Can Knowledge Awareness Tools Help Seek Learning Partners with Complementary Knowledge?

Autor: Jürgen Buder, Michail D. Kozlov, Daniel Thiemann
Rok vydání: 2018
Předmět:
Zdroj: IEEE Transactions on Learning Technologies. 11:334-341
ISSN: 2372-0050
DOI: 10.1109/tlt.2017.2740173
Popis: The present study aims to empiricaly demonstrate the viability and benefits of an awareness-based approach to diversify knowledge between potential learning partners. Groups of four learners studied lesson material on biology. After a knowledge test, the groups were to form collaborative learning dyads. Based on the test, a novel knowledge discrepancy calculation tool discerned group configurations with maximal and minimal discrepancies in knowledge between potential learning partners. In the experimental condition, groups were made aware of the results of this calculation; in the control condition, groups were asked to form dyads absent any additional guidance. The significant majority of groups in the experimental condition formed dyads with maximal knowledge discrepancy between partners. The majority of groups in the control condition formed dyads based on factors unrelated to learning. We anticipated that in dyads with discrepant knowledge, partners will be better able to fill each other's knowledge gaps. Although a significant difference between conditions did not transpire, there was a significant correlation between knowledge discrepancy and learning gain across conditions. The implications of these findings for effective learning group organization are discussed; implementation of the calculation tool in classrooms and MOOCs is proposed.
Databáze: OpenAIRE