CodERS: A hybrid recommender system for an E-learning system

Ansari, Mohammad Hossein; Moradi, Mohammad Hassan; NikRah, Omid; Kambakhsh, Keyvan M.

doi:10.1109/icspis.2016.7869884

Cited by 29 publications

(22 citation statements)

References 11 publications

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“…As a result of combining different recommendation techniques, it is possible to identify various combinations of recommender techniques in the literature. Noticeable are systems that combine clustering and CF filtering [254], CB and CF filtering [70], CF filtering and sequential pattern mining [256], clustering and machine learning [292], genetic algorithm and case‐based reasoning [135] or combination of RSs and other intelligent methods [27, 45, 65, 68, 82, 85, 96, 118, 253, 262]. These combined methods are mostly used to categorise learners with matching interests and goals [254], recommend learning resources [68, 237, 256], propose a classified list of recommendation data [82, 253], the adaptive suggestions for optimising problem‐solving abilities [136], offer adaptive feedback [20] or generate personalised learning path [108, 251, 267, 292].…”

Section: Resultsmentioning

confidence: 99%

Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

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Over the years, personalisation in e‐learning has evolved as a promising paradigm for matching learners' specific requirements. Although many literature reviews on personalised e‐learning have been conducted, there are still voids and limitations in the comprehensive literature survey regarding how personalisation might occur in e‐learning environments and benefit the teaching and learning process. In addition, no design guidelines, frameworks, or tools to guide its implementation exist, so practice can vary widely. The principal purpose of this literature review is to examine the subject, provide insights into the use of personalised techniques and systems in e‐learning, and investigate its impact on the engagement and success of the students. This study applies methods of precise selection criteria to determine which of the selected publications have the strongest interconnection and relevance with the topic of e‐learning personalisation. It also categorises the personalisation techniques employed by each identified system and highlights new perspectives and advances in the area.

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Section: Resultsmentioning

confidence: 99%

Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

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“…A database of 43 learners and 4644 assessments was used as an evaluation database. (Ansari et al, 2016) propose A CodERS recommendation system as part of an interactive programming learning platform. The test was performed on only 12 users.…”

Section: Hybrid Recommendation Systemsmentioning

confidence: 99%

Recommendation Systems on E-Learning and Social Learning: A Systematic Review

Souabi¹,

Retbi²,

Idrissi³

et al. 2021

EJEL

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E-learning is renowned as one of the highly effective modalities of learning. Social learning, in turn, is considered to be of major importance as it promotes collaboration between learners. For properly managing learning resources, recommender systems have been implemented in e-learning to enhance learners' experience. Whilst recommender systems are of widespread concern in online learning, it is still unclear to educators how recommender systems can improve the learning process and have a positive impact on learning. This paper seeks to provide an overview of the recommender systems proposed in e-learning between 2007 and the first part of 2021. Out of 100 initially identified publications for the period between 2007 and the first part of 2021, 51 articles were included for final synthesis, according to specific criteria. The descriptive results show that most of the disciplines involved in educational recommender systems papers have approached e-learning in a general way without putting as much emphasis on social learning, and that recommender systems based on explicit feedbacks and ratings were the most frequently used in empirical studies. The synthesis of results presents several recommender systems types in e-learning: (1) Content-based recommender systems, (2) Collaborative-filtering recommender systems, (3) Hybrid recommender systems and (4) Recommender systems based on supervised and unsupervised algorithms. The conclusions reflect on the almost lack of critical reflection on the importance of addressing recommender systems in social learning and social educational networks in particular, especially as social learning has particular requirements, the weak databases size used in some research work, the importance of acknowledging the strengths and weaknesses of each type of recommender system in an educational context and the need for further exploration of implicit feedbacks more than explicit learners’ feedbacks for more accurate recommendations.

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“…Many researchers restrict their efforts to testing approaches on small databases of non-significant size, which raises a genuine problem for machine learning algorithms as long as they require large databases. Many recommendation systems were tested only on databases with no more than 100 learners [15,16,17], whereas we require a lot of other data to measure the performance of a recommendation system. In what follows, we intend to combine genetic algorithms with our recommendation system in order to solve the problem of data scarcity.…”

Section: Related Workmentioning

confidence: 99%

Towards an Approach Based on Adjusted Genetic Algorithms to Improve the Quantity of Existing Data in the Context of Social Learning

Souabi¹,

Retbi²,

Idrissi

et al. 2021

Int. J. Emerg. Technol. Learn.

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In the current era, multiple disciplines struggle with the scarcity of data, particu-larly in the area of e-learning and social learning. In order to test their ap-proaches and their recommendation systems, researchers need to ensure the availability of large databases. Nevertheless, it is sometimes challenging to find-out large scale databases, particularly in terms of education and e-learning. In this article, we outline a potential solution to this challenge intended to improve the quantity of an existing database. In this respect, we suggest genetic algo-rithms with some adjustments to enhance the size of an initial database as long as the generated data owns the same features and properties of the initial data-base. In this case, testing machine learning and recommendation system ap-proaches will be more practical and relevant. The test is carried out on two da-tabases to prove the efficiency of genetic algorithms and to compare the struc-ture of the initial databases with the generated databases. The result reveals that genetic algorithms can achieve a high performance to improve the quantity of existing data and to solve the problem of data scarcity.

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CodERS: A hybrid recommender system for an E-learning system

Cited by 29 publications

References 11 publications

Personalisation methods in e‐learning‐A literature review

Personalisation methods in e‐learning‐A literature review

Recommendation Systems on E-Learning and Social Learning: A Systematic Review

Towards an Approach Based on Adjusted Genetic Algorithms to Improve the Quantity of Existing Data in the Context of Social Learning

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