A simple and effective remedial learning system with a fuzzy expert system
This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational...
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| Published in | Journal of computer assisted learning Vol. 32; no. 6; pp. 647 - 662 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.12.2016
Wiley-Blackwell Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0266-4909 1365-2729 |
| DOI | 10.1111/jcal.12160 |
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| Abstract | This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate the success of the proposed system where 88 students who studied a computer‐related program at a vocational high school in Taiwan participated in the experiment. The participating students came from three different types of classes. The fuzzy expert system determined the numbers of the remedial material units according to the scores of the pre‐test. Based on the results of the fuzzy expert system, each student then received personalized remedial learning materials by randomly selecting problem‐based learning units from a learning material repository. After reading the remedial learning materials, the students took the post‐test. The experimental results reveal that the students made significant progresses after studying the remedial learning materials. Both of high‐achieving students and low‐achieving students made significant progresses. Moreover, all of the three types of students made significant progresses.
Lay description
What is currently known about the subject matter:
Remedial learning systems are helpful.
Some systems are good for only low‐achieving students.
The remedial systems are complicated.
What their paper adds to this:
A fuzzy expert system has been added to produce remedial materials.
Automatically select the remedial learning units according to the pre‐test.
The implications of study findings for practitioners:
Our remedial system is good for both high‐achieving and low‐achieving students.
Our remedial system is good for different types of students.
Our remedial system is simple and easy‐to‐implement. |
|---|---|
| AbstractList | This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate the success of the proposed system where 88 students who studied a computer-related program at a vocational high school in Taiwan participated in the experiment. The participating students came from three different types of classes. The fuzzy expert system determined the numbers of the remedial material units according to the scores of the pre-test. Based on the results of the fuzzy expert system, each student then received personalized remedial learning materials by randomly selecting problem-based learning units from a learning material repository. After reading the remedial learning materials, the students took the post-test. The experimental results reveal that the students made significant progresses after studying the remedial learning materials. Both of high-achieving students and low-achieving students made significant progresses. Moreover, all of the three types of students made significant progresses. This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate the success of the proposed system where 88 students who studied a computer-related program at a vocational high school in Taiwan participated in the experiment. The participating students came from three different types of classes. The fuzzy expert system determined the numbers of the remedial material units according to the scores of the pre-test. Based on the results of the fuzzy expert system, each student then received personalized remedial learning materials by randomly selecting problem-based learning units from a learning material repository. After reading the remedial learning materials, the students took the post-test. The experimental results reveal that the students made significant progresses after studying the remedial learning materials. Both of high-achieving students and low-achieving students made significant progresses. Moreover, all of the three types of students made significant progresses. Lay description What is currently known about the subject matter: Remedial learning systems are helpful. Some systems are good for only low-achieving students. The remedial systems are complicated. What their paper adds to this: A fuzzy expert system has been added to produce remedial materials. Automatically select the remedial learning units according to the pre-test. The implications of study findings for practitioners: Our remedial system is good for both high-achieving and low-achieving students. Our remedial system is good for different types of students. Our remedial system is simple and easy-to-implement. This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate the success of the proposed system where 88 students who studied a computer‐related program at a vocational high school in Taiwan participated in the experiment. The participating students came from three different types of classes. The fuzzy expert system determined the numbers of the remedial material units according to the scores of the pre‐test. Based on the results of the fuzzy expert system, each student then received personalized remedial learning materials by randomly selecting problem‐based learning units from a learning material repository. After reading the remedial learning materials, the students took the post‐test. The experimental results reveal that the students made significant progresses after studying the remedial learning materials. Both of high‐achieving students and low‐achieving students made significant progresses. Moreover, all of the three types of students made significant progresses. Lay description What is currently known about the subject matter: Remedial learning systems are helpful. Some systems are good for only low‐achieving students. The remedial systems are complicated. What their paper adds to this: A fuzzy expert system has been added to produce remedial materials. Automatically select the remedial learning units according to the pre‐test. The implications of study findings for practitioners: Our remedial system is good for both high‐achieving and low‐achieving students. Our remedial system is good for different types of students. Our remedial system is simple and easy‐to‐implement. |
| Audience | High Schools Secondary Education |
| Author | Guo, K.-H. Lin, Y.-C. Lin, C.-C. |
| Author_xml | – sequence: 1 givenname: C.-C. surname: Lin fullname: Lin, C.-C. email: cclin@nknu.edu.tw, cclin@nknu.edu.tw organization: Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung, Taiwan, R.O.C – sequence: 2 givenname: K.-H. surname: Guo fullname: Guo, K.-H. organization: Department of Industrial Technology Education, National Kaohsiung Normal University, Kaohsiung, Taiwan, R.O.C – sequence: 3 givenname: Y.-C. surname: Lin fullname: Lin, Y.-C. organization: Department of Business Management, National Kaohsiung Normal University, Kaohsiung, Taiwan, R.O.C |
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and implementing a personalized remedial learning system for enhancing the programming learning publication-title: Educational Technology & Society |
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| Title | A simple and effective remedial learning system with a fuzzy expert system |
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