Adaptable learning assistant for item bank management

We present PKIP, an adaptable learning assistant tool for managing question items in item banks. PKIP is not only able to automatically assist educational users to categorize the question items into predefined categories by their contents but also to correctly retrieve the items by specifying the category and/or the difficulty level. PKIP adapts the “categorization learning model” to improve the system’s categorization performance using the incoming question items. PKIP tool has an advantage over the traditional document categorization methods in that it can correctly categorize the question item which lacks keywords since it adopts the feature selection technique and support vector machine approach to item bank text categorization. In our initial experimentation, PKIP was designed and implemented to manage the Thai high primary mathematics question items. PKIP was tested and evaluated in terms of both system accuracy and user satisfaction. The evaluation result shows that the system accuracy is acceptable and PKIP satisfies the need of the users.     

KEYWORD EXTRACTION STRATEGY FOR ITEM BANKS TEXT CATEGORIZATION

We proposed a feature selection approach, Patterned Keyword in Phrase ( PKIP ), to text categorization for item banks. The item bank is a collection of textual question items that are short sentences. Each sentence does not contain enough relevant words for directly categorizing by the traditional approaches such as "bag-of-words." Therefore, PKIP was designed to categorize such question item using only available keywords and their patterns. PKIP identifies the appropriate keywords by computing the weight of all words. In this paper, two keyword selection strategies are suggested to ensure the categorization accuracy of PKIP. PKIP was implemented and tested with the item bank of Thai high primary mathematics questions. The test results have proved that PKIP is able to categorize the question items correctly and the two keyword selection strategies can extract the very informative keywords.