# Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach

arXiv: https://arxiv.org/abs/2111.00901 · HTML: https://mlsanity.com/paper/2111.00901/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing](https://mlsanity.com/paper/2108.07386/) (2021)
- [Learning about social learning in MOOCs: From statistical analysis to generative model](https://mlsanity.com/paper/1312.2159/) (2013)
- [Personalized Thread Recommendation for MOOC Discussion Forums](https://mlsanity.com/paper/1806.08468/) (2018)
- [Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions](https://mlsanity.com/paper/1407.7131/) (2014)
- [Likely to stop? Predicting Stopout in Massive Open Online Courses](https://mlsanity.com/paper/1408.3382/) (2014)
- [Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features](https://mlsanity.com/paper/1809.00052/) (2018)
- [Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning](https://mlsanity.com/paper/2002.01598/) (2020)
- [Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos](https://mlsanity.com/paper/2002.01955/) (2020)
- [Social Interactions Clustering MOOC Students: An Exploratory Study](https://mlsanity.com/paper/2008.03982/) (2020)

## Built on by (most cited first, 4 of 4)

- [Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning](https://mlsanity.com/paper/2208.01182/) (2022)
- [Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics](https://mlsanity.com/paper/2212.02985/) (2022)
- [A review of clustering models in educational data science towards fairness-aware learning](https://mlsanity.com/paper/2301.03421/) (2023)
- [Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation](https://mlsanity.com/paper/2401.07456/) (2024)

## Abstract (arXiv)

We study the problem of predicting student knowledge acquisition in online courses from clickstream behavior. Motivated by the proliferation of eLearning lecture delivery, we specifically focus on student in-video activity in lectures videos, which consist of content and in-video quizzes. Our methodology for predicting in-video quiz performance is based on three key ideas we develop. First, we model students' clicking behavior via time-series learning architectures operating on raw event data, rather than defining hand-crafted features as in existing approaches that may lose important information embedded within the click sequences. Second, we develop a self-supervised clickstream pre-training to learn informative representations of clickstream events that can initialize the prediction model effectively. Third, we propose a clustering guided meta-learning-based training that optimizes the prediction model to exploit clusters of frequent patterns in student clickstream sequences. Through experiments on three real-world datasets, we demonstrate that our method obtains substantial improvements over two baseline models in predicting students' in-video quiz performance. Further, we validate the importance of the pre-training and meta-learning components of our framework through ablation studies. Finally, we show how our methodology reveals insights on video-watching behavior associated with knowledge acquisition for useful learning analytics.