Charbel Obeid

A hybrid recommender system for student academic advising supported by case-based reasoning and ontology

Academic advising at most high schools is limited in its ability to help students in identifying ... more Academic advising at most high schools is limited in its ability to help students in identifying appropriate academic pathways. For example, choosing a university and a university major is a challenging task rife with anxiety that gets students confused. Therefore, students at high school need assistance to match their interests with the available universities and university majors. This thesis presents a novel hybrid recommender system (RS) approach for students' academic advising supported by Case-based reasoning (CBR) and ontology. This Knowledge-based (KB) hybrid RS enables high school students to submit their demographic data, courses' ratings, preferences, interests, and other knowledge in order to get personalized recommendations. These recommendations consist of three essential suggestions namely the university/college, university major, and career choice that match the student interests and requirements. The five major contributions of our research are: (1) Proposing a novel hybridization approach that incorporates four-core technologies namely the KB recommender system, Collaborative Filtering (CF), CBR, and ontology; (2) Reducing the limitations and problems of the basic recommender systems' techniques; (3) Designing the ontology that describes the high school student's profile and interests, high school, university/college, and career domains; (4) Generating personalized recommendations based on the case-base and ontology; (5) Treating through the proposed hybrid RS high dimensional datasets that contain heterogeneous data types. A prototype of this hybrid RS named (COHRS) is developed and presented. The experimental assessments and comparisons with the existing traditional recommender systems' approaches, strongly demonstrate the high performance and accuracy of our hybrid RS that is supported by CBR and ontology.

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Ontology-based Recommender System in Higher Education

Academic advising is limited in its ability to assist students in identifying academic pathways. ... more Academic advising is limited in its ability to assist students in identifying academic pathways. Selecting a major and a university is a challenging process rife with anxiety. Students at high school are not sure how to match their interests with their working future or major. Therefore, high school students need guidance and support. Moreover, students need to filter, prioritize and efficiently get appropriate information from the web in order to solve the problem of information overload. This paper represents an approach for developing ontology-based recommender system improved with machine learning techniques to orient students in higher education. The proposed recommender system is an assessment tool for students' vocational strengths and weaknesses, interests and capabilities. The main objective of our ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one.

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A comparative analysis of different recommender systems for university major and career domain guidance

Education and Information Technologies, Dec 28, 2022

Academic advising is inhibited at most of the high schools to help students identify appropriate ... more Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the implemented approaches. The experimental results indicate that the knowledge-based hybrid recommender system, combined with the user-based collaborative filtering and braced with case-based reasoning as well as ontology, generated 98% of similar cases, 95% of them are personalized based on the interests of the high school students. The average usefulness feedback and satisfaction level of the students concerning this proposed hybrid approach reached 95% and 92.5% respectively, which could be a solution to similar problems, regardless of the application domain. Besides, the constructed ontology could be reused in other systems in the educational domain.

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Conceptual Clustering of University Graduate Students Trajectories Using Formal Concept Analysis: a Case Study in Lebanon

International Journal of Continuing Engineering Education and Lifelong Learning, 2020

Due to the weak academic advising systems in most schools, students need assistance to explore un... more Due to the weak academic advising systems in most schools, students need assistance to explore universities/colleges and study fields that match their interests and future careers. Unfortunately, the orientation programs in most schools are not well designed to cater to students' varied needs. While career orientation programs are much needed at schools, a large number of Lebanese schools have not integrated such programs in their educational systems yet. Thus, this research focuses on developing a hybrid recommender system based on machine learning techniques. Our research focuses on the implementation of the formal concept analysis (FCA) to study university graduates' trajectories. This data mining technique is applied in knowledge presentation and data processing. FCA enables the discovery of hidden knowledge by using association rule mining and machine learning. The objective of this study is to discover the university graduates' trajectories and demonstrate the effectiveness of the FCA technique in data clustering. C. Obeid et al.

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A comparative analysis of different recommender systems for university major and career domain guidance

Education and Information Technologies

Academic advising is inhibited at most of the high schools to help students identify appropriate ... more Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the i...

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A novel hybrid recommender system approach for student academic advising named COHRS, supported by case-based reasoning and ontology

Computer Science and Information Systems, 2022

The recent development of the WorldWideWeb, information, and communications technology have trans... more The recent development of the WorldWideWeb, information, and communications technology have transformed the world and moved us into the data era resulting in an overload of data analysis. Students at high school use, most of the time, the internet as a tool to search for universities/colleges, university?s majors, and career paths that match their interests. However, selecting higher education choices such as a university major is a massive decision for students leading them, to surf the internet for long periods in search of needed information. Therefore, the purpose of this study is to assist high school students through a hybrid recommender system (RS) that provides personalized recommendations related to their interests. To reach this purpose we proposed a novel hybrid RS approach named (COHRS) that incorporates the Knowledge base (KB) and Collaborative Filtering (CF) recommender techniques. This hybrid RS approach is supported by the Case based Reasoning (CBR) system and Ontolo...

Ontology-based Recommender System in Higher Education

Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18

Academic advising is limited in its ability to assist students in identifying academic pathways. ... more Academic advising is limited in its ability to assist students in identifying academic pathways. Selecting a major and a university is a challenging process rife with anxiety. Students at high school are not sure how to match their interests with their working future or major. Therefore, high school students need guidance and support. Moreover, students need to filter, prioritize and efficiently get appropriate information from the web in order to solve the problem of information overload. This paper represents an approach for developing ontology-based recommender system improved with machine learning techniques to orient students in higher education. The proposed recommender system is an assessment tool for students' vocational strengths and weaknesses, interests and capabilities. The main objective of our ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one.

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