Recommendation for Learners in E-Learning System

Chaudhary, Kamika; Gupta, Neena

doi:10.1109/icngcis.2017.8

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…(Di Mascio, Laura and Temperini, 2018) present a solution to support learners participating in a programming context by recommending the next problem to undertake. (Chaudhary and Gupta, 2017) carried out a study on the work carried out between 2001-2017 and concerning the different application software developed for learning as well as the evaluation parameters used. (Liu, 2008) proposes a self-assessment system with a recommendation system to adapt the platform to the needs of the learners.…”

Section: Other 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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Section: Other Recommendation Systemsmentioning

confidence: 99%

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

Souabi¹,

Retbi²,

Idrissi³

et al. 2021

EJEL

View full text Add to dashboard Cite

show abstract

“…Different plug-ins that manage authentication for Moodle are briefly discussed [10]. Though some users have common interests, it is revealed that some predictions cannot be proposed to those users [11]. It depends upon the learners' goals, history, and learning style.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model for a Good Learning Environment Using an Ensemble Approach

Subha¹,

Priya²

2023

Computer Systems Science and Engineering

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This paper presents an efficient prediction model for a good learning environment using Random Forest (RF) classifier. It consists of a series of modules; data preprocessing, data normalization, data split and finally classification or prediction by the RF classifier. The preprocessed data is normalized using minmax normalization often used before model fitting. As the input data or variables are measured at different scales, it is necessary to normalize them to contribute equally to the model fitting. Then, the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation (k = 10) is used to validate the model. The proposed Prediction Model for Course Selection (PMCS) system is considered a multi-class problem that predicts the course for a particular learner with three complexity levels, namely low, medium and high. It is operated under two modes; locally and globally. The former considers the gender of the learner and the later does not consider the gender of the learner. The database comprises the learner opinions from 75 males and 75 females per category (low, medium and high). Thus the system uses a total of 450 samples to evaluate the performance of the PMCS system. Results show that the system's performance, while using locally i.e., gender-wise has slightly higher performance than the global system. The RF classifier with 75 decision trees in the global system provides an average accuracy of 97.6%, whereas in the local system it is 97% (male) and 97.6% (female). The overall performance of the RF classifier with 75 trees is better than 25, 50 and 100 decision trees in both local and global systems.

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Comparative Analysis of Supervised Machine Learning Algorithms for Evaluating the Performance Level of Students

Subha¹,

Priya²

2021

2021 Fifth International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Recommendation for Learners in E-Learning System

Cited by 4 publications

References 21 publications

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

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

Prediction Model for a Good Learning Environment Using an Ensemble Approach

Comparative Analysis of Supervised Machine Learning Algorithms for Evaluating the Performance Level of Students

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