# Engaging with Massive Online Courses

arXiv: https://arxiv.org/abs/1403.3100 · HTML: https://mlsanity.com/paper/1403.3100/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2014.

## Built on by (most cited first, 20 of 20)

- [Enhancing Knowledge Tracing via Adversarial Training](https://mlsanity.com/paper/2108.04430/) (2021)
- [Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis](https://mlsanity.com/paper/2210.08169/) (2022)
- [Endowing Interpretability for Neural Cognitive Diagnosis by Efficient Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2405.14399/) (2024)
- [Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems](https://mlsanity.com/paper/2401.10840/) (2023)
- [Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts](https://mlsanity.com/paper/2308.03377/) (2023)
- [LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences](https://mlsanity.com/paper/2105.06266/) (2021)
- [Automatic Context-Driven Inference of Engagement in HMI: A Survey](https://mlsanity.com/paper/2209.15370/) (2022)
- [DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis](https://mlsanity.com/paper/2410.17564/) (2024)
- [Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2307.04429/) (2023)
- [Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attention](https://mlsanity.com/paper/2407.02547/) (2024)
- [A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments](https://mlsanity.com/paper/2410.15054/) (2024)
- [Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction](https://mlsanity.com/paper/2106.00263/) (2021)
- [DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms](https://mlsanity.com/paper/2210.12402/) (2022)
- [Subgroup Discovery in MOOCs: A Big Data Application for Describing Different Types of Learners](https://mlsanity.com/paper/2403.05555/) (2024)
- [Dual-State Personalized Knowledge Tracing with Emotional Incorporation](https://mlsanity.com/paper/2405.16799/) (2024)
- [MOOCRep: A Unified Pre-trained Embedding of MOOC Entities](https://mlsanity.com/paper/2107.05154/) (2021)
- [Heterogeneous Line Graph Transformer for Math Word Problems](https://mlsanity.com/paper/2208.05645/) (2022)
- [Modeling and predicting students' engagement behaviors using mixture Markov models](https://mlsanity.com/paper/2403.05556/) (2024)
- [Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression](https://mlsanity.com/paper/2408.05428/) (2024)
- [Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Education](https://mlsanity.com/paper/2604.07253/) (2026)

## Abstract (arXiv)

The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood. In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course. We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement.