Remembering the knowledge of experts and novices in computer-supported collaborative learning: A multinomial processing tree approach

Abstract

Accurately assessing learning partners’ knowledge profiles improves collaborative learning. Group awareness tools facilitate constructing such social context knowledge during learning and the formation of learning partner models retrievable afterwards. In this experimental study (N = 70), we investigated potential schema effects in partner modeling: Participants were first provided with descriptions of two learning partners (expert vs. novice of an area) and with their knowledge profiles consisting of knowledge levels (high vs. low) regarding certain topics of this area. In a memory test, participants had to remember the specific knowledge levels of both partners. Partner modeling was analyzed with multinomial processing tree models, as these models disentangle memory and guessing, which are often confounded when schema effects on memory are examined. High and low knowledge levels were equally well remembered for both partners. However, participants showed metacognitive biases, expecting their memory to be better for high knowledge levels. Additionally, knowledge profile estimations revealed that while novices’ knowledge was estimated accurately, experts’ knowledge was overestimated. We discuss the results and potential benefits of using multinomial processing tree models in the learning sciences for and beyond analyzing schema effects in partner modeling.