Modeling Metacognitive and Cognitive Processes in Data Science Problem Solving (Student Abstract)

Authors

  • Maryam Alomair University of Maryland Baltimore County King Faisal University
  • Shimei Pan University of Maryland Baltimore County
  • Lujie Karen Chen University of Maryland Baltimore County

DOI:

https://doi.org/10.1609/aaai.v37i13.26936

Keywords:

Educational Data Mining, Learning Analytics, Problem Solving, Metacognition, Cognition

Abstract

Data Science (DS) is an interdisciplinary topic that is applicable to many domains. In this preliminary investigation, we use caselet, a mini-version of a case study, as a learning tool to allow students to practice data science problem solving (DSPS). Using a dataset collected from a real-world classroom, we performed correlation analysis to reveal the structure of cognition and metacognition processes. We also explored the similarity of different DS knowledge components based on students’ performance. In addition, we built a predictive model to characterize the relationship between metacognition, cognition, and learning gain.
AAAI-23 Proceedings Cover

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Published

2024-07-15

How to Cite

Alomair, M., Pan, S., & Chen, L. K. (2024). Modeling Metacognitive and Cognitive Processes in Data Science Problem Solving (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16152-16153. https://doi.org/10.1609/aaai.v37i13.26936

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program