Document Details

Document Type : Thesis 
Document Title :
Enhancing Individual Learner Guidance through Self-Assessment Using Open Social Student Modeling Visualization
تعزیز الارشاد الفردي للمتعلم من خلال التقییم الذاتي باستخدام تصور النمذجة الاجتماعیة المفتوحة للطلاب
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : Continuous self-assessment is a key requirement for any successful learning process. It becomes crucial in self-paced online learning environments, where students often rely solely on themselves to assess their progress. Personalized e-learning systems aim to provide student guidance. They use user modeling techniques to create unique user profiles that the system would adapt to and present the learner with a tailored learning experience. The introduction of the latest user modeling technologies, such as Open Student Modeling (OSM) in personalized systems, is believed to improve students’ self-assessment. The Open Social Student Modeling (OSSM), as a derivative of OSM pushes the idea of personalized e-learning systems further. The traditional personalized systems hide student data under the hood; hence students are not exposed to a full picture of their progress. Moreover, they are not able to position themselves among their peers (for example, am I in the top 5% of my class?). This research used OSM & OSSM concepts in order to find a way for students’ self-assessment through an interesting interactive visual representation of their learning progress at KAU. Where the students will know their weaknesses, which can help them to improve their performance at both the individual and class level. This research aims to leverage awareness of self-assessment, through an interactive social visualization interface in the hope of promoting students’ motivation that pushes them to improve their academic performance. This approach has many benefits, but it also poses new challenges that need to be overcome to assure a quality learning process that promotes individual motivation. 
Supervisor : Dr. Maram Mekawi 
Thesis Type : Master Thesis 
Publishing Year : 1440 AH
2019 AD 
Co-Supervisor : Dr. Arwa Lengawi 
Added Date : Monday, June 17, 2019 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
ولاء أبوبكر باجنیدBagunaid, Wala AboBakrResearcherMaster 

Files

File NameTypeDescription
 44491.pdf pdf 

Back To Researches Page