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OSt |
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20231123143020.0 |
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231123b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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AIKTC-KRRC |
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AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
22174 |
| Author |
Attieh, Mahmoud |
| 245 ## - TITLE STATEMENT |
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| Title |
Forecasting of university students' performance using a hybrid model of neural networks and fuzzy logic |
| 250 ## - EDITION STATEMENT |
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| Volume, Issue number |
Vol.37(1), Jul |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Pune |
| Name of publisher, distributor, etc. |
Engineering Education Foundation |
| Year |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Pagination |
142-156p. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Artificial intelligence techniques can be applied in forecasting the academic performance of university students, with aim of detecting the factors that influence their learning process which allows instructors and university administration to take more effective actions to increase the university student's performance. Identifying the students' performance will improve the quality of education which will be through analyzing and forecasting the students' performance at the course level and degree level. This research focuses on first-year students' performance in two university-requirement courses, depending on features such as attendance, assessment marks, exams, assignments, and projects. Forecasting the students' performance in the whole degree will depend on these features; high school average, Grade Point Average (GPA) for each semester, drop courses, selected core courses in the degree, period of study, and final GPA. A hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used toperform the forecasting process. In this way, based on the datasets collected from the selected courses, or the whole degree, the future results can be forecasted and suggestions can be made to carry out corrective steps to improve the final results. The experiments result of the applied models performed that ANFIS-Grid outperforms the ANFIS-Cluster, wherein each model produces the lowest error of 0.7%, where it just fails in one sample from thirteen samples, while the ANFISCluster after modification produces an error equal to 0.15%. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 9 (RLIN) |
4642 |
| Topical term or geographic name entry element |
Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
22175 |
| Co-Author |
Awad, Mohammed |
| 773 0# - HOST ITEM ENTRY |
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| Place, publisher, and date of publication |
Sangli Rajarambapu Institute Of Technology |
| Title |
Journal of engineering education transformations (JEET) |
| International Standard Serial Number |
2349-2473 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| URL |
https://journaleet.in/articles/forecasting-of-university-students-performance-using-a-hybrid-model-of-neural-networks-and-fuzzy-logic |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Koha item type |
Articles Abstract Database |
