Symposia | Invited Symposia | Poster Sessions | Data Blitz Sessions

Engagement Fluctuations during Collaborative Learning: A Real-World EEG Study

Poster Session F - Tuesday, April 16, 2024, 8:00 – 10:00 am EDT, Sheraton Hall
Also presenting in Data Blitz Session 4 - Saturday, April 13, 2024, 1:00 – 2:30 pm EDT, Osgood Ballroom.

Yushuang Liu1 (yu-shuang.liu@uconn.edu), Ido Davidesco1, Kim Chaloner2, Emma Laurent3, Gabriella Amanda Ali3, Laura Noejovich3, Henry Valk3, Dana Bevilacqua3, David Poeppel3, Suzanne Dikker3; 1University of Connecticut, 2Grace Church School, 3New York University

Collaborative learning is known to be effective, but not all students engage and benefit from it. Here, we conducted electroencephalography (EEG) recordings in a real-world classroom to examine how students engage in a collaborative learning task. A total of 36 high school students participated in the study in groups of four. Students were equipped with portable, 32-channel EEG devices and were instructed to collaboratively make a model of a cell. Student learning was assessed at the end of the EEG session using a test. The sessions were video recorded, and the behavior of each student was coded in 5-second segments, and classified as “on-task,” “off-task,” or “idle.” Analysis of the video data indicated that students alternated between on- and off-task states every 10 to 20 seconds. The amount of time students were continuously on-task was positively correlated with their test performance. Additionally, students were idle for about 13% of the time, meaning that they were not explicitly engaged with the task. However, based on behavior alone, it is challenging to assess how engaged students are cognitively. EEG analyses revealed that students exhibited higher alpha (8-12 Hz) activity when they were off-task compared to on-task, especially in posterior EEG electrodes. During idle periods, students’ alpha brain activity was indistinguishable from their brain activity during off-task periods. These findings provide key new evidence for the value of brain data collected in real-world learning settings.

Topic Area: ATTENTION: Other

 

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