On the Effectiveness of Self-Training in MOOC Dropout Prediction

Goel, Yamini; Goyal, Rinkaj

doi:10.1515/comp-2020-0153

Cited by 26 publications

(22 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Alraimi, Zo, and Ciganek (2015) pointed out that retention rates are on average less than 10 percent and the completion rate is extremely low. Dropout rates of MOOCs are between 91 percent and 93 percent (Goel & Goyal, 2020). A high dropout rate leads to an average completion rate which is less than 13 percent (Krause, Mogalle, Pohl, & Williams, 2015).…”

Section: Introductionmentioning

confidence: 99%

Exploring the factors influencing users’ satisfaction and continuance intention of MOOCs in China

2022

KJSS

View full text Add to dashboard Cite

Recently, Massive Open Online Courses (MOOC) have received much attention from users. Users' continuance intention of using MOOC is one of the most important indicators to measure the success of MOOCs. At present, MOOCs are facing a low completion rate and retention rate problems. This calls for the importance in investigating factors that affect MOOC users' satisfaction, thereby enhancing the intention to continue using MOOCs. The purpose of this research was therefore to explore the factors affecting MOOC users' satisfaction and continuance intention towards the Expectation Confirmation Model. In total, 410 questionnaires were collected via an online survey and analysed with the Structural Equation Modelling (SEM). Participants were Chinese employees from different city tiers who had experienced using or now used MOOCs. The analysis indicated a good fit of the structural equation model. The analysis results showed that the confirmation factor has a significant impact on perceived usefulness, perceived service quality, and satisfaction. The analysis results highlighted that perceived service quality has the strongest impact on satisfaction, thereby enhancing the continuance intention in using MOOC. The results also revealed that perceived usefulness and perceived service quality can not only directly influence continuance intention, but also indirectly influenced satisfaction. The findings therefore suggested that MOOC providers should focus on improving MOOCs' overall quality to better meet MOOC users' satisfaction, thereby enhancing the intention to continue using MOOCs.

show abstract

Section: Introductionmentioning

confidence: 99%

Exploring the factors influencing users’ satisfaction and continuance intention of MOOCs in China

2022

KJSS

View full text Add to dashboard Cite

show abstract

“…As such, we employed the SMOTE technique on student learning data each week to make sure samples in the two target classes in the dataset are equal. This is in line with the research of Goel and Goyal (2020). Multiple approaches (i.e., Undersampling, Oversampling, and Hybrid methods) were employed by the authors to balance the different classes in the dataset to ensure that the model could better capture the underlying patterns.…”

Section: Discussionmentioning

confidence: 57%

An Interpretable Pipeline for Identifying At-Risk Students

Pei

Xing

2021

Journal of Educational Computing Research

View full text Add to dashboard Cite

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.

show abstract

“…Another research described by Gray and Perkins ( 2019 ) and Chui et al ( 2020 ) identified marginal students who are at-risk of low achievement. Authors in (Adnan et al, 2021 ; Dass et al, 2021 ; Goel & Goyal, 2020 ; Kabathova & Drlik, 2021 ) identified dropout students based on their academic results.…”

Section: Resultsmentioning

confidence: 99%

Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022)

2022

View full text Add to dashboard Cite

The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice in Learning Analytics and Educational Data Mining. The aim of this study is to review the most recent research body related to Predictive Analytics in Higher Education. Articles published during the last decade between 2012 and 2022 were systematically reviewed following PRISMA guidelines. We identified the outcomes frequently predicted in the literature as well as the learning features employed in the prediction and investigated their relationship. We also deeply analyzed the process of predictive modelling, including data collection sources and types, data preprocessing methods, Machine Learning models and their categorization, and key performance metrics. Lastly, we discussed the relevant gaps in the current literature and the future research directions in this area. This study is expected to serve as a comprehensive and up-to-date reference for interested researchers intended to quickly grasp the current progress in the Predictive Learning Analytics field. The review results can also inform educational stakeholders and decision-makers about future prospects and potential opportunities.

show abstract

On the Effectiveness of Self-Training in MOOC Dropout Prediction

Cited by 26 publications

References 38 publications

Exploring the factors influencing users’ satisfaction and continuance intention of MOOCs in China

Exploring the factors influencing users’ satisfaction and continuance intention of MOOCs in China

An Interpretable Pipeline for Identifying At-Risk Students

Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022)

Contact Info

Product

Resources

About