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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. 

As the need for database management skills continues to grow, there is an increasing demand for education on database models and their corresponding query languages. However, the body of research addressing the difficulties encountered by novice learners when working with query languages in database systems is still limited. In this study, we examined over 357215 submissions from 462 students’ homework problems during the Fall 2022 semester covering concepts in SQL, MongoDB, and Neo4j query languages. Our analysis through breaking down the most common syntax errors by concept confirms previous research and demonstrates that certain data operations pose challenges to students across different database systems. Specifically, we found that aggregation operations and Join operations were particularly difficult for students, which aligns with prior SQL education research. Therefore, we suggest that instructors consider incorporating visuals and assignments that enable students to build mental models for different database models. 

SQL is a crucial language for managing relational database systems, and is an essential skill for individuals in roles such as researchers, developers, and business professionals who work with databases. However, learning SQL can be a challenge, presenting an opportunity to study the various methods students use to arrive at semantically equivalent SQL queries. In this study, we examined students’ SQL submissions to homework assignments in the Database Systems course offered to upper-level undergraduate and graduate students at the University of Illinois Urbana-Champaign during the Fall 2022 semester. Our goal was to understand how students arrive at SQL solutions and overcome challenges in the learning process by building on prior research on line chart visualizations that instructors can use to increase visibility on students who are struggling. However, a major limitation of this approach was the difficulty for instructors to sift through a large number of visuals representing each student’s performance on a SQL problem and generate action items at scale, especially when dealing with enrollments of over 700 students. To overcome this limitation, we developed a novel technique to generate textual representations of the student submission sequence using global sequence alignment scores and regular expression algorithms to further compact these submission sequences. This allows instructors to gain insights quickly, on an aggregate level, and in an automated manner, enabling them to identify students who may be struggling with SQL based on their submission sequence characteristics and take appropriate action to improve database education. Our study discovered common textual submission patterns and pattern elements, and we present our recommendations to instructors to improve database education based on these findings. 

This Research Full Paper presents our findings of studying the effects of teaching modality on collaborative learning by comparing data from two sections of a Database Systems course offered simultaneously, with one offered fully face-to-face in a classroom setting while the other is offered online through a flipped-classroom model. Both sections utilized a collaborative learning approach where students work on group activities for part of the class meeting. Since the two sections were almost identical except for the teaching modality, we are provided with a unique opportunity to study the effect of teaching modalities on collaborative learning. As part of this study, we analyze four crucial data sources: 1) student performance data from the grade book 2) student performance data from the online learning management platform 3) an end-of-semester survey given by the instructor and 4) an end-of-semester survey given by the university. We extract insights on the impact of teaching modalities on collaborative learning in order to identify factors that can enhance collaborative learning. We also study the effect of teaching modalities on students’ performance. We visualize our findings to differentiate between the two modalities, and draw on the strengths of each section to establish recommendations for the instructors for course improvement efforts.