MOOC Student Success Prediction Using Knowledge Distillation

Kang, Tingting; Wei, Zhengang; Yao, Zhaoliang

doi:10.1109/cibda50819.2020.00088

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The idea of representing the online learning system as a graph to leverage students' relations, activities and courses could be extended further. Additionally, most studies mainly utilise the post-hoc prediction structures, while required features are not entirely knowable for predictive models in incomplete courses [23]. Addressing the problem, [23] proposed a new algorithm based on knowledge distillation, which only requires a few basic features, but still reaches promising forecasting results.…”

Section: Recent Improvementsmentioning

confidence: 99%

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs

Sun

Harit

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Massive Open Online Course (MOOC) systems have become prevalent in recent years and draw more attention, a.o., due to the coronavirus pandemic's impact. However, there is a well-known higher chance of dropout from MOOCs than from conventional off-line courses. Researchers have implemented extensive methods to explore the reasons behind learner attrition or lack of interest to apply timely interventions. The recent success of neural networks has revolutionised extensive Learning Analytics (LA) tasks. More recently, the associated deep learning techniques are increasingly deployed to address the dropout prediction problem. This survey gives a timely and succinct overview of deep learning techniques for MOOCs' learning analytics. We mainly analyse the trends of feature processing and the model design in dropout prediction, respectively. Moreover, the recent incremental improvements over existing deep learning techniques and the commonly used public data sets have been presented. Finally, the paper proposes three future research directions in the field: knowledge graphs with learning analytics, comprehensive social network analysis, composite behavioural analysis.

show abstract

Section: Recent Improvementsmentioning

confidence: 99%

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs

Sun

Harit

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs

2023

View full text Add to dashboard Cite

MOOCs are online learning environments which many students use, but the success rate of online learning is low. Machine learning can be used to predict learning success based on how people learn in MOOCs. Predicting the learning performance can promote learning through various methods, such as identifying low-performance students or by grouping students together. Recent machine learning has enabled the development of predictive models, and the ensemble method can assist in reducing the variance and bias errors associated with single-machine learning. This study uses a two-phase classification model with an ensemble technique to predict the learners’ grades. In the first phase, binary classification is used, and the non-majority class is then sent to the second phase, which is multi-class classification. The new features are computed based on the distance from the class’s center. The distance between the data and the center of an overlapping cluster is calculated using silhouette score-based feature selection. Lastly, Bayesian optimization boosts the performance by fine tuning the optimal parameter set. Using data from the HMPC- and the CNPC datasets, the experiment results demonstrate that the proposed design, the two-phase ensemble-based method, outperforms the state-of-the-art machine learning algorithms.

show abstract

MOOC Student Success Prediction Using Knowledge Distillation

Cited by 2 publications

References 15 publications

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs

A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs

Contact Info

Product

Resources

About