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 HSIAOJan.2007

agenda

Introduction Prediction methodology & Recommendation

Process Results & Evaluation Proposed Future Research

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 coursesFuzzy: 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

Prediction methodology

Datamining technique:

Apriori algorism (Agrawal & Srikant, 1994)

Recommendation process

Classifying courses/designing weights

Collecting Students’ Course Selection Records

Make Suggestions to Student

Construct Important Orders of Categories

Merge Rules into A Preference Sequence

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)

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

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?