Identifying Collaborative Problem-Solving Behaviors Using Sequential Pattern Mining

Yiqiu Zhou; Qianhui Liu; Sophia Yang; Abdussalam Alawini

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: The Best of Computers in Education
Tagged Division: Computers in Education Division (COED)
Tagged Topic: Diversity
Page Count: 11
DOI: 10.18260/1-2--43424
Permanent URL: https://peer.asee.org/43424
Download Count: 161

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Qianhui (Sophie) Liu is a PhD student in the Department of Curriculum & Instruction, College of Education at UIUC. Her research interests are learning analytics, educational data mining, computer science education, and explainable AI.

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Sophia Yang University of Illinois, Urbana-Champaign orcid.org/0000-0003-1274-4851

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Sophia Yang is a second-year Ph.D. candidate with research work focused in the areas of Computing Education, Database Systems, Bioinformatics algorithms, Human-Computer Interaction, and interesting intersections of the above. Current research work focuses on quantitatively and qualitatively studying how students learn SQL by utilizing Bioinformatics alignment algorithms, aiming to create instructor and student-facing tools. Her work aims to better improve students’ learning and instructors’ teaching experiences in large-scale database courses.

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Abdussalam Alawini University of Illinois, Urbana-Champaign

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Abdussalam Alawini is a Teaching Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. He holds a bachelor's degree from the University of Tripoli and master's and doctoral degrees in Computer Science from Portland State University. Dr. Alawini has worked in various roles in the tech industry, including as a database administrator, lead software developer, and IT Manager. He conducts research on data management systems and computing education. Dr. Alawini is passionate about building data-driven, AI-based systems for improving teaching and learning.

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Abstract

A vast body of research demonstrated the learning benefits and importance of collaborative learning (i.e., pedagogical approaches involving students learning collectively by working on collaborative assessments that require joint intellectual effort). Such benefits include increasing student retention, higher-level thinking skills, improved communication skills, responsibility for students' own learning, and more. Acknowledging these benefits, more instructors are integrating collaborative learning into their curriculum (Järvenoja et al., 2020).

With this increased adoption of collaborative learning approaches, instructors must understand their students' problem-solving approaches during collaborative learning activities to better design their class activities. Among the multiple ways to reveal collaborative problem-solving processes, temporal submission patterns is one that is more scalable and generalizable in Computer Science education. In this paper, we provide a temporal analysis of a large dataset of students' submissions to collaborative learning assignments in an upper-level database course offered at a large public university. This research uses sequential pattern mining techniques to identify temporal patterns that capture students' problem-solving strategies when working on collaborative assignments. We study the log data collected from an online assessment and learning system, which contains the timestamp data of each student's submissions to a problem on the collaborative assignment. Each submission was labeled as quick (Q), medium (M), or slow (S) based on its duration and whether it was shorter or longer than the 25th and 75th percentile.

Our analysis revealed seven submission patterns after applying two compacting rules: Q, Q-LNG, M, M-LNG, QM-LNG, S, and S-LNG, where "LNG" dictates that the compacting rule was applied. The compacting rule indicates that there were at least three consecutive submissions of the same pattern (i.e., Q-LNG is a compaction of Q-Q-Q). We then investigated how submission patterns link to become discriminating sequences by computing the transition probabilities with L* metrics. We identified transition pairs that include the same submission patterns in different transition positions (i.e., transit from and transit to) to construct more extended transition patterns by combining transition pairs. For example, the transition pairs M-LNG to S-LNG and S-LNG to Q have a correlation of 0.782 (p<0.001), so we can generate a three-pattern transition: M-LNG to S-LNG to Q. With these transition patterns, we can then better pinpoint collaborative learning problem-solving behaviors among students.

Citation
Format

Zhou, Y., & Liu, Q., & Yang, S., & Alawini, A. (2023, June), Identifying Collaborative Problem-Solving Behaviors Using Sequential Pattern Mining Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43424

TY  - CPAPER
AB  - A vast body of research demonstrated the learning benefits and importance of collaborative learning (i.e., pedagogical approaches involving students learning collectively by working on collaborative assessments that require joint intellectual effort). Such benefits include increasing student retention, higher-level thinking skills, improved communication skills, responsibility for students&#39; own learning, and more. Acknowledging these benefits, more instructors are integrating collaborative learning into their curriculum (Järvenoja et al., 2020).

With this increased adoption of collaborative learning approaches, instructors must understand their students&#39; problem-solving approaches during collaborative learning activities to better design their class activities. Among the multiple ways to reveal collaborative problem-solving processes, temporal submission patterns is one that is more scalable and generalizable in Computer Science education. In this paper, we provide a temporal analysis of a large dataset of students&#39; submissions to collaborative learning assignments in an upper-level database course offered at a large public university. This research uses sequential pattern mining techniques to identify temporal patterns that capture students&#39; problem-solving strategies when working on collaborative assignments. We study the log data collected from an online assessment and learning system, which contains the timestamp data of each student&#39;s submissions to a problem on the collaborative assignment. Each submission was labeled as quick (Q), medium (M), or slow (S) based on its duration and whether it was shorter or longer than the 25th and 75th percentile.

Our analysis revealed seven submission patterns after applying two compacting rules: Q, Q-LNG, M, M-LNG, QM-LNG, S, and S-LNG, where &quot;LNG&quot; dictates that the compacting rule was applied. The compacting rule indicates that there were at least three consecutive submissions of the same pattern (i.e., Q-LNG is a compaction of Q-Q-Q). We then investigated how submission patterns link to become discriminating sequences by computing the transition probabilities with L* metrics. We identified transition pairs that include the same submission patterns in different transition positions (i.e., transit from and transit to) to construct more extended transition patterns by combining transition pairs. For example, the transition pairs M-LNG to S-LNG and S-LNG to Q have a correlation of 0.782 (p&amp;lt;0.001), so we can generate a three-pattern transition: M-LNG to S-LNG to Q. With these transition patterns, we can then better pinpoint collaborative learning problem-solving behaviors among students.
AU  - Yiqiu Zhou
AU  - Qianhui Liu
AU  - Sophia Yang
AU  - Abdussalam Alawini
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Identifying Collaborative Problem-Solving Behaviors Using Sequential Pattern Mining
UR  - https://peer.asee.org/43424
DO  - 10.18260/1-2--43424
ER  -