Predicting Students Academic Performance Using Machine Learning Techniques / (Record no. 169521)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05471namaa2200409 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20250112141008.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 241217s2023 ua a|||f |m|| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloguing agency | EG-GICUC |
| Language of cataloging | eng |
| Transcribing agency | EG-GICUC |
| Modifying agency | EG-GICUC |
| Description conventions | rda |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of summary or abstract | eng |
| -- | ara |
| 049 ## - Acquisition Source | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 371.358 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 371.358 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.18.07.M.Sc.2023.Am.P. |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Amal Shaker A. M. Khamis, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Predicting Students Academic Performance Using Machine Learning Techniques / |
| Statement of responsibility, etc. | By Amal Shaker A. M. Khamis; Under The Supervision of Prof. Dr. Ahmed GadAllah, Prof. Dr. AbdelMoniem Helmy |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | تنبؤ الأداء الأكاديمي للطلاب باستخدام تقنيات التعلم الآلي / |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 160 Leaves : |
| Other physical details | illustrations ; |
| Dimensions | 30 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc.)-Cairo University, 2023. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 150-155. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 5-2 باللغه الانجليزيه :<br/>The current period has witnessed unprecedented progress in the use of e-learning in all domains utilizing personalized learning systems and recognizing the crucial role of effective guidance in Personal Learning Environments and educational settings. Nevertheless, the importance and impact of personalized content delivery in the educational field are still questionable. The quality of the personalized recommendations can be greatly improved by working on predicting academic performance by looking at data and other activities related to student’s performance, attitudes, and interactions. The use of machine learning algorithms to predict student outcomes based on current behavior and performance has been shown to be a valuable tool for predicting outcomes at various educational stages. Thus, machine learning (ML) techniques are the most appropriate approaches for achieving this goal. This research employed four supervised ML classifiers algorithms: Logistic Regression, Decision Tree, K-Nearest Neighbors, and Support Vector Machines to predict students’ academic performance based on their collected data. This data is an educational data collected from learning management system (LMS) through two educational semesters for 480 students studying levels G1-G12 from different 14 origin country. The collected data include demographic, academic, and behavioral characteristics for each student, and also their parents feedback. These models achieved f1-score results of 82.27, 83.91, 83.96, 83.17 respectively<br/> |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | شهدت الفترة الحالية تقدما غير مسبوق في استخدام التعلم الإلكتروني في جميع المجالات باستخدام أنظمة التعلم الشخصية والاعتراف بالدور الحاسم للتوجيه الفعال في بيئات التعلم الشخصية والبيئات التعليمية. ومع ذلك، فإن أهمية وتأثير تقديم المحتوى الشخصي في المجال التعليمي لا تزال موضع شك. يمكن تحسين جودة التوصيات الشخصية بشكل كبير من خلال العمل على التنبؤ بالأداء الأكاديمي من خلال النظر في البيانات والأنشطة الأخرى المتعلقة بأداء الطالب واتجاهاته وتفاعلاته. لقد ثبت أن استخدام خوارزميات التعلم الآلي للتنبؤ بنتائج الطلاب بناءً على السلوك والأداء الحالي هو أداة قيمة للتنبؤ بالنتائج في المراحل التعليمية المختلفة. وبالتالي، فإن تقنيات التعلم الآلي (ML) هي الأساليب الأكثر ملاءمة لتحقيق هذا الهدف. استخدم هذا البحث أربع خوارزميات لمصنفات تعلم الآلة تحت الإشراف: الانحدار اللوجستي، وشجرة القرار، وأقرب جيران K، وأجهزة المتجهات الداعمة للتنبؤ بالأداء الأكاديمي للطلاب بناءً على البيانات التي تم جمعها. هذه البيانات عبارة عن بيانات تعليمية تم جمعها من نظام إدارة التعلم (LMS) خلال فصلين دراسيين لـ 480 طالبًا يدرسون المستويات G1-G12 من 14 دولة مختلفة. وتشمل البيانات التي تم جمعها الخصائص الديموغرافية والأكاديمية والسلوكية لكل طالب، وكذلك ملاحظات أولياء أمورهم. حققت هذه النماذج نتائج f1 82.27، 83.91، 83.96، 83.17 على التوالي. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issued also as CD |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Student Performance Prediction |
| Source of heading or term | qrmak |
| 653 #0 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Learning Analytics |
| -- | Machine learning |
| -- | Education, Student Performance Prediction |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ahmed GadAllah |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | AbdelMoniem Helmy |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2023 |
| Supervisory body | Ahmed GadAllah |
| -- | AbdelMoniem Helmy |
| Universities | Cairo University |
| Faculties | Faculty of Graduate Studies for Statistical Research |
| Department | Department of Network Engineer / Software Engineering |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Eman El gebaly |
| Reviser Names | Huda |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 09.01.2025 | 89695 | Cai01.18.07.M.Sc.Am.P. | 01010110089695000 | 17.12.2024 | 09.01.2025 | Thesis |