| Title: | AUTOMATIC LEARNING OBJECT CATEGORIZATION FOR INSTRUCTION USING AN ENHANCED LINEAR TEXT CLASSIFIER |
| DOI No: | 10.1142/9789812701527_0025 |
| Source: | KNOWLEDGE MANAGEMENT: NURTURING CULTURE, INNOVATION AND TECHNOLOGY (pp 299-304)
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| Author(s): | THOMAS GEORGE KANNAMPALLIL
School of Information Sciences and Technology, Pennsylvania State University, University Park, Pa 16802, USA
ROBERT G. FARRELL
Next Generation Web Dept, IBM, T.J. Watson Research Center, Hawthorne, NY 10532, USA
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| Abstract: | This paper explores the use of a machine learning algorithm to automate the task of classifying learning materials into categories useful for instruction. A collection of documents was segmented manually into independent learning objects. A regularized linear text classifier was trained to recognize four topic categories and eleven instructional use categories using manual category labels as training data. The classifier was able to categorize text-based learning objects into topic categories with high accuracy, but initial performance for instructional use classification was poor. An enhanced classifier was able to distinguish between conceptual and procedural categories of instructional use with high accuracy. |
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