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Educational Data Mining for Predicting University Students' Performance, to Enhance University Admission Criteria
This thesis aims to propose and evaluate possible predictors of success for incoming university students to the American University in Cairo (AUC) who wish to enroll in its engineering programs, by considering their overall grade point average (GPA) at graduation as the measure of their success (out...
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| Format: | Thesis |
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AUC Knowledge Fountain
2022
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| _version_ | 1867613421707460608 |
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| access_status_str | Open Access |
| author | Amr Naga, Youssef |
| author_browse | Amr Naga, Youssef |
| author_facet | Amr Naga, Youssef |
| author_sort | Amr Naga, Youssef |
| collection | Thesis |
| description | This thesis aims to propose and evaluate possible predictors of success for incoming university students to the American University in Cairo (AUC) who wish to enroll in its engineering programs, by considering their overall grade point average (GPA) at graduation as the measure of their success (output variable). This study is composed of two phases. First, available university admission variables; i.e., gender, high school diploma, high school score, and proficiency level in the English language (the language of instruction at AUC) at the time of application are evaluated as a predictor of students’ performance using five different data mining techniques. The analysis suggests that the current input admission variables can only predict student performance with limited accuracy. Moreover, of all the university admission data available, the type of high school diploma exhibits the greatest statistical significance as a predictor of student success in AUC engineering programs.
The second phase of research was to conduct an analysis on the six high school Diplomas that are typically offered in Egypt, and which regularly feed into AUC. This phase was conducted on 60 current high school students and aimed to identify component-wise cognitive traits and habits of mind that could correlate diploma type to predicted success in studying engineering in general. The research findings suggest that student scores on aptitude tests which directly measure engineering knowledge in high school are the best predictor of success for studying engineering at the university level, rather than the more widely recognized general cognitive ability scores (e.g., logical, and verbal abilities). Nevertheless, the findings also identified that when student preparedness is uniformly above-average across all these general cognitive abilities, that situation too is a good indicator of their success in studying engineering at the university level. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-2932 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:53.165Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-2932 Educational Data Mining for Predicting University Students' Performance, to Enhance University Admission Criteria Amr Naga, Youssef This thesis aims to propose and evaluate possible predictors of success for incoming university students to the American University in Cairo (AUC) who wish to enroll in its engineering programs, by considering their overall grade point average (GPA) at graduation as the measure of their success (output variable). This study is composed of two phases. First, available university admission variables; i.e., gender, high school diploma, high school score, and proficiency level in the English language (the language of instruction at AUC) at the time of application are evaluated as a predictor of students’ performance using five different data mining techniques. The analysis suggests that the current input admission variables can only predict student performance with limited accuracy. Moreover, of all the university admission data available, the type of high school diploma exhibits the greatest statistical significance as a predictor of student success in AUC engineering programs. The second phase of research was to conduct an analysis on the six high school Diplomas that are typically offered in Egypt, and which regularly feed into AUC. This phase was conducted on 60 current high school students and aimed to identify component-wise cognitive traits and habits of mind that could correlate diploma type to predicted success in studying engineering in general. The research findings suggest that student scores on aptitude tests which directly measure engineering knowledge in high school are the best predictor of success for studying engineering at the university level, rather than the more widely recognized general cognitive ability scores (e.g., logical, and verbal abilities). Nevertheless, the findings also identified that when student preparedness is uniformly above-average across all these general cognitive abilities, that situation too is a good indicator of their success in studying engineering at the university level. 2022-05-31T07:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/1905 https://fount.aucegypt.edu/context/etds/article/2932/viewcontent/Youssef_Amr_Naga_Thesis.pdf Theses and Dissertations AUC Knowledge Fountain Educational Data Mining Predicting Student Performance Industrial Engineering |
| spellingShingle | Educational Data Mining Predicting Student Performance Industrial Engineering Amr Naga, Youssef Educational Data Mining for Predicting University Students' Performance, to Enhance University Admission Criteria |
| title | Educational Data Mining for Predicting University Students' Performance, to Enhance University Admission Criteria |
| title_full | Educational Data Mining for Predicting University Students' Performance, to Enhance University Admission Criteria |
| title_fullStr | Educational Data Mining for Predicting University Students' Performance, to Enhance University Admission Criteria |
| title_full_unstemmed | Educational Data Mining for Predicting University Students' Performance, to Enhance University Admission Criteria |
| title_short | Educational Data Mining for Predicting University Students' Performance, to Enhance University Admission Criteria |
| title_sort | educational data mining for predicting university students performance to enhance university admission criteria |
| topic | Educational Data Mining Predicting Student Performance Industrial Engineering |
| url | https://fount.aucegypt.edu/etds/1905 https://fount.aucegypt.edu/context/etds/article/2932/viewcontent/Youssef_Amr_Naga_Thesis.pdf |
| work_keys_str_mv | AT amrnagayoussef educationaldataminingforpredictinguniversitystudentsperformancetoenhanceuniversityadmissioncriteria |