Predicting student performance in a blended learning environment using learning management system interaction data

Fahd, Kiran; Miah, Shah Jahan; Ahmed, Khandakar

doi:10.1108/aci-06-2021-0150

Cited by 24 publications

(9 citation statements)

References 22 publications

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“…The diagnosis rate and prediction accuracy of the system were evaluated by comparing them with those of other studies [1,25,31,[40][41][42][43][44]. The results verified that the proposed ANFIS predictor is an improvement over other methods.…”

Section: Discussionmentioning

confidence: 63%

Prediction of Students’ Achievements in E-Learning Courses Based on Adaptive Neuro-Fuzzy Inference System

Raheema¹,

Al-Khazzar²,

Hussain³

2022

IJFIS

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A prediction of students' achievements is important for educational organizations. It helps to revise plans and improve students' achievements throughout their education period. A neurofuzzy system for predicting student achievement is presented in this study. The motivation behind it is to propose a promising achievement predictor for real-time systems associated with e-learning courses. The proposed neuro-fuzzy predictor uses the time that a student needs to answer a question and the difficulty level of that question as input variables. The predictor output was the level of the student's achievement. Real data were used from e-learning courses at the University of Kerbala, Iraq. The proposed system achieved an excellent accuracy of up to 99% and an root mean square error (RMSE) value of 0.0965 for recognizing unknown test samples. The proposed prediction system based on adaptive neuro-fuzzy inference system (ANFIS) achieved better results than previous techniques. It is hoped that the results of this work will improve college admission processes and support future planning in educational organizations.

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Section: Discussionmentioning

confidence: 63%

Prediction of Students’ Achievements in E-Learning Courses Based on Adaptive Neuro-Fuzzy Inference System

Raheema¹,

Al-Khazzar²,

Hussain³

2022

IJFIS

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“…Other languages include JAVA (15%) and SQL (5%). For tools used to prepare data and implement the predictive model, we noticed that most researchers used Waikato Environment for Knowledge Analysis (WEKA) ([16], [20], [98], [101], [104], [108]- [111], [113], [121], [129], [47], [50], [52], [54], [71], [76], [93], [94]) for ML approaches as it's the top tool with 27% of studies utilizing this. The second highest is IBM SPSS for statistical approaches with 13% ( [13], [66], [70], [82]- [84], [86], [95], [120], [136]).…”

Section: Prediction Approaches Algorithms Learning Outcome Type and T...mentioning

confidence: 99%

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

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Student retention is an essential measurement metric in education, indicated by retention rates, which are accumulated as students re-enroll from one academic year to the next. High retention rates can be obtained if institutions aim to provide appropriate support and teaching methods among the various practices to prevent students from deferring their studies. To address this pressing challenge faced by educational institutions, the underlying factors and the methodological aspects of building robust predictive models are reviewed and scrutinized. Educational Data Mining (EDM) and Learning Analytics (LA) have been widely adopted for knowledge discovery from educational data sources, improving the teaching practice, and identifying at-risk students. Various predictive techniques are applied in LA, such as Machine Learning (ML), Statistical Analysis, and Deep Learning (DL). To gain an in-depth review of these techniques, academic publications have been reviewed to highlight their potential to resolve Student Retention issues in education. Additionally, the paper presents a taxonomy of ML approaches and a comprehensive review of the success factors and the features that are not indicative of student performance in three different learning environments: Traditional Learning, Blended Learning, and Online Learning. The survey reveals that supervised ML and DL techniques are broadly applied in Student Retention. However, the application of ensemble and unsupervised learning clustering techniques supporting the heterogenous and homogenous groups of students is generally lacking. Moreover, static and traditional features are commonly used in student performance, ignoring vital factors such as educators-related, cognitive, and personal data. Furthermore, the paper highlights open challenges for future research directions.

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“…Regarding this framework, it is of significant importance to derive insights from learning data. Hence, there have been efforts to deal with that [11][12][13][14][15][16]. More specifically, research on learning analytics is frequently centered around Massive Open Online Courses (MOOCs), which are online courses designed to accommodate many participants over the Internet.…”

Section: Introductionmentioning

confidence: 99%

“…However, this approach is associated with challenges such as lower academic performance and higher dropout rates. Based on these characteristics, research has explored areas such as identifying at-risk students [13], developing intervention systems to enhance learner engagement [14], and predicting student performance using machine-learning models [15,16]. However, the central emphasis of these endeavors has predominantly been on learning outcomes-constructing predictive models aimed at discerning the salient features that influence these outcomes [15,16].…”

Section: Introductionmentioning

confidence: 99%

Integrating Business Analytics in Educational Decision-Making: A Multifaceted Approach to Enhance Learning Outcomes in EFL Contexts

Cho,

Kim,

Kim

et al. 2024

Mathematics

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This study introduces a framework that integrates business analytics into educational decision-making to improve learner engagement and performance in Massive Open Online Courses (MOOCs), focusing on learning environments in English as a Foreign Language (EFL). By examining three specific research questions, this paper delineates patterns in learner engagement, evaluates factors that affect these patterns, and examines the relationship between these factors and educational outcomes. The study provides an empirical analysis that elucidates the connection between learner behaviors and learning outcomes by employing machine learning, process mining, and statistical methods such as hierarchical clustering, process discovery, and the Mann–Kendall test. The analysis determines that learning patterns, characterized as single-phase or multi-phase, repetitive or non-repetitive, and sequential or self-regulated, are more closely associated with the nature of the educational content—such as books, series, or reading levels—than learner characteristics. Furthermore, it has been observed that learners exhibiting self-regulated learning patterns tend to achieve superior academic outcomes. The findings advocate for integrating analytics in educational practices, offer strategic insights for educational enhancements, and propose a new perspective on the connection between learner behavior and educational success.

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Predicting student performance in a blended learning environment using learning management system interaction data

Cited by 24 publications

References 22 publications

Prediction of Students’ Achievements in E-Learning Courses Based on Adaptive Neuro-Fuzzy Inference System

Prediction of Students’ Achievements in E-Learning Courses Based on Adaptive Neuro-Fuzzy Inference System

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Integrating Business Analytics in Educational Decision-Making: A Multifaceted Approach to Enhance Learning Outcomes in EFL Contexts

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