An evolutionary approach for personalization of content delivery in e-learning systems based on learner behavior forcing compatibility of learning materials

Christudas, Beulah Christalin Latha; Kirubakaran, E.; Thangaiah, P. Ranjit Jeba

doi:10.1016/j.tele.2017.02.004

Cited by 84 publications

(47 citation statements)

References 31 publications

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“…Personalised e-learning involves a range of educational technologies and pedagogical approaches that consider differences among individual students (Essalmi, Ayed, Jemni, Graf, & Kinshuk, 2015) and which can tailor the generic virtual education environment to their particular needs (Christudas, Kirubakaran, & Thangaiah, 2018), giving students the feeling that their individual learning requirements are being met (Ashman et al, 2014). One highly sought-after milestone among universities involved in personalised elearning activities, public authorities, and society in general is being able to provide elearners with opportunities in which to experience flow (OECD, 2007).…”

Section: Theoretical Background and Research Hypothesesmentioning

confidence: 99%

Flow experiences in personalised e-learning environments and the role of gender and academic performance

Rodríguez‐Ardura

2019

Interactive Learning Environments

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Section: Theoretical Background and Research Hypothesesmentioning

confidence: 99%

Flow experiences in personalised e-learning environments and the role of gender and academic performance

Rodríguez‐Ardura

2019

Interactive Learning Environments

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“…from related dataset and provides quality recommendations. Recommender systems are a significant part of E-Commerce that use machine learning [3] and data mining techniques [4] to filter the unseen information and predict whether the user would like a particular item. An intelligent system is a special type of recommender system used to exploit the historical user ratings on data that comes from mined-relevant data through data mining process [5].…”

Section: A Recommender System Extracts User's Interestmentioning

confidence: 99%

A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

et al. 2020

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Recommendation System (RS) has been broadly utilized in various areas and discovers product recommendations during an active user interaction in E-Commerce sites. Tremendous growth of users and products in recent years has faced some key challenges. There are numerous online sites that present many decisions to the user at once, which is strenuous. Moreover, finding active user or right product is an important task in RS. Existing works have been proposed to recommend a product by considering user inclination and socio-demographic behaviour. In this paper, we propose a Hybrid Action-Related K-Nearest Neighbour similarity (HAR-KNN) recommender that consolidates the simplicity of hybrid filtering to enrich user behaviour matrix with formation of the vector of features. It will classify the features using race classifiers from both quality and quantity aspects. The proposed approach also addresses the problems of the previous methods to efficiently evaluate user preference on products and balance feature analysis. The K-NN classification method has been qualified online and real-time to find user behaviour data coordinating to a specific user group containing the relationship between the similarity of many users and target users from a huge amount of data. The proposed experimental result is evaluated based on measures such as Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Squared Error (RMSE) with the lowest error of 0.7165, 0.7201 and 0.7322 separately. High predictive measures like Precision (P), Recall (R) and F1 are found to have values 0.8501, 0.2201 and 0.3507 respectively. INDEX TERMS Recommendation system (RS), user behaviour data, hybrid filtering, K-NN, behavioural matrix.

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“…Another work that considers LOs recommendation was proposed by Christalin et al (2017). This work took into account three important characteristics for recommending contents: the level of compatibility of LOs concerning the students LS, the level of complexity of LOs given the level of learners knowledge and the level of interactivity and satisfaction of the students.…”

Section: Related Workmentioning

confidence: 99%

A low complexity heuristic to solve a learning objects recommendation problem

Falci

Dorça

Andrade

et al. 2020

Smart Learn. Environ.

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The recommendation of learning objects in virtual learning environments has become the focus of research to improve online learning experience. Several approaches have been presented in an attempt to model the individual characteristics of the students and offer learning objects that best suit their particularities. Most of them, though, are impractical in real-world scenarios due to the high computational cost as a huge number of repositories offering learning objects such as Youtube, Wikipedia, Stackoverflow, Github, discussion forums, social networks and many others are available and each has a large amount of learning objects that can be retrieved. In this work, we propose a low complexity heuristic to solve this problem, comparing it to a classical mixed-integer linear programming model and classical genetic algorithm in varying dataset sizes that contain from 2000 to 1360000 learning objects. Performance and optimality were analyzed. The results showed that the proposed technique was only slightly suboptimal, while its computational cost was considerably smaller than the one presented by the linear optimization approach.

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An evolutionary approach for personalization of content delivery in e-learning systems based on learner behavior forcing compatibility of learning materials

Cited by 84 publications

References 31 publications

Flow experiences in personalised e-learning environments and the role of gender and academic performance

Flow experiences in personalised e-learning environments and the role of gender and academic performance

A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

A low complexity heuristic to solve a learning objects recommendation problem

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