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Designing a Course Recommendation System on Web based on the Students’ course Selection Records

Designing a Course Recommendation System on Web based on the Students’ course Selection Records. Ko-Kang Chu, Maiga Chang and Yen-The Hsia (Dept. of Information and Computer Engineering, Chung-Yuan Christian Univ. Taiwan). Presented by Sharon HSIAO Jan.2007. agenda. Introduction

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Designing a Course Recommendation System on Web based on the Students’ course Selection Records

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  1. Designing a Course Recommendation System on Web based on the Students’ course Selection Records Ko-Kang Chu, Maiga Chang and Yen-The Hsia (Dept. of Information and Computer Engineering, Chung-Yuan Christian Univ. Taiwan) Presented by Sharon HSIAO Jan.2007

  2. agenda • Introduction • Prediction methodology & Recommendation Process • Results & Evaluation • Proposed Future Research

  3. introduction • Focus on relation between course categories and student’s preferences • Preference: Mandatory courses should not be taken into consideration when analyzing students preference • Category: Classify courses>>Each course covers more than one category>>weigh courses Fuzzy: AI(90%),Research(85%),Math(70%) Neural Networks: AI(90%),Research(85%),Math(70%) Ken: Fuzzy and Neural Networks • Objective: construct a web-based course recommendation system that only depends on the courses chosen by students

  4. Prediction methodology • Datamining technique: Apriori algorism (Agrawal & Srikant, 1994)

  5. Construct Important Orders of Categories Merge Rules into A Preference Sequence Recommendation process Classifying courses/designing weights Collecting Students’ Course Selection Records Make Suggestions to Student

  6. Results and Evaluation • 4 consecutive terms, senior college students • Class 2001: 127 students’ course selection record, 34/83 questionnaires response • Class 2002: 102, 100% response rate • 6 categories: research, theory, math, hardware, software, network (information science)

  7. Accuracy rate for preference sequence • General assumption: 4th term should have the highest accuracy rate • Explanation: fewer prerequisites, more electives, tend to follow graduate school guidance • Class 2002: target 13 students who plan to go to graduate school straight after college

  8. Proposed Future Research • Student’s needs change analysis • How to find course categories classified by students? What are the relations among courses in student’s mind? • Time series analysis • Is it possible to develop or plan a series of courses depends on the student’s major interests?

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