Cognitive science

Mental model

Human–computer interaction

Humanities

Philosophy

Cognitive psychology

Artificial intelligence

Psychology

Programming language

Elaboration

Computer science

Comprehension

Computers & Education

Immersive virtual reality learning environments (VRLEs) have great potential but they do not guarantee effective or in-depth learning. Activating learners by including prompts, has been reported to increase learning in VR. Based on their promising effects in conventional settings, elaboration prompts should increase learning outcomes by activating learners to explain the learning content in their own words or to contrast it to prior knowledge. In our study, we investigated the effect of an elaboration prompt before entering the VRLE on learning robotics. Our between-subject study (prompt vs. no prompt) included N = 64 participants. We applied a multi-method approach (functional and structural) to measure mental models. We used a differentiated learning outcome measurement (knowledge, comprehension, and application) as a functional measurement. By using the structural assessment of knowledge technique, we deduce the structural accuracy of the developed mental model. We found an increase by the elaboration prompt on knowledge level but not on comprehension or application level. The interplay of pre-accuracy and prompt had a significant impact on structural accuracy after learning. Application level and structural accuracy after learning were positively correlated. Our multi-method approach delivered insights on different aspects of the developed mental model when prompting before entering the VRLE. For future research, further approaches to explore in-depth learning in VRLEs are recommended.

Prompting in-depth learning in immersive virtual reality: Impact of an elaboration prompt on developing a mental model