Adaptive Support for Student Learning in an e-Portfolio Platform

Author

Ke, Chih-Kun ; Wu, Mei-Yu

Author_Institution

Dept. of Inf. Manage., Nat. Taichung Inst. of Technol., Taichung, Taiwan

fYear

2010

fDate

9-10 Jan. 2010

Firstpage

55

Lastpage

58

Abstract

Constructing an e-portfolio platform for students is a modern educational trend. However, a student´s learning context is not analyzed in the current e-portfolio platform. In this research a model was designed for identifying the specific learning context and providing the corresponding knowledge support. A system framework which uses advanced information techniques is proposed. Information Retrieval (IR) technique extracts and analyzes key concepts from the student´s previous e-portfolio records. Data mining technique discovers hidden knowledge rules from key concepts. Various context-knowledge views were constructed based on discovered knowledge rules. Besides, Case-Based Reasoning (CBR) and profiling techniques were used to identify learning context and design adaptive knowledge recommendation mechanisms. Therefore, after identifying current learning contexts, the system would recommend previously documented knowledge to assist the student.

Keywords

case-based reasoning; computer aided instruction; data mining; information retrieval; adaptive knowledge recommendation; case-based reasoning; data mining; e-portfolio platform; e-portfolio record; hidden knowledge rule discovery; information retrieval; knowledge support; learning context; profiling technique; student learning; Artificial intelligence; Context modeling; Data mining; Educational institutions; Electronic mail; Frequency; Information analysis; Information management; Information retrieval; Learning; adaptive knowledge support; data mining; e-portfolio; learning context;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Discovery and Data Mining, 2010. WKDD '10. Third International Conference on

Conference_Location

Phuket

Print_ISBN

978-1-4244-5397-9

Electronic_ISBN

978-1-4244-5398-6

Type

conf

DOI

10.1109/WKDD.2010.11

Filename

5432737