Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems
An admissions system based on valid and reliable admissions criteria is very important to select candidates likely to perform well academically at institutions of higher education. This study focuses on ways to support universities in admissions decision making using data mining techniques to predic...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9042216/ |
| _version_ | 1818379561158574080 |
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| author | Hanan Abdullah Mengash |
| author_facet | Hanan Abdullah Mengash |
| author_sort | Hanan Abdullah Mengash |
| collection | DOAJ |
| description | An admissions system based on valid and reliable admissions criteria is very important to select candidates likely to perform well academically at institutions of higher education. This study focuses on ways to support universities in admissions decision making using data mining techniques to predict applicants' academic performance at university. A data set of 2,039 students enrolled in a Computer Science and Information College of a Saudi public university from 2016 to 2019 was used to validate the proposed methodology. The results demonstrate that applicants' early university performance can be predicted before admission based on certain pre-admission criteria (high school grade average, Scholastic Achievement Admission Test score, and General Aptitude Test score). The results also show that Scholastic Achievement Admission Test score is the pre-admission criterion that most accurately predicts future student performance. Therefore, this score should be assigned more weight in admissions systems. We also found that the Artificial Neural Network technique has an accuracy rate above 79%, making it superior to other classification techniques considered (Decision Trees, Support Vector Machines, and Naïve Bayes). |
| first_indexed | 2024-12-14T02:04:44Z |
| format | Article |
| id | doaj.art-040912c1542f4603b152d266cb090035 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:04:44Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-040912c1542f4603b152d266cb0900352022-12-21T23:20:55ZengIEEEIEEE Access2169-35362020-01-018554625547010.1109/ACCESS.2020.29819059042216Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission SystemsHanan Abdullah Mengash0https://orcid.org/0000-0002-4103-2434Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaAn admissions system based on valid and reliable admissions criteria is very important to select candidates likely to perform well academically at institutions of higher education. This study focuses on ways to support universities in admissions decision making using data mining techniques to predict applicants' academic performance at university. A data set of 2,039 students enrolled in a Computer Science and Information College of a Saudi public university from 2016 to 2019 was used to validate the proposed methodology. The results demonstrate that applicants' early university performance can be predicted before admission based on certain pre-admission criteria (high school grade average, Scholastic Achievement Admission Test score, and General Aptitude Test score). The results also show that Scholastic Achievement Admission Test score is the pre-admission criterion that most accurately predicts future student performance. Therefore, this score should be assigned more weight in admissions systems. We also found that the Artificial Neural Network technique has an accuracy rate above 79%, making it superior to other classification techniques considered (Decision Trees, Support Vector Machines, and Naïve Bayes).https://ieeexplore.ieee.org/document/9042216/Data mining techniqueseducational data miningperformance predictionpre-admission criteriastudent performance |
| spellingShingle | Hanan Abdullah Mengash Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems IEEE Access Data mining techniques educational data mining performance prediction pre-admission criteria student performance |
| title | Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems |
| title_full | Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems |
| title_fullStr | Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems |
| title_full_unstemmed | Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems |
| title_short | Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems |
| title_sort | using data mining techniques to predict student performance to support decision making in university admission systems |
| topic | Data mining techniques educational data mining performance prediction pre-admission criteria student performance |
| url | https://ieeexplore.ieee.org/document/9042216/ |
| work_keys_str_mv | AT hananabdullahmengash usingdataminingtechniquestopredictstudentperformancetosupportdecisionmakinginuniversityadmissionsystems |