Tracking college student's learning gains using EEG hyperscanning: An interpersonal neuroscience approach development and validation

Abstract

Background

Understanding college students' learning gains holds significant utility both for their self-regulation, and for teachers' online adaptation of instructional approach. EEG hyperscanning and brain-to-brain coupling (BBC) studies provide insight into students’ learning gains at the interpersonal level.

Aims

We proposed that a nonlinear BBC between teachers and students can reveal the shared cognitive processes during knowledge transmission and thus inform the learning gains. The main rationale is that the neural activities of teachers and students are dynamically coupled rather than aligned during teaching and learning.

Sample

Participants were 32 college students, in eight groups of four students each, and one lecturer. There was an asymmetric knowledge status between teacher and students in terms of the learning content.

Methods

Synchronized EEG and speech data were collected during eight well-scripted lessons, which involved in-class tests, teacher-led lectures, and self-reports. We introduced Cross-Sample Entropy to calculate the nonlinear BBC. The representative behavioral and neural features were quantified, including question-level and lesson-level learning gains, subjective experience, and BBC between student dyads, student-to-group, and student-to-teacher.

Results

Nonlinear BBC among students was complementary driven by shared attention and social closeness. Meanwhile, the teacher increased the interaction with students with less prior knowledge, thereby benefiting knowledge transmission. It was accompanied by a stronger nonlinear BBC. Importantly, only the nonlinear BBC, rather than the linear measure, showed sensitivity to differences in learning gains.

Conclusions

These findings demonstrated the feasibility of BBC tracking the students’ learning gains as a neural marker. Furthermore, it provided inter-brain neuroscience evidence supporting the Prediction-Transmission hypothesis and suggested enhancing interaction to facilitate knowledge transmission to students with less prior knowledge.