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Discovering unknown response patterns in progress test data to improve the estimation of student performance.

Miriam Sieg1,2, Iván Roselló Atanet1, Mihaela Todorova Tomova3

  • 1Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, AG Progress Test Medizin, Charitéplatz 1, 10117, Berlin, Germany.

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This study used k-means clustering on Progress Test Medizin (PTM) data to identify distinct student groups. Findings revealed performance-based clusters and drop-out patterns, offering insights into medical student learning trajectories.

Keywords:
Boosting algorithmClassificationClusteringEnsemble learningExplainerProgress testStudent groupsSupervised machine learningUnsupervised machine learningk-means

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Area of Science:

  • Medical Education
  • Educational Psychology
  • Data Science in Education

Background:

  • The Progress Test Medizin (PTM) is a large-scale formative assessment for medical students in German-speaking countries.
  • Students typically receive cohort-based feedback on their knowledge development.
  • This study leverages PTM data to uncover underlying student response patterns.

Purpose of the Study:

  • To identify distinct groups of medical students based on their response patterns in the PTM.
  • To understand the characteristics of these student groups in relation to their academic progression and test-taking behaviors.
  • To identify specific test questions that effectively differentiate these student clusters.

Main Methods:

  • Applied k-means clustering to a dataset of 5,444 medical students' PTM responses.
  • Utilized XGBoost and SHAP values to identify cluster-relevant questions for each of the five identified clusters.
  • Analyzed clusters based on total scores, response patterns, confidence levels, and question characteristics (difficulty, discrimination, competence).

Main Results:

  • Identified five distinct student clusters, with three categorized as 'performance' clusters (advanced, near-graduation, and beginners).
  • Discovered two 'drop-out' clusters, one showing initial good performance followed by test abandonment, and another with non-serious or early-semester students exhibiting poor performance.
  • Relevant questions for beginner clusters were generally easier, while those for advanced clusters were more difficult, with increased guessing observed in lower-performing groups.

Conclusions:

  • The identified clusters provide context for student performance across participating universities.
  • Specific PTM questions effectively separated student clusters, validating the 'performance' groupings.
  • This clustering approach offers a nuanced understanding of student learning and engagement within a large-scale medical assessment.