Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models

Adnan, Muhammad; Habib, Asad; Ashraf, Jawad; Mussadiq, Shafaq; Raza, Arsalan Ali; Abid, Мuhammad; Bashir, Maryam; Khan, Sana Ullah

doi:10.1109/access.2021.3049446

Cited by 87 publications

(48 citation statements)

References 54 publications

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“…In this step, we analysed the articles based on the quality of the dataset, in terms of the features used, the sample size, population, and collection methods. As can be seen in Figure 4, majority of researchers have used available data sources and records found in public dataset or through university/school repositories, precisely a total of seventy-three (73) studies indicate this ([10], [13], [56], [58]- [66], [20], [69], [71]- [78], [81], [27], [82], [83], [85], [87]- [93], [45], [94]- [97], [100]- [104], [106], [49], [107]- [110], [113]- [117], [119], [50], [121], [123]- [126], [128], [129], [131], [132], [134], [51], [135], [136], [138], [54], [55]). While these sources are easily accessible and provide expected accuracy of the prediction models, some researchers opted for a mixed-mode of data collection where both available sources and que...…”

Section: B Dataset Sample Population Size and Collection Methodsmentioning

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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Section: B Dataset Sample Population Size and Collection Methodsmentioning

confidence: 99%

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

et al. 2022

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“…The study used classification techniques such as decision trees, random forest, and Naive Bayes to compare their accuracy rates. Similarly, Adnan et al [28] proposed a predictive model that analyzes the problems faced by at-risk students. They trained and tested their model using various machine learning (ML) and deep learning (DL) algorithms to characterize the learning behavior.…”

Section: Background and Related Workmentioning

confidence: 99%

A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment

Evangelista

2021

Int. J. Emerg. Technol. Learn.

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Virtual Learning Environments (VLE), such as Moodle and Blackboard, store vast data to help identify students' performance and engagement. As a result, researchers have been focusing their efforts on assisting educational institutions in providing machine learning models to predict at-risk students and improve their performance. However, it requires an efficient approach to construct a model that can ultimately provide accurate predictions. Consequently, this study proposes a hybrid machine learning framework to predict students' performance using eight classification algorithms and three ensemble methods (Bagging, Boosting, Voting) to determine the best-performing predictive model. In addition, this study used filter-based and wrapper-based feature selection techniques to select the best features of the dataset related to students' performance. The obtained results reveal that the ensemble methods recorded higher predictive accuracy when compared to single classifiers. Furthermore, the accuracy of the models improved due to the feature selection techniques utilized in this study.

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“…Although the authors considered the sequential information by calculating the daily learning activities, the averaged/median and normalized alternatives of students' actual learning behaviors largely undermine the associations between the behaviors performed across different timestamps. Adnan et al (2021) calculated the sum and average of the clickstreams at the stages of 20%, 40%, 60%, 80%, and 100% according to the progress of the course, and built models to predict students' learning performance during each stage. Similarly, in this study, the authors did not consider the temporal associations among the learning behaviors during each stage.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To demonstrate the validity of the proposed method, we used the OULAD (see https://analyse.kmi.open.ac.uk/open_dataset) dataset collected by Open University (Kuzilek et al., 2017). Multiple studies (Adnan et al., 2021; Aljohani et al., 2019; Hlosta et al., 2018, 2017; Qiao & Hu, 2020) have been conducted using this dataset as a baseline to verify the effectiveness of their methods in predicting student learning performance. In this study, we selected four courses purposely from the dataset, where two of them (i.e., AAA_2013J and AAA_2014J) were in the social science field and the other two (i.e., CCC_2014B and CCC_2014J) were in the STEM field.…”

Section: Dataset Description and Feature Preparationmentioning

confidence: 99%

An Interpretable Pipeline for Identifying At-Risk Students

Pei

Xing

2021

Journal of Educational Computing Research

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This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for maintaining the consecutiveness among learning activities. To demonstrate the efficacy of our model in identifying at-risk students, we compare the weekly AUCs and averaged performance (i.e., accuracy, precision, recall, and f1-score) over each course with the baseline models (i.e., Random Forest, Support Vector Machine, and Decision Tree), respectively. Furthermore, we adopt a Top- K metric to examine the number of at-risk students that the model is able to identify with high precision during each week. Finally, the model output is interpreted through a model-agnostic interpretation approach to support instructors to make informed recommendations for students’ learning. The experimental results demonstrate the capability and interpretability of our model in identifying at-risk students in online learning settings. In addition to that our work also provides significant implications in building accountable machine learning pipelines that can be used to automatically generated individualized learning interventions while considering fairness between different learning groups.

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Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models

Cited by 87 publications

References 54 publications

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

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

A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment

An Interpretable Pipeline for Identifying At-Risk Students

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