Do Knowledge-Component Models Need to Incorporate Representational Competencies?
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Do Knowledge-Component Models Need to Incorporate Representational Competencies? Martina Angela Rau 1
# International Artificial Intelligence in Education Society 2016
Abstract Traditional knowledge-component models describe students’ content knowledge (e.g., their ability to carry out problem-solving procedures or their ability to reason about a concept). In many STEM domains, instruction uses multiple visual representations such as graphs, figures, and diagrams. The use of visual representations implies a Brepresentation dilemma^: students learn new content from visual representations they may not yet understand at the same time as they learn about visual representations that show content they do not yet understand. Therefore, students’ learning of content knowledge and of representational competencies (i.e., knowledge about representations) is invariably intertwined. Consequently, instruction may need to adapt not only to students’ acquisition of content knowledge but also to their acquisition of representational competencies. This claim corresponds to the hypothesis that knowledgecomponent models that describe content knowledge and representational competencies should be more accurate than knowledge-component models that describe only content knowledge. Yet, this hypothesis has not yet been tested. The work in this article tests this hypothesis by comparing knowledge-component models that describe representational competencies and content knowledge to knowledgecomponent models that describe only content knowledge. Analysis of log data from two experiments on chemistry learning with overall 203 undergraduate students suggests that including representational competencies into knowledge-component models increases model fit if the representational competencies are difficult. This finding suggests that students can learn abstract content knowledge only if they have a prerequisite level of representational competencies, and that educational technologies should use adaptive knowledgecomponent models that capture representational competencies the student has not yet mastered.
* Martina Angela Rau [email protected]
1
Department of Educational Psychology, University of Wisconsin Madison, Madison, WI, USA
Int J Artif Intell Educ
Keywords Multiple representations . Connection making . Sense-making processes . Inductive learning processes . Spatial skills
One of the major advantages of educational technologies is that they can adapt instruction to the individual student’s knowledge level while students solve domain-relevant problems (Koedinger and Corbett 2006; VanLehn 2011). To do so, adaptive educational technologies use a cognitive model that infers whether the student has learned target skills based on her/his interactions with the technology (Anderson et al. 1990; Koedinger and Corbett 2006; Koedinger et al. 2012; VanLehn 1990). These cognitive models, in turn, rely on so-called knowledge-component models; that is, a model of Bacquired units of cognitive function that can be inferred from performance on a set of
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