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

Maryam Alomair, Shimei Pan, Lujie Karen Chen

AAAI 2023 pp. 16152-16153

doi:10.1609/AAAI.V37I13.26936 /aaai/2023/alomair2023aaai-modeling/

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.

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Cite

Text

Alomair et al. "Modeling Metacognitive and Cognitive Processes in Data Science Problem Solving (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26936

Markdown

[Alomair et al. "Modeling Metacognitive and Cognitive Processes in Data Science Problem Solving (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/alomair2023aaai-modeling/) doi:10.1609/AAAI.V37I13.26936

BibTeX

@inproceedings{alomair2023aaai-modeling,
  title     = {{Modeling Metacognitive and Cognitive Processes in Data Science Problem Solving (Student Abstract)}},
  author    = {Alomair, Maryam and Pan, Shimei and Chen, Lujie Karen},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16152-16153},
  doi       = {10.1609/AAAI.V37I13.26936},
  url       = {https://mlanthology.org/aaai/2023/alomair2023aaai-modeling/}
}