Many educators have used the benefit offer by intelligent tutoring system. To
become more personalizing and effective tutoring system, student
characteristics need to be considered. One of important student characteristic
is motivation. Therefore, in this study a motivation assessment model based
on self-efficacy theory was proposed. Refer to the theory, effort, choice of
activities, performance and persistence were discussed as motivation
attributes. Further, time spend, difficulty level, number of correct answers and
number of questions skipped are the parameters was defined for each attribute.
The model was designed by taking the advantages of Mamdani inference
system as fuzzy logic technique to predict students’ motivation level. The
model able to inmates like a human tutor does in the traditional classroom to
understand students’ motivation level.
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Improving E-Learning by Integrating a Metacognitive Agent IJECEIAES
The major disadvantage of the current Learning Management Systems is the lack of learner assistance in their learning processes and, therefore, they can not replace the presence of the teacher who ensures the progress of learning. In fact, we proposed to integrate, for each learner, a metacognitive agent that supported the metacognitive assistance and extracts the defectsin the learning process and strategies. The goal is to invite the learner to correct himself and improve his learning method. Metacognitive questionnaires were distributed to a group of 100 students before, during and after a computer course. The goal is to evaluatethe metacognitive attributes and to determine their influence on the success of learning. Decision trees were used as data analysis tools to extract a set of rules and to discover the influence of these metacognitive attributes on the result obtained by the learners. The results indicate that there are relationships between the different metacognitive attributes and the learners’ success. We note there is the influence of metacognitive incitement on learner outcomes, which reflects the degree of understanding of a learning pedagogical unit by the learner.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
Student's Patterns of Interaction with a Mathematics Intelligent Tutor: Learn...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be predictors of final marks in the foundation mathematics course with = 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random. Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner were able to retain their mastery of learning after the summative assessment whereas the students who chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide them to choose the correct sequence of topics.
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Improving E-Learning by Integrating a Metacognitive Agent IJECEIAES
The major disadvantage of the current Learning Management Systems is the lack of learner assistance in their learning processes and, therefore, they can not replace the presence of the teacher who ensures the progress of learning. In fact, we proposed to integrate, for each learner, a metacognitive agent that supported the metacognitive assistance and extracts the defectsin the learning process and strategies. The goal is to invite the learner to correct himself and improve his learning method. Metacognitive questionnaires were distributed to a group of 100 students before, during and after a computer course. The goal is to evaluatethe metacognitive attributes and to determine their influence on the success of learning. Decision trees were used as data analysis tools to extract a set of rules and to discover the influence of these metacognitive attributes on the result obtained by the learners. The results indicate that there are relationships between the different metacognitive attributes and the learners’ success. We note there is the influence of metacognitive incitement on learner outcomes, which reflects the degree of understanding of a learning pedagogical unit by the learner.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
STUDENTS’PATTERNS OF INTERACTION WITH A MATHEMATICS INTELLIGENT TUTOR:LEARNIN...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
Student's Patterns of Interaction with a Mathematics Intelligent Tutor: Learn...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be predictors of final marks in the foundation mathematics course with = 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random. Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner were able to retain their mastery of learning after the summative assessment whereas the students who chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide them to choose the correct sequence of topics.
Student's Patterns of Interaction with a Mathematics Intelligent Tutor: Learn...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
Several instruments that measure the teaching, learning, and assessment quality have been developed and published. However, a psychometrically sound instrument to measure teaching, learning, and assessment quality in early childhood care and education that suitable for the Malaysian context needs to be validated. Therefore, this study aimed to validate on teaching, learning, and assessment quality in early childhood care and education instrument, which contains 68 items. The sample comprised 3,498, selected by stratified random sampling from a population of all Malaysian kindergarten teachers. Data were analyzed based on the Polytomous Item Response Theory (IRT) using the Xcalibre software. Samejima's Graded Rating Model (SRGM) was found to be the fit model with the data. Unidimensionality assumption and local independence were tested using the exploratory factor analysis and were fulfilled. The instrument’s reliability was overall very good (α=0.966) and the construct validity was also fairly fulfilled with the value of 58.17% total variance explained. Therefore, this instrument is suggested to be used as fairly to measure the quality of Malaysian early childhood care and education among teachers so that appropriate follow-up actions can be implemented towards the betterment of early childhood education quality.
This research aimed to find out whether or not there is an effect of Learning Infrastructure (LI) and Learning Motivation (LM) on Economics Learning Achievement (ELA), and which one has more dominant effect on Learning Achievement, Learning Infrastructure or Learning Motivation. This study was a descriptive quantitative research with survey method. The data of LI, LM and ELA were collected using questionnaire. The population of research consisted of 1192 economics students in Public Senior High Schools of Serdang Bedagai Regency applying the 2013 curriculum. The sample consisted of 300 respondents, taken using cluster areas sampling technique. From the result of research, it can be found that there was a positive significant effect of LI on ELA (tstatistic=9.597, P = 0.000), there was a positive significant effect of LM on ELA (tstatistic=6.990, P=0.000), there was a positive and significant effect of LI and LM on ELA (Fstatistic=114.281, P=0.000), and LI affected ELA more dominantly than LM did.
Exploring students’ emotional state during a test-related task using wearabl...IJECEIAES
Using wireless sensors for brain activity, brain signals associated with the mood states of engineering students have been captured before and during the taking of a mathematics exam. The characterization of brain lobule activity related to arousal/valence states was analyzed from reports on the literature of the horizontal dimensions of pleasure-displeasure and vertical dimensions representing arousal-sleep. The results showed a direct relationship of the level of students’ arousal with the event of taking an exam as well as feelings of negative emotions during the exam presentation. The development of this research can lead to the implementation of controlled spaces for the presentation of students’ exams in which arousal/valence states can be controlled so that they do not affect their performance and the fulfillment of the goals, achievements or objectives established in a program or subject.
EMOTION DETECTION AND OPINION MINING FROM STUDENT COMMENTS FOR TEACHING INNOV...ijejournal
Students can provide their opinions, comments, or suggestions about a course, course instructor, study environment, and available resources using the course evaluation at the end of every semester. This helps the course professors and other college authorities make appropriate changes or continue a particular approach to get the best experience in classrooms. These course evaluations are in both quantitative and qualitative forms. In quantitative feedback the evaluation is performed in terms of measurable outcomes and include a Likert-type scale to capture the level of agreement and disagreement. In qualitative feedback the students can convey their feelings, opinions or suggestions about the course, the course instructor, or their overall thoughts/comments towards the course. The qualitative feedbacks provide freedom for the students to express their honest thoughts on a course. The data collected in the qualitative form provides deeper insight into a student’s emotional state. In this work we focus on mining the qualitative student feedbacks and analyzing the student sentiments. We also analyze the efficiency of Light Weight teams and Flipped Classroom approach which are Active Learning methods. Results show that the implementation of these Active Learning methods is linked with increased positivity in student emotions.
The aim of this review was to identify the motivational constructs which were mostly associated with self-regulated learning and how these motivational constructs were related to self-regulated learning. There were 20 studies (N=8,759) met inclusion criteria for this review. In overall, the evidence of the included studies showed that motivational constructs such as self-efficacy, intrinsic goal orientation, task value, and control of learning beliefs were positively and significantly related to and in predicting self-regulated learning; test anxiety was negatively and insignificantly related to and in predicting self-regulated learning; inconsistent results were observed for extrinsic goal orientation as it could be positively or negatively related to and in predicting self-regulated learning.
Problem Based Learning In Comparison To Traditional Teaching As Perceived By ...iosrjce
Objectives: To compare lecture based learning (LBL) with problem based learning (PBL).
Methods: A cross sectional prospective study was carried out among 145 3rd year MBBS students in
Jawaharlal Nehru Medical College(JNMC), Aligarh. The study was performedfor a period of 60 days. Data was
collected by means of structured questionnaire.
Results: 65 (44.8%) students were girls while 80 (55.2%) were boys. 89 (61.4%) students liked only PBL
followed by both LBL and PBL by 104(71.7%) students. 59(40.7 %) students claimed that PBL has led to better
understanding of subject while 71(48.9%) respondents favored both LBL and PBL. 98(67.6%) respondents
admitted that PBL has led to more clarification of their concepts while 105(72.4%) students appreciated both.
Coverage of sufficient syllabus through PBL and both was claimed by 91(62.8%) and 105(72.4%) students
respectively. Majority 94(64.8%) was satisfied with training of the teacher for traditional teaching while
106(73.1%) were satisfied with training of facilitator for PBL. 69(47.5%) students were satisfied with
availability of resources for PBL while 71(48.9%) were for both methods combined together. 91(62.8%)
respondents preferred present scenario (LBL parallel with PBL)in JNMC.
Conclusion: LBL must be in symbiosis with PBL for better analytical approach and clarification of concepts.
There is need to improve the information resources for PBL and enhancement of practical knowledge of
students.
Evaluation of positive emotion in children mobile learning applicationjournalBEEI
This paper presents the evaluation of positive emotion in children's mobile learning applications. The mobile learning application is a teaching aid that can help students to self-study and increase the students’ interest in learning especially children. This paper will discuss how mobile learning application affects the children interest in school. The evaluation method implemented to evaluate the rate of positive emotion elicited by the children using mobile learning applications was a mixed method of qualitative and quantitative methods. Since emotion can be either negative or positive, the identification of a proper method or perspective was required to prove that positive emotion was really elicited. Next, the data was collected through the children’s assessment score, Electroencephalograms (EEG) device, Emotion identification using micro-expression (facial expression), Kort Scale and interview to confirm the positive emotion felt by the students. The result shows that all five perspectives or methods have shown that positive emotion is produced. It is found that the Mobile learning application can really trigger the children’s positive emotions.
Post-active Phase of Teaching and Learners’ Evaluation
a) Teacher roles and functions in the post-active phase: evaluation of pupil learning, evaluation
b) Generating feedback on all three phases of teaching
c) Reflection and appraisal for professional development in teaching: self-reflection, observation and feedback by peers
d) Analysis of teaching using different media, appraisal by students
DetailsThis assignment is a presentation that allows you to apply.docxgalinagrabow44ms
Details:
This assignment is a presentation that allows you to apply what you have learned in this course, as well as strengthen your presentation skills.
Introduction
Provide an overview of the portfolio.
Professional Presentation
1. Choose a topic from the course and define
an audience (e.g., educators, administration, parents, students, legislators)
for the presentation.
2. Design a professional presentation in the
format of a PowerPoint, workshop, or video. Within the presentation, include
specific evidence from coursework that demonstrates mastery of understanding in
the following areas: foundations and models (EBD), assessment, causes, facets,
interventions, and teaching strategies for students with EDB.
3. Conduct the presentation with at least one
member of your SPED team. Obtain feedback from participant(s). On the last slide before the reference page, include
a summary of the feedback you received.. Include the strengths and areas of
improvement.
https://lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=9501235198&site=ehost-live&scope=site
http://www.ed.gov/about/offices/list/osers/index.html
http://www.ccbd.net
http://www.eric.ed.gov/
http://www2.ed.gov/offices/OSERS/Policy/IDEA/index.html
https://lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=17308777&site=ehost-live&scope=site
http://files.eric.ed.gov/fulltext/ED386854.pdf
Assessment and Evaluation of Students With EBD
Introduction
Ambiguity and difficulty in defining emotional/behavioral disorders (EBD) causes the numbers of students with EBD to vary widely. Often this group can be the most under-identified category in a school. As stated in the previous lecture, factors causing students to exhibit emotional and behavioral disorder can be from five different theoretical frameworks, including
biological, psychoanalytical, behavioral, phenomenological,
and
sociological/ecological
(Smith, Polloway, Patton, Dowdy, 2004). The aforementioned factors may lead to numerous disorders that are all classified under the heading of
emotional disturbance
.
The debate ranges over which assessments to use and why. The purpose of assessment is not only to identify the disabilities but also to use that information to create a more individualized program for intervention and remediation. "Assessment of problem behaviors requires that the team collect and interpret functional information from a variety of sources" (Yell, Meadows, Drasgow, & Shriner, 2009, p. 76). These assessments include both formal and informal types, and the mandated team determines eligibility.
Overall, when considering students with ED, there are times when the team must determine if the behaviors are truly manifestations of students' disabilities in order to protect them from some disciplinary measures such as suspension and expulsion. No matter what assessments are used, there needs to be clear-cut guidelines and procedur.
Motivation to learn is one of the factors that can play a role in determining student employability. This study aimed to empirically test the role of motivation to learn on student employability. The population in this study were all grade twelve students in Vocational High School 1 Dlingo Bantul Yogyakarta, which is as many as 110 students. The sample in this study was 54 grade twelve students of Vocational High School 1 Dlingo Bantul, which consisted of two classes namely fashion and wood craft classes. The selection of the research sample was made by randomization using the cluster random sampling technique. Data collection was carried out by using the employability scale and motivation to learn scale. Data analysis was conducted using Pearson product-moment analysis technique. The analysis result shows that the magnitude of the correlation coefficient (r) between motivation to learn and employability was 0.747, p = 0.000 (p < 0.01). This finding indicates that there is a very significant positive correlation between motivation to learn and student employability. Motivation to learn contributes as large as 55.8 percent of employability. Thus, motivation to learn does contribute to explaining the level of employability of Vocational High School students.
This research is aimed at developing a learning model that encourages the skills of analytical thinking in science. The method used is research and development. The result is the ICAE (Incubation, Collection of data, Analysis, and Evaluation) model that promotes analytical thinking skills. Results of normalized gain tests show that the gain score is 0.28, which indicates that the ICAE learning model positively affects students’ analytical thinking, even though still within the lower category. The ICAE model also promotes the skills of analytical thinking in science and it has gained positive response from students.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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Student's Patterns of Interaction with a Mathematics Intelligent Tutor: Learn...IJITE
Purpose: The purpose of this paper is to determine potential identifiers of students’ academic success in
foundation mathematics course by analyzing the data logs of an intelligent tutor.
Design/ methodology/approach: A cross-sectional study design was used. A sample of 58 records was
extracted from the data-logs of the intelligent tutor, ALEKS. This data was triangulated with the data
collected from surveys. Two-step clustering, correlation and regression analysis, Chi-square analysis and
paired sample t-tests were applied to address the research questions.
Findings: The data-logs of ALEKS include information about number of topics practiced and number of
topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of
topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be
predictors of final marks in the foundation mathematics course with
= 42%.
Students were asked to report their preferred way of selecting topics as either sequential or random.
Results of paired sample t-test demonstrated that the students who selected topics in a sequential manner
were able to retain their mastery of learning after the summative assessment whereas the students who
chose topics randomly were not able to retain their mastery of learning.
Originality and value: This research has established three indicators of academic success in the course of
foundation mathematics which is delivered using the intelligent tutor ALEKS. Instructors can monitor
students’ progress and detect students at-risk who are not able to attain desired pace of learning and guide
them to choose the correct sequence of topics.
Several instruments that measure the teaching, learning, and assessment quality have been developed and published. However, a psychometrically sound instrument to measure teaching, learning, and assessment quality in early childhood care and education that suitable for the Malaysian context needs to be validated. Therefore, this study aimed to validate on teaching, learning, and assessment quality in early childhood care and education instrument, which contains 68 items. The sample comprised 3,498, selected by stratified random sampling from a population of all Malaysian kindergarten teachers. Data were analyzed based on the Polytomous Item Response Theory (IRT) using the Xcalibre software. Samejima's Graded Rating Model (SRGM) was found to be the fit model with the data. Unidimensionality assumption and local independence were tested using the exploratory factor analysis and were fulfilled. The instrument’s reliability was overall very good (α=0.966) and the construct validity was also fairly fulfilled with the value of 58.17% total variance explained. Therefore, this instrument is suggested to be used as fairly to measure the quality of Malaysian early childhood care and education among teachers so that appropriate follow-up actions can be implemented towards the betterment of early childhood education quality.
This research aimed to find out whether or not there is an effect of Learning Infrastructure (LI) and Learning Motivation (LM) on Economics Learning Achievement (ELA), and which one has more dominant effect on Learning Achievement, Learning Infrastructure or Learning Motivation. This study was a descriptive quantitative research with survey method. The data of LI, LM and ELA were collected using questionnaire. The population of research consisted of 1192 economics students in Public Senior High Schools of Serdang Bedagai Regency applying the 2013 curriculum. The sample consisted of 300 respondents, taken using cluster areas sampling technique. From the result of research, it can be found that there was a positive significant effect of LI on ELA (tstatistic=9.597, P = 0.000), there was a positive significant effect of LM on ELA (tstatistic=6.990, P=0.000), there was a positive and significant effect of LI and LM on ELA (Fstatistic=114.281, P=0.000), and LI affected ELA more dominantly than LM did.
Exploring students’ emotional state during a test-related task using wearabl...IJECEIAES
Using wireless sensors for brain activity, brain signals associated with the mood states of engineering students have been captured before and during the taking of a mathematics exam. The characterization of brain lobule activity related to arousal/valence states was analyzed from reports on the literature of the horizontal dimensions of pleasure-displeasure and vertical dimensions representing arousal-sleep. The results showed a direct relationship of the level of students’ arousal with the event of taking an exam as well as feelings of negative emotions during the exam presentation. The development of this research can lead to the implementation of controlled spaces for the presentation of students’ exams in which arousal/valence states can be controlled so that they do not affect their performance and the fulfillment of the goals, achievements or objectives established in a program or subject.
EMOTION DETECTION AND OPINION MINING FROM STUDENT COMMENTS FOR TEACHING INNOV...ijejournal
Students can provide their opinions, comments, or suggestions about a course, course instructor, study environment, and available resources using the course evaluation at the end of every semester. This helps the course professors and other college authorities make appropriate changes or continue a particular approach to get the best experience in classrooms. These course evaluations are in both quantitative and qualitative forms. In quantitative feedback the evaluation is performed in terms of measurable outcomes and include a Likert-type scale to capture the level of agreement and disagreement. In qualitative feedback the students can convey their feelings, opinions or suggestions about the course, the course instructor, or their overall thoughts/comments towards the course. The qualitative feedbacks provide freedom for the students to express their honest thoughts on a course. The data collected in the qualitative form provides deeper insight into a student’s emotional state. In this work we focus on mining the qualitative student feedbacks and analyzing the student sentiments. We also analyze the efficiency of Light Weight teams and Flipped Classroom approach which are Active Learning methods. Results show that the implementation of these Active Learning methods is linked with increased positivity in student emotions.
The aim of this review was to identify the motivational constructs which were mostly associated with self-regulated learning and how these motivational constructs were related to self-regulated learning. There were 20 studies (N=8,759) met inclusion criteria for this review. In overall, the evidence of the included studies showed that motivational constructs such as self-efficacy, intrinsic goal orientation, task value, and control of learning beliefs were positively and significantly related to and in predicting self-regulated learning; test anxiety was negatively and insignificantly related to and in predicting self-regulated learning; inconsistent results were observed for extrinsic goal orientation as it could be positively or negatively related to and in predicting self-regulated learning.
Problem Based Learning In Comparison To Traditional Teaching As Perceived By ...iosrjce
Objectives: To compare lecture based learning (LBL) with problem based learning (PBL).
Methods: A cross sectional prospective study was carried out among 145 3rd year MBBS students in
Jawaharlal Nehru Medical College(JNMC), Aligarh. The study was performedfor a period of 60 days. Data was
collected by means of structured questionnaire.
Results: 65 (44.8%) students were girls while 80 (55.2%) were boys. 89 (61.4%) students liked only PBL
followed by both LBL and PBL by 104(71.7%) students. 59(40.7 %) students claimed that PBL has led to better
understanding of subject while 71(48.9%) respondents favored both LBL and PBL. 98(67.6%) respondents
admitted that PBL has led to more clarification of their concepts while 105(72.4%) students appreciated both.
Coverage of sufficient syllabus through PBL and both was claimed by 91(62.8%) and 105(72.4%) students
respectively. Majority 94(64.8%) was satisfied with training of the teacher for traditional teaching while
106(73.1%) were satisfied with training of facilitator for PBL. 69(47.5%) students were satisfied with
availability of resources for PBL while 71(48.9%) were for both methods combined together. 91(62.8%)
respondents preferred present scenario (LBL parallel with PBL)in JNMC.
Conclusion: LBL must be in symbiosis with PBL for better analytical approach and clarification of concepts.
There is need to improve the information resources for PBL and enhancement of practical knowledge of
students.
Evaluation of positive emotion in children mobile learning applicationjournalBEEI
This paper presents the evaluation of positive emotion in children's mobile learning applications. The mobile learning application is a teaching aid that can help students to self-study and increase the students’ interest in learning especially children. This paper will discuss how mobile learning application affects the children interest in school. The evaluation method implemented to evaluate the rate of positive emotion elicited by the children using mobile learning applications was a mixed method of qualitative and quantitative methods. Since emotion can be either negative or positive, the identification of a proper method or perspective was required to prove that positive emotion was really elicited. Next, the data was collected through the children’s assessment score, Electroencephalograms (EEG) device, Emotion identification using micro-expression (facial expression), Kort Scale and interview to confirm the positive emotion felt by the students. The result shows that all five perspectives or methods have shown that positive emotion is produced. It is found that the Mobile learning application can really trigger the children’s positive emotions.
Post-active Phase of Teaching and Learners’ Evaluation
a) Teacher roles and functions in the post-active phase: evaluation of pupil learning, evaluation
b) Generating feedback on all three phases of teaching
c) Reflection and appraisal for professional development in teaching: self-reflection, observation and feedback by peers
d) Analysis of teaching using different media, appraisal by students
DetailsThis assignment is a presentation that allows you to apply.docxgalinagrabow44ms
Details:
This assignment is a presentation that allows you to apply what you have learned in this course, as well as strengthen your presentation skills.
Introduction
Provide an overview of the portfolio.
Professional Presentation
1. Choose a topic from the course and define
an audience (e.g., educators, administration, parents, students, legislators)
for the presentation.
2. Design a professional presentation in the
format of a PowerPoint, workshop, or video. Within the presentation, include
specific evidence from coursework that demonstrates mastery of understanding in
the following areas: foundations and models (EBD), assessment, causes, facets,
interventions, and teaching strategies for students with EDB.
3. Conduct the presentation with at least one
member of your SPED team. Obtain feedback from participant(s). On the last slide before the reference page, include
a summary of the feedback you received.. Include the strengths and areas of
improvement.
https://lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=9501235198&site=ehost-live&scope=site
http://www.ed.gov/about/offices/list/osers/index.html
http://www.ccbd.net
http://www.eric.ed.gov/
http://www2.ed.gov/offices/OSERS/Policy/IDEA/index.html
https://lopes.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=17308777&site=ehost-live&scope=site
http://files.eric.ed.gov/fulltext/ED386854.pdf
Assessment and Evaluation of Students With EBD
Introduction
Ambiguity and difficulty in defining emotional/behavioral disorders (EBD) causes the numbers of students with EBD to vary widely. Often this group can be the most under-identified category in a school. As stated in the previous lecture, factors causing students to exhibit emotional and behavioral disorder can be from five different theoretical frameworks, including
biological, psychoanalytical, behavioral, phenomenological,
and
sociological/ecological
(Smith, Polloway, Patton, Dowdy, 2004). The aforementioned factors may lead to numerous disorders that are all classified under the heading of
emotional disturbance
.
The debate ranges over which assessments to use and why. The purpose of assessment is not only to identify the disabilities but also to use that information to create a more individualized program for intervention and remediation. "Assessment of problem behaviors requires that the team collect and interpret functional information from a variety of sources" (Yell, Meadows, Drasgow, & Shriner, 2009, p. 76). These assessments include both formal and informal types, and the mandated team determines eligibility.
Overall, when considering students with ED, there are times when the team must determine if the behaviors are truly manifestations of students' disabilities in order to protect them from some disciplinary measures such as suspension and expulsion. No matter what assessments are used, there needs to be clear-cut guidelines and procedur.
Motivation to learn is one of the factors that can play a role in determining student employability. This study aimed to empirically test the role of motivation to learn on student employability. The population in this study were all grade twelve students in Vocational High School 1 Dlingo Bantul Yogyakarta, which is as many as 110 students. The sample in this study was 54 grade twelve students of Vocational High School 1 Dlingo Bantul, which consisted of two classes namely fashion and wood craft classes. The selection of the research sample was made by randomization using the cluster random sampling technique. Data collection was carried out by using the employability scale and motivation to learn scale. Data analysis was conducted using Pearson product-moment analysis technique. The analysis result shows that the magnitude of the correlation coefficient (r) between motivation to learn and employability was 0.747, p = 0.000 (p < 0.01). This finding indicates that there is a very significant positive correlation between motivation to learn and student employability. Motivation to learn contributes as large as 55.8 percent of employability. Thus, motivation to learn does contribute to explaining the level of employability of Vocational High School students.
This research is aimed at developing a learning model that encourages the skills of analytical thinking in science. The method used is research and development. The result is the ICAE (Incubation, Collection of data, Analysis, and Evaluation) model that promotes analytical thinking skills. Results of normalized gain tests show that the gain score is 0.28, which indicates that the ICAE learning model positively affects students’ analytical thinking, even though still within the lower category. The ICAE model also promotes the skills of analytical thinking in science and it has gained positive response from students.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
K-centroid convergence clustering identification in one-label per type for di...IAESIJAI
Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3 I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3 I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3 I system with the most important features and remain the same accuracy.
Plant leaf detection through machine learning based image classification appr...IAESIJAI
Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the enhanced k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.
Backbone search for object detection for applications in intrusion warning sy...IAESIJAI
In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Optically processed Kannada script realization with Siamese neural network modelIAESIJAI
Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Deep learning based biometric authentication using electrocardiogram and irisIAESIJAI
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Photoplethysmogram signal reconstruction through integrated compression sensi...IAESIJAI
The transmission of photoplethysmogram (PPG) signals in real-time is extremely challenging and facilitates the use of an internet of things (IoT) environment for healthcare- monitoring. This paper proposes an approach for PPG signal reconstruction through integrated compression sensing and basis function aware shallow learning (CSBSL). Integrated-CSBSL approach for combined compression of PPG signals via multiple channels thereby improving the reconstruction accuracy for the PPG signals essential in healthcare monitoring. An optimal basis function aware shallow learning procedure is employed on PPG signals with prior initialization; this is further fine-tuned by utilizing the knowledge of various other channels, which exploit the further sparsity of the PPG signals. The proposed method for learning combined with PPG signals retains the knowledge of spatial and temporal correlation. The proposed Integrated-CSBSL approach consists of two steps, in the first step the shallow learning based on basis function is carried out through training the PPG signals. The proposed method is evaluated using multichannel PPG signal reconstruction, which potentially benefits clinical applications through PPG monitoring and diagnosis.
Speaker identification under noisy conditions using hybrid convolutional neur...IAESIJAI
Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
Multi-channel microseismic signals classification with convolutional neural n...IAESIJAI
Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
Sophisticated face mask dataset: a novel dataset for effective coronavirus di...IAESIJAI
Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Deep neural network for lateral control of self-driving cars in urban environ...IAESIJAI
The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control.
Attention mechanism-based model for cardiomegaly recognition in chest X-Ray i...IAESIJAI
Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions. Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
1-dimensional convolutional neural networks for predicting sudden cardiacIAESIJAI
Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
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Motivation assessment model for intelligent tutoring system based on Mamdani inference system
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 12, No. 1, March 2023, pp. 189~200
ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i1.pp189-200 189
Journal homepage: http://ijai.iaescore.com
Motivation assessment model for intelligent tutoring system
based on Mamdani inference system
Rajermani Thinakaran1,2
, Suriayati Chupra2
, Malathy Batumalay1
1
Faculty of Data Science and Information Technology, INTI International University, Negeri Sembilan, Malayisa
2
Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
Article Info ABSTRACT
Article history:
Received Jul 26, 2021
Revised Aug 16, 2022
Accepted Sep 14, 2022
Many educators have used the benefit offer by intelligent tutoring system. To
become more personalizing and effective tutoring system, student
characteristics need to be considered. One of important student characteristic
is motivation. Therefore, in this study a motivation assessment model based
on self-efficacy theory was proposed. Refer to the theory, effort, choice of
activities, performance and persistence were discussed as motivation
attributes. Further, time spend, difficulty level, number of correct answers and
number of questions skipped are the parameters was defined for each attribute.
The model was designed by taking the advantages of Mamdani inference
system as fuzzy logic technique to predict students’ motivation level. The
model able to inmates like a human tutor does in the traditional classroom to
understand students’ motivation level.
Keywords:
Fuzzy logic
Intelligent tutoring system
Mamdani method
Motivation
Motivation assessment model This is an open access article under the CC BY-SA license.
Corresponding Author:
Rajermani Thinakaran
Faculty of Data Science and Information Technology, INTI International University
Persiaran Perdana BBN Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia
Email: rajermani.thina@newinti.edu.my
1. INTRODUCTION
The definition of motivation may take several forms and differ upon its application. According to
Keller and Litchfield [1], motivation can be defined as a persons’ desire to pursue a goal or accomplish a task.
Williams and Burden [2] define motivation as a “A state of cognitive and emotional encouragement, which
brings to a firm decision to act, and which gives rise to a period of sustained knowledge and/or physical effort
in order to reach a set of aim or aims”. Motivation has always been important for learning process and has a
great influence [3], [4]. In a real-world classroom, educators easily capture students’ motivation level during
learning process and adjusts lessons accordingly, in order to maximize the student’s interest and participation.
Educators usually understand student motivation level from observational cues such as student body language
or their behavior.
In e-learning environment mainly in intelligent tutoring system (ITS) the same consideration need to
be taken where the tutoring system able to recognize when the student is becoming demotivated. Vicente and
Pain [5] and Thinakaran and Ali [6] have argued that motivation components are as important as cognitive
components in ITS, and that important benefits would arise from considering techniques that track the students’
motivation. Thus, the authors claim that ITS should include a mechanism for detecting the students’
motivational level, and appropriately responding to that level. This study tries to address aforesaid issues by
proposing a model for motivation assessment in ITS that takes the active and successive environment of
motivation into account.
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2. REVIEW RELATED WORK
The capability to assess the students’ motivational level in ITS can bring numerous benefits. Since
motivation characterizes an important factor in learning process, different researchers have recommended
different motivation assessment to examine student motivation level in e-learning. From the literature, different
approach was proposed in order to measure and assess students’ motivation level and they can be grouped in
questionnaire-based approach, interaction-based approach, sentic modulation approach (physical assessment
of a persons’ emotional changes via sensors) and also hybrid-based approach. The followings are some of the
tutoring systems presented base on stated approaches.
Vicente and Pain [5] developed motivation diagnosis study (MOODS) for learning Japanese numbers
with an added motivation self-report facility. The motivation self-report facility is based on a number of
motivational factors consists of trait and state variables. First, student need to answer traits questionnaire before
carrying the exercises. In between answering the exercise, the student are required to feedback on their state
motivation factor. The state factors can be changed as often as possible since it is necessary for the computer
to understand student current motivation level in order to modify the instruction accordingly.
While, M-Ecolab was designed for teaching pupils aged between 9 to 11 years old related to food
chains and food-webs. M-Ecolab is the extension of Ecolab developed by Rebolledo-Mendez et al. [7] to
provide motivational scaffolding by an on-screen character called Paul at interaction time. The motivational
modeling was based on three motivational traits: effort, independence and the confidence. The system provides
Paul’s spoken feedback and gestures at pre- and post-activity according to the motivation model’s perception.
For example, if the motivation model determines a low state of motivation due to the quality of the actions
which was poor, Paul’s post-activity feedback states: “For the next node try to make fewer errors”. Under these
situations, Paul’s face would reflect concern.
Hurley and Weibelzahl [8] developed a motivational strategy recommender tool known as MotSaRT.
Its functionality enables the teacher to specify the students’ motivation profile. By observing the students’
activities and interaction, teacher would evaluate students’ motivation in terms of their self-efficacy, goal-
orientation, locus of control and perceived task difficulty. In the recommended strategies, depending on the
profile entered, a list of strategies will appear. MotSaRT would then classify this situation and sort the strategies
in terms of their applicability and plan their interventions according to the recommendations.
E-learning with motivational adaptation also known as ELMA developed by Endler et al. [9] presents
a fixed number of tasks and measures the student's motivational level during learning process. The system used
self-assessed motivation questionnaire. The questionnaire containing 7-point Likert scales with 18 questions
covering four motivation factors, anxiety, probability of success, interest, and challenge. In the questionnaire,
the student will be ask to report their current motivation based on the previous block of tasks. The complete
questionnaire could assess the student's motivation at the beginning and at the end of the program. Motivational
questionnaire covering each of the motivational factors was presented several times during the program to
make sure that the program always captured the learner's current motivation.
Derbali and Frasson [10] assessed student motivation level in ITS gameplay called Food-Force. To
assess student motivation level, physiological sensors which consists heart rate, skin conductance, and
electroencephalogram also known as EEG and self-reported scores of the ARCS model consist of attention,
relevance, confidence, and satisfaction have been considered. To assess motivation level, galvanic skin
resistance (GSR) electrodes and the blood volume pulse (BVP) sensor were attached to the fingers of
participant’s nondominant hands. GSR used to measure the conductance across the skin and BVP to measure
heart rate. An EEG cap fitted on learners’ heads to measure brainwaves. Self-reported scores of the ARCS
model used to identify four factors of motivation: attention, relevance, confidence, and satisfaction.
The intervention of students’ motivation assessment in ITS can bring many benefits but have some
drawbacks. MOODS [5] and ELMA [9] assess students’ motivation by asking how their feeling was in between
their learning process. These self-motivation reports cause interruption in student concentration in the learning
process. The interruption also can make student lost interest to continue the learning process. MotSaRT [8] is
a motivation strategy recommender tool, where the teacher has to enter students’ motivation level according
student activity in the tutoring system. Then the tool will suggest appropriate strategies to motivate the student.
In this intervention, the teacher still has to evaluate the students’ motivation level manually by interpreting
students’ activates in e-learning. Derbali and Frasson [10] used physiological sensors to assess students’
motivation level. Even though the intervention brings new dimension in student motivation assessment but in
real world is not applicable. Imagine that, student need to attach the particular devices at their body during in
their learning process and again this situation can disturb the student concentration. As conclusion, a motivation
assessment in ITS should be construct in the system itself without interruption students’ learning process. In
the following session, a motivation assessment model was proposed to assess students’ motivation level
without interruption students’ learning process.
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3. METHOD
In this study a deductive approach is used to reach a logical true conclusion [11]. The approach holds
a theory and based on it, make a prediction of its consequences. Figure 1 illustrated how the study carried out
using the deductive approach.
Figure 1. Deduction approach
The proposed motivation assessment model was design base on a well-known self-efficacy theory by
Bandura [12], a Canadian psychologist. He has claimed that self-efficacy beliefs effect on choice of activities
a student takes part in; the level of student effort expended in performing a task, persistence in the face of
difficulties in completing a task, and student performance in the task. Through research on self-efficacy as
learning motivations factor, many scholars have demonstrated their relationship. For example, Emre and
Ayverdi [13]; Durak et al. [14]; Gorson and O'Rourke [15], had state that individuals with a high perception
of self-efficacy on a particular situation strive to accomplish a task. They do not easily give up and are persistent
and patient. While Hattie [16], from 800 meta-analyses, the researcher has identified self-efficacy as the
strongest predictor of educational achievement.
Base on self-efficacy theory as motivation factor, choice of activities, effort, performance and
persistence were identified as motivation attributes. These motivation attributes were used in this study to
determine students’ motivation level. Choice of activities is defined as the level of challenging task the student
chooses [17]. Difficulty level of tasks such as low, medium, high, has been considered as a parameter to
measure choice of activities [18]. Effort define as the amount that the student is employing their self in order
to perform the learning activities [19]. To measure effort, the amount of time spent to perform a task [20] has
been considered as a parameter. Performance explains the student’s achievement on a specific topic [21]. To
measure performance, the number of correct answers has been considered as parameter [17], [21]. Persistence,
describe as a constant in performing an activity [21]. The number of questions skipped was used as a parameter
to measure persistence [17], [20].
Fuzzy logic (FL) as artificial intelligent technique applied to predict the students’ motivation level.
This technique was introduced by Zadeh [22] and used when conventional logic fails. It is a computational
paradigm which is based on human thinking. The aim of using FL technique in this study is to capture the
vagueness of effort, performance, choice of activities and persistence, then determine students’ self-efficacy
which are used together to draw the conclusion of students’ motivation level. The main advantage of FL is that
it uses reasoning that closely resembles human. Furthermore, motivation is characterized by ambiguity thus
difficult to quantify. Consequently, Wang and Hsieh [23] suggested the use of FL technique to help in solving
this problem.
In general FL technique consist of [24]: i) fuzzification which translates crisp (real-valued) inputs into
fuzzy values; ii) rule evaluation is an engine that applies a fuzzy reasoning mechanism to obtain a fuzzy output;
and iii) defuzzification which translates this latter output into a crisp value. There are 3 different inference
system which are widely used in FL which are Mamdani inference system [25], Sugeno inference system [26]
and Tsukamoto inference system [27]. The most widely used system is Mamdani inference system [28]. This
inference system also known as Max Min inference system which was introduced by Professor Ebrahim
Mamdani from London University [25]. The advantages are, it is intuitive; it has widespread acceptance, and
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it is well suited to human input. Hence, in this study Mamdani's Fuzzy inferences system as students’
motivation prediction technique was applied.
4. PROPOSED MODEL
To assess students’ motivation level, the authors has applied Mamdani's fuzzy inferences system. The
main advantage of Mamdani's fuzzy inferences system is that it uses reasoning that closely resembles the
presence of human. The aim of using Mamdani's fuzzy inferences system in this study is to capture the
vagueness of effort, performance, choice of activities and persistence which will therefore determines students’
self-efficacy to draw a conclusion on students’ motivation level. The following are steps describes how the
motivation assessment model was developed based on Mamdani fuzzy Inference System.
4.1. Determining the linguistic variables and fuzzy sets
Choice of activities (CA) parameter depends on the difficulty of each particular question. This
parameter is calculated as a weightage average difficulty of all solved questions by the student as in (1). The
weightage value for easy question is 1, medium question is 2 and hard question is 3. The weightage average
equation is given (1) where ans will be assigned as 1 if the question is answered correctly or else it will be
assigned as 0. The value of weightage average (wa) becomes a crisp value for CA.
𝑤𝑎
1
𝑛
∑ (𝑞𝑖
𝑛
𝑖=1 = 𝑤𝑓 ∗ 𝑎𝑛𝑠) (1)
Effort (EF) parameters depends on the time (t) taken by a student to answer a set of tutorial questions.
The maximum time depends on the time that the teacher has defined for solving a set of questions. For this
study an average of 1.2 minutes is given to answer each question. As in (2) is used to calculate time taken by
the student for answering the given questions. The time taken becomes a crisp value for EF.
𝑡 = ∑ 𝑡𝑖𝑚𝑒𝑖
𝑛
𝑖=1 (2)
𝑡 = (𝑡𝑖𝑚𝑒1 + time2 + ⋯ + 𝑡𝑖𝑚𝑒𝑛)
Performance (PF) parameter depends on the number of correct answers answered by the student on
the particular set of tutorial questions. As in (3) is used to calculate total number of correct answers (cAns)
answered by the student over the total number of generated questions (numOfQuest) by the system times by
100%. The percentage of correct answers (%cAns) will be the crispy value for PF.
𝑝𝑒𝑟𝐶𝑎𝑛𝑠 =
∑ 𝑐𝐴𝑛𝑠𝑖
𝑛
𝑖=1
𝑛𝑢𝑚𝑂𝑓𝑄𝑢𝑒𝑠𝑡𝑛
× 100 (3)
Persistence (PS) parameter depends on the number of skipped questions on a given tutorial. As in (4)
is used to calculated as the total number of skipped questions (sQuest) by the student over number of generated
questions (numOfQuest) by the system times by 100%. The percentage of skipped questions (%sQuest) will
be the crispy value for PS.
𝑝𝑒𝑟𝑆𝑞𝑢𝑒𝑠𝑡 =
∑ 𝑠𝑄𝑢𝑒𝑠𝑡𝑖
𝑛
𝑖=1
𝑛𝑢𝑚𝑂𝑓𝑄𝑢𝑒𝑠𝑡𝑛
× 100 (4)
4.2. Fuzzification
Fuzzification, translates crisp (real-valued) inputs into fuzzy values using a membership function [23].
In this study, triangular and trapezoidal with R- and L- functions were used to translate each linguistic variable
value as crisp value into fuzzy values. The membership functions have proven popular with fuzzy logic and
have been in use extensively due to their simple formula and computational efficiency [24]. The following are
fuzzification for each input linguistic variable.
CA has 3 fuzzy sets shows in Figure 2 with possible values of easy, medium and hard which are
denoted as CA(x)={easy, medium, hard}. These distributions are formulated as in (5).
𝐶𝐴𝑒𝑎𝑠𝑦(𝑥) = {
0, 𝑥 > 0.8
0.8−𝑥
0.8−0.2
, 0.2 ≤ 𝑥 ≤ 0.8
1, 𝑥 < 0.2
(5)
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𝐶𝐴𝑚𝑒𝑑𝑖𝑢𝑚(𝑥) =
{
0, 𝑥 < 0.4
𝑥 − 0.4
1.0 − 0.4
, 0.4 ≤ 𝑥 < 1.0
1.6 − 𝑥
1.6 − 1.0
, 1.0 ≤ 𝑥 ≤ 1.6
0, 𝑥 > 1.6
𝐶𝐴ℎ𝑎𝑟𝑑(𝑥) = {
0, 𝑥 < 1.2
𝑥 − 1.2
1.8 − 1.2
, 1.2 ≤ 𝑥 < 1.8
1, 𝑥 > 1.8
Figure 2. Membership function for CA
EF has 3 fuzzy sets shows in Figure 3 with possible values of short, medium and long which are
denoted as EF(x)={short, medium, long}. These distributions are formulated as in (6).
𝐸𝐹𝑠ℎ𝑜𝑟𝑡(𝑥) = {
0, 𝑥 > 9.0
9.0 −𝑥
9.0−3.6
, 3.6 ≤ 𝑥 ≤ 9.0
1, 𝑥 < 3.6
(6)
𝐸𝐹𝑚𝑒𝑑𝑖𝑢𝑚(𝑥) =
{
0, 𝑥 < 5.4
𝑥 − 5.4
10.8 − 5.4
, 5.4 ≤ 𝑥 < 10.8
16.2 − 𝑥
16.2 − 10.8
, 10.8 ≤ 𝑥 ≤ 16.2
0, 𝑥 > 16.2
𝐸𝐹𝑙𝑜𝑛𝑔(𝑥) = {
0, 𝑥 < 12.6
𝑥 − 12.6
18.0 − 12.6
, 12.6 ≤ 𝑥 < 18.0
1, 𝑥 > 18.0
PF has 3 fuzzy sets shows in Figure 4 with possible values of poor, good and excellent which are
denoted as PF(x)={poor, good, excellent}. These distributions are formulated as in (7).
𝑃𝐹𝑝𝑜𝑜𝑟(𝑥) = {
0, 𝑥 > 40
40 −𝑥
40−20
, 20 ≤ 𝑥 ≤ 40
1, 𝑥 < 20
(7)
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𝑃𝐹𝑔𝑜𝑜𝑑(𝑥) =
{
0, 𝑥 < 30
𝑥 − 30
50 − 30
, 30 ≤ 𝑥 < 50
70 − 𝑥
70 − 50
, 50 ≤ 𝑥 ≤ 70
0, 𝑥 > 70
𝑃𝐹𝑒𝑥𝑐𝑒𝑙𝑙𝑒𝑛𝑡(𝑥) = {
0, 𝑥 < 60
𝑥 − 60
80 − 60
, 60 ≤ 𝑥 < 80
1, 𝑥 > 80
Figure 3. Membership function for EF
Figure 4. Membership function for PF
PS has 3 fuzzy sets shows in Figure 5 which are low, medium and high and are denoted as PS(x) =
{low, average, high}. These distributions are formulated as in (8).
𝑃𝑆𝑙𝑜𝑤(𝑥) = {
0, 𝑥 > 40
40 −𝑥
40−20
, 20 ≤ 𝑥 ≤ 40
1, 𝑥 < 20
(8)
𝑃𝑆𝑎𝑣𝑒𝑟𝑎𝑔𝑒(𝑥) =
{
0, 𝑥 < 30
𝑥 − 30
50 − 30
, 30 ≤ 𝑥 < 50
70 − 𝑥
70 − 50
, 50 ≤ 𝑥 ≤ 70
0, 𝑥 > 70
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𝑃𝑆ℎ𝑖𝑔ℎ(𝑥) = {
0, 𝑥 < 60
𝑥 − 60
80 − 60
, 60 ≤ 𝑥 < 80
1, 𝑥 > 80
Figure 5. Membership function for PS
The output variable which is called as motivation level (ML) of a student is also determined by the
fuzzy logic. The motivation level of a student has three fuzzy sets shows in Figure 6 which are low, medium
and high and are denoted as ML(x) = {Low, medium, high}. These distributions are formulated as in (9).
𝑀𝐿𝑙𝑜𝑤(𝑥) = {
0, 𝑥 > 1
1 −𝑥
1−0.5
, 0.5 ≤ 𝑥 ≤ 1
1, 𝑥 < 0.5
(9)
𝑀𝐿𝑚𝑒𝑑𝑖𝑢𝑚(𝑥) =
{
0, 𝑥 < 0.75
𝑥 − 0.75
1.5 − 0.75
, 0.75 ≤ 𝑥 < 1.5
2.25 − 𝑥
2.25 − 1.5
, 1.5 ≤ 𝑥 ≤ 2.25
0, 𝑥 > 2.25
𝑀𝐿ℎ𝑖𝑔ℎ(𝑥) = {
0, 𝑥 < 2
𝑥 − 2
2.25 − 2
, 2 ≤ 𝑥 < 2.25
1, 𝑥 > 2.25
Figure 6. Membership function for ML
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4.3. Fuzzy inferencing or evaluate rules
The logic for assessing students’ motivation level is encoded as a set of if-then rules. The antecedents
of the production rules consist of CA, EF, PF, PS and one set of values representing the conclusion and, the
rules consequent (motivation level-ML). A rule is defined as every possible combination of antecedents that
may occur. In this study, 81 rules were obtained as the combination of each value (difficulty level, time, number
of correct answer and number of skipped questions) from CA, EF, PF and PS. However, only 26 rules have
been logically accepted. The following shows one of linguistic rule used whereby the inputs (antecedents) are
combined logically using the AND operator in order to get students’ motivation level as output (consequent).
The output of students’ motivation level is denoted as ML(x)={low, medium, high}.
Rule Linguistic rules
1 IF CA is easy AND EF is short AND PF is poor AND PS is low THEN ML is low.
4.4. Rules output
The min method is applied as an implication function. It combines each degree of memberships to
each if-then rule then truncates the output. For example, a student manages to answer 4 easy questions correctly
out of 12 questions within 15 minutes and skips all the medium and hard questions. The following is Rule 1
using min method while Figure 7 illustrates in a graphical view. This method is repeated so that the output
membership functions are determined for all 26 rules as shown in Figure 8 in a graphical view.
Rule 1 = IF CA is easy AND EF is short AND PF is poor
AND PS is low
THEN ML is low.
= min (CA(x) ∩ EF(x) ∩ PF(x) ∩ PS(x))
= min (CA (4) ∩ EF (15) ∩ PF (4) ∩ PS (8))
= min (0.33 ∩ 15.00 ∩ 16.70 ∩ 66.70)
= 0.33
On the other hand, the max method is applied as an aggregation function. The input for the aggregation
process is the list of truncated output returned by the implication process for each rule. Figure 9 shows all 26
rules which are displayed to show how the rule outputs are aggregated into a single fuzzy set whose
membership function is assigned for every output (motivation) value and are represented in a graphical view.
Figure 7. Implication function using min method for rule 1
4.5. Defuzzification
Defuzzification functions to convert the fuzzy values into crisp values. The input for the
defuzzification process is the aggregate output. In this study, a Centroid method was applied which is one of
the most common methods used. The Centroid method which returns the center of area under the curve is
shown in Figure 10 in a graphical view. From the example given, the defuzzified value is between 0 and 1.
Therefore, it can be concluded that the students’ motivation level is recorded to be at 0.452 which is considered
to be at a low level.
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Figure 8. Implication function using min method for overall rules
Figure 9. Aggregation function using max method
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Figure 10. Defuzzify the motivation level using centroid method
Figure 11 display the steps how the motivation assessment model was developed based on Mamdani
Fuzzy Inference System. The steps started with deciding linguistic variables and fuzzy sets; translates crisp
inputs into fuzzy values using a membership function; Fuzzy inferencing; and defuzzification. Following with
motivation assessment algorithm shows in Figure 11 derived from motivation assessment model shows in
Figure 12. While Figure 11 is motivation assessment algorithm derived from motivation assessment model
which was illustrated in Figure 11. Figure 12 as shown in Appendix.
Figure 11. Motivation assessment model based mamdani fuzzy inference system
5. CONCLUSION AND FUTURE WORK
Predicting student motivation level in holds great promise for ITSs. The proposed model can be used
to detect student motivation level during their learning process. This model describes all the steps of inference
starting from fuzzification, rule evaluation and defuzzifiction. Future work will involve implementation of the
proposed model into ITS. The model will be incorporated with ITS architecture specifically in student or user
model. Besides detection of student motivation level, the tutoring system aims some recommendations in
automatic manner based on student motivation level, much like in the traditional classroom.
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APPENDIX
BEGIN
start time
//generate 12 mcqs one by one
for (q = 1; q <= 12; q++){
display question
read(ans)
//calculate weightage factor
wfans = (wf * ans) + wfans
if (ans == True) //calculate correct answer
cAns = cAns +1
//calculate number of skipped questions
if (ansSkipp == True)
sQuest= sQuest +1
}
stop time
wa = wfans /12 // As in (1)
t = stop time – start time // As in (2)
perCans = (cAns /12) *100 // As in (3)
perSquest = (sQuest / 12) *100 // As in (4)
/*translates crisp inputs into fuzzy values using membership function*/
CA(x)← difficulty level (wa)
EF(x)← time taken (t)
PF(x)← number of correct answered (perCans)
PS(x) ← number of skipped question (perSquest)
//rules output
(min method) ← 26 rules //implication function
// aggregation function
(max method) ←output of min method on 26 rules
/*Defuzzification is converts the fuzzy values to crisp values */
ML(x)← (Centroid method)
display (ML(x))
END
Figure 12. Motivation assessment algorithm
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BIOGRAPHIES OF AUTHORS
Rajermani Thinakaran holds a doctor degree from Universiti Teknologi Malaysia
(UTM), Malaysia in 2019. She also received her Master in IT from Universiti Kebangsaan
Malaysia (UKM) and Bachelor in Science (Computer Science) from UTM in 2012 and 1995,
respectively. She is currently a senior lecturer at Faculty of Data Science and Information
Technology in INTI International University, Negeri Sembilan, Malaysia. Her research interests
lie in the area of artificial intelligent, assistive technology in empowering disabled students, e-
learning and gamming ranging from theory to design to implementation. She supervises both
undergraduate and postgraduate students (Masters and PhD levels). She can be contacted at email:
rajermani.thina@newinti.edu.my or rajermani@yahoo.com.
Suriayati Chuprat is an Associate Professor at Advanced Informatics Department
of Razak Faculty of Technology Informatics, Universiti Teknologi Malaysia. She holds a
Bachelor Degree in Computer Science, with concentration in Software Engineering and
Management Information Systems, a Master in Software Engineering and a PhD in Mathematics.
She was attached to the University of North Carolina, USA, as part of her PhD research, where
she worked with Professor Sanjoy K. Baruah on real-time scheduling in parallel computing. She
can be contacted at email: suriayati.kl@utm.my.
Ir. Dr. Malathy Batumalay holds a BEng. (Electrical Engineering) form University
Tun Hussein Onn, MEng. (Telecommunication) from University Malaya and Ph.D. (Photonics)
from University Malaya. Currently she is attached as Associate Professor with the Faculty of Data
Science and Information Technology in INTI International University, Negeri Sembilan,
Malaysia. She focuses on the research of Photonics Engineering, Fiber Optics and Lasers
technology. In her previous research work, she developed fiber optics into sensors to monitor the
relative humidity, temperature and also as biosensor. She is currently collaborating with local
Universities to further enhance the performance of sensors for several applications. She can be
contacted at email: malathy.batumalay@newinti.edu.my.