A Personalized Group-Based Recommendation Approach for Web Search in E-Learning

Rahman, Mohammad Mustaneer; Abdullah, Nor Aniza

doi:10.1109/access.2018.2850376

Cited by 62 publications

(32 citation statements)

References 48 publications

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“…More research is warranted to explore the possibility of offering personalization and recommendations in MOOCs; as personalization in other domains (e.g. [73]) and recommendations in e-learning systems (e.g. [74]) has proven to be an effective means of lessening technological complexity, connecting and engaging students to online learning platforms.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Mapping Study of the Empirical MOOC Literature

et al. 2019

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Massive open online courses (MOOCs) have revolutionized todays education by offering a global accessible form of online learning. Over the years, MOOCs have been an attractive research area and have yielded an ample amount of research publications. However, the existing review studies in MOOCs are characterized by short year coverage or focusing on a specific theme. As such, a systematic mapping methodology was adopted to provide a fine-grain overview of MOOC research domain by identifying the quantity, types of research, available results and publication trends in educational aspects of MOOCs from 2009 to 2018. Key findings show that I) MOOC research have been on the rise since MOOCs became mainstream in 2011. II) MOOC research largely resides in the United States and few European countries. II) Most of MOOC studies focused on addressing learners' completion/dropout/retention. In addition, we proposed some recommendations for future research on MOOCs.INDEX TERMS E-learning, distance education, massive open online course, MOOC, systematic mapping study.The associate editor coordinating the review of this article and approving it for publication was Utku Kose.

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Section: Discussionmentioning

confidence: 99%

A Systematic Mapping Study of the Empirical MOOC Literature

et al. 2019

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“…Another example is a hybrid recommender system [11] for course recommendation with professor and student information dataset to enhance the effectiveness of information access to learners. A personalized group-based recommendation system is implemented in [16] to improve students' search experience on the Web based on their behaviour patterns and competences.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Targeted sample Smart Recommender System for Learning objects [10] Personal learning patterns of students Canada Pcrs: Course selection recommender [11] Personal information, knowledge and expertise India Career-path recommender for engineering students [12] student's interest and skills, influence from peers and family India Career-path recommender for high-school students [13] Age, gender, grades and peer influence Philippines Career-path recommender [14] Career test Malaysia GSTEM-CAT: university-program recommender [15] student's personality type and knowledge test Philippines Personalized group-based recommender [16] Personalized recommendations based on students' competences and behaviour Malaysia civil, chemical, computer and electrical, and industrial engineering. Students do not have knowledge on the difference between engineering disciplines which affects their choice negatively [24].…”

Section: Factorsmentioning

confidence: 99%

PCRS: Personalized Career-Path Recommender System for Engineering Students

2020

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Choosing a university specialization is a challenging decision for high-school students. Due to the lack of guidance and limited online resources, students base their decisions on subjective perceptions of family and friends. This increases the risk of high university dropout rates, and students changing their university disciplines. To address the aforementioned drawbacks, this research paper presents a Personalized Career-path Recommender System (PCRS) to provide guidance and help high school students choose engineering discipline. The design of PCRS is based on fuzzy intelligence of N-layered architecture and uses students' academic performance, personality type, and extra-curricular skills. The association between personality type and engineering discipline was built using a sample of 1250 engineering students enrolled in seven engineering disciplines at An-Najah National University. PCRS is implemented as a mobile application and it is tested against an evaluation sample of 177 engineers. The sample consists of graduate or undergraduate engineers who are satisfied with their engineering disciplines. The evaluation examined the agreement between the recommendations generated by PCRS and the 177 actual engineering discipline of the sample. The evaluation results proved a slight agreement between the suggested recommendations of PCRS and the actual discipline of the research sample. Hence, PCRS is capable of providing guidance to high-school students who are interested in pursuing their studies in Engineering. The PCRS application is the first career-path recommender to target Palestinian community and other developing countries in the MENA region. The design of PCRS is scalable and it can be expanded in the future to consider other academic majors of higher education.

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“…scenarios [2], [8]- [10], [28]- [37]. Ibrahim et al [2] developed a framework of an ontology-based hybrid-filtering system, which integrates information from multiple sources based on hierarchical ontology similarity so as to personalize course recommendations that will match the individual needs of students.…”

Section: Recommendation Based Personalized Learningmentioning

confidence: 99%

A Score Prediction Approach for Optional Course Recommendation via Cross-User-Domain Collaborative Filtering

et al. 2019

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Optional course selection is a critical activity for college students due to a large number of available but unfamiliar optional courses. Improper selection of optional courses would seriously affect the students' optional course achievements, which enforces students to drop out the improperly selected optional courses. Therefore, there is an urgent need to develop an optional course recommendation system. In this paper, we develop an optional course recommendation system based on score prediction. In particular, a novel cross-user-domain collaborative filtering algorithm is designed to accurately predict the score of the optional course for each student by using the course score distribution of the most similar senior students. After generating the predicted scores of all optional courses, the top t optional courses with the highest predicted scores without time conflict will be recommended to the student. The extensive experiments have been conducted to evaluate the effectiveness of the proposed method, and the results show that the proposed method is able to accurately recommend optional courses to students who will achieve relatively high scores. INDEX TERMS Optional course recommendation, score prediction, collaborative filtering, personalized learning.

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A Personalized Group-Based Recommendation Approach for Web Search in E-Learning

Cited by 62 publications

References 48 publications

A Systematic Mapping Study of the Empirical MOOC Literature

A Systematic Mapping Study of the Empirical MOOC Literature

PCRS: Personalized Career-Path Recommender System for Engineering Students

A Score Prediction Approach for Optional Course Recommendation via Cross-User-Domain Collaborative Filtering

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