IJLTER.ORG Vol 21 No 8 August 2022

International Journal
of
Learning, Teaching
And
Educational Research
p-ISSN:
1694-2493
e-ISSN:
1694-2116
IJLTER.ORG
Vol.21 No.8

International Journal of Learning, Teaching and Educational Research
(IJLTER)
Vol. 21, No. 8 (August 2022)
Print version: 1694-2493
Online version: 1694-2116
IJLTER
International Journal of Learning, Teaching and Educational Research (IJLTER)
Vol. 21, No. 8
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VOLUME 21 NUMBER 8 August 2022
Table of Contents
Mathematical Knowledge for Teaching in Further Education and Training Phase: Evidence from Entry Level
Student Teachers’ Baseline Assessments .............................................................................................................................1
Folake Modupe Adelabu, Jogymol Kalariparampil Alex
Exploring the Use of Chemistry-based Computer Simulations and Animations Instructional Activities to Support
Students’ Learning of Science Process Skills..................................................................................................................... 21
Flavia Beichumila, Eugenia Kafanabo, Bernard Bahati
Issues Surrounding Teachers’ Readiness in Implementing the Competency-Based ‘O’ Level Geography Syllabus
4022 in Zimbabwe.................................................................................................................................................................43
Paul Chanda, Tafirenyika Mafugu
Exploring Headteachers, Teachers and Learners’ Perceptions of Instructional Effectiveness of Distance Trained
Teachers ................................................................................................................................................................................. 58
Vincent Mensah Minadzi, Ernest Kofi Davis, Bethel Tawiah Ababio
The Role of Middle Managers in Strategy Execution in two Colleges at a South African Higher Education
Institution (HEI)....................................................................................................................................................................75
Ntokozo Mngadi, Cecile N. Gerwel Proches
Learners’ Active Engagement in Searching and Designing Learning Materials through a Hands-on Instructional
Model...................................................................................................................................................................................... 92
Esther S. Kibga, Emmanuel Gakuba, John Sentongo
The Development of e-Reading to Improve English Reading Ability and Energise Thai Learners’ Self-Directed
Learning Strategies ............................................................................................................................................................. 109
Pongpatchara Kawinkoonlasate
The Role of Mother’s Education and Early Skills in Language and Literacy Learning Opportunities ................... 129
Dyah Lyesmaya, Bachrudin Musthafa, Dadang Sunendar
Exploring Assessment Techniques that Integrate Soft Skills in Teaching Mathematics in Secondary Schools in
Zambia.................................................................................................................................................................................. 144
Chileshe Busaka, Septimi Reuben Kitta, Odette Umugiraneza
Generic Competences of University Students from Peru and Cuba............................................................................ 163
Miguel A. Saavedra-López, Xiomara M. Calle-Ramírez, Karel Llopiz-Guerra, Marieta Alvarez Insua, Tania Hernández
Nodarse, Julio Cjuno, Andrea Moya, Ronald M. Hernández
Representation of Nature of Science Aspects in Secondary School Physics Curricula in East African Community
Countries.............................................................................................................................................................................. 175
Jean Bosco Bugingo, Lakhan Lal Yadav, K.K Mashood

Using Graphic Oral History Texts to Operationalize the TEIL Paradigm and Multimodality in the Malaysian
English Language Classroom............................................................................................................................................ 202
Said Ahmed Mustafa Ibrahim, Azlina Abdul Aziz, Nur Ehsan Mohd Said, Hanita Hanim Ismail
Remote Teaching and Learning at a South African University During Covid-19 Lockdown: Moments of
Resilience, Agency and Resignation in First-Year Students’ Online Discussions ...................................................... 219
Pineteh E. Angu
Enhancing Upper Secondary Learners’ Problem-solving Abilities using Problem-based Learning in Mathematics
............................................................................................................................................................................................... 235
Aline Dorimana, Alphonse Uworwabayeho, Gabriel Nizeyimana
The Development of Mobile Applications for Language Learning: A Systematic Review of Theoretical
Frameworks......................................................................................................................................................................... 253
Kee-Man Chuah, Muhammad Kamarul Kabilan
The Effect of Professional Training on In-service Secondary School Physics 'Teachers' Motivation to Use Problem-
Based Learning.................................................................................................................................................................... 271
Stella Teddy Kanyesigye, Jean Uwamahoro, Imelda Kemeza
Knowledge of Some Evidence-Based Practices Utilized for Managing Behavioral Problems in Students with
Disabilities and Barriers to Implementation: Educators' Perspectives ........................................................................ 288
Hajar Almutlaq
Exploring Virtual Reality-based Teaching Capacities: Focusing on Survival Swimming during COVID-19 ........ 307
Yoo Churl Shin, Chulwoo Kim
Math Anxiety, Math Achievement and Gender Differences among Primary School Children and their Parents
from Palestine...................................................................................................................................................................... 326
Nagham Anbar, Lavinia Cheie, Laura Visu-Petra
Investigating the Tertiary Level Students’ Practice of Collaborative Learning in English Language Classrooms,
and Its Implications at Public Universities and at Arabic Institutions ........................................................................ 345
Md Anwar, Md Nurul Ahad, Md. Kamrul Hasan
Navigating the New Covid-19 Normal: The Challenges and Attitudes of Teachers and Students during Online
Learning in Qatar................................................................................................................................................................ 368
Caleb Moyo, Selaelo Maifala
The Classical Test or Item Response Measurement Theory: The Status of the Framework at the Examination
Council of Lesotho.............................................................................................................................................................. 384
Musa Adekunle Ayanwale, Julia Chere-Masopha, Malebohang Catherine Morena
Addressing the Issues in Democratic Civilian Control in Ukraine through Updating the Refresher Course for
Civil Servants ...................................................................................................................................................................... 407
Valentyna I. Bobrytska, Leonid V. Bobrytskyi, Andriy L. Bobrytskyi, Svitlana M. Protska
Supervisory Performance of Cooperative Teachers in Improving the Professional Preparation of Student Teachers
............................................................................................................................................................................................... 425
Ali Ahmad Al-Barakat, Rommel Mahmoud Al Ali, Mu’aweya Mohammad Al-Hassan, Omayya M. Al-Hassan

1
©Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License (CC BY-NC-ND 4.0).
Vol. 21, No. 8, pp. 1-20, August 2022
https://doi.org/10.26803/ijlter.21.8.1
Received Mar 7, 2022; Revised May 31, 2022; Accepted Jul 17, 2022
Mathematical Knowledge for Teaching in
Further Education and Training Phase: Evidence
from Entry Level Student Teachers’ Baseline
Assessments
Folake Modupe Adelabu*
Walter Sisulu University, Nelson Mandela Drive, Mthatha, South Africa
Jogymol Kalariparampil Alex
Walter Sisulu University, Nelson Mandela Drive, Mthatha, South Africa
Abstract. This paper investigates entry-level student teachers'
mathematical knowledge for teaching in the Further Education and
Training phase (FET) through Baseline Assessment. The study employed
a quantitative research technique. The data collection instrument was a
mathematics subject knowledge test (Baseline Assessment) for FET phase
student teachers. Purposive and convenient sampling methods were
employed in the study. The study enlisted the participation of 222 first-
year mathematics education student teachers from a rural Higher
Education Institution (HEI) specialising in FET phase mathematics
teaching. One hundred and seventy-five (175) student teachers completed
the Baseline Assessment for all grades in this study (10, 11, and 12). The
Baseline Assessment findings were examined using descriptive statistics.
The results revealed that student teachers have a moderate knowledge of
mathematics topics in the FET phase at the entry-level. In addition, an
adequate level of understanding for teaching Grades 10 and 12 Patterns,
Functions, Algebra, Space and Shape (Geometry), and Functional
Relationships. While the elementary level of understanding for teaching
grade 10 Measurement, Grade 11 Patterns, Functions, Algebra, and
Trigonometry and Grade 12 Space and Shape (Geometry). There is no
level of understanding for teaching FET phase Data and Statistics and
Probability. The paper suggests that student teachers must develop a
comprehensive understanding of the mathematics curriculum with the
assistance of teacher educators in HEIs.
Keywords: Mathematical knowledge; student teachers’ entry-level;
Baseline Assessments; Further Education and Training
*
Corresponding author: Folake Modupe Adelabu, fadelabu@wsu.ac.za

2
http://ijlter.org/index.php/ijlter
1. Introduction
There was much emphasis on the teachers’ content knowledge in mathematics in
2013, according to Julie (2019). The focus on content knowledge was due to the
Diagnostic Measures for the Trends in Mathematics and Science Study (TIMSS)
2011, which focused mainly on student and mathematics teacher performance in
public schools. Based on the test results, Reddy et al. (2016) concluded a need for
significant improvement in teachers' content knowledge of classroom
mathematics. They found that most teachers' lack of mathematical content
knowledge is a contributing factor to learners' poor mathematics performance in
most South African schools. According to research, several studies in developed
countries and developing countries suggest that teachers’ content knowledge for
teaching mathematics contributes significantly and is a good predictor of student
achievement (Mullens, Murnane and Willett, 1996; Altinok, 2013). (e.g. Norton
2019, Shepherd, 2013) (Monk, 1994; Wayne & Youngs, 2003; Hill, Rowan & Ball,
2005; Rivkin, Hanushek & Kain, 2005). This paper presents the findings of a
baseline assessment that investigated the mathematical subject content knowledge
of entry-level student teachers who are being trained to teach mathematics in the
Further Education and Training (FET) phase in South Africa.
The South African educational system is divided into three hierarchical phases:
General Education and Training (GET), Further Education and Training (FET), and
s Higher Education (HE). The national matriculation examination takes place at
the end of Grade 12 to mark the shift from the GET to the FET phase of schooling
(DBE, 2011). Secondary school is known as the FET phase, where learners' abilities
are improved to prepare them for careers of their choice. During this stage, learners
lay the groundwork for future success. At the end of the FET phase, learners
prepare to transition into university and higher education. According to the DBE
(2011), it is expected that all learners will have a sound foundational grasp of the
fundamentals that will assist them in choosing courses or study programmes at a
higher education institution. Therefore, at this stage, learners concentrate on
course selections consistent with their unique professional objectives and goals,
whether in Commerce, Humanities, or Sciences.
To advance to the HE level for Bachelor’s degree in South Africa, learners must
attain at least 40% minimum passes in three or four subjects, including one official
home language in the national matriculation and school-leaving examination
(DBE, 2012). Therefore, the teachers who specialise in the FET phase during the
Bachelor of Education degree teach subjects in the FET phase in secondary schools.
For example, a student teacher with a degree in FET phase mathematics learns how
to teach mathematics to learners in Grades 10 to 12. As a result, the student-teacher
devotes themselves to mathematics as a subject specialist. The student-teacher
concentrates on merging basic mathematics knowledge with efficiently
communicating the knowledge to prospective Grades 10 to 12 learners. According
to DBE (2011), the link between the Senior Phase and the Higher Education band
is FET. Therefore, all learners who complete this phase gain a functional
understanding of mathematics, allowing them to make sense of society. FET
learners get exposed to various mathematical experiences that provide them with
numerous possibilities to build mathematical reasoning and creative skills in

3
preparation for abstract mathematics in higher education. In this regard, the
student teachers for the FET phase need to be prepared for the task and the
comprehensive role ahead since studies show that learners' poor performance in
mathematics is due to the teachers' poor mathematical content knowledge (Pino-
Fan, Assis & Castro, 2015; Reddy et al., 2016; Siyepu & Vimbelo, 2021; Verster,
2018). In recent times, there has been increasing attention to investigating
knowledge that mathematics teachers should have to execute an adequate control
of the learners’ learning. Hence, to quantify the mathematical knowledge content
for teaching and understanding level of the student-teacher for the FET phase, this
paper reports on the Baseline Assessment that investigated the mathematical
content knowledge of entry-level FET phase student teachers for teaching
mathematics in South Africa.
Specifically, it sought an answer to the following questions:
1. What is the mathematical content knowledge of student teachers for
teaching FET phase Mathematics through baseline assessment?
2. What is the level of understanding of the entry-level student teachers'
mathematical content knowledge for teaching FET phase mathematics
through baseline assessment?
2. Literature review and theoretical framework
2.1 Mathematical Knowledge for Teaching
The theoretical framework that underpins this study is Mathematical Content
Knowledge. Mathematical Content Knowledge (MCK) was built on Shulman’s
pedagogical content knowledge by Ball and colleagues in 2005. Mathematical
Content Knowledge (MCK) is an essential factor to consider when teaching
mathematics because it influences teachers' decisions towards teaching and
learning mathematics. The entry-level mathematics subject knowledge of the
student teachers for teaching in the FET phase is crucial because it determines the
student achievement in mathematics (Reddy et al., 2016). Jacinto & Jakobsen (2020)
argues that several studies highlight that teachers should be able to teach what
they know and comprehend. Jakimovik (2013) further supports this, who states
that teachers should have the appropriate MCK for effective teaching and learning.
(According to Narh-Kert (2021), effective mathematics teachers know the
mathematics relevant to the grade level and the value of the mathematics courses
they teach. Therefore, the authors believe that the quality of FET mathematics
teaching depends on teachers' knowledge of the content in the phase.
Deborah Ball and colleagues in Michigan created a test for mathematics teachers'
professional expertise aimed at elementary school teachers in the United States
(Ball, Hill & Bass, 2005) to assess their MCK for the grades they teach. The test was
a multiple-choice measure of number and operation, pattern, function, algebra and
geometry. This test became a measure and was used to evaluate the MCK of
mathematics educators, mathematicians, professional developers, project staff,
and classroom teachers. Ball et al. (2005) discovered that teachers lack sound
mathematical knowledge and skills. The test results led to the definition of
mathematical content knowledge and its two components, Common Content
Knowledge (CCK) and Specialised Content Knowledge (SCK) (Ball et al. 2005).

4
These researchers further explained that most of the in-service mathematics
teachers in the U.S are graduates of a weak system. Therefore, there is a dire need
to improve the mathematical knowledge of educators. Ball et al. (2005) state that
the system clarifies that these in-service teachers learned mathematics with
irregularity and insufficient mathematical knowledge, leading to many teachers'
weak mathematical knowledge. To improve teachers' MCK, Ball et al. (2005) test
approach is embedded in Shulman's (1986, 1987) taxonomy of teacher knowledge.
Shulman makes a theoretical distinction between pedagogical content knowledge
(PCK), which is the knowledge of how to make the subject accessible to others, and
content knowledge (CK), which is the knowledge of deep comprehension of the
domain itself (Shulman 1986). As a result, Shulman (1986, 1987) and Ball et al.
(2005) use mathematical subject knowledge to assess teachers’ performance. Both
rely on a distinct teaching philosophy that emphasises teachers' capacity to
translate content knowledge into pedagogical strategies that help students learn
effectively. Jacinto and Jakobsen (2020) state that Mathematical Knowledge for
Teaching (MKfT) also provides a long-term theoretical foundation and practical
ramifications for teacher preparation programs. (. Hence, the theory of MKfT
proposed by Ball, Thames, and Phelps (2008) is used in this study.
According to the MK, the following domains are the key focus: common content
knowledge (CCK), horizon content knowledge (HCK), specialised content
knowledge (SCK), knowledge of content and students (KCS), knowledge of
content and teaching (KCT), and knowledge of content and curriculum (KCC)
(Jacinto & Jakobsen, 2020).
• The first domain (CCK) refers to mathematical knowledge that is
frequently utilised and created in various settings, including outside of
formal education. This form of knowledge consists of questions that can be
answered by those who know mathematics rather than specialised
understandings (Ball et al., 2008).
• CCK is demonstrated by using an algorithm to solve an addition problem.
• Horizon content knowledge (HCK) is the knowledge of "how the content
being taught fits into and is connected to the larger disciplinary domain."
This domain includes knowing the origins and concepts of the subject and
how useful it may be to students' learning. HCK allows teachers to "make
judgements about the value of particular concepts" raised by students, as
well as address "the discipline with integrity, all resources for balancing
the core goal of linking students to a large and highly developed area" (Ball
et al., 2008: 400; Jacinto & Jakobsen, 2020).
• Specialized Content Knowledge (SCK) is defined as "the mathematical
knowledge specific to the teaching profession." It entails an unusual form
of mathematical unpacking that is not required in environments other than
education. It necessitates knowledge that extends beyond a thorough
understanding of the subject matter. Teachers' roles include being able to
present mathematical ideas during instruction and responding to students'
queries, both of which necessitate mathematical expertise specific to
teaching mathematics (Ball et al., 2008: 400; Jacinto & Jakobsen, 2020).

5
• Knowledge of content and students (KCS) was another sub-construct that
needed to be redefined because it did not fit the criterion for one-
dimensionality. For instance, respondents such as teachers, non-teachers,
and mathematicians used standard mathematical procedures to answer the
items designed to reflect KCS, according to cognitive tracing interviews.
Furthermore, the use of multiple-choice items in KCS measurement was
reviewed in favour of open-ended questions.
Teachers utilise CCK to plan and teach mathematics concepts, allowing them to
evaluate students' answers, respond to concept definitions, and complete a
mathematical approach. Therefore, any adult with a well-developed CCK but not
the knowledge required to educate, such as new student teachers entering Higher
Education Institutions (HEI), may have a well-developed CCK but lack the
necessary knowledge to teach. Hence, this study investigates the mathematical
content knowledge of entry-level student teachers in the FET phase training phase
for teaching mathematics through Baseline Assessment in South Africa.
2.2. Educational assessment
Educational assessment supports knowledge, skills, attitudes, and beliefs, usually
in measurable terms. Assessment is an essential component of a coherent
educational experience (Sarka, Lijalem & Shibiru, 2017). According to Sarka et al.
(2017), assessment methods considerably influence the breadth and depth of
students' learning, that is, the approach to studying and retention, with either a
strong influence or a lack thereof. Assessments are used in a variety of ways, which
include motivating students and focusing their attention on what is essential,
providing feedback on the students' thinking, determining what understandings
and ideas that are within the zone of proximal development, and gauging the
effectiveness of teaching, including identifying parts of lessons that could be
improved. (Patterson, Parrott & Belnap, 2020).
Assessment is a process of collecting, analysing and interpreting information to
assist teachers, parents and other stakeholders in making decisions about the
progress of learners (DBE, 2011). Therefore, assessment serves a wide range of
functions, including permission to progress to the next level, classifying students'
performance in ranked order, improving their learning and evaluating the success
of a particular technique for improvement (Sarka et al., 2017). Furthermore, the
assessment goals include curriculum development, teaching, gathering data to aid
decision-making, communication with stakeholders, instructional improvement,
program support, and motivation (Pattersonet et al., 2020; Sarka et al., 2017;
Wilson, 2018).
According to the DBE (2011), there are various types of assessments. These include
formative assessment, summative assessment, diagnostic and baseline assessment.
Formative assessment is assessing students' progress and knowledge regularly to
identify learning needs and adapt teaching accordingly (Wilson, 2018). The Centre
for Educational Research and Innovation (CERI) (2008) states that teachers who
use formative assessment methods and strategies are better equipped to address
the requirements of a wide range of students. This can be done by differentiating

6
and adapting their instruction to enhance students' achievement to achieve more
significant equity in their learning outcomes. Formative assessment can also be
defined as the activity that supports learning by giving information that can be
utilised as feedback by teachers and students to evaluate themselves and each
other to improve the teaching and learning activities. Therefore, formative
assessment is one of the primary core activities in teachers' work (Wilson, 2018).
Summative assessments are used to determine what students have learned at the
end of a unit and are used as a measure for promotion purposes. Dolin et al. (2018)
state that summative assessment ensures that students have fulfilled the
requirements to achieve certification for school completion or admittance into
higher education institutions or occupations. In addition, when an assessment
activity is used to provide a summary of what a student knows, understands, and
can do rather than to aid in the modification of the teaching and learning activities
in which the student is engaged by providing feedback, it is considered summative
(CERI, 2008; Wilson, 2018). Summative assessments are used in education for a
variety of reasons. Individual students and their parents discuss progress and
receive an overall assessment that includes praise, inspiration, and guidance for
what has been accomplished. Summative assessments provide a comprehensive
guide to the effectiveness of the students' work, which may be externally
standardised ((Dolin et al., 2018; Wilson, 2018). Wilson (2018) agrees that
summative assessments assist schools in making the best possible grouping and
subject choices for the learners. Both a school and a public authority employ
summative assessments to inform teachers and the school’s accountability. As a
result, a common element of summative assessments is that the results are utilised
to guide future decisions.
The initial assessment occurs when a student begins a new learning program. The
initial assessment is a comprehensive process in which students start to piece
together a picture of an individual's accomplishments, abilities, interests, prior
learning experiences, ambitions, and the learning requirements associated with
those ambitions. The information from the initial assessment is used to negotiate a
program or course (Quality Improvement Agency (QI), 2008). Diagnostic
assessment supports the identification of individual learning strengths and
weaknesses. It provides learning objectives and the necessary teaching and
learning strategies for achievement. This is necessary because many students excel
in some areas but struggle in others. Diagnostic evaluation occurs at the start of a
learning program and again when required. It has to do with the specialised talents
needed for specific tasks. The information acquired from the initial examination is
supplemented by diagnostic testing (QIA, 2008).
Baseline assessment commonly used in early childhood education gathers
information regarding a child's development or achievement as they transition to
a new environment or grade. These assessments are conducted in various ways,
ranging from casual observations to standardised examinations. The information
gathered from these assessments assists educators in fulfilling the learner's
requirements, highlighting their strengths and areas for improvement. All these
assessments are helpful in their capacity to assess the learners. Baseline

7
assessments assist schools in understanding the students' requirements. It also aids
in determining learners' learning capability and potential and assessing the
influence the schools have on learners. Information from baseline assessment
facilitates schools in customising planning, teaching, and learning, including
determining the most effective resource allocation to track learners’ progress
throughout the school year. According to Khuzwayo and Khuzwayo (2020) and
Tomlinson (2020), the baseline assessment findings provide information to the
teacher regarding the learners’ abilities and knowledge gaps. This evidence assists
the teacher in organising learning content, selecting, and matching teaching and
learning approaches with the learning needs of individual students or groups of
students.
The three assessments (The Initial, Diagnostic, and Baseline assessments) are
interrelated in education. The assessments are always administered at the
beginning or entry of students into the school, measure the strengths and
weaknesses, and deduce places for improvement in a learner. The assessments are
embedded in formative assessment.
The baseline assessment (CAMI) utilised in this paper is in accordance with the
Curriculum and Assessment Policy Statement (CAPS) for Further Education and
Training in South Africa. The licensed online Computer Aided Mathematics
Instruction (CAMI) software is used to program the baseline assessments. CAMI
is a high-productivity software system that can improve mathematics grades in a
minimal amount of time. One of the software's functions is to correct extension
work for a more advanced student. CAMI employs the computer as a "Drill and
Practice" system rather than a tutoring system because it focuses on knowledge
retention (see www.cami.co.za).
The main mathematics topics in the FET phase are Functions; Number Patterns,
Sequences, and Series; Finance, growth, and decay; Algebra; Differential Calculus;
Probability; Euclidean Geometry and Measurement; Analytical Geometry;
Trigonometry; and Statistics. The topics constitute Papers 1 and 2 of the national
examinations in South Africa. The weighting of content areas is shown in Table 1
below:
Table 1: The weight of content areas description of FET’s mathematics topics
The weighting of Content Areas
Description Grade 10 Grade 11 Grade 12
Paper 1 (Grades 12: bookwork: maximum 6 marks)
Algebra and Equations (and Inequalities) 30 ± 3 45 ± 3 25 ± 3
Patterns and Sequences 15 ± 3 25 ± 3 25 ± 3
Finance and Growth 10 ± 3
Finance, growth, and decay 15 ± 3 15 ± 3
Functions and Graphs 30 ± 3 45 ± 3 35 ± 3
Differential Calculus 35 ± 3
Probability 15 ± 3 20 ± 3 15 ± 3
TOTAL 100 150 150

8
Paper 2: Grade 11 and 12: theorems and /or trigonometric proofs: maximum 12 marks
Description Grade 10 Grade 11 Grade 12
Statistics 15 ± 3 20 ± 3 20 ± 3
Analytical Geometry 15 ± 3 30 ± 3 40 ± 3
Trigonometry 40 ± 3 50 ± 3 40 ± 3
Euclidean Geometry and Measurement 30 ± 3 50 ± 3 50 ± 3
TOTAL 100 150 150
(Source: CAPS Documents, DBE, 2011)
3. Research methodology
3.1 Research design and sampling
A quantitative research design and methodology were used in this study. The data
collection instrument was a mathematics subject knowledge test (Baseline
Assessment by CAMI) for FET phase student teachers. The Baseline Assessment
was used to assess the entry-level student teachers' mathematical content
knowledge through online Computer Aided Mathematics Instruction (CAMI)
software. The CAMI programme is part of the ongoing research conducted in the
Mathematics Education and Research Centre established in rural higher education
(HEI) in South Africa. Two hundred and twenty-two (222) first-year mathematics
student teachers specialising in FET phase mathematics teaching participated in
the study. This paper included 175 student teachers who completed the Baseline
Assessment for all grades (10, 11, and 12). Purposive and convenience samplings
were utilised to collect data. Participation in the CAMI Baseline Assessment was
done in a controlled environment in an invigilated computer lab for two weeks.
The majority of the student teachers enrolled in the FET Teaching Bachelor of
Education Course came from rural secondary schools and had not experienced
computer-assisted learning.
3.2 Data collection
3.2.1 Baseline Assessment through CAMI
Computer-Aided Mathematics Instruction (CAMI) baseline assessment is an
online assessment available in the CAMI EduSuite program (further information
is available from www.cami.co.za). The FET baseline assessment consisted of a 60-
minute online test with 25 items that student teachers can easily access through
internet connectivity. CAMI was installed on the lab computers, and all student
teachers participating in the FET Mathematics courses were given credentials to
log in and access the FET Baseline Test (Grades 10, 11, and 12). After completing
the Baseline Assessment, the teacher can access their results.
The navigation to the FET Baseline Test on the CAMI package is illustrated in the
figure below.

9
Figure 1: The navigation to the senior phase Baseline Test on the CAMI package
After logging into the system, student teachers should go to the Assessment box
and click ‘Do assessment’, which will bring up the Baseline and Grades
assessments. After that, the student teachers choose Grades 10, 11, and 12 from the
Baseline Assessment and complete the test items one by one, as shown in figure 1.
Each of the Baseline Assessments for Grades 10, 11, and 12 has 25 items.
3.3. Data analysis
The findings of the Baseline Assessment were analyses using descriptive statistics.
The frequency distributions were used to establish the mathematical content
knowledge and the level of understanding of the contents for teaching
mathematics in the FET phase. One-way ANOVA was used to establish the
variability of the mean performance of the student teachers from grade to grade.
Because the program includes the Baseline Assessment, all the questions on each
grade are valid. All ethical requirements were completed, and the student teachers
participated (Ethical Clearance Number: FEDSRECC001-06-21).
Below are some of the sample items from the CAMI Baseline Assessment.
Figure 2: assessment items no. 9 and 16 (source: www.cami.co.za)

10
Figure 3: assessment items no. 20 and 25 (source: www.cami.co.za)
According to international benchmarks, 60 per cent was used as the
understanding level of mathematical content knowledge in the FET phase in this
study. The national codes and descriptions of the percentages that qualify learner
performance can be found in Table 2 (DBE, 2011).
Table 2: Codes and percentages for recording and reporting in Grades R-12
performances
Achievement level Achievement description Marks %
7 Outstanding achievement 80 – 100
6 Meritorious achievement 70 – 79
5 Substantial achievement 60 – 69
4 Adequate achievement 50 – 59
3 Moderate achievement 40 – 49
2 Elementary achievement 30 – 39
1 Not achieved 0 – 29
(Source: DBE, 2011)
According to the benchmarking, "Substantial achievement" was the minimum
score for student teachers' subject content knowledge mastery at a specific grade
level.
4. Results
4.1. Baseline assessment of the mathematical content knowledge of student
teachers for teaching FET phase Mathematics
The mean of the Baseline Assessment in the three grades of the FET phase was
determined using a one-way single factor ANOVA. The following tables depict the
outcome:
Table 3: ANOVA Summary table
Groups Count Sum Average Variance
Grade 10 175 7832 44.75429 113.3588
Grade 11 175 6580 37.6 166.9885
Grade 12 175 5756 32.89143 171.7985

11
Table 4: One-way ANOVA single factor
Source of
Variation
Sum of
Squares
Df Mean
Squares
F P-value F crit
Between Groups 12488.11 2 6244.053 41.42947 2E-17 3.012991
Within Groups 78673.37 522 150.7153
Total 91161.48 524
Notes: Df – Degree of freedom; P-value: p < 0.05
As shown in Table 3, the mean strengths range from 32.89 for Grade 12 to 44.75 for
Grade 10, indicating that the sample means are different. That is to say; the average
score is not the same. Table 4 shows that the p-value of 2 ×10-17 is less than the
significant level of 0.05, implying that the Baseline Assessment mean scores for
FET student teachers are not equal. This means that student teachers' average
performance in the FET phase varies from grade to grade. The mean percentage
scores of student teachers in the FET phase Baseline Assessment are shown in the
graph below.
Figures 4: The mean percentage scores of the student teachers in the FET phase
Baseline Assessment according to content areas
The mean percentage scores of student teachers in the FET phase Baseline
Assessment according to the content areas are shown in Figure 4 above. The results
revealed that the students' mean percentage in Space and Shape (Geometry) Grade
10 was 59.18%, Patterns, Functions, and Algebra at 50.96%, measurement at 36.48
per cent, data and statistics at 19.13%, and probability at 2.55%. Patterns,
Functions, and Algebra (39.59%), Trigonometry (30.35%), and Space and Shape
(Geometry) (27.69%) are the average percentage scores of student teachers in
Grade 11. The student teachers had the highest mean percentage in Functional
Relationships Grade 12 (54.28%), 38.07%percent in Space and Shape (Geometry),
27.60% in Trigonometry, and 22.68% in Patterns, Functions, and Algebra (see
Figure 4).

12
According to the above findings, student teachers scored better in Grade 10
concepts than in Grades 11 and 12 during the FET phase. Patterns, Functions, and
Algebra in Grade 12 and Measurement and Space and Shape (Geometry) in Grade
11 were all below average. Students in Grades 11 and 12 should study
trigonometry and functional relationships, whereas, in Grade 10, students should
study Data Statistics and Probability. The frequency distribution of the student
teachers' achievements was analysed to corroborate the study's findings. Figure 5
depicts the frequency distribution of student-teacher marks for the FET phase:
Figure 5: Frequency distribution of the student teachers’ achievements in Grades 10,
11, and 12
The percentage marks from the CAMI Baseline Assessment for Grades 10, 11, and
12 for entry-level student teachers are shown in different percentiles in Figure 5.
As indicated in the graph, most student teachers' achievements for Grades 10, 11,
and 12 are within 40% and 49% of each other, corresponding to 66, 56, and 48 in
Grades 10, 11, and 12, respectively. For the three FET Grades (10, 11 and 12), the
number of student-teacher marks above 50% is 61, 29, and 18. The number of
student teachers with scores below 30% in Grades 10, 11, and 12 is 16, 56, and 71.
In Grades 10, 11, and 12, - 32, 34, and 38, student teachers within 30 per cent and
39 per cent, respectively. In Grades 10 and 12, no student-teacher receives a score
higher than 70%. In Grade 11, just two student teachers receive a score of more
than 70%. The signal denotes moderate achievement in Grades 10, 11, and 12.
According to national codes and descriptions (DBE, 2011), the number of 'not
achieved' student teachers in the FET phase Baseline Assessment is 16, 56, and 71
in Grades 10, 11, and 12, respectively as shown in Figure 5 above. In Grades 10, 11,
and 12; 32, 34, and 38 of the student teachers have elementary achievement, 66, 56,
and 48 have moderate achievement, 43, 18, and 17 have adequate achievement, 18,
9 and 1 have substantial achievement, respectively and just two have meritorious
achievement at Grade 11 level. In the FET phase Baseline Assessment, no student-
teacher achieved the outstanding achievement (80% and above). According to the
findings, the student teachers have a moderate level of accomplishment. As a

13
result, student teachers' entry-level mathematical content knowledge in the FET
phase is of modest achievement.
4.2. Level of understanding of student teachers’ mathematical content
knowledge for teaching FET phase mathematics through baseline assessment
Table 5 shows the student teachers' mathematical content knowledge level for
teaching the FET phase in each grade according to the content areas.
Table 5: The understanding level of student teachers’ mathematical content knowledge
for teaching FET phase mathematics according to content areas
Achievement
level
Achievement
description
Grade 10 Grade 11 Grade 12
7 Outstanding
achievement
- - -
6 Meritorious
achievement
- - -
5 Substantial
achievement
- - -
4 Adequate
achievement
Patterns,
Functions, and
Algebra; Space
and Shape
(Geometry)
- Functional
Relationships
3 Moderate
achievement
- - -
2 Elementary
achievement
Measurement Patterns,
Functions, and
Algebra;
Trigonometry
Space and Shape
(Geometry)
1 Not achieved Data and
statistics;
Probability;
Trigonometry;
Functional
Relationships
Data and
statistics; Space
and Shape
(Geometry);
Measurement
Probability;
Functional
Relationships
Data and
statistics;
Measurement;
Probability;
Patterns,
Functions, and
Algebra;
Trigonometry
The results given in Table 5 show the level of understanding of the student teachers
according to the content areas. The findings revealed that student teachers have an
adequate level of understanding of Patterns, Functions, Algebra and Space and
Shape (Geometry) in Grade 10 and Functional Relationships in Grade 12.
Furthermore, the student teachers have an elementary level of understanding of
Measurement in Grade 10, Patterns, Functions, and Algebra, Trigonometry in
Grade 11, and Space and Shape (Geometry) in Grade 12. The student teachers have
no level of understanding of Data and statistics and Probability in any of the
grades, that is, Grade 10, 11 and 12. The finding indicated that the level of
understanding of the student teachers’ mathematical content knowledge for
teaching Grade 10 Patterns, Functions, and Algebra, as well as Space and Shape
(Geometry) and Grade 12 Functional Relationships, is adequate level. While the
level of understanding of the student teachers’ mathematical content knowledge

14
for teaching Grade 10 Measurement, Grade 11 Patterns, Functions, Algebra, and
Trigonometry and Grade 12 Space and Shape (Geometry) is elementary level. In
addition, the results revealed that the student teachers did not have sufficient
understanding of the mathematical content knowledge for teaching FET phase
Data and Statistics and Probability.
5. Discussion
The evidence can be drawn from the findings that the entry-level student teachers'
mathematical knowledge for the FET phase is at the 'moderate level' of
achievement. In contrast, the actual level of understanding was not attainable.
However, the findings in table 5 revealed an adequate level of understanding of
the entry-level student teachers’ mathematical content knowledge for teaching
grades 10 and 12 Patterns, Functions, and Algebra, Space and Shape (Geometry)
Functional Relationships. Elementary level of understanding for teaching Grade
10 Measurement, Grade 11 Patterns, Functions, and Algebra, including
Trigonometry and Grade 12 Space and Shape (Geometry). The entry-level student
teachers do not have adequate mathematical content knowledge for teaching FET
phase Data and Statistics and Probability.
The result of the mean percentage from the Baseline Assessment (Figure 4)
determined the mathematical content knowledge of the student teachers to be in
Grade 10 Space and Shape (Geometry) and Patterns, Functions, and Algebra with
(59.18%) and (50.96%) respectively as well as Grade 12 Functional Relationships
with (54.28%). Similar results were obtained by Fonseca, Maseko, and Roberts
(2018) in their study ‘Students’ mathematical knowledge in a Bachelor of
Education (Foundation or intermediate phase) programme’ that there is a good
distribution of attainment for the first-year students in their pilot test. In contrast,
the findings in this study disagree with Alex and Roberts (2019), where low
percentage performance and poor mathematical knowledge for teaching were
recorded in their research. There is a need to improve entry-level first-year student
teachers’ mathematical content knowledge. The finding also revealed that none of
the student teachers achieved the “outstanding achievement", and only two have
“meritorious achievement” at Grade 11 level.
The results of the student teachers' level of understanding are in agreement with
Reid and Reid (2017). They found that student teachers had difficulty
understanding mathematical content knowledge, such as probability and standard
algorithms. According to the above researchers, the student teachers performed
below the expected standard. As a result, student teachers must have a strong
understanding of mathematical concepts and be able to express and explain them
in a variety of ways in their future teaching.
According to studies, the primary purpose of a baseline assessment in teaching
and learning is to get to know students at the entry level of a new school year
(Khuzwayo & Khuzwayo, 2020; Nguare, Hungi & Matisya, 2018; Tiymms, 2013;
Tomlinson, 2020). Therefore, the goal of baseline assessment in this study is to
assist HEIs teacher educators in developing learning activities inclusive of various
learning styles. This would also assist in detecting student teachers' special needs

15
at an early stage so that a remediation program can be implemented (DBE, 2019).
Taylor (2021) states that in South Africa, a vicious-cycle system problem is evident.
Due to the negative public perception of teaching, ITE programs cannot attract
competent matriculants to study for a teaching qualifications. Most of the students
intending to study teaching as a career are often rejected in their first and second
choices at the university level. Often universities are forced to recruit a lesser
quality of pre-service teachers into the programme, which demands a reduction in
the rigour of their training. A lower-quality or competent teacher is thus deployed
into schools, resulting in poor quality teaching, thus lowering the learner
performance and the prestige of the teaching profession. Matriculant quality while
also lowering the perceived prestige of teaching. Taylor & Robinson (2016) opine
that the inability to recruit qualified pre-service teachers enhances the cycle of poor
quality teaching and learning.
According to Deacon (2016), the entrance requirements for Initial Teacher
Education programs are generally lower than most other entry-level degree
programs. The evidence suggested that the weakest students enter education
faculties as a last resort, motivated by a desire to earn a university qualification
rather than a desire to make a difference in students' lives. Taylor (2021) supported
his claim with data from the Centre for Educational Testing for Access and
Placement's National Benchmark Tests (NBTs) (CETAP, 2020). Most university
applicants take the NBTs, which require a minimum to gain admission into a
particular programme. However, this is not applicable to most Initial Teacher
Development Programme. Over 75 000 university applicants took the Academic
Literacy (AL) and Quantitative Literacy (QL) examinations, while over 58 000 took
the Mathematics Test (MAT) during the 2019 NBT entry cycle. Candidates
planning to study Education had the second-lowest average score of all
applications to all faculties, with only those intending to study Allied Healthcare
or Nursing having a lower average (CETAP, 2020). Basic, Intermediate, and
Proficient are the three tiers of NBT scores, with applicants in the Basic band
defined as:
“Test performance reveals serious learning challenges: it is predicted that
students will not cope with degree-level study without extensive and
long-term support, perhaps best provided through bridging programmes
(i.e., non-credit preparatory courses, special skills provision) or FET
provision. Institutions admitting students performing at this level need
to provide such support themselves.” (CETAP, 2020, p. 18).
Due to the low mathematics achievement of students entering teacher education
programs, the goal of creating a deep understanding of mathematics required for
teaching should become an essential aspect of the mathematics course design and
implementation (Jakimovik, 2013). Furthermore, Jakimovik (2013) claims that the
complete lack of a link between mathematics and methods courses is a long-
standing trend in teacher preparation programs. The only stipulation is that
students complete the mathematics course’s exams before enrolling in the methods
courses. The mathematics courses are taught by university mathematicians and
academics who teach the techniques courses, which place less emphasis on the
interaction between subject matter expertise and teaching.

16
According to Ma (1999), teachers should possess a Profound Understanding of
Fundamental Mathematics (PUFM). This means teachers' mathematical content
knowledge should be a thorough understanding of mathematics that has breadth,
depth, connectedness, and thoroughness, not on the average level. Jakimovik
(2013) maintains that one of the most critical aspects of teaching is understanding
what will be taught. In addition, mathematics is one of the fundamental realms of
human thought and investigation. Learners need to build intellectual resources for
knowing about and actively engaging in mathematics. The above researcher
explains that the future teachers must use their mathematical knowledge in
conducting classroom discourse in a learning community, mentioning students'
educational needs by involving them in genuine mathematics learning, analysing
students' productions, examining students' mathematical knowledge and skills in
lesson preparation, or in evaluating curriculum materials. Consequently, to
provide successful learning for future teachers, educators must establish
specialised instructional methodologies in the HEIs.
According to Burghes and Geach (2011), the requirements for being a good
mathematics teacher are confidence, competency, commitment and a passion for
mathematics at a level much higher than the one being taught. Furthermore,
knowledge of the topic to be taught is a significant factor in determining the quality
of training. Goldsmith, Doerr and Lewis (2014) believe that teacher’s capacity to
recognise and analyse student’s thinking also their ability to engage in effective
professional conversations are hampered by a lack of mathematical content
understanding.
6. Conclusions
In conclusion, to become a FET mathematics teacher, student teachers must be
exposed to many mathematical experiences. They should be offered a variety of
opportunities to hone their mathematical reasoning and creative abilities in
preparation for teaching mathematics in the FET phase. Their low level of
mathematical knowledge and understanding may make it difficult for the student
teachers to teach the FET phase in the future. To teach in the FET phase, student
teachers must have mathematical solid foundational knowledge and
understanding. Since FET is the link between the Senior Phase and the Higher
Education band, the student teachers should have an appropriate achievement
level, namely, adequate, substantial, meritorious, and outstanding achievement
level, to link FET learners to the Higher Education band.
Consequently, student teachers will need to improve their ability to teach
mathematics effectively and ensure that it is meaningful for learners. They will be
able to effectively teach mathematics in the future, even further than their current
level of knowledge and ability. Then the mathematics performance of the learners
will improve.
7. Recommendations
This paper showed that the mathematical content knowledge of the student
teachers at the entry-level is at a moderate level, and the level of understanding
was low. Therefore, this paper recommends that with the assistance of teacher

17
educators in HEIs, student teachers must gain a thorough understanding of the
mathematics curriculum. Furthermore, the mathematics appropriate to the grade
level and mathematical courses that the student teachers are responsible for
teaching should be known and well understood. This study also recommends that
only those students who have attained substantial achievement in mathematics
should be allowed to study FET mathematics at higher education institutions. HEI
should consider those students who have applied for teaching as their 1st option
rather than their last option as an entry requirement. Stricter entry-level to FET
teaching programmes should be implemented at HEIs, such as good mathematics
attainment levels in the matriculation examination. Finally, every university
should build into their entry-level programme a 'Baseline assessment’ for all
students intending to study towards teaching mathematics in the FET phase.
8. Implications and contribution of the study
In conclusion, the authors believe that teachers with a low entry-level and a low
level of understanding will have poor content knowledge of mathematics. As a
result, there will be ineffective classroom teaching and poor mathematics
performance in secondary schools. Therefore, for learner performance
improvement, HEIs and the Department of Higher Education and Training
(DHET) should ensure that student teachers have a solid entry-level level of
understanding of the mathematics curriculum. Student teachers' entry level
should be investigated for all educational system stages, including general
education and training, further education and training, and higher education for
future studies.
9. Limitations of the study
This research is confined to student teachers who enrolled in a FET mathematics
teaching programme and came from poor, disadvantaged backgrounds. The
majority of the student teachers had not experienced computer-assisted learning,
which may have contributed to their performance in the baseline test.
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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).
Vol. 21, No. 8, pp. 21-42, August 2022
https://doi.org/10.26803/ijlter.21.8.2
Received Mar 30, 2022; Revised July 12, 2022; Accepted July 27, 2022
Exploring the Use of Chemistry-based Computer
Simulations and Animations Instructional Activities to
Support Students’ Learning of Science Process Skills
Flavia Beichumila
African Centre of Excellence for Innovative Teaching and Learning Mathematics
and Science (ACEITLMS), University of Rwanda, College of Education
Eugenia Kafanabo
University of Dar es Salaam, School of Education, Tanzania
Bernard Bahati
University of Rwanda, College of Education, Rwanda
Abstract. This study aimed at exploring the instructional activities that
could support students’ learning of science process skills by using
chemistry-based computer simulations and animations. A total of 160
students were randomly selected and 20 teachers were purposively
selected to participate in the study. Data were gathered in both qualitative
and quantitative formats. This was accomplished through the use of a
classroom observation checklist as well as a lesson reflection sheet. The
qualitative data were analyzed thematically, while the quantitative data
were analyzed using percentages. The key findings from the study
indicated that chemistry-based computer simulations and animations
through instructional activities, particularly formulating hypotheses,
planning experiments, identifying variables, developing operational
definitions and interpretations, and drawing conclusions, support
students in learning science process skills. It was found that during the
teaching and learning process, more than 70% of students were able to
perform well in the aforementioned types of instructional activities, while
60% performed well in planning experiments. On the other hand, as
compared to other instructional activities, planning experiments was least
observed among students and teachers. Students can be engaged in
knowledge construction while learning science process skills through the
use of chemistry-based computer simulations and animations
instructional activities. Therefore, the current study strongly
recommends the use of chemistry-based computer simulations and
animations by teachers to facilitate students’ learning of chemistry
concepts in Tanzanian secondary schools.
Keywords: chemistry-based computer simulations; instructional
activities; science process skills

22
1. Introduction
The possibility of involving students in the acquisition of knowledge and scientific
skills, particularly science process skills (SPSs), has grown in importance in
chemistry curricula globally (Aydm, 2013; Bete, 2020). This is owing to the science
process skills' alignment with students' learning and application in everyday life.
As a result, different countries' chemistry curricula include science process skills
in both basic and integrated SPSs. Basic SPSs includes observing, classifying,
measuring, calculating, inferring, and communicating. Integrated SPSs include
formulating hypotheses, identifying and controlling variables, designing
experiments, data recording and interpretation (Abungu et al., 2014; Athuman,
2019; Aydm, 2013). During chemistry teaching and learning, effective
instructional strategies that engage students in inquiry activities are essential for
the development of science process skills. Therefore, inquiry-based approaches to
teaching and learning, such as practical work and hands-on activities, are critical
for engaging students in active learning (Abungu et al., 2014; Irwanto et al., 2018;
& Seetee et al., 2016).
Chemistry includes abstract concepts such as chemical kinetics, equilibrium and
energetics which students find difficult to learn (Lati et al., 2012). Along the same
line, teacher-centeredness dominates chemistry teaching and learning in
Tanzanian classrooms, with the teacher remaining the primary source of
information through the chalk-and-talk technique. Moreover, inquiry learning
tasks such as observations, hypotheses, testing, data collection, interpretations,
discourse, and conclusions are similarly restricted in the learning process (Kalolo,
2015; Kinyota, 2020). Consequently, memorization learning persists, and there is
little effort to support learners with science process skills (Mkimbili et al., 2018;
Kinyota, 2020; Semali & Mehta, 2012). In this regard, inappropriate teaching
strategies which rely on teacher-centeredness and occasional practical work,
shortages of laboratories and teaching aids, as well as large class size, are among
the contributing causes (Mkimbili et al., 2018; Semali & Mehta, 2012).
Chemistry-based computer simulations and animations are examples of an
information and communication technology (ICT) invention that has been
explored and used as alternative teaching and learning resources in classrooms
globally. Computer simulations are computational models of real or hypothesized
situations or natural phenomena that allow users to explore the implications by
manipulating or changing parameters within them (Nkemakolam et al., 2018). In
addition, animations are dynamic displays of graphics, images, and colors that
are used to create certain visual effects over a series of frames (Trindade et al.,
2002). Computer simulations and animations include virtual laboratories and
visualizations of phenomena. Further, the interactivity feature of computer
simulations in involving students in hands-on activities has promoted their
importance as they are essential for inquiry learning and a learner-centered
environment in the classroom (Moore et al., 2014; Plass et al., 2012). Based on the
significance of ICT, the competence-based curriculum in Tanzania recommends
the availability and use of ICT, including computer simulations and animations.
This is to ensure smooth teaching and learning as well as giving learners real-
world experience in learning (MoEST, 2015; MoEST, 2019).

23
Despite Tanzania's government's initiatives to integrate ICT into classrooms, little
is known about how chemistry content may be presented effectively in an inquiry-
based setting (Ngeze, 2017). ICT uses encompasses specific instructional strategies
that support students in learning science process skills through inquiry learning
in the chemistry classroom. This follows the fact that blending proper
instructional activities when using computer simulations is an important factor in
engaging students in learning chemistry concepts and specific science process
skills (Çelik, 2022). The reviewed literature (Beichumila et al., 2022; Çelik, 2022;
Moore et al., 2014) advocates the use of computer simulations and animations in
chemistry learning to improve students’ acquisition of science process skills.
In the above regard, Çelik (2022) and Sreelekha (2018) emphasize teaching
strategies for students to acquire science process skills through computer
simulations and animations. In such a learning context, little is known about
instructional strategies that support the learning of these integrated science
process skills through computer simulations and animations. Therefore, the goal
of this study was to investigate the chemistry-based computer instructional
activities used to engage students in building integrated science process skills
during chemistry teaching and learning. The study sought to address the
following research question: What are the chemistry-based computer simulation
and animation instructional activities used to engage students in building
integrated science process skills during chemistry teaching and learning?
2. Literature Review
2.1 Chemistry-computer simulations, animations and science process skills
development
The interactivity feature of computer simulations and animations has ability to
enable students to observe process, events, and activities during learning
(Smetana & Bell, 2012). As students interact with computer simulations and
animations, they become engaged in the exploration of the world around them
through inquiry activities (Moore et al., 2014). In this sense, students get the
opportunity to engage in inquiry learning and gather scientific evidence that are
important for learning science concepts. Through computer simulations students
develop scientific knowledge as well as science process skills (Beichumila et al.,
2022; Çelik, 2022; Supriyatman & Sukarino, 2014). However, aspects of inquiry are
not the focus in most of the lessons in science classrooms. As a result, instructional
strategies as advocated by Yadav and Mishra (2013) in teaching and learning
processes are critical towards using any inquiry-based approach, including
computer simulations and animations to develop science process skills. Students
learn less in terms of science process skills by using computer simulations in a
teacher-centered format in which students’ complete recipe-type tasks that require
them to verify solutions (Çelik, 2022; Smetana & Bell, 2012). Thus, instructional
activities for inquiry learning are important.
2.2 The importance of instructional activities and development of science
process skills
Instructional activities relate to all activities that support the teaching and learning
process (Akdeniz, 2016). These instructional activities are teaching and learning
activities and assessment activities that play a significant role in engaging

24
students in the construction of knowledge and the acquisition of skills.
Instructional activities that engage teachers in explaining or lecturing students
while students are passive listeners do not help students to acquire science process
skills. One way to develop the science process skills among students is to use
appropriate instructional activities that engage students in inquiry activities (Bete,
2020; Coil et al., 2010; Irwanto et al., 2018; Seok, 2010). Activating students'
background knowledge, offering analogies, asking questions, and encouraging
students to use alternative forms of representation are some of the teaching
strategies. According to Supriyatman and Sukarino, (2014), teachers can use
computer simulations to assist students in predictions to generate inquiry.
Furthermore, Brien and Peter (1994) and Jiang and McComas (2015) advocated the
need for instructional activities that integrate well into lessons for inquiry
learning. The approach allows students to gain a deeper and broader
understanding of science content with real-world applications, as well as learning
about the scientific inquiry process. This includes developing general
investigative skills (such as posing and pursuing open-ended questions,
synthesizing information, planning and conducting experiments, analyzing, and
presenting results). For example, during classroom lessons, students were
engaged in tasks such as making observations and inferences, planning
experiments, and generating predictions (Abungu et al., 2014., Chebii et al., 2012,
Rauf et al. 2013, Saputri, 2021). As a consequence of involving students in these
learning activities, they work collaboratively in groups, interact with each other
through discussion and carrying out experiments under the guidance of the
teacher. In addition, the instructional activities mentioned develop critical
thinking skills and learning curiosity among learners (Higgins & Moeed, 2017;
Pradana et al., 2020). Thus, in the Tanzanian context it was important to explore
instructional activities that support students’ learning of science process skills
while using computer simulations and animations to learn chemistry concepts.
2.3 Theoretical Framework
This study was framed within social constructivism theory by Vygotsky (1978)
who believed that knowledge construction is an active process conducted through
social interaction among learners themselves, learners and teachers or learners
and materials. This indicates that scientific knowledge and skills are socially
constructed and verified under social constructivism in science learning. As a
result, Onwioduokit (2013) suggested that when students are taught science, they
should participate in inquiry activities. This becomes possible when learners are
encouraged to learn by doing something as a means of learning instead of only
listening (Demirci, 2009). In essence, these instructional activities are essential to
enable teachers and learners to interact with computer simulations and
animations during teaching and learning.
Vygotsky (1978) explained the role of teachers in using instructional activities and
learner-centered strategies to enable students to construct knowledge and skills.
Therefore, using social constructivism theory, it was believed that it could help to
understand instructional activities that engage learners in knowledge
construction and learning science process skills as they learn using computer
simulations. These are essential learning environments to create a social learning

25
environment that facilitates students' construction of knowledge and skills that
can be applied from a classroom context to real life experiences.
3. Methodology
3.1 Participants, sampling and sample size
The study was carried out at four secondary schools from the Dodoma and
Singida regions of Tanzania's central part. The area was chosen because students
perform poorly in science, including chemistry, and there is a shortage of
instructional materials (MoEST, 2019, 2020). The selection of schools was based on
the availability of computer laboratories and other ICT equipment or tools such
as projectors. The assumption was that by using computer laboratories, students
could be subjected to the teaching and learning of chemistry using computer
simulations as one way to engage learners in hands-on activities.
The challenging topic of chemical kinetics, equilibrium, and energetics was the
focal point of the current study (Beichumila et al., 2022; Lati et al., 2012), which is
taught at level three of secondary education in Tanzania (MoEVT, 2010). This
served the choice of 160 Form Three students (level 3 of ordinary secondary
education), who were rondomly selected to be involved in this study.
Furthermore, 20 chemistry teachers were purposely involved in the study based
on the criteria that they had prior training in ICT integration in the classroom.
3.2 Research approach and design
The study employed a mixed method through both quantitative and qualitative
approaches to collect data. This was done through classroom observations
focusing on both teachers' and students' learning activities (Cresswell, 2013;
Cresswell & Clark, 2018). In addition, a lesson reflection sheet was used to explore
students’ insights on lesson instructional activities. The focus was to explore the
instructional activities that could support students’ learning of science process
skills by using chemistry-based computer simulations and animations. This
generated information that helped the research team to explore the instructional
strategies that could engage students in learning chemistry concepts using
computer simulations and animations. The use of both classroom observation and
a lesson reflection sheet was considered as triangulation of information (Cohen et
al., 2011). The design of the study followed two steps, namely pre-intervention
and post-intervention.
3.3 Data Collection Procedure
Step 1: Pre-intervention
The first four sessions, which were utilized as a pre-intervention, focused on the
topics of chemical kinetics, equilibrium, and energetics, with conducted one
lesson per school being conducted. The four lessons in pre-intervention were
purposely used to capture an actual picture of instructional activities used by
teachers to support students’ learning of science process skills through computer
simulations. This was a baseline setting. At this stage a classroom observation
checklist was used as a data collection tool. The classroom observation checklist
was developed by the researcher from existing literature, for example, Chebii et
al. (2012). Classroom observation was chosen as the method since it provides first-

26
hand evidence of what the teacher and students perform in class as compared to
a questionnaire (Atkinson & Bolt, 2010).
Step 2: Post-intervention
In post-intervention, seven consecutive series of lessons were conducted at school
level, making a total of 28 lessons in four secondary schools. Teachers and
researchers were involved in the process of lesson planning, classroom teaching,
and reflection. During lesson planning, teachers collaborated to prepare a lesson.
It was to ensure that the lesson was prepared based on inquiry learning, focusing
on achieving science process skills. Classroom teaching involved observations of
different instructional activities and how students were learning chemistry
concepts as well as science process skills. During lesson reflection, students were
given a lesson reflection sheet on which they identified their favorite learning
activities from the lesson. This was also time for the research team to reflect on the
lesson and plan for the next one. Therefore, in this study, students were required
to acquire knowledge as well as to formulate hypothesis, plan experiments,
identify variables, define operationally, make interpretations, and draw
conclusions. Table 1 indicates the nature of teaching strategies that accompanied
the lessons adapted from Jiang and McComas’s (2015) framework on inquiry
instructional strategies for learning science concepts and process skills in the
classroom context. This was to engage students in a more discursive context, as
supported by chemistry-based computer simulations and animations in each
lesson.
Table 1: Instructional strategies and science process indicators
Item Instructional strategies in classroom context Indicators of science process skills
1 Students were required to formulate a
hypothesis in relation to the question under
investigation
Formulating a hypothesis
2 Students were required to think of scientific
procures, plan an investigation, and
conduct experiments for the purpose of
testing the hypothesis
Identifying procedures and planning
for investigation
3 Students were required to identify associated
variables of the investigation that could be
controlled variables, dependent or
independent variables
Identifying variables
4 Students were required to make
interpretations of the collected evidence or
data through tables, graphs, or words in
order to obtain meaningful information and
thereafter draw conclusions basing on
collected evidence
Making interpretations and
conclusions
5 Students were required to develop
statements presenting a concrete description
of an event that indicates what to observe/do
as the evidence towards their observations
and conclusion in relation to the question
under investigation
Developing operational definitions

27
Furthermore, computer simulations from Yenka chemistry
(https://www.yenka.com/en/Yenka_Chemistry), and one model of PhET
simulation of reactions and rates
(https://phet.colorado.edu/en/simulations/reactions-and rates) were used
during the teaching and learning process in this study. Figures 1 and 2 are samples
of these simulations in which students were engaged to learn chemical kinetics,
equilibrium and energetics.
Figure 1: Computer simulation of the effect of a catalyst on the rate of reaction
Figure 2: Computer simulation of the effect of concentration on the rate of reaction
3.4 Validity and reliability of data collection tools
In the case of ensuring validity, the classroom observation checklist and reflection
sheets were evaluated by three chemistry teachers. Later on, the tools were piloted
in two secondary schools that were not part of the selected schools in the study.
This helped to identify and remove irrelevant items. In addition, inter-observer
reliability which is a measure of consistency between two or more observers of
the same construct was calculated (Cohen, 1988). The value of the Kappa
coefficient (ka) across three observer pairs was found to be 0.80, 0.78, and 0.79
which are acceptable. The use of three observers (the researcher and two assistant
researchers) independently during classroom observation helped to improve the
internal reliability of the findings from classroom observation (Cresswell, 2013).
3.5 Data analysis
For the quantitative data, percentages (Pallant, 2020) were used to show the
number of students and teachers in relation to instructional activities and science

28
process skills indicators in the teaching and learning process. The qualitative data
generated from classroom observations were thematically analyzed according to
Braun and Clarke (2012). Information from the classroom observation and
reflection sheet were transcribed and coded after a thorough discussion among
the research team. This included notes and comments from observers on specific
instructional activities that engaged students to learn science process skills
through computer simulations. Finally, the agreed themes were used to conclude
specific instructional activities supporting the learning of chemistry concepts with
computer simulations and animations.
4. Results and Discussion
The general findings from this study indicate that instructional activities,
particularly formulating hypothesis, planning experiments, identifying variables,
developing operational definitions, making interpretations, and drawing
conclusions, support students in learning integrated science process skills using
chemistry-based computer simulations. It was found that during the teaching and
learning process, generally more than 70% of students were able to perform the
aforementioned activities well while 60% performed well in planning
experiments. On the other hand, as compared to other instructional activities,
planning experiments was the least observed among students and teachers.
Tables 2-6 indicate the findings under each instructional activity.
Formulating hypothesis
The findings from this study indicated that the hypothesis formulation as an
instructional activity involved students in predictions skill as 75% of students in
post-interventions were able to formulate hypotheses. It was observed that,
initially, 70% of students had no idea on how to hypothesize; however, their
ability improved as they were involved in this learning activity. The activity
helped students to make their predictions that could be scientifically tested. It was
found in this study that using chemistry-based computer simulations to learn and
understand chemical kinetics, equilibrium, and energetics made students more
engaged in the teaching and learning process. Students were more involved in the
lesson when they were asked to formulate a hypothesis in relation to the
experiment’s aim, rather than doing experiments by following predetermined
sequence of procedures, as is the case in most science classrooms (Table 2).
Table 2: Formulating hypothesis
Teaching activities Learning activities
Indicators of science
process skills in the
classroom context
90% of teachers guided
students in small groups
of 3-5 students through the
process of writing down
the aim of the experiment
to be explored.
Then teachers guided
students to observe the
Students in small groups
of 3-5 students were
required to think and
write down the question
to be investigated and the
aim of the experiment.
Students discussed in
groups what the
Before:
The majority of students
(70%) were not able to
formulate a hypothesis
correctly. For example, one
of students in school C
wrote:

29
computer simulation
models, for example, the
simulation that exhibited
the effect of temperature
and rate of reaction. They
began writing down their
hypothesis in relation to
the question being
investigated. For example,
investigating how the
temperature affect the rate
of reaction.
hypothesis could be in
relation to the aim of the
experiment they
determined by observing
the computer simulations.
“Surface area and rate of
reaction are related”.
After:
75% of students could
formulate a hypothesis.
Captured sentences from
students formulating a
hypothesis:
“The higher the temperature,
the higher the rate of a
chemical reaction”
In another group:
“The higher the temperature,
the fast the chemical reaction”
“Temperature affects the rate
of a chemical reaction”
Another group in another
lesson:
“The presence of catalyst will
speed up the decomposition of
hydrogen peroxide”
Observations from
students: “Increasing the
rate of a reaction means
increasing the number of
fruitful collisions between
particles, therefore increasing
the temperature will increase
the rate of reaction”.
The teacher used probing
questions to help students
use their prior knowledge
to understand how they
could formulate the
hypothesis before further
activity, for example:
“From the collision theory
what do you think will
happen if the temperature is
lower or high in the reaction
of calcium carbonate and
hydrochloric acid?”
The majority of students
(75%) were able to think
and discuss in their small
groups how collision
theory relates with
temperature and rate of
any chemical reaction.
The findings from this study support Seok (2010), who found that engaging
students in formulating a hypothesis on the question to be investigated in the
science classroom helps develop this science process skill. Moreover, the findings
indicated that through this instructional activity students developed a sense of
collaboration and ownership of the lesson. This was revealed through learning
from each other and arguing to reach a conclusion on the kind of hypothesis being
formulated. This helped students to construct knowledge while at the same time
developing a hypothesis-formulation skill. Darus and Saat (2014) found that
teaching strategies that could be used by teachers to help students in hypothesis
formulation to generate inquiry include activating students’ background
knowledge, providing analogies, questioning, and encouraging students to use
alternative forms of representation. Thus, hypothesizing as learning with
computer simulations in science classroom is one way to promote active learning
and reasoning among students (Moore et al., 2014; Sreelekha, 2018).

30
Furthermore, collaboration is discussed under social-constructivism theory as one
of the essential elements in the learning process as it changes the dynamics of the
classroom by encouraging discussion among the learners. Vygotsky (1978) further
explained that collaboration impacts students’ learning. As a result, one of active
learning strategies that promote students' curiosity in learning chemistry is their
ability to make predictions. As has been suggested in the literature, students'
interest in the subject matter contributes significantly to their ability to learn the
subject when they are exposed to a social learning environment through active
learning activities (Anderhag et al., 2015; Higgins & Moeed, 2017).
Planning experiment
The findings revealed that students (60%) learned to plan experiments through
interaction with their peers during the investigation process since students could
brainstorm with each other and work cooperatively in their small group to ensure
that they come up with a good procedure to test their hypothesis. For example,
when investigating how a catalyst affects the rate of reaction, a student told his
group members that they needed to use the same amount of hydrogen peroxide
in both test tubes, but one test tube needed to be added with a catalyst while the
other did not, so that they could observe the difference. This is because some
students understand the procedures more easily than others. Therefore, it was
observed that this process helped students to share their ideas in the lesson which
was also another way of being aware of the procedures and important related
aspects such as materials, variables to consider and how to conduct their
experiment (Table 3).
Table 3: Planning experiment
Teaching activities Learning activities Indicators of science
process skills in the
classroom context
60% of teachers guided
students in groups of 3-4 to
devise procedures to
investigate the scientific
question being explored to
test their
hypotheses/predictions. For
instance, in a scientific
question where students were
to investigate how the catalyst
affects the rate of a chemical
reaction,
teachers guided students to
use their plans and computer
simulations to conduct simple
experiments, make
observations, record data and
write simple reports.
60% of students in
groups of 3-4 students
were able to discuss and
critically think of the best
plan they could use for
the procedure to test
their hypothesis with the
computer simulation.
Evidence from students’
work in one of the
groups:
“We have to put the same
amount of hydrogen
peroxide in two test tubes,
then in one of the test tubes
put certain amount of
catalyst manganese (IV)
oxide, then we will start the
reaction and observe the
time taken for the reaction
between the two test tubes
to complete.”
In another group
“We will put 25mls of
hydrogen peroxide (H2O2)
in two test tubes, then we
will add 2g of manganese
(IV) oxide (catalyst) and
The majority of teachers were
insisting students use specific
measurements to obtain
justifiable scientific
Students were able to
discuss and decide the
amount of solution or
solute to be used in their

31
conclusions, for example one
of the teachers: “Do you think
if you use a different amount of
hydrogen peroxide in the two test
tubes and a different amount of
catalyst you will come up with a
good scientific conclusion?”
experiment to come up
with scientific
conclusion.
observe the reaction in both
test tubes.”
Observations from
students’ group
discussion “… no, we
need to take the same
amount of hydrogen
peroxide in both test tubes
and measure specific
amount of catalyst to be
added in one of the test
tubes”.
Another student: “Yes,
this is good, let us use 2g of
manganese (IV) oxide as a
catalyst.”
Observations from
students:
“We can scientifically
investigate a good soap to
remove stains on clothes if
we use same amount and
types of water, the same
clothes but we vary the
soaps.”
60% of teachers used probing
questions to help students
understand how to plan
scientific
investigation/experiments by
relating various concepts of
kinetics in daily life activities
in their homes.
Students were listening
to teacher’s questions
and trying to think of
and give examples of
short plans for scientific
investigation or
experiments from daily
life experiences in
society.
It was found that planning and performing experiments as an instructional
activity enabled students to use concrete activities through computer simulations
to test their hypotheses and come up with evidence. Students could learn other
skills such as measuring substances, knowing when to mix chemicals and start the
reaction, making observations, keeping records on what they observed, either in
tables or in words and making relevant decisions. Irwanto et al. (2018) and Seetee
et al. (2016) suggested that students’ experimenting skill is developed when a
science teacher guides them to write out detailed steps to their procedure and
determine the variables, including what needs to be controlled, and thinking of
the data to be collected. The capacity to design an experiment is essential for
comprehending the scientific process and developing critical thinking abilities
(Pradana et al., 2020).
In addition, experimentation, a process which engages students directly with the
physical world has been found to be effective in developing various students’
science process skills (Chebii et al., 2012). Moreover, the study mentioned did not
explain the students’ abilities to plan experiments based on their own experience
and understanding rather than following predetermined procedures. The use of
these instructional procedures during practical activities is teacher-centered and
does not match directly with social-constructivist theory as used in the context of
the present study. As a result, the current study has revealed that experimentation
instructional activity through computer simulations is one way to enable students
to think critically and devise procedures to test their hypotheses. As students
engage in these learning activities, they learn to reason and think critically.

IJLTER.ORG Vol 21 No 8 August 2022

IJLTER.ORG Vol 21 No 8 August 2022

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