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ERIC Number: EJ1131289
Record Type: Journal
Publication Date: 2017
Pages: 15
Abstractor: As Provided
ISSN: ISSN-1539-3100
Revealing Learner Interests through Topic Mining from Question-Answering Data
Dun, Yijie; Wang, Na; Wang, Min; Hao, Tianyong
International Journal of Distance Education Technologies, v15 n2 p18-32 Apr-Jun 2017
In a question-answering system, learner generated content including asked and answered questions is a meaningful resource to capture learning interests. This paper proposes an approach based on question topic mining for revealing learners' concerned topics in real community question-answering systems. The authors' approach firstly preprocesses all questions associated with learners. Afterwards, it analyzes each question with text features and generates a weight feature matrix using a revised TF/IDF method. In order to decrease the sparsity issue of data distribution, the authors employ three concept-mapping strategies including named entity recognition, synonym extension, and hyponym replacement. Applying an SVM classifier, their approach categorizes user questions into representative topics. Three experiments are conducted based on a TREC dataset and an actual dataset containing 1,120 questions posted by learners from a commercial question-answering community. Results demonstrate the effectiveness of the method compared with conventional classifiers as baselines.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A