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

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https://ojs.aaai.org/index.php/AAAI/article/view/26936

Permanent Link

https://doi.org/10.1609/aaai.v37i13.26936
 http://hdl.handle.net/11603/31699

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UMBC Information Systems Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0002-5989-8543
 https://orcid.org/0000-0002-7185-8405

Date

2023-09-06

Type of Work

conference papers and proceedings
journal articles
Text

Department

Program

Citation of Original Publication

Alomair, Maryam, Shimei Pan, and Lujie Karen Chen. 2023. “Modeling Metacognitive and Cognitive Processes in Data Science Problem Solving (Student Abstract)”. Proceedings of the AAAI Conference on Artificial Intelligence 37 (13):16152-53. https://doi.org/10.1609/aaai.v37i13.26936.

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Subjects

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.