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.

[1]  R C Atkinson,et al.  Computerized instruction and the learning process. , 1968, The American psychologist.

[2]  J. Lave Cognition in Practice: Outdoors: a social anthropology of cognition in practice , 1988 .

[3]  Beau Jones Designing Learning and Technology for Educational Reform. , 1994 .

[4]  J. Reid Computer-assisted instruction. , 1993, Missouri medicine.

[5]  CerconeNick,et al.  Adaptable learning assistant for item bank management , 2008 .

[6]  Donald A. Erickson Educational Organization and Administration , 1977 .

[7]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[8]  M. Patton,et al.  Qualitative evaluation and research methods , 1992 .

[9]  H. Simon,et al.  Applications and Misapplications of Cognitive Psychology to Mathematics Education , 2000 .

[10]  Damras Wongsawang,et al.  PKIP: feature selection in text categorization for item banks , 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).

[11]  Masaki Murata,et al.  Comparison of three machine-learning methods for Thai part-of-speech tagging , 2002, TALIP.

[12]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[13]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[14]  Andrew R. Molnar Computers in education: A brief history , 1997 .

[15]  Grant Wiggins,et al.  Educative Assessment: Designing Assessments to Inform and Improve Student Performance , 1998 .

[16]  Roberto Basili,et al.  Lexical Acquisition and Information Extraction , 1997, SCIE.

[17]  Kevin Knight,et al.  Mining online text , 1999, Commun. ACM.

[18]  E. Glasersfeld Radical constructivism in mathematics education , 2002 .

[19]  M. Patton Qualitative evaluation and research methods, 2nd ed. , 1990 .

[20]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[21]  Yiming Yang,et al.  A re-examination of text categorization methods , 1999, SIGIR '99.