Improving final grade prediction accuracy in blended learning environment using voting ensembles

Predić, Bratislav; Dimić, Gabrijela; Rančić, Dejan; Štrbac, Perica; Maček, Nemanja; Spalević, Petar

doi:10.1002/cae.22042

Cited by 14 publications

(10 citation statements)

References 45 publications

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“…The results revealed that reviewed articles made several contributions on predicting academic performance, identifying learning behaviours, and improving learning environments. With regards to predicting academic performance, machine learning-based predictive models was proven to be effective but with low portability across courses, whereas visualisation-based methods required teacher assistance [2,77]. Moreover, data variables effectiveness on performance prediction is based on course structure; however, social network metrics and variables related to LMS engagement, self-regulated learning, collaborative learning are found significantly correlated with academic performance [86,90,102].…”

Section: Rq1 and Rq2mentioning

confidence: 99%

“…81. Understanding and predicting performance: To predict students' academic performance, random forest, linear and logistic regression, and ensemble modelling based predictive models provided satisfying results (over 70% accuracy)[2,51,77,80,81,102]. Similarly, a forecast learning outcome model (FLOM) was developed using interactive data to predict at-risk students…”

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confidence: 99%

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Learning Analytics for Blended Learning: A Systematic Review of Theory, Methodology, and Ethical Considerations

Bergdahl

Nouri

Karunaratne

et al. 2020

iJAI

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<p>Learning Analytics (LA) approaches in Blended Learning (BL) research is becoming an established field. In the light of previous critiqued toward LA for not being grounded in theory, the General Data Protection and a renewed focus on individuals’ integrity, this review aims to explore the use of theories, the methodological and analytic approaches in educational settings, along with surveying ethical and legal considerations. The review also maps and explores the outcomes and discusses the pitfalls and potentials currently seen in the field. Journal articles and conference papers were identified through systematic search across relevant databases. 70 papers met the inclusion criteria: they applied LA within a BL setting, were peer-reviewed, full-papers, and if they were in English. The results reveal that the use of theoretical and methodological approaches was disperse, we identified approaches of BL not included in categories of BL in existing BL literature and suggest these may be referred to as hybrid blended learning, that ethical considerations and legal requirements have often been overlooked. We highlight critical issues that contribute to raise awareness and inform alignment for future research to ameliorate diffuse applications within the field of LA.</p>

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Section: Rq1 and Rq2mentioning

confidence: 99%

mentioning

confidence: 99%

Learning Analytics for Blended Learning: A Systematic Review of Theory, Methodology, and Ethical Considerations

Bergdahl

Nouri

Karunaratne

et al. 2020

iJAI

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show abstract

“…that is widely used as it is simple and able to make fast predictions. It is suitable for small datasets that combine complexity with a flexible probabilistic model [24]. • Decision Tree (J48) a widely used in several multi-class classification that can handle missing values with high…”

Section: Performance Analysismentioning

confidence: 99%

Multiclass Prediction Model for Student Grade Prediction Using Machine Learning

et al. 2021

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Today, predictive analytics applications became an urgent desire in higher educational institutions. Predictive analytics used advanced analytics that encompasses machine learning implementation to derive high-quality performance and meaningful information for all education levels. Mostly know that student grade is one of the key performance indicators that can help educators monitor their academic performance. During the past decade, researchers have proposed many variants of machine learning techniques in student grade prediction. However, there is a lack of studies in identifying the effective predictive model, especially in addressing imbalanced multi-classification for student grade prediction. Therefore, this paper presents a comprehensive analysis of machine learning techniques to predict the final student grades in the first semester courses by providing better prediction accuracy. Two modules will be highlighted in this paper. First, we compare the accuracy performance of five well-known machine learning techniques, namely Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (N.B.), K-Nearest Neighbor (kNN), and Logistic Regression (L.R.), to our real dataset. Second, we proposed a multi-class prediction model for an imbalanced multi-class dataset using two types of data-level solutions to improve the prediction accuracy performance. The obtained results indicate that the proposed Synthetic Minority Oversampling Technique (SMOTE) and wrapper-based feature selection (F.S.) integrates with kNN shows significant improvement with the highest accuracy of 99.6%. In contrast, all F.S. algorithms perform 99.4% robust performance when working with SVM independently. These findings indicate that the proposed model generally discovers various F.S. algorithms and SMOTE in addressing imbalanced multi-classification, which will help the researcher to improve the performance for student grade prediction.

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“…Studies under EDM proposed several data mining approaches ( Chanlekha and Niramitranon 2018 ; Kaviyarasi and Balasubramanian 2018 ; Predić et al, 2018 ; Zaffar et al, 2018 ; Fernandes et al, 2019 ; Inyang et al, 2019 ; Li et al, 2019 ) to predict academic performance. In contrast to AIED, specific subject-related data were collected (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…It has also been shown that effective feedback and action recommendations are essential for self-regulated learning (SRL) and are significantly correlated with students’ learning and performance ( Algayres and Triantafyllou 2020 ). Therefore, to address these challenges, a number of studies within the fields of learning analytics (LA), artificial intelligence in education (AIED) and educational data mining (EDM) have investigated how students’ self-regulation could be supported through, for instance, dashboards that provide predictive student performance ( Lakkaraju et al, 2015 ; Johnson et al, 2015 ; Kim et al, 2016 ; Marbouti et al, 2016 ; Akhtar et al, 2017 ; Chanlekha and Niramitranon 2018 ; Choi et al, 2018 ; Howard et al, 2018 ; Nguyen et al, 2018 ; Predić et al, 2018 ; Villamañe et al, 2018 ; Xie et al, 2018 ; Baneres et al, 2019 ; Bennion et al, 2019 ; Rosenthal et al, 2019 ; Nouri et al, 2019 ; D.). These studies employed various data mining, machine learning (ML), clustering and visualisation techniques on a diverse variety of learning management system (LMS) data sources to predict student success and failure in a course or in an entire academic year.…”

Section: Introductionmentioning

confidence: 99%

Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation

Afzaal

Nouri

Zia

et al. 2021

Front. Artif. Intell.

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Formative feedback has long been recognised as an effective tool for student learning, and researchers have investigated the subject for decades. However, the actual implementation of formative feedback practices is associated with significant challenges because it is highly time-consuming for teachers to analyse students’ behaviours and to formulate and deliver effective feedback and action recommendations to support students’ regulation of learning. This paper proposes a novel approach that employs learning analytics techniques combined with explainable machine learning to provide automatic and intelligent feedback and action recommendations that support student’s self-regulation in a data-driven manner, aiming to improve their performance in courses. Prior studies within the field of learning analytics have predicted students’ performance and have used the prediction status as feedback without explaining the reasons behind the prediction. Our proposed method, which has been developed based on LMS data from a university course, extends this approach by explaining the root causes of the predictions and by automatically providing data-driven intelligent recommendations for action. Based on the proposed explainable machine learning-based approach, a dashboard that provides data-driven feedback and intelligent course action recommendations to students is developed, tested and evaluated. Based on such an evaluation, we identify and discuss the utility and limitations of the developed dashboard. According to the findings of the conducted evaluation, the dashboard improved students’ learning outcomes, assisted them in self-regulation and had a positive effect on their motivation.

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Improving final grade prediction accuracy in blended learning environment using voting ensembles

Cited by 14 publications

References 45 publications

Learning Analytics for Blended Learning: A Systematic Review of Theory, Methodology, and Ethical Considerations

Learning Analytics for Blended Learning: A Systematic Review of Theory, Methodology, and Ethical Considerations

Multiclass Prediction Model for Student Grade Prediction Using Machine Learning

Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation

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