# Analyzing Student Strategies In Blended Courses Using Clickstream Data

arXiv: https://arxiv.org/abs/2006.00421 · HTML: https://mlsanity.com/paper/2006.00421/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 5 of 5)

- [RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education](https://mlsanity.com/paper/2212.01133/) (2022)
- [Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design](https://mlsanity.com/paper/2212.08955/) (2022)
- [Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection](https://mlsanity.com/paper/2407.11979/) (2024)
- [ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMs](https://mlsanity.com/paper/2505.15410/) (2025)
- [Scalable and Explainable Learner-Video Interaction Prediction using Multimodal Large Language Models](https://mlsanity.com/paper/2604.04482/) (2026)

## Abstract (arXiv)

Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern mining and models borrowed from Natural Language Processing (NLP) to understand student interactions and extract frequent strategies from a blended college course. Fine-grained clickstream data is collected through Diderot, a non-commercial educational support system that spans a wide range of functionalities. We find that interaction patterns differ considerably based on the assessment type students are preparing for, and many of the extracted features can be used for reliable performance prediction. Our results suggest that the proposed hybrid NLP methods can provide valuable insights even in the low-data setting of blended courses given enough data granularity.