A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles

Nafea, Shaimaa M.; Siewe, François; He, Ying

doi:10.1109/itce.2019.8646355

Cited by 22 publications

(22 citation statements)

References 27 publications

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“…In recent years, ML techniques have been applied [88] or incorporated into course RS [89] to enhance performance and the quality of the final recommendation. In this section, we briefly discuss how DM approaches including ML, DL and ARM are used independently in RS and followed by some examples for course RS with DM [46,90].…”

Section: Data Mining (Dm) Approachesmentioning

confidence: 99%

“…A ML-based RS for improving student learning experiences is introduced in [94]. The researchers in [46,90] applied K-means clustering to group learners with similar preferences and behavior to recommend the learning objects which are most similar to the active learners based on the selected similarity metrics.…”

Section: Machine Learning (Ml) and Deep Learning (Dl) Approachesmentioning

confidence: 99%

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The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Guruge

Kadel

Halder

2021

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In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

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Section: Data Mining (Dm) Approachesmentioning

confidence: 99%

Section: Machine Learning (Ml) and Deep Learning (Dl) Approachesmentioning

confidence: 99%

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Guruge

Kadel

Halder

2021

Data

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“…5. The new student profile SP will be used to recommend personalised learning objects to the student the next time s/he logs in, using the learning style based recommender system proposed in [46].…”

Section: Updating Student Profilementioning

confidence: 99%

“…Step 1-Recommend course learning objects according to their similarity to SP learning styles as explained in [46] and in Table VIII.…”

Section: A Profile Adaptation Scenariomentioning

confidence: 99%

A Novel Algorithm for Dynamic Student Profile Adaptation Based on Learning Styles

Nafea

Siewe

2019

Advances in Intelligent Systems and Computing

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E-learning recommendation systems are used to enhance student performance and knowledge by providing tailormade services based on the students' preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students' changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students' learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method.

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“…Esteban et al [37] combined CF and content-based CF to propose a hybrid recommendation system; it is used to recommend the most suitable courses to students, which improved the recommendation's reliability and performance. Nafea et al [38] designed an effective recommendation algorithm based on the K-means clustering algorithm, cosine similarity measure, and Pearson correlation coefficient. They recommended personalized course learning objects according to students' learning styles, which improved the recommendation accuracy.…”

mentioning

confidence: 99%

Teaching Teacher Recommendation Method Based on Fuzzy Clustering and Latent Factor Model

Yao

Deng

2020

IEEE Access

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A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles

Cited by 22 publications

References 27 publications

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

A Novel Algorithm for Dynamic Student Profile Adaptation Based on Learning Styles

Teaching Teacher Recommendation Method Based on Fuzzy Clustering and Latent Factor Model

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