Volume 2 Number 5 (Jul 2007)
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JCP 2007 Vol.2(5): 17-26 ISSN: 1796-203X
doi: 10.4304/jcp.2.5.17-26

A Study on the Possibility of Automatically Estimating the Confidence Value of Students’ Knowledge in Generated Conceptual Models

Diana P´erez-Mar´ın, Enrique Alfonseca, Pilar Rodr´ıguez, Ismael Pascual-Nieto
1Department of Computer Science, Universidad Aut´onoma de Madrid, Spain

Abstract—We propose a new metric to automatically evaluate the confidence that a student knows a certain concept included in his or her conceptual model. The conceptual model is defined as a simplified representation of the concepts and relationships among them that a student keeps in his or her mind about an area of knowledge. Each area of knowledge comprises several topics and each topic several concepts. Each concept can be identified by a term that the students should use. A concept can belong to one topic or to several topics. Terms are automatically extracted from the answers provided to an automatic and adaptive free-text scoring system using Machine Learning techniques. In fact, the conceptual model is fully generated from the answers provided by the students to this system. In the paper, the automatic procedure that makes it possible is reviewed in detail. Finally, concept maps are used to graphically display the conceptual model to teachers and students. In this way, they can instantly see which concepts have already been assimilated and which ones should still be reviewed.

Index Terms—metrics of concept assimilation; generation of conceptual models; free-text scoring; blended learning; e-assessment; e-learning

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Cite: Diana P´erez-Mar´ın, Enrique Alfonseca, Pilar Rodr´ıguez, Ismael Pascual-Nieto, "A Study on the Possibility of Automatically Estimating the Confidence Value of Students’ Knowledge in Generated Conceptual Models," Journal of Computers vol. 2, no. 5, pp. 17-26, 2007.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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