Abstract
Cognitive diagnosis as an instance of model-based diagnosis, it works on the model of students’ courses in terms of knowledge items that they may learn, tests them and helps them to understand their faults in cognition. In this paper, courses are formally defined as set of knowledge items with requirement constraints, knowledge items are associated with a set of exam questions. Moreover, diagnostic algorithms are used to help a student understand what knowledge item within a course the student does not master. Each knowledge item has a degree of correlation with knowledge items have not mastered, which can be used in computing suspicious degree of knowledge items as a selection criteria for the final results. Find the root reason of his/her test errors, and the recommendations like what should be done next. Experimental results show that the group of students with such understanding can improve their testing performance greatly in an E-learning environment.
This work is supported by National Natural Science Foundation of China (60775028), the Major Projects of Technology Bureau of Dalian No.2007A14GXD42, and IT Industry Development of Jilin Province.
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Xu, J., Chen, R. (2011). Introducing Probability for Model-Based Cognitive Diagnosis of Students’ Test Performance. In: Zhou, M., Tan, H. (eds) Advances in Computer Science and Education Applications. Communications in Computer and Information Science, vol 202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22456-0_47
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DOI: https://doi.org/10.1007/978-3-642-22456-0_47
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