A Learning Fuzzy Cognitive Map (LFCM) approach to predict student performance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F21%3A00121670" target="_blank" >RIV/00216224:14560/21:00121670 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.informingscience.org/Publications/4760?Source=%2FJournals%2FJITEResearch%2FArticles%3FVolume%3D0-0" target="_blank" >https://www.informingscience.org/Publications/4760?Source=%2FJournals%2FJITEResearch%2FArticles%3FVolume%3D0-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.28945/4760" target="_blank" >10.28945/4760</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Learning Fuzzy Cognitive Map (LFCM) approach to predict student performance

Popis výsledku v původním jazyce

Aim/Purpose This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student's overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students' total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student's overall performance. Recommendations Academic institutions can use the results and approach developed in this paper for Practitioners to identify students' performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendations Researchers can use the proposed approach in this research to deal with the for Researchers problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model's elements.

Název v anglickém jazyce

A Learning Fuzzy Cognitive Map (LFCM) approach to predict student performance

Popis výsledku anglicky

Aim/Purpose This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student's overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students' total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student's overall performance. Recommendations Academic institutions can use the results and approach developed in this paper for Practitioners to identify students' performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendations Researchers can use the proposed approach in this research to deal with the for Researchers problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model's elements.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Information Technology Education: Research

ISSN

1547-9714

e-ISSN

1539-3585

Svazek periodika

20

Číslo periodika v rámci svazku

n/a

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

221-243

Kód UT WoS článku

000669616700001

EID výsledku v databázi Scopus

2-s2.0-85107398448