Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods
Sijil Pelajaran Malaysia (SPM) or Malaysia Certificate of Education is a national examination required to be seated by all Form Five secondary school students in Malaysia. This examination is compulsory for them to pursue post-secondary education. However, students’ performance on this examination...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 语言: | English |
| 出版: |
2022
|
| 主题: | |
| 在线阅读: | http://ir.unimas.my/id/eprint/39214/5/Master%20Sc%20Thesis_Muhammad%20Haziq%20Roslan%20-%20fulltext.pdf |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| id |
my-unimas-ir.39214 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-unimas-ir.392142023-03-13T03:33:14Z Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods 2022-08-08 Muhammad Haziq, Bin Roslan LB1603 Secondary Education. High schools Sijil Pelajaran Malaysia (SPM) or Malaysia Certificate of Education is a national examination required to be seated by all Form Five secondary school students in Malaysia. This examination is compulsory for them to pursue post-secondary education. However, students’ performance on this examination particularly in English and Mathematics subjects has been concerning. According to the literature, advances in educational technology can improve students’ performance. Therefore, this study attempts to predict Form Four students’ SPM performance in English and Mathematics subjects using data mining (DM) techniques. Three main attributes namely students’ past academic performance, demographics, and psychological attributes were scrutinised to identify their impact on the prediction. Moreover, the predictive performance of the DM techniques was evaluated to find the best technique for predicting students’ SPM performance. The relationship between students’ SPM performance in English and Mathematics subjects was also examined. This study found that by using Decision Tree (DT) rules, the characteristics of students with low, moderate, and high performance in English and Mathematics subjects could be identified. Next, DT and Nave Bayes (NB) had the best predictive performance among the DM approaches in predicting students’ SPM performance in English and Mathematics respectively. Moreover, there is a connection between students’ SPM English and Mathematics performance. The findings may provide stakeholders with new insight into how to improve students’ performance in these subjects. Educators can intervene early with students who are at risk of receiving low performance for these subjects in SPM. Education and Information Technologies 2022-08 Thesis http://ir.unimas.my/id/eprint/39214/ http://ir.unimas.my/id/eprint/39214/5/Master%20Sc%20Thesis_Muhammad%20Haziq%20Roslan%20-%20fulltext.pdf text en validuser masters University Malaysia Sarawak Faculty of Cognitive Sciences and Human Development Malaysian Ministry of Higher Education, Fundamental Research Grant Scheme, FRGS/1/2020/SS10/UNIMAS/01/1, and UNIMAS Zamalah Scholarship |
| institution |
Universiti Malaysia Sarawak |
| collection |
UNIMAS Institutional Repository |
| language |
English |
| topic |
LB1603 Secondary Education High schools |
| spellingShingle |
LB1603 Secondary Education High schools Muhammad Haziq, Bin Roslan Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods |
| description |
Sijil Pelajaran Malaysia (SPM) or Malaysia Certificate of Education is a national examination required to be seated by all Form Five secondary school students in Malaysia.
This examination is compulsory for them to pursue post-secondary education. However, students’ performance on this examination particularly in English and Mathematics
subjects has been concerning. According to the literature, advances in educational technology can improve students’ performance. Therefore, this study attempts to predict
Form Four students’ SPM performance in English and Mathematics subjects using data mining (DM) techniques. Three main attributes namely students’ past academic
performance, demographics, and psychological attributes were scrutinised to identify their impact on the prediction. Moreover, the predictive performance of the DM techniques was evaluated to find the best technique for predicting students’ SPM performance. The relationship between students’ SPM performance in English and Mathematics subjects was also examined. This study found that by using Decision Tree (DT) rules, the characteristics of students with low, moderate, and high performance in English and Mathematics subjects could be identified. Next, DT and Nave Bayes (NB) had the best predictive performance among the DM approaches in predicting students’ SPM performance in English and Mathematics respectively. Moreover, there is a connection between students’ SPM English and Mathematics performance. The findings may provide stakeholders with new insight into how to improve students’ performance in these subjects. Educators can intervene early with students who are at risk of receiving low performance for these subjects in SPM. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Muhammad Haziq, Bin Roslan |
| author_facet |
Muhammad Haziq, Bin Roslan |
| author_sort |
Muhammad Haziq, Bin Roslan |
| title |
Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods |
| title_short |
Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods |
| title_full |
Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods |
| title_fullStr |
Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods |
| title_full_unstemmed |
Prediction of Students’ Performance in SPM English and Mathematics Using Data Mining Methods |
| title_sort |
prediction of students’ performance in spm english and mathematics using data mining methods |
| granting_institution |
University Malaysia Sarawak |
| granting_department |
Faculty of Cognitive Sciences and Human Development |
| publishDate |
2022 |
| url |
http://ir.unimas.my/id/eprint/39214/5/Master%20Sc%20Thesis_Muhammad%20Haziq%20Roslan%20-%20fulltext.pdf |
| _version_ |
1783728511208390656 |