| 000 -LEADER |
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| fixed length control field |
01837nam a22001457a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
221031b ||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--AUTHOR |
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| Author |
BALOGUN, Okikiola Elizabeth |
| 245 ## - TITLE STATEMENT |
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| Title |
Development Of Machine Learning Model For Predicting Students Learning Style |
| 250 ## - SUPERVISOR |
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| Supervisor |
Dr. F. A. Kasali |
| 260 ## - IMPRINT |
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| Place of publication |
Ibafo |
| Department (College) |
Computer science |
| Date of publication |
2022 |
| 300 ## - COLLATION |
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| Pagination |
xv; 79pgs. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
ABSTRACT<br/>The aim of this project is to develop a machine learning model with the purpose of predicting student’s learning styles. The study identified the various user and system requirements, specified the system design and implemented the system. <br/>A review of the literature was being done to identify and understand existing machine learning models. The user and system requirements of the system were identified from system users using informal interviews. The system design was specified using UML diagrams, such are use case, sequence and class diagram. The database was implemented using Firebase. The implementation of the frontend was done using HTML, CSS, and Bootstrap. The backend was implemented using Node JS and Express JS. <br/>The results of the system showed the implementation of the system’s database for storing the information alongside the front-end of the web and mobile application. The results revealed that the system was able to uniquely identify each hostel residents using a uniquely generated QR code with which the movement of the students in and out of the hostels was easily monitored. <br/>The study concluded that using the machine learning prediction system to predict students learning style will help to improve students learning performance. The system will detect and classify student learning styles based on the learner’s preference. <br/><br/> <br/> |
| 650 ## - TRACINGS |
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| Main Subject |
Applied science |
| Subdivision (1st) |
Computer science |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Students Thesis |
