Identifying Collaborative Problem-Solving Behaviors Using Sequential Pattern Mining

Yiqiu Zhou, Qianhui Liu, Sophia Yang, Abdussalam Alawini

Research output: Contribution to journalConference articlepeer-review


With the increasing adoption of collaborative learning approaches, instructors must understand students' problem-solving approaches during collaborative activities to better design their class. 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. The log data was collected from an online assessment and learning system, containing the timestamps 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. Sequential compacting and mining techniques were employed to identify pairs of transitions highly associated with one another. This preliminary research sheds light on the recurring submission patterns derived from the amount of time spent on each problem, warranting further examination on these patterns to unpack collaborative problem-solving behaviors. Our study demonstrates the potential of temporal analysis to identify meaningful problem-solving patterns based on log traces, which may help flag key moments and alert instructors to provide in-time scaffolding when students work on group assignments.

Original languageEnglish (US)
JournalASEE Annual Conference and Exposition, Conference Proceedings
StatePublished - Jun 25 2023
Event2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023 - Baltimore, United States
Duration: Jun 25 2023Jun 28 2023

ASJC Scopus subject areas

  • General Engineering


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