2026 ASEE Annual Conference & Exposition

This paper involves Preliminary Analysis of how elementary students' affective and collaborative responses are managed while solving engineering design problems regarding simple physics machines. In the United States, there is a growing trend to include engineering as a part of the K–12 curriculum in line with the Next Generation Science Standards. Elementary teachers face challenges when integrating engineering, such as limited resources, a lack of student experience with engineering and teamwork, and limited class time. Collaborative, open-ended design projects are particularly difficult, as students must learn to handle ambiguity and navigate disagreements with group members.
In this research, we analyze by the MindLabs(ML) platform, which supports students developing engineering design skills by providing an augmented reality interface that captures student collaborations, design actions and self-reported emotional states through event data-logging. In a Midwestern school, 487 elementary students collaborated in teams on design challenges. For example, they were tasked with developing a system for transporting a ball using MindLabs. Assignments were primarily based on science concepts including forces and motion and culminating with a design challenge.
Previous research utilized exploratory data visualization to analyze student collaboration and affective responses, identifying varying levels of collaboration or dysfunction (e.g., teams dominated by one or a few members) and mixed but often helpful use of affective reporting tools.
Our study examines student actions through sequence analysis, applying regular expressions. Sequence analysis is powerful for educational research because these techniques can identify sets of ordered actions students undertake that represent some higher order skill. While other sequence analysis techniques exist, like Markov chains or optimal matching exist, these techniques struggle with complex or long sequences. Regular expressions (regex) uses several user-defined rules, making it more flexible than other sequence analysis techniques, to search character sequences to find patterns. This allows users to automatically locate patterns in large amounts of text, such as email addresses or phone numbers. We transform raw student action data by extracting and encoding key features like tile type, additions, and rotations into simplified representations (e.g., single letters). This enables efficient analysis using regex to identify behavioral patterns like "A:L:2+" (add, lever, rotate clockwise180), providing insights into student strategies and interactions. Regex is powerful for tasks like data validation, text parsing, search and replace operations. Our results highlight teams’ sequences that demonstrate collaborative skills such as teams exhibiting patterns like "collapse all and start over." We further examine their engagement with affect reporting tools. This allows us to uncover fine-grained patterns, like “Equal Contributions" where team members shared equal levels of actions of adding and editing within a sequence and report primarily positive affect.
We believe this research can help teachers support younger students in engineering design by understanding their sequences of actions and team dynamics. It can also aid students in recognizing their own and teammates' affective responses. Furthermore, it informs software development to automatically detect and assist students facing difficulties. More generally, regex may prove useful for other educational technologies that use data-logging to track student learning processes.