Radhamani et al. (2014) reported that after virtual lab classes, 44% of
the students who participated in their study scored 90%, with an average class
score of about 70% in the post-test evaluation. In the pre-test evaluation, the
majority of the students (88%) had scored below 70%.
Indeed, the topic to be taught with the use of virtual labs depends on the nature
of the experiment. For instance, considering the topic of dissection, this topic
raises many debates and disagreements regarding ethical issues among
researchers, educators, and animal rights activists. Virtual laboratories that dissect
animal specimens provide a viable alternative to real dissections and relieve
0
1
2
3
4
5
6
7
8
9
10
Number of studies
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ethics-related issues. Studies comparing the value of virtual frog dissections with
traditional dissections using real specimens have revealed mixed results,
however. Some supported that real dissections in the physical laboratory are
effective (Cross & Cross, 2004), while others agreed that the simulated dissections
are effective for improving students’ performance in the virtual laboratories
(Akpan & Strayer, 2010).
3.3. Learning Outcomes Enhanced by the Use of Virtual Laboratories
The learning outcomes identified in the reviewed studies were grouped into three
categories (Figure 3). These are: 1) knowledge and conceptual understanding;
2) laboratory skills, knowledge transfer, and self-efficacy in laboratory activities;
and 3) students’ motivation, perceptions, and attitudes towards biology and the
learning environment. Some of the reviewed studies assessed more than one of
the above learning outcomes. The total number of studies indicated in Figure 3
therefore exceed the number of reviewed studies. The overall findings indicated
that the learning outcomes varied, but in most studies, knowledge and conceptual
understanding were frequently assessed.
Figure 3: Learning outcomes identified in the reviewed studies
3.3.1 Knowledge and conceptual understanding
From our analysis, 21 out of the 26 reviewed studies reported that the use of
virtual labs enhances students’ conceptual understanding (Figure 3). Indeed,
virtual lab exercises have been proven essential for students to understand
biology concepts. Virtual labs present multiple opportunities for students to gain
access to learning resources easily, and to get enough time to do and repeat
activities, thereby nurturing deeper learning (Muhamad et al., 2012).
Furthermore, biology is a molecular science; most of its topics require
visualizations, videos, and illustrations for students to understand how processes
work at the molecular level (Evans et al., 2004; Muhamad et al., 2012). Many
studies have shown that virtual laboratories are effective, low-cost tools to
enhance students’ understanding of biology concepts. This is because they
provide students with visualizations of abstract concepts through animations,
simulations, and virtual practices of simulated laboratory experiments for some
21
8
5
0 5 10 15 20 25
Knowledge/ conceptual understanding
Laboratory skills, knowledge transfer, and self
efficacy
Motivation, perceptions, and attitudes
Learning outcome Number of reviewed studies
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topics, which could not be done even in normal classes (Akhigbe & Ogufere, 2019;
Collier et al., 2012; Makransky et al., 2016; Oser & Fraser, 2015; Špernjak & Šorgo,
2018; Tan & Waugh, 2013).
In the study conducted by Tan and Waugh (2013), students admitted that before
exposure to visualization exercises, molecular biology was a dry topic, too
abstract and daunting for them. This resulted in some of them giving up biology
altogether. Nonetheless, Tan and Waugh confirmed that after viewing the
animations and participating in the visualization exercises, the students
demonstrated increased interest, understanding, and engagement in the subject.
Whitworth et al. (2018) reported a varied use of simulations in laboratory activities
after seeing a significant increase in post-test scores of the experimental group of
students over the control group of students. The experimental group was taught
using standard lab instruction coupled with simulated lab instruction, while the
control group was taught with only standard lab instruction. The increased
post-test scores of the experimental group had an average standard deviation of
1.59. Based on their study results, Whitworth et al. (2018) concluded that
computer simulations improve students’ conceptual understanding of enzyme
kinetics.
Moreover, various studies have shown that virtual labs are adequate for
improving understanding of biology topics that are difficult to observe directly in
the classroom context (Collier et al., 2012; Pope et al., 2017; Radhamani et al., 2014).
For example, evolution by natural selection has been shown to be notoriously
difficult for students to understand, and its processes have been described as not
directly observable (Krist & Showsh, 2007; Nehm & Schonfeld, 2008; Plunkett &
Yampolsky, 2010). However, Pope et al. (2017) clearly showed that simulations of
natural phenomena are effective tools that support an active teaching approach to
help students overcome natural selection misconceptions.
3.3.2 Laboratory skills, knowledge transfer, and self-efficacy in laboratory activities
Eight out of the twenty-six reviewed studies indicated that virtual laboratories
enhance students’ laboratory skills, knowledge transfer, and self-efficacy
(Figure 3). These studies suggested that virtual laboratories are effective tools for
pre-lab preparation and transferring knowledge and skills from an idealized
environment into physical reality (Makransky et al., 2016). Research has affirmed
that for meaningful laboratory learning to occur, students should be prepared
before performing the required laboratory tasks (Jones & Edwards, 2010).
According to O’Brien and Cameron (2008), laboratory practices help students to
move from abstract to concrete settings. However, if students are not prepared,
they could experience stress and confusion during laboratory activities instead of
expected manipulative and process skills. The students become overloaded with
too much information about the assigned task and may become overwhelmed as
they try to handle new manipulative tasks as well as master new concepts
(Pogačnik & Cigić, 2006).
Virtual labs are crucial for the preparation of students before embarking on a
physical experiment. Researchers have affirmed that to perform the required
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practical tasks, science classes should blend real and virtual experiments so that
students acquire the skills necessary. Several of the reviewed studies suggested
the desirability of integrating hands-on laboratories with virtual ones and the
effectiveness of engaging in virtual experiences before the real, hands-on
investigation (Akpan & Strayer, 2010; Toth et al., 2009). In addition, other
researchers have indicated that students prepared using virtual labs do not waste
time on how to handle apparatus in organizing the experiment; rather, they focus
on testing hypotheses through practicing and making important observations
(Johnstone & Al-Shuaili, 2001). Prepared students begin the procedures faster and
ask questions on a higher level than those who are less or not prepared (Dyrberg
et al., 2017).
In their post-test, Akpan and Strayer (2010) discovered that students who engaged
first in simulated dissection outperformed their peers who only performed
conventional dissection. Similarly, Maldarelli et al. (2009) found that visual
demonstration of laboratory techniques via instructional videos before the actual
physical laboratory activity was sufficient to mediate significant increases in
knowledge, self-efficacy, and experience in basic biology laboratory procedures.
However, not surprisingly, some studies found that students believed that
traditional labs offer more effective pedagogical techniques in teaching them how
to use biology laboratory equipment than virtual labs (Flowers, 2011). Researchers
have also criticized virtual labs, claiming that they have limited potential for
teaching students how to handle specimens and perform techniques such as
fixing, staining, and thin sectioning (Scheckler, 2003). However, other scholars
have indicated that with simulations, students have opportunities to repeatedly
learn all steps of an experiment, enabling them to transfer knowledge and skills
gained from virtual learning to physical applications (Makransky et al., 2016).
3.3.3 Students’ motivation, perceptions, and attitudes towards biology and the learning
environment
In this study, 5 out of the 26 reviewed studies reported about virtual laboratories
as related to students’ motivation, perceptions, and attitudes towards biology and
the learning environment (Figure 3). According to these studies, virtual labs are
important for enhancing students’ attitudes, stimulating interest and enjoyment,
and motivating them to learn biology, improving their performance. Toth et al.
(2009) performed a study about myDNA by using virtual labs to show the
separation of DNA fragments. They found that students were happy to learn and
efficiently repeated experiments and studied the effects of the variables. In a
recent study, Akhigbe and Ogufere (2019) assessed the effect of computer
simulations on students’ attitudes towards biology, finding that computer
simulations improve students’ attitudes towards genetics. A significant
improvement in performance was seen with the students who were exposed to
the computer simulation instructional strategy over their counterparts who were
taught using traditional methodologies.
The majority of the reviewed studies revealed that students have positive
perceptions towards virtual labs. Stuckey-Mickell and Stuckey-Danner (2007)
made a contrary finding in their qualitative study analyzing open-ended
qualitative responses by students after completion of several virtual lab sessions
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in human biology. This allowed them to investigate how students perceive virtual
labs as compared to hands-on laboratory activities. They found that with virtual
labs, students lacked the enjoyment of student-teacher interaction and the ability
to ask questions and receive direct feedback from the instructor.
4. Conclusion and Recommendation
Based on the study’s findings, we conclude that virtual laboratories are commonly
effective in teaching difficult and abstract biology topics related to cell and
molecular biology. Furthermore, conceptual understanding is the learning
outcome most enhanced when using virtual labs. Studies have further affirmed
that virtual labs improve students’ motivation, self-efficacy, and attitudes towards
learning biology topics. Virtual laboratories deserve the attention of researchers,
teachers, and instructional designers due to their appealing nature as a means of
actively involving students in safer and more cost-effective scientific inquiry. We
suggest that future research assesses teachers’ preparedness to use virtual labs in
teaching and learning processes. The effectiveness of virtual labs, like any other
instructional tool, may be greatly influenced by how they are used in the
classroom. This study did not address the limitations of the virtual laboratory in
teaching and learning biology. Thus, we recommend further research into the
negative effects of using virtual laboratories in teaching and learning.
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©Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License (CC BY-NC-ND 4.0).
International Journal of Learning, Teaching and Educational Research
Vol. 21, No. 6, pp. 18-33, June 2022
https://doi.org/10.26803/ijlter.21.6.2
Received Mar 3, 2022; Revised May 29, 2022; Accepted Jun 22, 2022
Mindset and Levels of Conceptual Understanding in
the Problem-Solving of Preservice Mathematics
Teachers in an Online Learning
Environment
Ma Luisa Mariano-Dolesh
Distance, Open and Transnational University, Central Luzon State University
Science City of Muñoz, Nueva Ecija, Philippines
Leila M. Collantes
College of Education, Central Luzon State University
Science City of Muñoz, Nueva Ecija, Philippines
Edwin D. Ibañez
College of Science, Central Luzon State University
Science City of Muñoz, Nueva Ecija, Philippines
Jupeth T. Pentang*
College of Education, Western Philippines University
Puerto Princesa City, Philippines
Abstract. Mindset plays a vital role in tackling the barriers to improving
the preservice mathematics teachers’ (PMTs) conceptual understanding
of problem-solving. As the COVID-19 pandemic has continued to pose a
challenge, online learning has been adopted. This led this study to
determining the PMTs’ mindset and level of conceptual understanding in
problem-solving in an online learning environment utilising Google
Classroom and the Khan Academy. A quantitative research design was
employed specifically utilising a descriptive, comparative, and
correlational design. Forty-five PMTs were chosen through simple
random sampling and willingly took part in this study. The data was
gathered using validated and reliable questionnaires and problem-
solving tests. The data gathered was analysed using descriptive statistics,
analysis of variance, and simple linear regression. The results revealed
that the college admission test, specifically numerical proficiency,
influences a strong mindset and a higher level of conceptual
understanding in problem-solving. Additionally, this study shows that
mindset predicts the levels of conceptual understanding in problem-
*
Corresponding author: Jupeth T. Pentang, jupeth.pentang@wpu.edu.ph
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solving in an online environment where PMTs with a growth mindset
have the potential to solve math problems. The use of Google Classroom
and the Khan Academy to aid online instruction is useful in the
preparation of PMTs as future mathematics teachers and problem-
solvers. Further studies may be conducted to validate these reports and
to address the limitations of this study.
Keywords: conceptual understanding; growth mindset; mathematics
education; online learning; preservice teachers
1. Introduction
Future math teachers must be equipped with the right mindset and a full
understanding of problem-solving. Mindset and conceptual understanding have
a crucial role in the preparation of preservice mathematics teachers (PMTs). The
academic mindset is critical in deeper learning (Farrington, 2013) where
understanding the mindset of preservice teachers improves their morale as future
educators (Yazon et al., 2021). Sadly, preservice teachers have a mindset that they
cannot do mathematics (Cutler, 2020). Considering that a positive mindset is a
gateway to mathematical achievement (Sun, 2018) and problem-solving
performance (Pentang et al., 2021), an exploration of this matter is necessary to
guide the teacher educators in empowering the PMTs. Poor conceptual
understanding may also be a product of a negative mindset. Ibañez and Pentang
(2021) have reported this among preservice teachers in the Philippines.
Discovering ways to develop a strong mindset and conceptual understanding
among PMTs was disrupted by the occurrence of the novel coronavirus disease in
2019 (COVID-19). Nevertheless, it opened up opportunities for teacher education
institutions (TEIs) to explore alternative teaching and learning modalities.
TEIs in the locality suspended face-to-face classes and limited academic
exchanges to mitigate the public health effects of COVID-19 (Tan et al., 2021).
Institutions adopted a purely online modality while some blended it with self-
learning modules to aid the instructions which may have affected the mindset and
level of conceptual understanding among PMTs. Although online learning has
been configured under a wide variety of different formats over half a century, one
could say that COVID-19 has made educational institutions aware of the new
normal way of academic exchange. Given the challenges due to the pandemic’s
impact, experts in educational institutions have been forced to adopt remote
teaching strategies maximising online resources as a teaching-learning tool. As
online classrooms promote a healthy mindset and encourage learning motivation
(Bacsal et al., 2022; De Souza et al., 2021), TEIs have begun to adopt online
technology methods for disseminating the teaching-learning processes such as
Google Classroom and the Khan Academy.
On the other hand, educators who wish to improve their learning outcomes must
consider approaches to establish a growth mindset (Dimitriadis, 2015). A person
with a strong mindset shows grit, hard work, and perseverance. Embedded in
each of these beliefs, or mindsets, are networks of beliefs and assumptions that
shape how people approach learning (Tabrizi, 2020). In contrast, those who
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believe that intelligence is fixed tend to focus on judgment. They are more
concerned with proving that they are intelligent or concealing that they are not,
which means that they avoid circumstances in which they might fail or have to
work hard (Dweck, 2016). The faculty and staff require more than just
technological knowledge; they must also be fully prepared to apply instructional
approaches that improve the students’ online experiences (DeBrock et al., 2020;
De Souza et al., 2021). Thus, there is a need for teachers, including those in the
preservice, to assess their beliefs about intelligence. Their mindset will drive how
they teach and facilitate learning in the mathematics classroom.
Studies about mindset have not yet been fully explored, especially in the field of
mathematics education. It is noticeable that growth mindset research emerged
recently, less than ten years ago. Likewise, the conceptual understanding of
problem-solving in an online environment has not yet been examined. It will be
interesting to find out whether mindset has a connection with the level of
conceptual understanding in an online setup. Moreover, the research will likely
be compelling if the study is done in a group of preservice teachers who are taking
mathematics majors. Considering that these future teachers will probably teach
mathematics in the K-12 program in a few years (Bacsal et al., 2022; Domingo et
al., 2021; Ibañez & Pentang, 2021; Pentang et al., 2021), it would bring in great
benefits to the students, parents, and administrators if their mindset and levels of
conceptual understanding are found to be related.
As an academic institution that trains and prepares preservice teachers, Central
Luzon State University (CLSU) has been dramatically affected by the pandemic
due to the lockdown and school closures that started in March 2020. Online
resources are needed to address the unprecedented pandemic issues in the
teaching-learning process (Manca & Meluzzi, 2020; Pentang, 2021b). Given the
uncertainty of how long the pandemic lasts, online learning plays a vital role in
the continuity of teaching and learning (Bacsal et al., 2022). Google Classroom and
the Khan Academy was used to facilitate continuous learning despite the ongoing
closure and lockdown in schools, colleges, and universities. These scenarios have
compelling reasons to study the mindset and levels of conceptual understanding
in problem-solving in an online learning environment using readily free available
tools like Google Classroom and the Khan Academy in a mathematics classroom
at CLSU, specific to PMTs, who are deemed to be able to recuperate the status of
Philippine mathematics education.
Research Questions
1. What is the PMTs’ mindset when problem-solving in terms of growth and a
fixed mindset?
2. What is the PMTs’ levels of conceptual understanding when problem-solving
regarding best, partial, complete/incomplete, functional, and no
understanding?
3. Is there a significant difference in the PMTs’ mindset when problem-solving
when grouped according to socio-demographic characteristics?
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4. Is there a significant difference in the PMTs’ levels of conceptual
understanding of problem-solving when grouped according to socio-
demographic characteristics?
5. Do the PMTs’ mindsets significantly predict their conceptual understanding
of problem-solving?
2. Conceptual Framework
The inequalities in the Filipino students’ mathematical literacy can be attributed
to their unawareness of a growth mindset and lack of conceptual understanding,
both of which are linked to their teachers’ means of imparting knowledge and
skills in mathematics. With the unprecedented move to online learning brought
about by the pandemic, mathematics educators have been obligated to employ
online learning management systems such as Google Classroom with the Khan
Academy to train and prepare future maths teachers who are deemed able to
address the mathematics illiteracy among young Filipinos. It is an opportunity to
assess the growth mindset and conceptual understanding of problem-solving of
the preservice mathematics teachers (PMTs). The Khan Academy existed prior to
the pandemic but was not commonly used in formal mathematics instruction.
The PMTs’ mindsets can be influenced by what they believe about their academic
ability. Intelligence may be strengthened by a growth mindset (Dweck, 2016). A
person with a growth mindset knows that intelligence may be attained through
hard work and the assistance of others (Romero, 2015). Knowing a student’s
mindset will assist a teacher in developing techniques to promote learning
(Tabrizi, 2020). Growth mindset techniques enable the students to engage in risk-
taking activities (Hennessey, 2019). Thus, it is vital to consider the right mindset
when pursuing academic success in mathematics, especially in relation to
problem-solving. The PMTs’ mindset may be found to be helpful in problem-
solving activities with the aid of the Khan Academy.
PMT's conceptual understanding of problem-solving also has implications for
mathematics education. Conceptual understanding denotes a comprehensive and
functional knowledge of mathematical notions (National Research Council, 2001).
Conceptual understanding is critical to solving a problem and understanding why
the algorithms and approaches used work. Conceptual understanding, in which
learners grasp ideas in a transferable manner, enables them to apply what they
learn in class across domains (Moser & Chen, 2016). Problem-solving and deep
conceptual understanding is demonstrated when a student decides how to solve
a problem (Ibañez & Pentang, 2021; Pentang et al., 2021). The PMTs should be able
to monitor their process and judge whether the procedure is the right method to
answer the question or if a new way is needed (Pentang, 2021a; Schoenfeld, 1989).
Through the Khan Academy, it is deemed that the PMTs’ conceptual
understanding will be estimated.
The socio-demographic characteristics such as sex, number of siblings, birth order,
family monthly income, father’s and mother’s educational attainment, and CAT
Numerical Proficiency, are essential factors to consider when determining the
PMTs’ mindset and level of conceptual understanding. Considering that both
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mindset and conceptual understanding are essential in mathematics education,
this study resolves the gap in the literature where no exploration has established
the influence of socio-demographic characteristics in relation to the PMTs mindset
and conceptual understanding of problem-solving as well as to establish whether
mindset is a predictor of the PMT’s conceptual understanding. The study also
conceptualised the vital role of online learning in problem-solving through the
use of Google Classroom and the Khan Academy (Figure 1).
Figure 1: Conceptual Framework of the Study
3. Methodology
3.1. Research Design
This study employed a quantitative research design combining descriptive,
comparative, and regression methods to address the research questions and
conceptual framework of the study (Magulod et al., 2021). The descriptive
analysis addressed the first two research questions which described the
participants’ mindset and their level of conceptual understanding of problem-
solving in an online learning environment. Additionally, the comparative analysis
answered the third and fourth research questions which distinguished between
the socio-demographic characteristic differences in the participants’ mindset and
level of conceptual understanding, respectively. Moreover, the regression
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analysis answered the fifth question which showed whether the PMT’s mindset
predicts their conceptual understanding of problem-solving.
3.2. Participants and Sampling Procedure
The participants of the study were preservice mathematics teachers (third-year
Bachelor of Secondary Education major in Mathematics students) from Central
Luzon State University. The study targeted respondents who had taken
mathematics college courses and who were currently enrolled in Problem-solving,
Mathematical Investigation, and Modelling in their first semester of the school
year 2020-2021. The simple random sampling employed drew 45 participants
(Table 1).
Table 1: Participants’ socio-demographic characteristics (n = 45)
Socio-Demographic Characteristics Frequency Percentage
Sex
Male 14 31.11
Female 31 68.89
Number of Siblings
0 - 2 10 22.22
3 - 5 31 68.89
6 and above 4 8.89
Birth Order
Last-born (Youngest) 12 26.67
Middle-born 21 46.67
First-born (Eldest) 12 26.67
Family Monthly Income
Less than ₱11,690 34 75.56
Between ₱11,690 to ₱23,380 9 20.00
Between ₱23,381 to ₱46,761 2 4.44
Father’s Educational
Attainment
Did not finish Elementary 7 15.56
Elementary Graduate 7 15.56
High School Graduate 26 57.78
College Graduate 5 11.11
Mother’s Educational
Attainment
Elementary Undergraduate 2 4.44
Elementary Graduate 6 13.33
High School Graduate 30 66.67
College Graduate 7 15.56
CAT Numerical
Proficiency
Below Average 8 17.78
Average 24 53.33
Above Average 13 28.89
3.3. Research Instrument
The instrument utilised in this study was a survey questionnaire (for Part I and
Part II) and a problem-solving test (for Part III). Part I determined the socio-
demographic characteristics of the participants. Part II focused on the
participants’ mindset following the example of Dweck (2016). It consisted of two
subscales: Entity Self Beliefs (items number 1 to 4) and Incremental Self Beliefs
(items number 5 to 8). The entity or fixed mindset items were reverse coded so
then the students who answered strongly disagree for these items showed
agreement with the growth mindset. Higher scores for this subscale showed
agreement with the incremental or growth mindset items. Part III was aimed at
the participants’ levels of conceptual understanding of problem-solving in terms
of their best understanding, partial understanding, incomplete understanding,
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functional misconception, and no understanding. The levels were determined
based on Jensen and Finley’s (1995) theory. The problems provided focused on
the following topics: expressions in multiple variables, systems of equations,
graph labels and scales, quadratics, and exponential graphs. These problems were
among the difficult items included in the work of Bacsal et al. (2022), Domingo et
al. (2021), Ibañez and Pentang (2021), and Pentang et al. (2021) in their studies on
mathematics problems concerning elementary preservice teachers in the same
institution. The research instrument was pilot tested which demonstrated a high
internal consistency (Cronbach’s alpha = 0.891).
3.4. Data Gathering Procedures
The researchers secured approval and consent from the institution and the
participants, respectively. Upon approval, the course professor assisted the
researchers in gathering the data. At the start of the class, the participants
familiarised themselves with the course expectations of the online learning
environment. The participants completed an online survey about their socio-
demographic characteristics and mindset towards problem-solving. In the
following meetings in the first week, the course professor facilitated discussions
on mathematical investigation, developing critical thinking and problem-solving
skills, as well as math problem-solving techniques and strategies. Examples of
how to solve different mathematics problems were presented which served as a
review of the PMTs’ prior knowledge regarding their mathematics courses.
The researchers oriented the participants of the Khan Academy online resource in
the first meeting of the second week of class. Given how the participants have
prior knowledge of the mathematics concepts from previous years, the Khan
Academy platform offered them an opportunity to practice mathematical skills
repeatedly to master the concepts. It also allowed them to track their progress as
it provided instant feedback. Thus, the participants could fill in the gaps in their
understanding by watching the related videos and getting hints or moving ahead.
During the two weeks of the class meetings, the students independently practiced
their problem-solving skills. The PMTs continued to do the practice exercises and
watch videos, if necessary. In the next two weeks of the classes, the students
answered the problem-solving questions in Google Classroom through Google
Forms. Each problem set had four multiple-choice questions. The students wrote
the solutions and explanations to their chosen answers in the multiple-choice area
for each item question. After a month of online learning, the researchers gathered
the data on the number of times each participant tried to answer the given five
sets of problems to achieve mastery learning using the Khan Academy.
3.5. Data Analysis
Descriptive statistics such as the mean and standard deviation were utilised to
determine the PMTs’ mindset regarding the presence of a growth mindset or
absence of a growth mindset, equivalently a fixed mindset, whereas frequency
count and percentage were used to describe the PMTs’ level of conceptual
understanding of problem-solving in an online environment. Besides this, a series
of Analysis of Variance tests were employed to distinguish between the significant
differences in the PMTs’ (a) mindset and (b) conceptual understanding in
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problem-solving when grouped according to their socio-demographic
characteristics. Follow-up post hoc analysis was conducted using the Scheffe test.
Furthermore, simple linear regression was utilised to find out whether the PMTs’
mindset was able to predict their level of conceptual understanding of problem-
solving in an online environment.
4. Results and Discussion
4.1. PMTs’ Mindset
The study found alarming results where the PMTs recorded a weak growth
mindset (Mean = 3.98, SD = 0.16). The PMTs have limited their perspective
regarding their intelligence and ability to do problem-solving. Still, the Khan
Academy online intervention showed that the PMTs performed the exercises
several times to reach the mastery level. As Table 2 reflects, the PMTs have a
strong growth mindset regarding the time and effort needed to improve
themselves. This demonstrates the PMTs' readiness to maximise their resources,
learn from their mistakes, and accept challenges, as they consider failure as a
chance to learn (Boaler, 2022; Dweck, 2016). Also, the PMTs seemed determined
and persevering when it came to accomplishing whatever they set their minds to.
Hence, the PMTs showed that they are most likely to demonstrate the
characteristics of people with a growth mindset, such as hard work, perseverance,
seeking help from others, and learning from feedback (Boaler, 2022; Dweck, 2016;
Wilkins, 2014).
There is still a need to cultivate a growth mindset among the PMTs. The PMTs’
growth mindset will be vital when addressing the poor status of mathematics
education in the Philippines. Several online resources relevant to mathematics
instructions may be adopted to fully prepare prospective math teachers. With “the
teacher’s crucial role in facilitating and monitoring the student’s development”
(Agayon et al., 2022), this weak growth mindset may be replicated in the PMTs’
students. Thus, the institution may provide ample training and activities to
strengthen the PMTs’ growth mindset. In line with De Souza et al. (2021) and
Pentang (2021b), the course professors concerned may further utilise several
online teaching-learning tools and integrate available technology to communicate
effective instructions.
Table 2: PMTs’ mindset
Parameters Mean SD Description
*1. I don’t think I can do much to increase my intelligence. 3.84 1.26 WGM
*2. I can learn new things but I can’t change my basic
intelligence.
3.76 1.28 WGM
*3. My intelligence is something about me that I can’t
change very much.
3.98 1.29 WGM
*4. To be honest, I don’t think I can change how intelligent
I am.
3.93 1.25 WGM
5. With enough time and effort, I think I could
significantly improve my intelligence level.
5.24 0.98 SGM
6. I believe I can always substantially improve my
intelligence.
4.89 0.71 AGM
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7. Regardless of my current intelligence level, I think I can
change it quite a bit.
4.80 0.50 AGM
8. I believe I can change my basic intelligence level
considerably over time.
4.87 0.69 AGM
Pooled Mean 3.98 0.16 WGM
Note: 5.16–6.00 = Strong Growth Mindset (SGM) *Reversely Coded
4.33–5.15 = Average Growth Mindset (AGM)
3.50–4.32 = Weak Growth Mindset (WGM)
2.67–3.49 = Weak Fixed Mindset (WFM)
1.84–2.66 = Average Fixed Mindset (AFM)
1.00–1.83 = Strong Fixed Mindset (SFM)
4.2. PMTs’ Level of Conceptual Understanding
Most (40 out of 45) PMTs recorded their best conceptual understanding in
problem-solving (Table 3). This shows that the PMTs have prior knowledge of the
concepts and mastered the skills needed in problem-solving, which opposes the
work of Ibañez and Pentang (2021) and Pentang et al. (2021) who revealed that
the majority of the preservice teachers have functional misconceptions and an
incomplete understanding of problem-solving. This result approves the effective
use of Google Classroom with the Khan Academy as employed by the PMTs’
professors where the institution they belong to has led to the standard of being
one of the best universities in Asia. The PMTs have shown their ability to impart
knowledge and skills in mathematical problem-solving to their future students.
Meanwhile, five PMTs had an incomplete to partial understanding, which can be
attributed to a lack of contextual comprehension of the mathematical topics
(Domingo et al., 2021; Pentang, 2021a; Pentang et al., 2021). This unwanted result
may infer that the PMTs are not yet ready for the challenge to empower young
Filipinos in their mathematics courses. Since partial understanding hampers the
students’ understanding of the subsequent mathematical knowledge (Shockey &
Pindiprolu, 2015), there is a need for an intervention to facilitate the preparation
of the PMTs as math teachers. Other online-based platforms and resources may
be utilised in the teaching-learning process to improve the PMTs’ conceptual
understanding as well as to effectively strengthen their growth mindset in
mathematics.
Table 3: PMTs’ level of conceptual understanding
Levels Frequency (n = 45) Percentage
Best Understanding 40 88.89
Partial Understanding 4 8.89
Incomplete Understanding 1 2.22
Functional Misconception 0 0
No Understanding 0 0
4.3. Mindset in Problem-Solving When Grouped According to Socio-
Demographic Characteristics
ANOVA found there to be a significant difference in the PMTs’ mindset in terms
of the CAT numerical proficiency of the PMTs, F(2,42) = 1.002, p < 0.05 (Table 4).
PMTs with an above-average CAT numerical proficiency (Mean = 4.430, SD =
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0.139) tended to have a stronger growth mindset in relation to problem-solving
compared to the PMTs with an average (Mean = 3.973, SD = 0.155) and below-
average (Mean = 3.937, SD = 0.197) CAT numerical proficiency. This means that
numerical proficiency can influence mindset in relation to problem-solving.
Overall, the study results suggested that there is no statistical evidence to say that
there is a significant difference between the PMTs’ mindsets when grouped
according to their socio-demographic characteristics except for their CAT
Numerical Proficiency.
The results can be related to the work of Boaler (2022) and Bower (2017) where
people who have a growth mindset directly impact how they face academic
challenges, including college examinations. However, this finding contradicts Li
and Bates (2020) where admission test scores throughout the transition from high
school to college were not found to be connected to a growth mindset. When
establishing the PMTs' mindset, it would be beneficial to focus more on their
academic profile, such as college admission test scores. The PMTs’ high school
background may be included, and a stringent retention policy in the mathematics
teacher education program may be implemented.
Table 4: Socio-demographic characteristic differences in relation to the PMTs’
mindset towards problem-solving
Socio-Demographic Characteristics Mean SD df F p
Sex
Male 3.946 0.137
1,43 -1.044 0.302
Female 4.000 0.168
Number of Siblings
0 - 2 4.000 0.150
2,42 0.496 0.613
3 - 5 3.989 0.162
6 and above 3.916 0.176
Birth Order
Youngest 3.923 0.148
2,42 1.297 0.284
Middle 4.015 0.153
Eldest 3.983 0.178
Monthly Family Income
Less than ₱11,690 3.890 0.152
2,42 0.409 0.667
Between ₱11,690 to ₱23,380 3.972 0.186
Between ₱23,381 to ₱46,761 4.083 0.235
Father’s Educational Attainment
Did not finish Elementary 4.023 0.133
3,41 0.537 0.219
Elementary Graduate 3.964 0.249
High School Graduate 4.003 0.127
College Graduate 3.850 0.170
Mother’s Educational Attainment
Did not finish Elementary 4.000 0.235
3,41 0.058 0.981
Elementary Graduate 4.000 0.166
High School Graduate 3.983 0.165
College Graduate 3.964 0.143
CAT Numerical Proficiency
Below Average 3.609b 0.197 2,42 1.002 0.037
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Average 3.903b 0.155
Above Average 4.430a 0.137
Note: Means with the same subscript do not differ using Scheffe post hoc analysis.
4.4. Socio-Demographic Differences in the PMTs’ Conceptual Understanding
of Problem-Solving
ANOVA found there to be a significant difference in the PMTs’ conceptual
understanding of problem-solving when grouped according to CAT Numerical
Proficiency, F(2,42) = 3.464, p < 0.05 (Table 5). The post hoc analysis using Scheffe
showed that PMTs with an above-average CAT Numerical Proficiency (Mean =
3.792, SD = 0.238) tended to have higher conceptual understanding of problem-
solving compared to the PMTs with an average (Mean = 3.644, SD = 0.423) and
below-average (Mean = 3.306, SD = 0.545) CAT numerical proficiency. This
suggests that there is no significant difference between the PMTs’ conceptual
understanding of problem-solving in an online environment when grouped
according to the socio-demographic characteristics, except according to their CAT
Numerical Proficiency.
College admissions tests have a long track record of bringing value to higher
education institutions by giving a predictive value of student success in entry-
level college courses. This conforms to the work of Allen and Bond (2001),
Mengash (2020), Montalbo et al. (2018), and Tesema (2014) but opposes Laus
(2021). The college admission test is indeed a good measure for admitting
potential preservice teachers. However, the institution may opt to accept those
with a higher numerical proficiency to ensure that the PMTs are ready not only in
their college preparation but also for the board exam and their anticipated
teaching career. A strict admission policy may be implemented considering other
backgrounds such as their high school grade point average and national
achievement test results.
Table 5: Socio-demographic characteristic differences in relation to the PMTs’
conceptual understanding of problem-solving
Socio-Demographic Characteristics Mean SD df F p
Sex
Male 3.739 0.208
1,43 1.490 0.229
Female 3.571 0.494
Number of Siblings
0 - 2 3.783 0.130
2,42 0.989 0.380
3 - 5 3.565 0.503
6 and above 3.700 0.127
Birth Order
Youngest 3.691 0.270
2,42 2.115 0.133
Middle 3.478 0.554
Eldest 3.800 0.126
Monthly Family Income
Less than ₱11,690 3.576 0.475
2,42 0.862 0.429
Between ₱11,690 to ₱23,380 3.750 0.208
Between ₱23,381 to ₱46,761
3.850 0.212
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Father’s Educational Attainment
Did not finish Elementary 3.521 0.655
3,41 0.237 0.870
Elementary Graduate 3.707 0.302
High School Graduate 3.617 0.439
College Graduate 3.680 0.148
Mother’s Educational Attainment
Did not finish Elementary 3.875 0.354
3,41 0.258 0.855
Elementary Graduate 3.575 0.194
High School Graduate 3.610 0.472
College Graduate 3.650 0.475
CAT Numerical Proficiency
Below Average 3.306b 0.545
2,42 3.464 0.041
Average 3.444b 0.423
Above Average 3.792a 0.238
Note: Means with the same subscript do not differ using the Scheffe post hoc analysis.
4.5. Mindset as a Predictor of the PMTs’ Conceptual Understanding of Problem-
solving
A simple linear regression analysis was performed to determine whether the
PMTs’ mindset predicts their conceptual understanding of problem-solving in an
online learning environment. Table 6 shows that the model is significant, R2 =
0.515, Adjusted R2 = 0.407, F(1,43) = 4.781, p < 0.05, indicating that PMTs who have
a growth mindset tend to have higher conceptual understanding of problem-
solving. The coefficient of determination (R2) means that about 51.5% of the
variance in the PMTs’ levels of conceptual understanding in problem-solving in
an online learning environment is explained or accounted for by their mindset.
Similar to Hennessey (2019), the results show that a growth mindset is associated
with better educational outcomes. The study also agrees that an individual with a
growth mindset is inspired by mastery goals, finds inspiration in others’ success,
and learns from feedback (Wilkins, 2014). This inspiration and reflection is
cultivated in an online learning environment. Thus, the growth mindset must be
instilled among PMTs while they are in their formative years in the teacher
education program. This measure will be helpful as part of encouraging a full
understanding of problem-solving.
The results further prove that people who have a growth mindset accomplish
much (Boaler, 2022) as the PMTs pursue becoming excellent math teachers.
However, this study is contrary to the research conducted at the same institution
concerning elementary preservice teachers. Although the preservice teachers try
to develop a positive disposition, they find it hard to learn mathematics (Ibañez
& Pentang, 2021). Even preservice teachers who have a growth mindset toward
mathematics do not show a full conceptual understanding when solving problems
(Pentang et al., 2021). The study still needs validation due to the limited sample
size.
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Table 6: Simple linear regression analysis of the PMTs’ conceptual understanding in
problem-solving as the criterion with mindset as the predictor
Model
Unstandardised
Coefficients
Standardised
Coefficients t-value p-value
B Std. Error Beta
Constant 2.365 1.729 -1.983 0.055
Mindset 0.273 0.420 0.558 4.490 0.049
Note: R2 = 0.515, Adjusted R2 = 0.407, F(1,43) = 4.781, p < 0.05
5. Conclusion and Recommendations
The PMTs have to develop a strong growth mindset which is necessary for them
as future teachers. The PMTs’ preparedness to teach mathematics to young
Filipinos cannot be assured with a fixed mindset. To foster a growth mindset
among the PMTs, this may be integrated into the Psychology Course that the
preservice teachers are taking. The PMTs with a growth mindset are more likely
to know that academic success is no accident – it is related to learning, studying,
hard work, perseverance, sacrifice, and love of what you are doing or learning to
do. Additionally, the inclusion of growth mindset activities in the Mathematics
Education Courses would be beneficial to the PMTs. This may result in more
awareness that intelligence can be developed. This may lead to a stronger growth
mindset among the PMTs who will shape the younger generation’s minds in the
upcoming K-12 program.
The PMTs attained the expected level of conceptual understanding in problem-
solving. The PMTs showed a mastery of skills in mathematical problem-solving
due to their strong academic background combined with the online intervention
via the Khan Academy activities. Nevertheless, it is noteworthy that there are still
a handful of them who have gaps in their conceptual understanding of problem-
solving. It is good to advocate the use of an open-source platform like the Khan
Academy to enhance the PMTs’ conceptual understanding. They are likely to be
motivated to have mastery skills through independent learning. It is also a useful
intervention for those who exhibit a partial or incomplete understanding of the
mathematics concepts.
Since the PMTs with a higher CAT Numerical Proficiency tend to have a stronger
growth mindset and higher conceptual understanding of problem-solving, it is
proposed that the college admission test is used in the admission of potential PMT
applicants. Besides this, mindset predicts the level of conceptual understanding
in problem-solving in an online environment. With the use of online resources
through Google Classroom and the Khan Academy, it is profitable to develop and
implement online mathematics lessons that incorporate a growth mindset and
conceptual understanding.
The continuous use of online resources (e.g., lesson videos and practice exercises)
via the Khan Academy even in the post-pandemic time is highly recommended
even after limited face-to-face classes are implemented. Online resources are
beneficial for the PMTs’ growth mindset and conceptual understanding of
mathematical problem-solving. This may also help the PMTs to prepare for the
board examinations and their future teaching career. With the limitations posed
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by the current study, further research on online learning may be looked to,
considering a larger sample size and the adoption of similar variables and
methods to validate this report. Other online learning tools such as maths
applications and software as well as academic and non-academic factors that
possibly influence the mindset and conceptual understanding may also be
considered.
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IJLTER.ORG Vol 21 No 6 June 2022

  • 1. International Journal of Learning, Teaching And Educational Research p-ISSN: 1694-2493 e-ISSN: 1694-2116 IJLTER.ORG Vol.21 No.6
  • 2. International Journal of Learning, Teaching and Educational Research (IJLTER) Vol. 21, No. 6 (June 2022) Print version: 1694-2493 Online version: 1694-2116 IJLTER International Journal of Learning, Teaching and Educational Research (IJLTER) Vol. 21, No. 6 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically those of translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying machines or similar means, and storage in data banks. Society for Research and Knowledge Management
  • 3. International Journal of Learning, Teaching and Educational Research The International Journal of Learning, Teaching and Educational Research is a peer-reviewed open-access journal which has been established for the dissemination of state-of-the-art knowledge in the fields of learning, teaching and educational research. Aims and Objectives The main objective of this journal is to provide a platform for educators, teachers, trainers, academicians, scientists and researchers from over the world to present the results of their research activities in the following fields: innovative methodologies in learning, teaching and assessment; multimedia in digital learning; e-learning; m-learning; e-education; knowledge management; infrastructure support for online learning; virtual learning environments; open education; ICT and education; digital classrooms; blended learning; social networks and education; e- tutoring: learning management systems; educational portals, classroom management issues, educational case studies, etc. Indexing and Abstracting The International Journal of Learning, Teaching and Educational Research is indexed in Scopus since 2018. The Journal is also indexed in Google Scholar and CNKI. All articles published in IJLTER are assigned a unique DOI number.
  • 4. Foreword We are very happy to publish this issue of the International Journal of Learning, Teaching and Educational Research. The International Journal of Learning, Teaching and Educational Research is a peer-reviewed open-access journal committed to publishing high-quality articles in the field of education. Submissions may include full-length articles, case studies and innovative solutions to problems faced by students, educators and directors of educational organisations. To learn more about this journal, please visit the website http://www.ijlter.org. We are grateful to the editor-in-chief, members of the Editorial Board and the reviewers for accepting only high quality articles in this issue. We seize this opportunity to thank them for their great collaboration. The Editorial Board is composed of renowned people from across the world. Each paper is reviewed by at least two blind reviewers. We will endeavour to ensure the reputation and quality of this journal with this issue. Editors of the June 2022 Issue
  • 5. VOLUME 21 NUMBER 6 June 2022 Table of Contents Effectiveness of Virtual Laboratories in Teaching and Learning Biology: A Review of Literature .............................1 Celine Byukusenge, Florien Nsanganwimana, Albert Paulo Tarmo Mindset and Levels of Conceptual Understanding in the Problem-Solving of Preservice Mathematics Teachers in an Online Learning Environment.......................................................................................................................................18 Ma Luisa Mariano-Dolesh, Leila M. Collantes, Edwin D. Ibanez, Jupeth T. Pentang Exploring the Teacher Professional Development Activities: Perspectives of Nigerian High School Teachers ......34 Oluwatoyin Ayodele Ajani Mental Health and Wellbeing of Secondary School Teachers in Malaysia ...................................................................50 Kee Pau, Aslina Binti Ahmad, Hsin-Ya Tang, Ahmad Jazimin Bin Jusoh, Asma Perveen, Kong Kwoi Tat Structure, Activities and Teacher Development in the Philippine Science Teachers’ Community of Practice ........ 71 Rhea F. Confesor, Rosario M. Belmi Physics Course Content of University Physics Education Programme as Reference to Content Distribution of JUPEB and WAEC Syllabi.................................................................................................................................................... 90 Olalekan T. Badmus, Abiodun A. Bada, Loyiso C. Jita Rasch Validation of Instrument Measuring Gen-Z Science, Technology, Engineering, and Mathematics (STEM) Application in Teaching during the Pandemic ............................................................................................................... 104 Hilman Qudratuddarsi, Riyan Hidayat, Raja Lailatul Zuraida binti Raja Maamor Shah, Nurihan Nasir, Muh Khairul Wajedi Imami, Rusdi bin Mat Nor The Level of Sports Participation and Academic Success among Malaysian Student-Athletes............................... 122 Jorrye Jakiwa, Siti Azilah Atan, Mohd Syrinaz Azli, Shahrulfadly Rustam, Norhafizah Hamzah, Aizuddin Amri Zainuddin ‘Publish or Perish’: a Transformation of Professional Value in Creating Literate Academics in the 21st Century138 Asep Kurnia Jayadinata, Kama Abdul Hakam, Tatang Muhtar, Tedi Supriyadi, J. Julia E-learning Outcomes during the COVID-19 Pandemic.................................................................................................160 Sang Tang My, Hung Nguyen Tien, Ha Tang My, Thang Le Quoc Saudi Teachers’ Attitudes towards using Online Learning for Young Children during the Covid-19 Pandemic 178 Ahlam A. Alghamdi A Survey of Teachers’ Perceptions of a Learning Portfolio in Lesotho Classrooms .................................................. 194 Julia Mathabo Chere-Masopha Purposeful Collaboration through Professional Learning Communities: Teacher Educators’ Challenges............ 210 Carolina Botha, Carisma Nel
  • 6. Trends of Educational Technology (EdTech): Students’ Perceptions of Technology to Improve the Quality of Islamic Higher Education in Indonesia............................................................................................................................ 226 Susanto ., Evi Muafiah, Ayu Desrani, Apri Wardana Ritonga, Arif Rahman Hakim High School Students’ Mathematics Anxiety: Discouragement, Abuse, Fear, and Dilemma Induced through Adults’ Verbal Behaviour .................................................................................................................................................. 247 Boj Bahadur Budhathoki, Bed Raj Acharya, Shashidhar Belbase, Mukunda Prakash Kshetree, Bishnu Khanal, Ram Krishna Panthi Entrepreneurship Education in Ghana: A Case Study of Teachers’ Experiences....................................................... 270 R J (Nico) Botha, M Obeng-Koranteng Enhancing Students’ Attitudes in Learning 3-Dimension Geometry using GeoGebra............................................. 286 Marie Sagesse Uwurukundo, Jean Francois Maniraho, Michael Tusiime Pre-Service Teachers' Perspectives towards the Use of GammaTutor in Teaching Physical Sciences in South African Secondary Schools ................................................................................................................................................ 304 Sakyiwaa Boateng, Jogymol Kalariparampil Alex, Folake Modupe Adelabu, Thamsanqa Sihele, Vuyokazi Momoti Continuing Professional Development of the Teacher Education Faculty among Philippine State Universities and Colleges................................................................................................................................................................................ 324 Ninez B. Tulo, Jiyoung Lee
  • 7. 1 ©Authors This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). International Journal of Learning, Teaching and Educational Research Vol. 21, No. 6, pp. 1-17, June 2022 https://doi.org/10.26803/ijlter.21.6.1 Received Mar 3, 2022; Revised May 22, 2022; Accepted Jun 22, 2022 Effectiveness of Virtual Laboratories in Teaching and Learning Biology: A Review of Literature Céline Byukusenge African Centre of Excellence for Innovative Teaching and Learning Mathematics and Science (ACEITLMS), University of Rwanda College of Education (URCE), Kayonza, Rwamagana, Rwanda Florien Nsanganwimana University of Rwanda College of Education (URCE), Kayonza, Rwamagana, Rwanda Albert Paulo Tarmo Educational Psychology and Curriculum Studies, School of Education, University of Dar es Salaam, Dar es Salaam, Tanzania Abstract. Scholars have debated whether virtual laboratories are educationally effective tools and if they should be continuously developed. In this paper, we comprehensively review literature about the effectiveness of virtual labs in teaching and learning biology to identify the topics often taught and the linked learning outcomes. We used Google Scholar, ERIC, and Web of Science electronic databases to access journal articles and conference proceeding papers. Through a systematic analysis, we obtained 26 articles solely related to virtual lab use in biology education. The overall findings from the reviewed literature indicated that virtual laboratories are often used on topics that seem abstract. These include cell and molecular biology topics, followed by microbiology, genetics, and other practical topics such as dissection and biotechnology. This review study revealed that virtual labs are effective as they improve students’ conceptual understanding, laboratory or practical skills, and motivation and attitudes towards biology. We recommend the use of virtual labs in teaching as a means of actively involving students in safer and more cost-effective scientific inquiry. Keywords: Biology topics; computer simulations; learning outcomes; virtual laboratories/labs
  • 8. 2 http://ijlter.org/index.php/ijlter 1. Introduction 1.1 Background Information and communication technology is increasingly penetrating almost all domains of human life, including education. In addition, with the current global trend of achieving twenty-first century learning skills, where digital literacy is one of the core goals, there is an increasing, understandable desire to bring more educational technologies into the classroom (Dakhi et al., 2020; Smetana & Bell, 2012; Tarbutton, 2018). Globally, researchers and practitioners agree that educational technology can transform the learning process by providing teachers and students with access to relevant resources when integrated into teaching. However, to be successful, educational technology should enhance the achievement of learning objectives (Griffin, 2003), because effective technology should enable students to achieve critical thinking by creating a shift from memorizing factual knowledge to understanding principles and applications. Like any other science subject, the teaching of biology inevitably requires laboratory exercises as a part of the practical skills acquisition process (Borgerding et al., 2013). Indeed, most biology topics heavily rely on practical activities, especially in laboratories (Cavanagh et al., 2005; Çimer, 2012; Vijapurkar et al., 2014). In addition, research has shown that laboratory activities can potentially develop students’ intellectual abilities, such as critical thinking, scientific inquiry, and practical skills. For instance, Hofstein and Mamlok-Naaman (2007) revealed that science cannot be significant to students without practical experiences in the school laboratory. When students have no access to laboratory activities and experiences, they often meet with difficulties in the learning of biology, especially in molecular biology topics (Boulay et al., 2010; Öztap et al., 2003; Sammet & Dreesmann, 2017; Tibell & Rundgren, 2009). Literature has shown that technology can provide students with laboratory experience and enhance learning (Keller & Keller, 2005). However, the question to be asked is which kind of technology can provide students with authentic scientific practice and help them move from memorization to a deeper understanding of concepts and applications. Research has shown that using inquiry-based and learner-centered technologies that allow students to manipulate and observe scientific phenomena (Flick & Bell, 2000; Sivin et al., 2000) bring about a deeper understanding of concepts and applications. Virtual laboratories, commonly called virtual labs, meet the criteria in this context. Virtual lab technologies were proposed by the National Science Foundation’s (NSF) task force to upgrade the state of STEM education as a dynamic response to the sustainable preparation of the population for complex global challenges in the twenty-first century (Borgman et al., 2008). Researchers have shown that virtual labs could help make science concepts in general and biology in particular more concrete (Olympiou et al., 2013) and meaningful for students without requiring complex and costly equipment (Elangovan & Ismail, 2014; Makransky et al., 2019; Marbach-Ad et al., 2008).
  • 9. 3 http://ijlter.org/index.php/ijlter Several pedagogical advantages have been highlighted regarding virtual lab use in education. For instance, by using virtual labs, teachers can easily explain complex theoretical concepts through a visual and immersive experience that can make it simpler for students to understand the subject (Smetana & Bell, 2012). With virtual labs, students try various experiments in risk–free environments without fear of damaging equipment. In addition, students can conduct the same experiment multiple times to ensure an understanding of the concept. Virtual labs allow teachers to capture students’ attention and ensure their engagement and motivation (Babateen, 2011). Furthermore, virtual labs help students to learn at their own pace as they can prepare and perform laboratory experiments at any time and place. With virtual lab technology, teachers and students can explore topics that would otherwise be unworkable in conventional classes (Smetana & Bell, 2012). Radhamani et al. (2014) and Pearson and Kudzai (2015) emphasized the need for virtual labs in teaching biology, especially in developing countries. They argued that, generally, science education in developing countries faces many limitations. These include shortage of laboratory equipment and reagents, space and time constraints, insufficient laboratory protocol, inadequate technical support, and safety, among other limitations. According to Radhamani et al. (2014), virtual labs are asset tools to mitigate the challenges of insufficient laboratory equipment needed in teaching biology topics such as biotechnology. This is despite some drawbacks of virtual labs, such as students not being able to feel, smell, or touch as in a physical laboratory. While physical laboratories are absent or not fully equipped in many schools due to the high costs of their equipment and maintenance, virtual labs have been affirmed to lessen financial constraints related to laboratory equipment, space, and maintenance (Fisher et al., 2012). These potential advantages have triggered research interest, and a good number of empirical studies have been conducted about the effectiveness of virtual laboratories (Breakey et al., 2008; Dyrberg et al., 2017; Muhamad et al., 2010, 2012; Pope et al., 2017; Radhamani et al., 2014; Ray et al., 2012; Triola & Holloway, 2011). Along this vein, several review studies on the effect of virtual laboratories in teaching sciences have been carried out (Brinson, 2015; De Jong et al., 2013; Ma & Nickerson, 2006; Smetana & Bell, 2012; Udin et al., 2020). However, most reviews only included laboratory practices of many other disciplines, such as physics, chemistry, and engineering, with few review studies about the effectiveness of virtual laboratories in teaching and learning biology (Udin et al., 2020). There is a need to know for which topics of biology virtual labs are more useful and what outcomes are brought about by virtual labs in the teaching and learning of biology. Therefore, we assume that this study will shed light on the effectiveness of virtual labs and in which preferred topics teachers are called to use the virtual labs. This relates especially to those biology topics which seem difficult to be taught by teachers and those which are too hard to understand for students because they are too abstract. The following specific questions guide this literature review:
  • 10. 4 http://ijlter.org/index.php/ijlter 1. In which topics of biology are virtual laboratories the most useful? 2. What learning outcomes are best achieved using virtual laboratories in biology? 1.2 Theoretical Context The use of virtual laboratories in teaching and learning is based on David Kolb’s (1984) experiential learning theory, which is rooted in the constructivist approach and John Dewey’s work (Ouyang & Stanley, 2014). Around 1938, Dewey showed that no learning happens without practice and the active involvement of students. Kolb advocated and applied Dewey’s concept of “learning by doing”, believing that learning occurs through cognitive and experiential learning (Kolb & Kolb, 2005). The core of experiential learning theory is the individual learner’s participation and experiences (Ouyang & Stanley, 2014). The application of virtual labs in teaching ensures students’ active learning (Evans et al., 2004). The use of virtual labs allows learners to experiment with immediate feedback and interactivity (Dyrberg et al., 2017; Tan & Waugh, 2013). Thus, virtual labs help students to learn by doing and to become more engaged in their studies (Gallagher et al., 2005; Marchevsky et al., 2003). 2. Methodology We applied preferred reporting items for systematic reviews and meta-analyses (PRISMA) principles and guidelines in our review (Moher et al., 2009). PRISMA guidelines assist researchers in conducting transparent and comprehensive systematic review reporting. These guidelines help researchers define research strategies, eligibility criteria, the selection process, and the data collection process. 2.1. Literature Search We used an open federated search in this review study to find relevant articles from trusted databases. This type of search involves searching various electronic databases for information relevant to the review study. We used certain keywords to search and retrieve articles related to our study. These included “biology laboratory”, “virtual laboratory in teaching biology”, “virtual labs and biology topics”, “biology education and virtual laboratory”, “virtual and physical laboratory”, “virtual lab and real lab”, and “effectiveness of virtual labs in biology education”. We used trusted electronic databases such as Google Scholar, ERIC, and Web of Science to access reliable articles and conference proceedings. 2.2 Inclusion and Exclusion Criteria Using a systematic selection process and the elimination of duplicates, the first stage of searching yielded 161 papers. Manual filtering was applied based on how an article is relevant to our study. In selecting the relevant articles for inclusion in the review, we screened the titles and abstracts of all recorded articles. We used several inclusion and exclusion criteria to filter irrelevant articles (Table 1).
  • 11. 5 http://ijlter.org/index.php/ijlter Table 1: Inclusion and exclusion criteria used to select relevant studies Inclusion criteria Exclusion criteria Empirical studies in peer-reviewed journals, and conference proceedings Reviews in non-peer-reviewed journals Virtual labs used for biology education - Virtual lab development procedures, design, or architecture - Virtual labs used for medical biology Articles published in English Articles that are not in English The screening of titles and abstracts yielded 38 publications. The publications were further subjected to screening by checking their full-text content. The articles that focused only on biology virtual lab development procedures, design, or architecture without any relation to teaching biology were excluded. In this regard, 12 publications were filtered out. Eventually, we gathered 26 studies relevant to our review study, and each study was recorded to categorize information for further analysis (see Table 2 and Figure 1). The PRISMA diagram in Figure 1 shows the selection process. The obtained articles are dated from 2002 to 2019 Figure 1: PRISMA diagram of the selection process of the reviewed studies
  • 12. 6 http://ijlter.org/index.php/ijlter 3. Results and Discussion 3.1. The Use of Virtual Laboratories in Teaching Biology Topics In response to the first research question, we present in Table 2 the biology topics in which virtual laboratories are most commonly used for effective teaching. We also present the related learning outcomes that are most commonly enhanced by the use of virtual labs. Table 2. Biology topics in which virtual labs are used and related learning outcomes SN Study Biology topic Measured learning outcome 1 Akhigbe and Ogufere (2019) Genetics Student attitudes and academic achievement in genetics 2 Akpan and Strayer (2010) Frog dissection Actual dissection practices and attitudes towards dissection 3 Breakey et al. (2008) Genetics Understanding of experimental genetics procedures 4 Collier et al. (2012) Histology Content mastery and time management 5 Diwakar et al. (2011) Biotechnology (No learning outcomes were identified) 6 Dyrberg et al. (2017) Microbiology and pharmaceutical toxicology Enhanced student positive attitudes, motivation, and self-efficacy 7 Elangovan and Ismail (2014) Cell division Student conceptual understanding of cell division 8 Flowers (2011) Various topics, most of which are related to cell and molecular biology (DNA, cell structure, enzyme- controlled reaction, cell reproduction) Student perceptions of biology 9 Havlícková et al. (2018) Dissection Student motivation 10 Huppert et al. (2002) Microbiology Student science process skills and academic achievement 11 Ismail et al. (2016) Microbiology (dissolving pathogenic bacteria) Enhancing student scientific literacy 12 Kiboss et al. (2006) Cell division Conceptual understanding and perceptions 13 Makransky et al. (2016) Microbiology Knowledge transfer and practical skills 14 Makransky et al. (2019) Microbiology Student knowledge, motivation, and self-efficacy in microbiology 15 Marbach et al. (2008) Molecular biology Enhanced student achievement 16 Meir et al. (2005) Introductory biology (osmosis and diffusion) Student understanding of how these processes work at a molecular level
  • 13. 7 http://ijlter.org/index.php/ijlter 17 Muhamad et al. (2012) Cell division Student understanding of cell division, specifically applications of mitosis in cloning 18 Oser and Fraser (2015) Genetics Student perception of the learning environment, attitudes towards the topic, and achievement 19 Pope et al. (2017) Evolution Student understanding of natural selection concepts 20 Radhamani et al. (2014) Biotechnology Enhanced student achievement 21 Shelden et al. (2019) Cell division Understanding of cell division phases 22 Stuckey-Mickell and Stuckey- Danner (2007) Introductory biology Enhanced student perceptions 23 Tan and Waugh (2013) Molecular biology Student conceptual understanding and attitudes in molecular biology 24 Toth et al. (2009) DNA and gel electrophoresis Student understanding and laboratory skills 25 White et al. (2007) Genetics Conceptual understanding 26 Whitworth et al. (2018) Enzyme kinetics Conceptual understanding Table 2 displays the topics in which virtual labs were used and the learning outcomes that were attained as a result of their use. The reviewed articles are dated from 2002 to 2019. We did not find literature for the years 2020 to 2022. In the reviewed studies, virtual labs were used to teach genetics, dissection, microbiology, cell division, osmosis, DNA and gel electrophoresis, enzyme kinetics, biotechnology, evolution, histology, and introduction to biology. Virtual labs were used most frequently in teaching microbiology and cell division. Moreover, some of the learning outcomes that were attained using virtual labs included conceptual understanding, knowledge transfer, practical skills acquisition, and enhanced positive attitudes, motivation, and self-efficacy among students. The topics and learning outcomes are further described in the following sections, respectively. 3.2. Topics in Which Virtual Labs are the Most Useful We analyzed the reviewed studies to identify which biology topics were most taught using virtual labs. Figure 2 shows the different topics that were facilitated using virtual labs.
  • 14. 8 http://ijlter.org/index.php/ijlter Figure 2. Biology topics in which virtual labs were used as per the reviewed studies It is not by coincidence that the identified topics in Figure 2 employ virtual laboratories. The listed topics are perceived by both teachers and students to be difficult, abstract, and daunting due to their complexity, difficulty to visualize, and not being practicable in normal physical school laboratories. For instance, before conducting their study on developing and implementing a scenario-based biology virtual lab, Muhamad et al. (2012) carried out a preliminary investigation of a survey type involving 72 students and 10 high school teachers. Their investigation aimed to identify the biology topic that was most difficult to teach and learn and to focus on developing a virtual lab for it. Their preliminary study findings indicated cell division as the most difficult topic for both teachers and students (Muhamad et al., 2010). Tan and Waugh (2013) undertook research employing virtual reality simulations in teaching and learning molecular biology in Singapore high schools. Teachers claimed that the topic of molecular biology was challenging and difficult to teach. They also indicated different complaints by students about teaching materials used by their teachers, such as diagrams and 2D presentations, which do not enable them to see DNA and protein molecules. Tan and Waugh (2013) argued that before studying molecular biology by use of virtual reality simulations, it was difficult for students to relate the structure and molecular interactions for cell functioning. Radhamani et al. (2014) reported that after virtual lab classes, 44% of the students who participated in their study scored 90%, with an average class score of about 70% in the post-test evaluation. In the pre-test evaluation, the majority of the students (88%) had scored below 70%. Indeed, the topic to be taught with the use of virtual labs depends on the nature of the experiment. For instance, considering the topic of dissection, this topic raises many debates and disagreements regarding ethical issues among researchers, educators, and animal rights activists. Virtual laboratories that dissect animal specimens provide a viable alternative to real dissections and relieve 0 1 2 3 4 5 6 7 8 9 10 Number of studies
  • 15. 9 http://ijlter.org/index.php/ijlter ethics-related issues. Studies comparing the value of virtual frog dissections with traditional dissections using real specimens have revealed mixed results, however. Some supported that real dissections in the physical laboratory are effective (Cross & Cross, 2004), while others agreed that the simulated dissections are effective for improving students’ performance in the virtual laboratories (Akpan & Strayer, 2010). 3.3. Learning Outcomes Enhanced by the Use of Virtual Laboratories The learning outcomes identified in the reviewed studies were grouped into three categories (Figure 3). These are: 1) knowledge and conceptual understanding; 2) laboratory skills, knowledge transfer, and self-efficacy in laboratory activities; and 3) students’ motivation, perceptions, and attitudes towards biology and the learning environment. Some of the reviewed studies assessed more than one of the above learning outcomes. The total number of studies indicated in Figure 3 therefore exceed the number of reviewed studies. The overall findings indicated that the learning outcomes varied, but in most studies, knowledge and conceptual understanding were frequently assessed. Figure 3: Learning outcomes identified in the reviewed studies 3.3.1 Knowledge and conceptual understanding From our analysis, 21 out of the 26 reviewed studies reported that the use of virtual labs enhances students’ conceptual understanding (Figure 3). Indeed, virtual lab exercises have been proven essential for students to understand biology concepts. Virtual labs present multiple opportunities for students to gain access to learning resources easily, and to get enough time to do and repeat activities, thereby nurturing deeper learning (Muhamad et al., 2012). Furthermore, biology is a molecular science; most of its topics require visualizations, videos, and illustrations for students to understand how processes work at the molecular level (Evans et al., 2004; Muhamad et al., 2012). Many studies have shown that virtual laboratories are effective, low-cost tools to enhance students’ understanding of biology concepts. This is because they provide students with visualizations of abstract concepts through animations, simulations, and virtual practices of simulated laboratory experiments for some 21 8 5 0 5 10 15 20 25 Knowledge/ conceptual understanding Laboratory skills, knowledge transfer, and self efficacy Motivation, perceptions, and attitudes Learning outcome Number of reviewed studies
  • 16. 10 http://ijlter.org/index.php/ijlter topics, which could not be done even in normal classes (Akhigbe & Ogufere, 2019; Collier et al., 2012; Makransky et al., 2016; Oser & Fraser, 2015; Špernjak & Šorgo, 2018; Tan & Waugh, 2013). In the study conducted by Tan and Waugh (2013), students admitted that before exposure to visualization exercises, molecular biology was a dry topic, too abstract and daunting for them. This resulted in some of them giving up biology altogether. Nonetheless, Tan and Waugh confirmed that after viewing the animations and participating in the visualization exercises, the students demonstrated increased interest, understanding, and engagement in the subject. Whitworth et al. (2018) reported a varied use of simulations in laboratory activities after seeing a significant increase in post-test scores of the experimental group of students over the control group of students. The experimental group was taught using standard lab instruction coupled with simulated lab instruction, while the control group was taught with only standard lab instruction. The increased post-test scores of the experimental group had an average standard deviation of 1.59. Based on their study results, Whitworth et al. (2018) concluded that computer simulations improve students’ conceptual understanding of enzyme kinetics. Moreover, various studies have shown that virtual labs are adequate for improving understanding of biology topics that are difficult to observe directly in the classroom context (Collier et al., 2012; Pope et al., 2017; Radhamani et al., 2014). For example, evolution by natural selection has been shown to be notoriously difficult for students to understand, and its processes have been described as not directly observable (Krist & Showsh, 2007; Nehm & Schonfeld, 2008; Plunkett & Yampolsky, 2010). However, Pope et al. (2017) clearly showed that simulations of natural phenomena are effective tools that support an active teaching approach to help students overcome natural selection misconceptions. 3.3.2 Laboratory skills, knowledge transfer, and self-efficacy in laboratory activities Eight out of the twenty-six reviewed studies indicated that virtual laboratories enhance students’ laboratory skills, knowledge transfer, and self-efficacy (Figure 3). These studies suggested that virtual laboratories are effective tools for pre-lab preparation and transferring knowledge and skills from an idealized environment into physical reality (Makransky et al., 2016). Research has affirmed that for meaningful laboratory learning to occur, students should be prepared before performing the required laboratory tasks (Jones & Edwards, 2010). According to O’Brien and Cameron (2008), laboratory practices help students to move from abstract to concrete settings. However, if students are not prepared, they could experience stress and confusion during laboratory activities instead of expected manipulative and process skills. The students become overloaded with too much information about the assigned task and may become overwhelmed as they try to handle new manipulative tasks as well as master new concepts (Pogačnik & Cigić, 2006). Virtual labs are crucial for the preparation of students before embarking on a physical experiment. Researchers have affirmed that to perform the required
  • 17. 11 http://ijlter.org/index.php/ijlter practical tasks, science classes should blend real and virtual experiments so that students acquire the skills necessary. Several of the reviewed studies suggested the desirability of integrating hands-on laboratories with virtual ones and the effectiveness of engaging in virtual experiences before the real, hands-on investigation (Akpan & Strayer, 2010; Toth et al., 2009). In addition, other researchers have indicated that students prepared using virtual labs do not waste time on how to handle apparatus in organizing the experiment; rather, they focus on testing hypotheses through practicing and making important observations (Johnstone & Al-Shuaili, 2001). Prepared students begin the procedures faster and ask questions on a higher level than those who are less or not prepared (Dyrberg et al., 2017). In their post-test, Akpan and Strayer (2010) discovered that students who engaged first in simulated dissection outperformed their peers who only performed conventional dissection. Similarly, Maldarelli et al. (2009) found that visual demonstration of laboratory techniques via instructional videos before the actual physical laboratory activity was sufficient to mediate significant increases in knowledge, self-efficacy, and experience in basic biology laboratory procedures. However, not surprisingly, some studies found that students believed that traditional labs offer more effective pedagogical techniques in teaching them how to use biology laboratory equipment than virtual labs (Flowers, 2011). Researchers have also criticized virtual labs, claiming that they have limited potential for teaching students how to handle specimens and perform techniques such as fixing, staining, and thin sectioning (Scheckler, 2003). However, other scholars have indicated that with simulations, students have opportunities to repeatedly learn all steps of an experiment, enabling them to transfer knowledge and skills gained from virtual learning to physical applications (Makransky et al., 2016). 3.3.3 Students’ motivation, perceptions, and attitudes towards biology and the learning environment In this study, 5 out of the 26 reviewed studies reported about virtual laboratories as related to students’ motivation, perceptions, and attitudes towards biology and the learning environment (Figure 3). According to these studies, virtual labs are important for enhancing students’ attitudes, stimulating interest and enjoyment, and motivating them to learn biology, improving their performance. Toth et al. (2009) performed a study about myDNA by using virtual labs to show the separation of DNA fragments. They found that students were happy to learn and efficiently repeated experiments and studied the effects of the variables. In a recent study, Akhigbe and Ogufere (2019) assessed the effect of computer simulations on students’ attitudes towards biology, finding that computer simulations improve students’ attitudes towards genetics. A significant improvement in performance was seen with the students who were exposed to the computer simulation instructional strategy over their counterparts who were taught using traditional methodologies. The majority of the reviewed studies revealed that students have positive perceptions towards virtual labs. Stuckey-Mickell and Stuckey-Danner (2007) made a contrary finding in their qualitative study analyzing open-ended qualitative responses by students after completion of several virtual lab sessions
  • 18. 12 http://ijlter.org/index.php/ijlter in human biology. This allowed them to investigate how students perceive virtual labs as compared to hands-on laboratory activities. They found that with virtual labs, students lacked the enjoyment of student-teacher interaction and the ability to ask questions and receive direct feedback from the instructor. 4. Conclusion and Recommendation Based on the study’s findings, we conclude that virtual laboratories are commonly effective in teaching difficult and abstract biology topics related to cell and molecular biology. Furthermore, conceptual understanding is the learning outcome most enhanced when using virtual labs. Studies have further affirmed that virtual labs improve students’ motivation, self-efficacy, and attitudes towards learning biology topics. Virtual laboratories deserve the attention of researchers, teachers, and instructional designers due to their appealing nature as a means of actively involving students in safer and more cost-effective scientific inquiry. We suggest that future research assesses teachers’ preparedness to use virtual labs in teaching and learning processes. The effectiveness of virtual labs, like any other instructional tool, may be greatly influenced by how they are used in the classroom. This study did not address the limitations of the virtual laboratory in teaching and learning biology. Thus, we recommend further research into the negative effects of using virtual laboratories in teaching and learning. 5. References Akhigbe, J. N., & Ogufere, J. A. (2019). Effect of computer simulation instructional strategy on students’ attitude and academic achievement in genetics. KIU Journal of Social Sciences, 5(4), 305-315. Akpan, J., & Strayer, J. (2010). Which comes first: The use of computer simulation of frog dissection or conventional dissection as academic exercise? Journal of Computers in Mathematics and Science Teaching, 29(2), 113-138. https://eric.ed.gov/?id=EJ885724 Babateen, M. H. (2011). The role of virtual laboratories in science education [Conference session]. IACSIT Press, Singapore. http://www.ipcsit.com/vol12/19- ICDLE2011E10013.pdf Borgerding, L. A., Sadler, T. D., & Koroly, M. J. (2013). Teachers’ concerns about biotechnology education. Journal of Science Education and Technology, 22(2), 133-147. https://doi.org/10.1007/s10956-012-9382-z Borgman, C. L., Abelson, H., Dirks, L., Johnson, R., Koedinger, K., & Linn, M. C. (2008). Fostering learning in the networked world: The cyberlearning opportunity and challenge. https://escholarship.org/uc/item/32t8b4bt Boulay, R., Parisky, A., & Campbell, C. (2010, June). Developing teachers’ understanding of molecular biology: Building a foundation for students [Conference session]. ASCILITE 2010 – The Australasian Society for Computers in Learning in Tertiary Education, Sydney. https://www.ascilite.org/conferences/sydney10/procs/Boulay-full.pdf Breakey, K. M., Levin, D., Miller, I., & Hentges, K. E. (2008). The use of scenario-based- learning interactive software to create custom virtual laboratory scenarios for teaching genetics. Genetics, 179(3), 1151-1155. https://doi.org/10.1534/genetics.108.090381 Brinson, J. R. (2015). Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers & Education, 87, 218-237. https://doi.org/10.1016/j.compedu.2015.07.003
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  • 22. 16 http://ijlter.org/index.php/ijlter laboratory class. Evolution: Education and Outreach, 10(1), 1-16. https://doi.org/10.1186/s12052-017-0067-1 Radhamani, R., Sasidharakurup, H., Sujatha, G., Nair, B., Achuthan, K., & Diwakar, S. (2014). Virtual labs improve student’s performance in a classroom. E-Learning, E-Education, and Online Training, 138, 138-146. https://doi.org/doi:10.1007/978-3- 319-13293-8_17 Ray, S., Koshy, N. R., Reddy, P. J., & Srivastava, S. (2012). Virtual labs in proteomics: New e-learning tools. Journal of Proteomics, 75(9), 2515-2525. https://doi.org/10.1016/j.jprot.2012.03.014 Sammet, R., & Dreesmann, D. (2017). What do secondary students really learn during investigations with living animals? Parameters for effective learning with social insects. Journal of Biological Education, 51(1), 26-43. https://doi.org/10.1080/00219266.2016.1150873 Scheckler, R. K. (2003). Virtual labs: A substitute for traditional labs? International Journal of Developmental Biology, 47(2-3), 231-236. Shelden, E. A., Offerdahl, E. G., & Johnson, G. T. (2019). A virtual laboratory on cell division using a publicly-available image database. CourseSource. https://doi.org/10.24918/cs.2019.15 Sivin, J. P., Bialo, E., & Langford, J. (2000). 2000 research report on the effectiveness of technology in schools (7th ed.). Software and Information Industry Association. Smetana, L. K., & Bell, R. L. (2012). Computer simulations to support science instruction and learning: A critical review of the literature. International Journal of Science Education, 34(9), 1337-1370. https://doi.org/10.1080/09500693.2011.60518 Špernjak, A., & Šorgo, A. (2018). Differences in acquired knowledge and attitudes achieved with traditional, computer-supported and virtual laboratory biology laboratory exercises. Journal of Biological Education, 52(2), 206-220. https://doi.org/10.1080/00219266.2017.1298532 Stuckey-Mickell, T. A., & Stuckey-Danner, B. D. (2007). Virtual labs in the online biology course: Student perceptions of effectiveness and usability. MERLOT Journal of Online Learning and Teaching, 3(2), 105-111. https://jolt.merlot.org/vol3no2/stuckey.pdf Tan, S., & Waugh, R. (2013). Use of virtual-reality in teaching and learning molecular biology. In Y. Cai (Ed.), 3D immersive and interactive learning (pp. 17-43). https://doi.org/10.1007/978-981-4021-90-6_2 Tarbutton, T. (2018). Leveraging 21st century learning & technology to create caring diverse classroom cultures. Multicultural Education, 25(2), 4-6. Tibell, L. A. E., & Rundgren, C.-J. (2009). Educational challenges of molecular life science: Characteristics and implications for education and research. CBE—Life Sciences Education, 9(1), 55-61. https://doi.org/https://doi.org/10.1187/cbe.08-09-0055 Toth, E., Morrow, B. L., & Ludvico, L. R. (2009). Designing blended inquiry learning in a laboratory context: A study of incorporating hands-on and virtual laboratories. Innovative Higher Education, 33(5), 333-344. https://doi.org/10.1007/s10755-008- 9087-7 Triola, M. M., & Holloway, W. J. (2011). Enhanced virtual microscopy for collaborative education. BMC Medical Education, 11(1), 9-12. https://doi.org/10.1186/1472- 6920-11-4 Udin, W. N., Ramli, M., & Muzzazinah. (2020). Virtual laboratory for enhancing students’ understanding on abstract biology concepts and laboratory skills: A systematic review. Journal of Physics: Conference Series, 1521(4), 042025. https://doi.org/10.1088/1742-6596/1521/4/042025 Vijapurkar, J., Kawalkar, A., & Nambiar, P. (2014). What do cells really look like? An inquiry into students’ difficulties in visualising a 3-D biological cell and lessons
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  • 24. 18 ©Authors This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). International Journal of Learning, Teaching and Educational Research Vol. 21, No. 6, pp. 18-33, June 2022 https://doi.org/10.26803/ijlter.21.6.2 Received Mar 3, 2022; Revised May 29, 2022; Accepted Jun 22, 2022 Mindset and Levels of Conceptual Understanding in the Problem-Solving of Preservice Mathematics Teachers in an Online Learning Environment Ma Luisa Mariano-Dolesh Distance, Open and Transnational University, Central Luzon State University Science City of Muñoz, Nueva Ecija, Philippines Leila M. Collantes College of Education, Central Luzon State University Science City of Muñoz, Nueva Ecija, Philippines Edwin D. Ibañez College of Science, Central Luzon State University Science City of Muñoz, Nueva Ecija, Philippines Jupeth T. Pentang* College of Education, Western Philippines University Puerto Princesa City, Philippines Abstract. Mindset plays a vital role in tackling the barriers to improving the preservice mathematics teachers’ (PMTs) conceptual understanding of problem-solving. As the COVID-19 pandemic has continued to pose a challenge, online learning has been adopted. This led this study to determining the PMTs’ mindset and level of conceptual understanding in problem-solving in an online learning environment utilising Google Classroom and the Khan Academy. A quantitative research design was employed specifically utilising a descriptive, comparative, and correlational design. Forty-five PMTs were chosen through simple random sampling and willingly took part in this study. The data was gathered using validated and reliable questionnaires and problem- solving tests. The data gathered was analysed using descriptive statistics, analysis of variance, and simple linear regression. The results revealed that the college admission test, specifically numerical proficiency, influences a strong mindset and a higher level of conceptual understanding in problem-solving. Additionally, this study shows that mindset predicts the levels of conceptual understanding in problem- * Corresponding author: Jupeth T. Pentang, jupeth.pentang@wpu.edu.ph
  • 25. 19 http://ijlter.org/index.php/ijlter solving in an online environment where PMTs with a growth mindset have the potential to solve math problems. The use of Google Classroom and the Khan Academy to aid online instruction is useful in the preparation of PMTs as future mathematics teachers and problem- solvers. Further studies may be conducted to validate these reports and to address the limitations of this study. Keywords: conceptual understanding; growth mindset; mathematics education; online learning; preservice teachers 1. Introduction Future math teachers must be equipped with the right mindset and a full understanding of problem-solving. Mindset and conceptual understanding have a crucial role in the preparation of preservice mathematics teachers (PMTs). The academic mindset is critical in deeper learning (Farrington, 2013) where understanding the mindset of preservice teachers improves their morale as future educators (Yazon et al., 2021). Sadly, preservice teachers have a mindset that they cannot do mathematics (Cutler, 2020). Considering that a positive mindset is a gateway to mathematical achievement (Sun, 2018) and problem-solving performance (Pentang et al., 2021), an exploration of this matter is necessary to guide the teacher educators in empowering the PMTs. Poor conceptual understanding may also be a product of a negative mindset. Ibañez and Pentang (2021) have reported this among preservice teachers in the Philippines. Discovering ways to develop a strong mindset and conceptual understanding among PMTs was disrupted by the occurrence of the novel coronavirus disease in 2019 (COVID-19). Nevertheless, it opened up opportunities for teacher education institutions (TEIs) to explore alternative teaching and learning modalities. TEIs in the locality suspended face-to-face classes and limited academic exchanges to mitigate the public health effects of COVID-19 (Tan et al., 2021). Institutions adopted a purely online modality while some blended it with self- learning modules to aid the instructions which may have affected the mindset and level of conceptual understanding among PMTs. Although online learning has been configured under a wide variety of different formats over half a century, one could say that COVID-19 has made educational institutions aware of the new normal way of academic exchange. Given the challenges due to the pandemic’s impact, experts in educational institutions have been forced to adopt remote teaching strategies maximising online resources as a teaching-learning tool. As online classrooms promote a healthy mindset and encourage learning motivation (Bacsal et al., 2022; De Souza et al., 2021), TEIs have begun to adopt online technology methods for disseminating the teaching-learning processes such as Google Classroom and the Khan Academy. On the other hand, educators who wish to improve their learning outcomes must consider approaches to establish a growth mindset (Dimitriadis, 2015). A person with a strong mindset shows grit, hard work, and perseverance. Embedded in each of these beliefs, or mindsets, are networks of beliefs and assumptions that shape how people approach learning (Tabrizi, 2020). In contrast, those who
  • 26. 20 http://ijlter.org/index.php/ijlter believe that intelligence is fixed tend to focus on judgment. They are more concerned with proving that they are intelligent or concealing that they are not, which means that they avoid circumstances in which they might fail or have to work hard (Dweck, 2016). The faculty and staff require more than just technological knowledge; they must also be fully prepared to apply instructional approaches that improve the students’ online experiences (DeBrock et al., 2020; De Souza et al., 2021). Thus, there is a need for teachers, including those in the preservice, to assess their beliefs about intelligence. Their mindset will drive how they teach and facilitate learning in the mathematics classroom. Studies about mindset have not yet been fully explored, especially in the field of mathematics education. It is noticeable that growth mindset research emerged recently, less than ten years ago. Likewise, the conceptual understanding of problem-solving in an online environment has not yet been examined. It will be interesting to find out whether mindset has a connection with the level of conceptual understanding in an online setup. Moreover, the research will likely be compelling if the study is done in a group of preservice teachers who are taking mathematics majors. Considering that these future teachers will probably teach mathematics in the K-12 program in a few years (Bacsal et al., 2022; Domingo et al., 2021; Ibañez & Pentang, 2021; Pentang et al., 2021), it would bring in great benefits to the students, parents, and administrators if their mindset and levels of conceptual understanding are found to be related. As an academic institution that trains and prepares preservice teachers, Central Luzon State University (CLSU) has been dramatically affected by the pandemic due to the lockdown and school closures that started in March 2020. Online resources are needed to address the unprecedented pandemic issues in the teaching-learning process (Manca & Meluzzi, 2020; Pentang, 2021b). Given the uncertainty of how long the pandemic lasts, online learning plays a vital role in the continuity of teaching and learning (Bacsal et al., 2022). Google Classroom and the Khan Academy was used to facilitate continuous learning despite the ongoing closure and lockdown in schools, colleges, and universities. These scenarios have compelling reasons to study the mindset and levels of conceptual understanding in problem-solving in an online learning environment using readily free available tools like Google Classroom and the Khan Academy in a mathematics classroom at CLSU, specific to PMTs, who are deemed to be able to recuperate the status of Philippine mathematics education. Research Questions 1. What is the PMTs’ mindset when problem-solving in terms of growth and a fixed mindset? 2. What is the PMTs’ levels of conceptual understanding when problem-solving regarding best, partial, complete/incomplete, functional, and no understanding? 3. Is there a significant difference in the PMTs’ mindset when problem-solving when grouped according to socio-demographic characteristics?
  • 27. 21 http://ijlter.org/index.php/ijlter 4. Is there a significant difference in the PMTs’ levels of conceptual understanding of problem-solving when grouped according to socio- demographic characteristics? 5. Do the PMTs’ mindsets significantly predict their conceptual understanding of problem-solving? 2. Conceptual Framework The inequalities in the Filipino students’ mathematical literacy can be attributed to their unawareness of a growth mindset and lack of conceptual understanding, both of which are linked to their teachers’ means of imparting knowledge and skills in mathematics. With the unprecedented move to online learning brought about by the pandemic, mathematics educators have been obligated to employ online learning management systems such as Google Classroom with the Khan Academy to train and prepare future maths teachers who are deemed able to address the mathematics illiteracy among young Filipinos. It is an opportunity to assess the growth mindset and conceptual understanding of problem-solving of the preservice mathematics teachers (PMTs). The Khan Academy existed prior to the pandemic but was not commonly used in formal mathematics instruction. The PMTs’ mindsets can be influenced by what they believe about their academic ability. Intelligence may be strengthened by a growth mindset (Dweck, 2016). A person with a growth mindset knows that intelligence may be attained through hard work and the assistance of others (Romero, 2015). Knowing a student’s mindset will assist a teacher in developing techniques to promote learning (Tabrizi, 2020). Growth mindset techniques enable the students to engage in risk- taking activities (Hennessey, 2019). Thus, it is vital to consider the right mindset when pursuing academic success in mathematics, especially in relation to problem-solving. The PMTs’ mindset may be found to be helpful in problem- solving activities with the aid of the Khan Academy. PMT's conceptual understanding of problem-solving also has implications for mathematics education. Conceptual understanding denotes a comprehensive and functional knowledge of mathematical notions (National Research Council, 2001). Conceptual understanding is critical to solving a problem and understanding why the algorithms and approaches used work. Conceptual understanding, in which learners grasp ideas in a transferable manner, enables them to apply what they learn in class across domains (Moser & Chen, 2016). Problem-solving and deep conceptual understanding is demonstrated when a student decides how to solve a problem (Ibañez & Pentang, 2021; Pentang et al., 2021). The PMTs should be able to monitor their process and judge whether the procedure is the right method to answer the question or if a new way is needed (Pentang, 2021a; Schoenfeld, 1989). Through the Khan Academy, it is deemed that the PMTs’ conceptual understanding will be estimated. The socio-demographic characteristics such as sex, number of siblings, birth order, family monthly income, father’s and mother’s educational attainment, and CAT Numerical Proficiency, are essential factors to consider when determining the PMTs’ mindset and level of conceptual understanding. Considering that both
  • 28. 22 http://ijlter.org/index.php/ijlter mindset and conceptual understanding are essential in mathematics education, this study resolves the gap in the literature where no exploration has established the influence of socio-demographic characteristics in relation to the PMTs mindset and conceptual understanding of problem-solving as well as to establish whether mindset is a predictor of the PMT’s conceptual understanding. The study also conceptualised the vital role of online learning in problem-solving through the use of Google Classroom and the Khan Academy (Figure 1). Figure 1: Conceptual Framework of the Study 3. Methodology 3.1. Research Design This study employed a quantitative research design combining descriptive, comparative, and regression methods to address the research questions and conceptual framework of the study (Magulod et al., 2021). The descriptive analysis addressed the first two research questions which described the participants’ mindset and their level of conceptual understanding of problem- solving in an online learning environment. Additionally, the comparative analysis answered the third and fourth research questions which distinguished between the socio-demographic characteristic differences in the participants’ mindset and level of conceptual understanding, respectively. Moreover, the regression
  • 29. 23 http://ijlter.org/index.php/ijlter analysis answered the fifth question which showed whether the PMT’s mindset predicts their conceptual understanding of problem-solving. 3.2. Participants and Sampling Procedure The participants of the study were preservice mathematics teachers (third-year Bachelor of Secondary Education major in Mathematics students) from Central Luzon State University. The study targeted respondents who had taken mathematics college courses and who were currently enrolled in Problem-solving, Mathematical Investigation, and Modelling in their first semester of the school year 2020-2021. The simple random sampling employed drew 45 participants (Table 1). Table 1: Participants’ socio-demographic characteristics (n = 45) Socio-Demographic Characteristics Frequency Percentage Sex Male 14 31.11 Female 31 68.89 Number of Siblings 0 - 2 10 22.22 3 - 5 31 68.89 6 and above 4 8.89 Birth Order Last-born (Youngest) 12 26.67 Middle-born 21 46.67 First-born (Eldest) 12 26.67 Family Monthly Income Less than ₱11,690 34 75.56 Between ₱11,690 to ₱23,380 9 20.00 Between ₱23,381 to ₱46,761 2 4.44 Father’s Educational Attainment Did not finish Elementary 7 15.56 Elementary Graduate 7 15.56 High School Graduate 26 57.78 College Graduate 5 11.11 Mother’s Educational Attainment Elementary Undergraduate 2 4.44 Elementary Graduate 6 13.33 High School Graduate 30 66.67 College Graduate 7 15.56 CAT Numerical Proficiency Below Average 8 17.78 Average 24 53.33 Above Average 13 28.89 3.3. Research Instrument The instrument utilised in this study was a survey questionnaire (for Part I and Part II) and a problem-solving test (for Part III). Part I determined the socio- demographic characteristics of the participants. Part II focused on the participants’ mindset following the example of Dweck (2016). It consisted of two subscales: Entity Self Beliefs (items number 1 to 4) and Incremental Self Beliefs (items number 5 to 8). The entity or fixed mindset items were reverse coded so then the students who answered strongly disagree for these items showed agreement with the growth mindset. Higher scores for this subscale showed agreement with the incremental or growth mindset items. Part III was aimed at the participants’ levels of conceptual understanding of problem-solving in terms of their best understanding, partial understanding, incomplete understanding,
  • 30. 24 http://ijlter.org/index.php/ijlter functional misconception, and no understanding. The levels were determined based on Jensen and Finley’s (1995) theory. The problems provided focused on the following topics: expressions in multiple variables, systems of equations, graph labels and scales, quadratics, and exponential graphs. These problems were among the difficult items included in the work of Bacsal et al. (2022), Domingo et al. (2021), Ibañez and Pentang (2021), and Pentang et al. (2021) in their studies on mathematics problems concerning elementary preservice teachers in the same institution. The research instrument was pilot tested which demonstrated a high internal consistency (Cronbach’s alpha = 0.891). 3.4. Data Gathering Procedures The researchers secured approval and consent from the institution and the participants, respectively. Upon approval, the course professor assisted the researchers in gathering the data. At the start of the class, the participants familiarised themselves with the course expectations of the online learning environment. The participants completed an online survey about their socio- demographic characteristics and mindset towards problem-solving. In the following meetings in the first week, the course professor facilitated discussions on mathematical investigation, developing critical thinking and problem-solving skills, as well as math problem-solving techniques and strategies. Examples of how to solve different mathematics problems were presented which served as a review of the PMTs’ prior knowledge regarding their mathematics courses. The researchers oriented the participants of the Khan Academy online resource in the first meeting of the second week of class. Given how the participants have prior knowledge of the mathematics concepts from previous years, the Khan Academy platform offered them an opportunity to practice mathematical skills repeatedly to master the concepts. It also allowed them to track their progress as it provided instant feedback. Thus, the participants could fill in the gaps in their understanding by watching the related videos and getting hints or moving ahead. During the two weeks of the class meetings, the students independently practiced their problem-solving skills. The PMTs continued to do the practice exercises and watch videos, if necessary. In the next two weeks of the classes, the students answered the problem-solving questions in Google Classroom through Google Forms. Each problem set had four multiple-choice questions. The students wrote the solutions and explanations to their chosen answers in the multiple-choice area for each item question. After a month of online learning, the researchers gathered the data on the number of times each participant tried to answer the given five sets of problems to achieve mastery learning using the Khan Academy. 3.5. Data Analysis Descriptive statistics such as the mean and standard deviation were utilised to determine the PMTs’ mindset regarding the presence of a growth mindset or absence of a growth mindset, equivalently a fixed mindset, whereas frequency count and percentage were used to describe the PMTs’ level of conceptual understanding of problem-solving in an online environment. Besides this, a series of Analysis of Variance tests were employed to distinguish between the significant differences in the PMTs’ (a) mindset and (b) conceptual understanding in
  • 31. 25 http://ijlter.org/index.php/ijlter problem-solving when grouped according to their socio-demographic characteristics. Follow-up post hoc analysis was conducted using the Scheffe test. Furthermore, simple linear regression was utilised to find out whether the PMTs’ mindset was able to predict their level of conceptual understanding of problem- solving in an online environment. 4. Results and Discussion 4.1. PMTs’ Mindset The study found alarming results where the PMTs recorded a weak growth mindset (Mean = 3.98, SD = 0.16). The PMTs have limited their perspective regarding their intelligence and ability to do problem-solving. Still, the Khan Academy online intervention showed that the PMTs performed the exercises several times to reach the mastery level. As Table 2 reflects, the PMTs have a strong growth mindset regarding the time and effort needed to improve themselves. This demonstrates the PMTs' readiness to maximise their resources, learn from their mistakes, and accept challenges, as they consider failure as a chance to learn (Boaler, 2022; Dweck, 2016). Also, the PMTs seemed determined and persevering when it came to accomplishing whatever they set their minds to. Hence, the PMTs showed that they are most likely to demonstrate the characteristics of people with a growth mindset, such as hard work, perseverance, seeking help from others, and learning from feedback (Boaler, 2022; Dweck, 2016; Wilkins, 2014). There is still a need to cultivate a growth mindset among the PMTs. The PMTs’ growth mindset will be vital when addressing the poor status of mathematics education in the Philippines. Several online resources relevant to mathematics instructions may be adopted to fully prepare prospective math teachers. With “the teacher’s crucial role in facilitating and monitoring the student’s development” (Agayon et al., 2022), this weak growth mindset may be replicated in the PMTs’ students. Thus, the institution may provide ample training and activities to strengthen the PMTs’ growth mindset. In line with De Souza et al. (2021) and Pentang (2021b), the course professors concerned may further utilise several online teaching-learning tools and integrate available technology to communicate effective instructions. Table 2: PMTs’ mindset Parameters Mean SD Description *1. I don’t think I can do much to increase my intelligence. 3.84 1.26 WGM *2. I can learn new things but I can’t change my basic intelligence. 3.76 1.28 WGM *3. My intelligence is something about me that I can’t change very much. 3.98 1.29 WGM *4. To be honest, I don’t think I can change how intelligent I am. 3.93 1.25 WGM 5. With enough time and effort, I think I could significantly improve my intelligence level. 5.24 0.98 SGM 6. I believe I can always substantially improve my intelligence. 4.89 0.71 AGM
  • 32. 26 http://ijlter.org/index.php/ijlter 7. Regardless of my current intelligence level, I think I can change it quite a bit. 4.80 0.50 AGM 8. I believe I can change my basic intelligence level considerably over time. 4.87 0.69 AGM Pooled Mean 3.98 0.16 WGM Note: 5.16–6.00 = Strong Growth Mindset (SGM) *Reversely Coded 4.33–5.15 = Average Growth Mindset (AGM) 3.50–4.32 = Weak Growth Mindset (WGM) 2.67–3.49 = Weak Fixed Mindset (WFM) 1.84–2.66 = Average Fixed Mindset (AFM) 1.00–1.83 = Strong Fixed Mindset (SFM) 4.2. PMTs’ Level of Conceptual Understanding Most (40 out of 45) PMTs recorded their best conceptual understanding in problem-solving (Table 3). This shows that the PMTs have prior knowledge of the concepts and mastered the skills needed in problem-solving, which opposes the work of Ibañez and Pentang (2021) and Pentang et al. (2021) who revealed that the majority of the preservice teachers have functional misconceptions and an incomplete understanding of problem-solving. This result approves the effective use of Google Classroom with the Khan Academy as employed by the PMTs’ professors where the institution they belong to has led to the standard of being one of the best universities in Asia. The PMTs have shown their ability to impart knowledge and skills in mathematical problem-solving to their future students. Meanwhile, five PMTs had an incomplete to partial understanding, which can be attributed to a lack of contextual comprehension of the mathematical topics (Domingo et al., 2021; Pentang, 2021a; Pentang et al., 2021). This unwanted result may infer that the PMTs are not yet ready for the challenge to empower young Filipinos in their mathematics courses. Since partial understanding hampers the students’ understanding of the subsequent mathematical knowledge (Shockey & Pindiprolu, 2015), there is a need for an intervention to facilitate the preparation of the PMTs as math teachers. Other online-based platforms and resources may be utilised in the teaching-learning process to improve the PMTs’ conceptual understanding as well as to effectively strengthen their growth mindset in mathematics. Table 3: PMTs’ level of conceptual understanding Levels Frequency (n = 45) Percentage Best Understanding 40 88.89 Partial Understanding 4 8.89 Incomplete Understanding 1 2.22 Functional Misconception 0 0 No Understanding 0 0 4.3. Mindset in Problem-Solving When Grouped According to Socio- Demographic Characteristics ANOVA found there to be a significant difference in the PMTs’ mindset in terms of the CAT numerical proficiency of the PMTs, F(2,42) = 1.002, p < 0.05 (Table 4). PMTs with an above-average CAT numerical proficiency (Mean = 4.430, SD =
  • 33. 27 http://ijlter.org/index.php/ijlter 0.139) tended to have a stronger growth mindset in relation to problem-solving compared to the PMTs with an average (Mean = 3.973, SD = 0.155) and below- average (Mean = 3.937, SD = 0.197) CAT numerical proficiency. This means that numerical proficiency can influence mindset in relation to problem-solving. Overall, the study results suggested that there is no statistical evidence to say that there is a significant difference between the PMTs’ mindsets when grouped according to their socio-demographic characteristics except for their CAT Numerical Proficiency. The results can be related to the work of Boaler (2022) and Bower (2017) where people who have a growth mindset directly impact how they face academic challenges, including college examinations. However, this finding contradicts Li and Bates (2020) where admission test scores throughout the transition from high school to college were not found to be connected to a growth mindset. When establishing the PMTs' mindset, it would be beneficial to focus more on their academic profile, such as college admission test scores. The PMTs’ high school background may be included, and a stringent retention policy in the mathematics teacher education program may be implemented. Table 4: Socio-demographic characteristic differences in relation to the PMTs’ mindset towards problem-solving Socio-Demographic Characteristics Mean SD df F p Sex Male 3.946 0.137 1,43 -1.044 0.302 Female 4.000 0.168 Number of Siblings 0 - 2 4.000 0.150 2,42 0.496 0.613 3 - 5 3.989 0.162 6 and above 3.916 0.176 Birth Order Youngest 3.923 0.148 2,42 1.297 0.284 Middle 4.015 0.153 Eldest 3.983 0.178 Monthly Family Income Less than ₱11,690 3.890 0.152 2,42 0.409 0.667 Between ₱11,690 to ₱23,380 3.972 0.186 Between ₱23,381 to ₱46,761 4.083 0.235 Father’s Educational Attainment Did not finish Elementary 4.023 0.133 3,41 0.537 0.219 Elementary Graduate 3.964 0.249 High School Graduate 4.003 0.127 College Graduate 3.850 0.170 Mother’s Educational Attainment Did not finish Elementary 4.000 0.235 3,41 0.058 0.981 Elementary Graduate 4.000 0.166 High School Graduate 3.983 0.165 College Graduate 3.964 0.143 CAT Numerical Proficiency Below Average 3.609b 0.197 2,42 1.002 0.037
  • 34. 28 http://ijlter.org/index.php/ijlter Average 3.903b 0.155 Above Average 4.430a 0.137 Note: Means with the same subscript do not differ using Scheffe post hoc analysis. 4.4. Socio-Demographic Differences in the PMTs’ Conceptual Understanding of Problem-Solving ANOVA found there to be a significant difference in the PMTs’ conceptual understanding of problem-solving when grouped according to CAT Numerical Proficiency, F(2,42) = 3.464, p < 0.05 (Table 5). The post hoc analysis using Scheffe showed that PMTs with an above-average CAT Numerical Proficiency (Mean = 3.792, SD = 0.238) tended to have higher conceptual understanding of problem- solving compared to the PMTs with an average (Mean = 3.644, SD = 0.423) and below-average (Mean = 3.306, SD = 0.545) CAT numerical proficiency. This suggests that there is no significant difference between the PMTs’ conceptual understanding of problem-solving in an online environment when grouped according to the socio-demographic characteristics, except according to their CAT Numerical Proficiency. College admissions tests have a long track record of bringing value to higher education institutions by giving a predictive value of student success in entry- level college courses. This conforms to the work of Allen and Bond (2001), Mengash (2020), Montalbo et al. (2018), and Tesema (2014) but opposes Laus (2021). The college admission test is indeed a good measure for admitting potential preservice teachers. However, the institution may opt to accept those with a higher numerical proficiency to ensure that the PMTs are ready not only in their college preparation but also for the board exam and their anticipated teaching career. A strict admission policy may be implemented considering other backgrounds such as their high school grade point average and national achievement test results. Table 5: Socio-demographic characteristic differences in relation to the PMTs’ conceptual understanding of problem-solving Socio-Demographic Characteristics Mean SD df F p Sex Male 3.739 0.208 1,43 1.490 0.229 Female 3.571 0.494 Number of Siblings 0 - 2 3.783 0.130 2,42 0.989 0.380 3 - 5 3.565 0.503 6 and above 3.700 0.127 Birth Order Youngest 3.691 0.270 2,42 2.115 0.133 Middle 3.478 0.554 Eldest 3.800 0.126 Monthly Family Income Less than ₱11,690 3.576 0.475 2,42 0.862 0.429 Between ₱11,690 to ₱23,380 3.750 0.208 Between ₱23,381 to ₱46,761 3.850 0.212
  • 35. 29 http://ijlter.org/index.php/ijlter Father’s Educational Attainment Did not finish Elementary 3.521 0.655 3,41 0.237 0.870 Elementary Graduate 3.707 0.302 High School Graduate 3.617 0.439 College Graduate 3.680 0.148 Mother’s Educational Attainment Did not finish Elementary 3.875 0.354 3,41 0.258 0.855 Elementary Graduate 3.575 0.194 High School Graduate 3.610 0.472 College Graduate 3.650 0.475 CAT Numerical Proficiency Below Average 3.306b 0.545 2,42 3.464 0.041 Average 3.444b 0.423 Above Average 3.792a 0.238 Note: Means with the same subscript do not differ using the Scheffe post hoc analysis. 4.5. Mindset as a Predictor of the PMTs’ Conceptual Understanding of Problem- solving A simple linear regression analysis was performed to determine whether the PMTs’ mindset predicts their conceptual understanding of problem-solving in an online learning environment. Table 6 shows that the model is significant, R2 = 0.515, Adjusted R2 = 0.407, F(1,43) = 4.781, p < 0.05, indicating that PMTs who have a growth mindset tend to have higher conceptual understanding of problem- solving. The coefficient of determination (R2) means that about 51.5% of the variance in the PMTs’ levels of conceptual understanding in problem-solving in an online learning environment is explained or accounted for by their mindset. Similar to Hennessey (2019), the results show that a growth mindset is associated with better educational outcomes. The study also agrees that an individual with a growth mindset is inspired by mastery goals, finds inspiration in others’ success, and learns from feedback (Wilkins, 2014). This inspiration and reflection is cultivated in an online learning environment. Thus, the growth mindset must be instilled among PMTs while they are in their formative years in the teacher education program. This measure will be helpful as part of encouraging a full understanding of problem-solving. The results further prove that people who have a growth mindset accomplish much (Boaler, 2022) as the PMTs pursue becoming excellent math teachers. However, this study is contrary to the research conducted at the same institution concerning elementary preservice teachers. Although the preservice teachers try to develop a positive disposition, they find it hard to learn mathematics (Ibañez & Pentang, 2021). Even preservice teachers who have a growth mindset toward mathematics do not show a full conceptual understanding when solving problems (Pentang et al., 2021). The study still needs validation due to the limited sample size.
  • 36. 30 http://ijlter.org/index.php/ijlter Table 6: Simple linear regression analysis of the PMTs’ conceptual understanding in problem-solving as the criterion with mindset as the predictor Model Unstandardised Coefficients Standardised Coefficients t-value p-value B Std. Error Beta Constant 2.365 1.729 -1.983 0.055 Mindset 0.273 0.420 0.558 4.490 0.049 Note: R2 = 0.515, Adjusted R2 = 0.407, F(1,43) = 4.781, p < 0.05 5. Conclusion and Recommendations The PMTs have to develop a strong growth mindset which is necessary for them as future teachers. The PMTs’ preparedness to teach mathematics to young Filipinos cannot be assured with a fixed mindset. To foster a growth mindset among the PMTs, this may be integrated into the Psychology Course that the preservice teachers are taking. The PMTs with a growth mindset are more likely to know that academic success is no accident – it is related to learning, studying, hard work, perseverance, sacrifice, and love of what you are doing or learning to do. Additionally, the inclusion of growth mindset activities in the Mathematics Education Courses would be beneficial to the PMTs. This may result in more awareness that intelligence can be developed. This may lead to a stronger growth mindset among the PMTs who will shape the younger generation’s minds in the upcoming K-12 program. The PMTs attained the expected level of conceptual understanding in problem- solving. The PMTs showed a mastery of skills in mathematical problem-solving due to their strong academic background combined with the online intervention via the Khan Academy activities. Nevertheless, it is noteworthy that there are still a handful of them who have gaps in their conceptual understanding of problem- solving. It is good to advocate the use of an open-source platform like the Khan Academy to enhance the PMTs’ conceptual understanding. They are likely to be motivated to have mastery skills through independent learning. It is also a useful intervention for those who exhibit a partial or incomplete understanding of the mathematics concepts. Since the PMTs with a higher CAT Numerical Proficiency tend to have a stronger growth mindset and higher conceptual understanding of problem-solving, it is proposed that the college admission test is used in the admission of potential PMT applicants. Besides this, mindset predicts the level of conceptual understanding in problem-solving in an online environment. With the use of online resources through Google Classroom and the Khan Academy, it is profitable to develop and implement online mathematics lessons that incorporate a growth mindset and conceptual understanding. The continuous use of online resources (e.g., lesson videos and practice exercises) via the Khan Academy even in the post-pandemic time is highly recommended even after limited face-to-face classes are implemented. Online resources are beneficial for the PMTs’ growth mindset and conceptual understanding of mathematical problem-solving. This may also help the PMTs to prepare for the board examinations and their future teaching career. With the limitations posed
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