Publications de Bahar Radmehr | EPFL Graph Search

PharmaSimText: A Text-Based Educational Playground filled with RL-LLM Agents That Work Together Even in Disagreement

Bahar Radmehr, Tanja Christina Käser Jacober

There has been a growing interest in developing simulated learners to enhance learning and teaching experiences in educational environments. However, existing works have primarily focused on structured environments relying on meticulously crafted represent ...

2025

ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMs

Bahar Radmehr, Tanja Christina Käser Jacober

Clickstream data from digital learning environments provide valuable insights into student behavior but are challenging to interpret due to their granularity. Prior methods mainly relied on handcrafted features, expert labeling, clustering, or supervised m ...

Springer Science and Business Media Deutschland GmbH2025

How Instructional Sequence and Personalized Support Impact Diagnostic Strategy Learning

Tatjana Nazaretsky, Bahar Radmehr, Tanja Christina Käser Jacober

Supporting students in developing effective diagnostic reasoning is a key challenge in various educational domains. Novices often struggle with cognitive biases such as premature closure and over-reliance on heuristics. Scenario-based learning (SBL) can ad ...

Springer2025

An Interpretable Approach to Identify Performance Indicators Within Unstructured Learning Environments

Bahar Radmehr, Tanja Christina Käser Jacober

As educational technology becomes increasingly integrated into curricula, more students are engaging with online learning platforms, interactive simulations, and MOOCs. These unstructured environments record students’ behaviors, providing a rich source of ...

Springer Science and Business Media Deutschland GmbH2024

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Vinitra Swamy, Tanja Christina Käser Jacober, Bahar Radmehr, Mirko Marras, Natasa Krco

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

2022