# Predicting Long-Term Student Outcomes from Short-Term EdTech Log Data

arXiv: https://arxiv.org/abs/2412.15473 · HTML: https://mlsanity.com/paper/2412.15473/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 42 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Early Performance Prediction using Interpretable Patterns in Programming Process Data](https://mlsanity.com/paper/2102.05765/) (2021)
- [Evaluating the Surrogate Index as a Decision-Making Tool Using 200 A/B Tests at Netflix](https://mlsanity.com/paper/2311.11922/) (2023)

## Built on by (most cited first, 2 of 2)

- [Modeling Student Learning with 3.8 Million Program Traces](https://mlsanity.com/paper/2510.05056/) (2025)
- [LLM-based Multimodal Feedback Produces Equivalent Learning and Better Student Perceptions than Educator Feedback](https://mlsanity.com/paper/2601.15280/) (2026)

## Abstract (arXiv)

Educational stakeholders are often particularly interested in sparse, delayed student outcomes, like end-of-year statewide exams. The rare occurrence of such assessments makes it harder to identify students likely to fail such assessments, as well as making it slow for researchers and educators to be able to assess the effectiveness of particular educational tools. Prior work has primarily focused on using logs from students full usage (e.g. year-long) of an educational product to predict outcomes, or considered predictive accuracy using a few minutes to predict outcomes after a short (e.g. 1 hour) session. In contrast, we investigate machine learning predictors using students' logs during their first few hours of usage can provide useful predictive insight into those students' end-of-school year external assessment. We do this on three diverse datasets: from students in Uganda using a literacy game product, and from students in the US using two mathematics intelligent tutoring systems. We consider various measures of the accuracy of the resulting predictors, including its ability to identify students at different parts along the assessment performance distribution. Our findings suggest that short-term log usage data, from 2-5 hours, can be used to provide valuable signal about students' long-term external performance.