Dyads composed of members with high prior knowledge are most conducive to digital game-based collaborative learning

Abstract

To develop better understanding of the links among collaborative antecedents, processes, and outcomes, this study aims to investigate the effects of group composition on game-based collaborative learning and to explore the underlying interbrain dynamics between group members. A one-factor (group composition based on prior knowledge) between-subjects design experimental study was conducted. 106 students were assigned to one of the three conditions (high-high: both members with high level of prior knowledge; high-low: one member with high level, and the other with low level, of prior knowledge; low-low: both members with low level of prior knowledge) and performed digital game tasks while wearing near-infrared devices on their heads. The results showed that.

(1) Learners in the high-high composition condition showed advantages over those in high-low and low-low composition conditions (i.e., higher interpersonal neural synchronization, more positive learning performance, lower perceived intrinsic cognitive load, and higher perceived learning motivation).

(2) Learners in the high-low composition condition showed some advantages over those in the low-low composition condition (i.e., more positive learning performance).

(3) Interpersonal neural synchronization in the high-high composition condition was positively related to perceived interaction quality and learning retention; and

(4) The perceived interaction quality played a mediating role in interpersonal neural synchronization predicting learning retention.

Recent studies have revealed that the optimal learning effect was achieved when students possessing a high prior knowledge collaborate. These findings, while aligned with the principles of transaction cost hypothesis, provided pedagogical guidance on how to conduct collaborative learning in DGBL and enhanced our understanding about the underlying cognitive and neural mechanisms.