A Support Mechanism for Decision-Making of Stratified Teaching Using Online Examination Diagnosis
Date Issued
2015
Date
2015
Author(s)
Lin, Wei-Yen
Abstract
Stratified teaching is a new option which enable to integrate the advantages of traditional teaching and personalized instruction and avoid their disadvantages, but teacher needs to spend more time on data management. Because of the properties of easier recombination and data analysis, online examination can be used to reduce the data management burden on teachers. Using online examination diagnosis, this thesis provides a supporting mechanism for decision-making for stratified teaching to help the teachers who intend to adopt stratified teaching. The mechanism consists of three modules: item combination module, learning achievement module and effect evaluation module. The item combination module uses linear programming simplex method to combine items. The learning achievement module set up students’ achievement tracking criteria by interviewing with experts. The effect evaluation module use two-way ANOVA to evaluate the teaching effect. Experiments show that the teaching effect with the proposed mechanism is better than the traditional teaching. Furthermore, there exists significant interaction correlation effect between stratified teaching effect and difficulty of the course unit. Stratified teaching has better effect on most course units except of especially difficult ones. Besides, there is no significant interaction correlation effect between stratified teaching effect and learning ability group, which shows that stratified teaching effect doesn’t depend on specific learning ability group.
Subjects
Stratified teaching
Online examination
Linear programming
Two-way ANOVA
Type
thesis
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ntu-104-R02525090-1.pdf
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