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1. Generalisable Methods for Early Prediction in Interactive Simulations for Education

Author

Cock, Jade Maï, Marras, Mirko, Giang, Christian, and Käser, Tanja

Abstract

Interactive simulations allow students to discover the underlying principles of a scientific phenomenon through their own exploration. Unfortunately, students often struggle to learn effectively in these environments. Classifying students' interaction data in the simulations based on their expected performance has the potential to enable adaptive guidance and consequently improve students' learning. Previous research in this field has mainly focused on a-posteriori analyses or investigations limited to one specific predictive model and simulation. In this paper, we investigate the quality and generalisability of models for an early prediction of conceptual understanding based on clickstream data of students across interactive simulations. We first measure the students' conceptual understanding through their in-task performance. Then, we suggest a novel type of features that, starting from clickstream data, encodes both the state of the simulation and the action performed by the student. We finally propose to feed these features into GRU-based models, with and without attention, for prediction. Experiments on two different simulations and with two different populations show that our proposed models outperform shallow learning baselines and better generalise to different learning environments and populations. The inclusion of attention into the model increases interpretability in terms of effective inquiry. The source code is available on Github. [For the full proceedings, see ED623995.] more...

Published
2022

2. Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling

Author

Cock, Jade Maï, Bilal, Muhammad, Davis, Richard, Marras, Mirko, and Käser, Tanja

Subjects
Computer Science - Computers and Society
Abstract

Algorithms deployed in education can shape the learning experience and success of a student. It is therefore important to understand whether and how such algorithms might create inequalities or amplify existing biases. In this paper, we analyze the fairness of models which use behavioral data to identify at-risk students and suggest two novel pre-processing approaches for bias mitigation. Based on the concept of intersectionality, the first approach involves intelligent oversampling on combinations of demographic attributes. The second approach does not require any knowledge of demographic attributes and is based on the assumption that such attributes are a (noisy) proxy for student behavior. We hence propose to directly oversample different types of behaviors identified in a cluster analysis. We evaluate our approaches on data from (i) an open-ended learning environment and (ii) a flipped classroom course. Our results show that both approaches can mitigate model bias. Directly oversampling on behavior is a valuable alternative, when demographic metadata is not available. Source code and extended results are provided in https://github.com/epfl-ml4ed/behavioral-oversampling}{https://github.com/epfl-ml4ed/behavioral-oversampling ., Comment: Accepted as a full paper at LAK 2023: The 13th International Learning Analytics and Knowledge Conference, 13-17 of March 2023, Arlington more...

Published
2022
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3. A Systematic Review of Empirical Studies Using Log Data from Open-Ended Learning Environments to Measure Science and Engineering Practices

Author

Wang, Karen D., Cock, Jade Maï, Käser, Tanja, and Bumbacher, Engin

Abstract

Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry and problem solving. How to parse and analyse the log data to reveal evidence of multifaceted constructs like inquiry and problem solving holds the key to making interactive learning environments useful for assessing students' higher-order competencies. In this paper, we present a systematic review of studies that used log data generated in OELEs to describe, model and assess scientific inquiry and problem solving. We identify and analyse 70 conference proceedings and journal papers published between 2012 and 2021. Our results reveal large variations in OELE and task characteristics, approaches used to extract features from log data and interpretation models used to link features to target constructs. While the educational data mining and learning analytics communities have made progress in leveraging log data to model inquiry and problem solving, multiple barriers still exist to hamper the production of representative, reproducible and generalizable results. Based on the trends identified, we lay out a set of recommendations pertaining to key aspects of the workflow that we believe will help the field develop more systematic approaches to designing and using OELEs for studying how students engage in inquiry and problem-solving practices. more...

Published
2023
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6. Early Prediction of Conceptual Understanding in Interactive Simulations

Author

Cock, Jade Maï L, Marras, Mirko, Giang, Christian, Käser, Tanja, Hsia, I-Han, Sahebi, Shaghayerg, Bouchet, François, and Vie, Jill-Jênn

Subjects
early classification, skip-grams, interactive simulations, conceptual understanding
Abstract

Interactive simulations allow students to independently ex- plore scientific phenomena and ideally infer the underlying principles through their exploration. Effectively using such environments is challenging for many students and there- fore, adaptive guidance has the potential to improve stu- dent learning. Providing effective support is, however, also a challenge because it is not clear how effective inquiry in such environments looks like. Previous research in this area has mostly focused on grouping students with similar strategies or identifying learning strategies through sequence mining. In this paper, we investigate features and models for an early prediction of conceptual understanding based on clickstream data of students using an interactive Physics simulation. To this end, we measure students’ conceptual understanding through a task they need to solve through their exploration. Then, we propose a novel pipeline to transform clickstream data into predictive features, using latent feature represen- tations and interaction frequency vectors for different com- ponents of the environment. Our results on interaction data from 192 undergraduate students show that the proposed approach is able to detect struggling students early on. more...