Predicting MOOC Dropout over Weeks Using Machine Learning Methods

By Marius Kloft, Felix Stiehler, Zhilin Zheng, Niels Pinkwart. Conference on Empirical Methods in Natural Language Processing, 2014

323 citations recorded by the source corpus.

With high dropout rates as observed in many current larger-scale online courses, mechanisms that are able to predict student dropout become increasingly important. While this problem is partially solved for students that are active in online forums, this is not yet the case for the more general student population. In this paper, we present an approach that works on click-stream data. Among other features, the machine learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behavior over time. In the later phases of a course (i.e., once such history data is available), this approach is able to predict dropout significantly better than baseline methods.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/3ffe41c72ace4b5ccf8e76d713f48dc644bf5a51

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Srijan Kumar
2. Jure Leskovec
3. Dit-Yan Yeung
4. Xikun Zhang
5. Mung Chiang
6. Minrui Fei
7. Yu Zhang
8. Qiang Yang

In Computer Science Applications

1. Jure Leskovec
2. Dit-Yan Yeung
3. Minrui Fei
4. Jane Yin-Kim Yau
5. Dirk Ifenthaler

Citations in the corpus

• A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques (2021, methodology): “Kloft et al. [7] considered the clickstream data for dropout classification in MOOCs environment using EMNLP 2014 dataset.”
• Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks (2017, background): “In this regard, there have been a few recent works on predictive analytics for MOOC, proposing algorithms to predict dropoff rates [21], [22] and assessment scores [4], [10], [22], [23].”
• Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (2019, background): “re fundamental problems in these domains [5, 6, 22, 30, 36, 40, 43]. For instance, predicting when a student is likely to drop out of a MOOC course is important to develop early intervention measures [23, 46] and predicting when a user is likely to turn malicious on social platforms, like Reddit and”
• Temporal Models for Predicting Student Dropout in Massive Open Online Courses (2015, methodology): “The student has some course activities in week 1 and 3 but not in all other weeks (shown as ‘Zeros’ for the features).”