Education content provider based on particular weaknesses of students: a unsupervised machine learning appro
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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| বিন্যাস: | গবেষণাপত্র |
| ভাষা: | English |
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Brac University
2024
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| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/10361/23592 |
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10361-235922024-06-26T21:03:04Z Education content provider based on particular weaknesses of students: a unsupervised machine learning appro Rahman, Shabab Intishar Fariha, Tasnim Akter Haque, Muhammad Nayeem Mubasshirul Mohammad, Ammar Ahmed, Shadman Noor, Jannatun Department of Computer Science and Engineering, Brac University Threshold Weaknesses Unsupervised algorithms Associative pattern E-learning sphere Data structures (Computer science) Algorithms This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-41). To uncover underlying patterns in large datasets, a procedure called data mining is often utilized. By analyzing data gathered through Online Learning (OL) systems, data mining can be used to unearth hidden relationships between topics and trends in student performance. Here in this paper, we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system module. In our implementation, we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%. By focusing on marks below this threshold, we aim to identify and establish associations based on the patterns of weakness observed in the past data. Additionally, we leverage K-means clustering to provide instructors with visual representations of the generated associations. This strategy aids teachers in better comprehending the information and associations produced by the algorithms. K-means clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights, enabling them to support the verification of the relationship between topics. This can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their pedagogy. This paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system. B.Sc in Computer Science 2024-06-26T04:22:28Z 2024-06-26T04:22:28Z ©2023 2023-09 Thesis ID 18241010 ID 23341072 ID 19101115 ID 19301063 ID 20101031 http://hdl.handle.net/10361/23592 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 52 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Threshold Weaknesses Unsupervised algorithms Associative pattern E-learning sphere Data structures (Computer science) Algorithms |
| spellingShingle |
Threshold Weaknesses Unsupervised algorithms Associative pattern E-learning sphere Data structures (Computer science) Algorithms Rahman, Shabab Intishar Fariha, Tasnim Akter Haque, Muhammad Nayeem Mubasshirul Mohammad, Ammar Ahmed, Shadman Education content provider based on particular weaknesses of students: a unsupervised machine learning appro |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Noor, Jannatun |
| author_facet |
Noor, Jannatun Rahman, Shabab Intishar Fariha, Tasnim Akter Haque, Muhammad Nayeem Mubasshirul Mohammad, Ammar Ahmed, Shadman |
| format |
Thesis |
| author |
Rahman, Shabab Intishar Fariha, Tasnim Akter Haque, Muhammad Nayeem Mubasshirul Mohammad, Ammar Ahmed, Shadman |
| author_sort |
Rahman, Shabab Intishar |
| title |
Education content provider based on particular weaknesses of students: a unsupervised machine learning appro |
| title_short |
Education content provider based on particular weaknesses of students: a unsupervised machine learning appro |
| title_full |
Education content provider based on particular weaknesses of students: a unsupervised machine learning appro |
| title_fullStr |
Education content provider based on particular weaknesses of students: a unsupervised machine learning appro |
| title_full_unstemmed |
Education content provider based on particular weaknesses of students: a unsupervised machine learning appro |
| title_sort |
education content provider based on particular weaknesses of students: a unsupervised machine learning appro |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23592 |
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