Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A Review

Alhothali, Areej; Albsisi, Maram; Assalahi, Hussein; Aldosemani, Tahani

doi:10.3390/su14106199

Cited by 35 publications

(21 citation statements)

References 99 publications

(126 reference statements)

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“…It seems likely that network analysis modelling has not investigated enough for solving educational data related problems. Indeed, network view and especially link prediction methods may be used for several research problems in educational data mining such as learner consultation and performance prediction which has been comprehensively checked out by machine learning methods (Alhothali et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Negative link prediction to reduce dropout in Massive Open Online Courses

Khoushehgir

Sulaimany

2023

Educ Inf Technol

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In recent years, the rapid growth of Massive Open Online Courses (MOOCs) has attracted much attention for related research. Besides, one of the main challenges in MOOCs is the high dropout or low completion rate. Early dropout prediction algorithms aim the educational institutes to retain the students for the related course. There are several methods for identification of the resigning students. These methods are often based on supervised machine learning, and require student activity records to train and create a prediction model based on the features extracted from the raw data. The performance of graph-based algorithms in various applications to discover the strong or weak relationships between entities using limited data encouraged us to turn to these algorithms for this problem. Objective of this paper is proposing a novel method with low complexity, negative link prediction algorithm, for the first time, utilizing only network topological data for dropout prediction. The idea is based on the assumption that entities with similar network structures are more likely to establish or remove a relation. Therefore, we first convert the data into a graph, mapping entities (students and courses) to nodes and relationships (enrollment data) to links. Then we use graph-based algorithms to predict students' dropout, utilizing just enrollment data. The experimental results demonstrate that the proposed method achieves significant performance compared to baseline ones. However, we test the supervised link prediction idea, and show the competitive and promising results in this case as well. Finally, we present important future research directions to improve the results.

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Section: Discussionmentioning

confidence: 99%

Negative link prediction to reduce dropout in Massive Open Online Courses

Khoushehgir

Sulaimany

2023

Educ Inf Technol

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“…According to Alhothali et al (2022), MOOC and SPOC are the primary forms of online courses used in online education. Present research of online courses focus primarily on the delivery style of the courses, students' motivation, and how to design and evaluate the quality of online courses and students' performance and dropout in online courses ( Ruiz-Palmero et al, 2022;Martin et al, 2019;Bao, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The present research of online courses focus primarily on the delivery style of the courses, their motivation, and how to design and evaluate the quality of online courses and students' performance and dropout in online courses (Ruiz-Palmero et al, 2020;Alhothali et al, 2022;Martin et al, 2019;Bao, 2021). Previous study has looked into the elements that influence adoption of online learning and online courses Ifinedo, 2016;Yang et al, 2019;Yin et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Students’ Acceptance towards Small Private Online Course: A Case Study in one of Chinese Universities

Tan,

Tasir

2024

IJARBSS

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Online learning has gained widespread acceptance among educators and learners, yet research on student acceptance within the context of Small Private Online Courses (SPOCs) remains limited. Understanding student acceptance toward SPOCs is crucial as it serves as a foundational step before further large-scale research endeavors in this area. This study investigated students' acceptance of SPOCs integrated with problem-solving tasks in the field of Educational Technology. The SPOC was developed following the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model and IDEAL (Identify, Define, Explore, Act, Look) problem-solving phases. Utilizing a quantitative research design, a survey based on the Technology Acceptance Model (TAM) was administered to 161 undergraduate students at a Chinese university. Analysis of the data using SPSS version 25.0, focusing on 128 responses, reveals high levels of acceptance among students. They demonstrate strong endorsement of the SPOC in terms of perceived usefulness (mean = 3.879), perceived ease of use (mean = 3.84), behavioral intention (mean = 3.829), and usage (mean = 4.04). Notably, correlation analysis indicates a significant relationship; students who find the SPOC easy to use also perceive it as useful and express a strong intention to use it for learning. This finding underscores the importance of user experience in driving acceptance and adoption of SPOCs in educational settings. Moreover, this study addresses a significant gap in understanding SPOC acceptance among Chinese university students, suggesting avenues for future research to explore further the utilization of SPOCs in China's higher education landscape.

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“…Many previous studies focused on developing prediction models and evaluating results using the ML technique, with little attention focused on comprehending classi cation models for understanding predictive features [6][7][8][9][10][11][12][13][14][15][16][17][18]. Understanding the black-box output of a machine-learning model was crucial for computing and examining the in uence of features on individual and overall predictions, as well as evaluating useful features and investigating their interpretability and characteristics.…”

Section: Barnabásmentioning

confidence: 99%

“…It is the eld of revealing new and meaningful results from data. ML in education has evolved into a powerful tool for uncovering hidden patterns in educational data and forecasting students' academic performance [6,7]. Previous researchers [8] developed machine learning approaches to predict the nal test grades of undergraduate students.…”

Section: Introductionmentioning

confidence: 99%

Machine learning's model-agnostic interpretability on The Prediction of Students' Academic Performance in Video-Conference-Assisted Online Learning During the Covid-19 Pandemic

Miranda,

Aryuni,

Rahmawati

et al. 2023

Preprint

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The Covid-19 pandemic had an immediate impact on higher education. Although online technology has made contributions to higher education, its adoption has had a significant impact on learning activities during the Covid-19 pandemic. This paper proposed a predictive model for predicting students’ academic performance in video-conference-assisted online learning (VCAOL) during Covid-19 pandemic based on machine learning approach. We investigated: Random Forest (RF), Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB). There were 361 data gathered as a dataset from September 2022 to January 2023. The overall result revealed RF outperformed SVM and GNB with accuracy score of 60.27%, precision 59.46%, recall 60.27%, F1-score 59.51% and ROC AUC 87%. Understanding a machine learning model's black-box output was crucial for providing predictions that explain why and how they were developed. SHAP value of global interpretability to visualize global feature importance revealed that students' performance while using VCAOL (Performance) was the most critical attribute for predicting students' academic performance. The SHAP local interpretability bar plot revealed that ‘student academic performance was still well achieved during the learning process, despite using video conferencing during the Covid-19 pandemic’ (Performance), when Performance decreased it contributed negative impact on students’ academic performance in VCAOL during Covid-19.

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Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A Review

Cited by 35 publications

References 99 publications

Negative link prediction to reduce dropout in Massive Open Online Courses

Negative link prediction to reduce dropout in Massive Open Online Courses

Students’ Acceptance towards Small Private Online Course: A Case Study in one of Chinese Universities

Machine learning's model-agnostic interpretability on The Prediction of Students' Academic Performance in Video-Conference-Assisted Online Learning During the Covid-19 Pandemic

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