Journal ArticleDOI
Advance in Detecting Key Concepts as an Expert Model: Using Student Mental Model Analyzer for Research and Teaching (SMART)
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TLDR
This study investigated which filtering method extract key concepts most accurately from experts’ concept maps and showed the PageRank filtering method outperformed the other methods in all accuracy measures.Abstract:
While key concepts embedded within an expert’s textual explanation have been considered an aspect of expert model, the complexity of textual data makes determining key concepts demanding and time consuming. To address this issue, we developed Student Mental Model Analyzer for Teaching and Learning (SMART) technology that can analyze an expert’ textual explanation to elicit an expert concept map from which key concepts are automatically derived. SMART draws on four graph-based metrics (i.e., clustering coefficient, betweenness, PageRank, and closeness) to automatically filter key concepts from experts’ concept maps. This study investigated which filtering method extract key concepts most accurately. Using 18 expert textual data, we compared the accuracy levels of those four competing filtering methods by referring to four accuracy measures (i.e., precision, recall, F-measure, and N-similarity). The results showed the PageRank filtering method outperformed the other methods in all accuracy measures. For example, on average, PageRank derived 79% of key concepts as accurately as human experts. SMART’s automatic filtering methods can help human experts save time when building an expert model, and it can validate their decision making on a list of key concepts.read more
Citations
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Journal ArticleDOI
Improving summary writing through formative feedback in a technology-enhanced learning environment
Min Kyu Kim,Kathryn S. McCarthy +1 more
Journal ArticleDOI
Using graph centrality as a global index to assess students’ mental model structure development during summary writing
Min Kyu Kim,Kathryn S. McCarthy +1 more
TL;DR: This paper explored the potential of a global index, Graph Centrality (GC), as a measure to describe mental model structure and its relation to the quality of student summaries (e.g., the amount of content-coverage).
Book ChapterDOI
Predicting Reading Comprehension from Constructed Responses: Explanatory Retrievals as Stealth Assessment
TL;DR: Results indicate that the linguistic features of post-reading explanatory retrievals were more predictive of comprehension outcomes than self-explanations, and these models relied on different indices to predict performance.
Journal ArticleDOI
Dialogue between smart education and classical education
Svetlana Sharonova,Elena Avdeeva +1 more
TL;DR: In this paper, the authors show the fundamental paradigmatic differences between classical education and smart education, and build a bridge of dialogue between these two paradigms, and propose a framework to bridge the gap between them.
References
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