Animated Pedagogical Agents to Assist Learners and to keep them motivated on Online Learning Environments (LMS or MOOC)

Bendou, K.; Megder, El Hassan; Cherkaoui, Chihab

doi:10.5120/ijca2017914477

Cited by 7 publications

(9 citation statements)

References 10 publications

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“…The identified studies applied community detection methods or clustering techniques to analyze the learners' or users' interactions in online learning environments (OLE). Any learning environment that contains rich and open different learning content as well as technologies for interacting and collaborating can be considered as online learning environment (Bendou et al, 2017). This includes virtual and blended classrooms and universities, social learning networks, or any technological assisted learning initiative.…”

Section: Resultsmentioning

confidence: 99%

Detecting communities using social network analysis in online learning environments: Systematic literature review

Yassine¹,

Kadry²,

Sicilia³

2021

WIREs Data Min & Knowl

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Uncovering community structure has made a significant advancement in explaining, analyzing, and forecasting behaviors and dynamics of networks related to different fields in sociology, criminology, biology, medicine, communication, economics, and academia. Detecting and clustering communities is a powerful step toward identifying the structural properties and the behavioral patterns in social networks. Recently, online learning has been progressively adopted by a lot of educational practices which raise many questions about assessing the learners' engagement, collaboration, and behaviors in the new emerging learning communities. This systematic literature review aims to assess the use of community detection techniques in analyzing the network's structure in online learning environments. It provides a comprehensive overview of the existing research that adopted those techniques with identifying the educational objectives behind their application as well as suggesting possible future research directions. Our analysis covered 65 studies that found in the literature and applied different community discovery techniques on various types of online learning environments to analyze their users' interactions patterns. Our review revealed the potential of this field in improving educational practices and decisions and in utilizing the massive amount of data generated from interacting with those environments. Finally, we highlighted the need to include automated community discovery techniques in online learning environments to facilitate and enhance their use as well as we stressed on the urge for further advance research to uncover a lot of hidden opportunities. This article is categorized under: Algorithmic Development > Statistics Algorithmic Development > Web Mining Application Areas > Education and Learning

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Section: Resultsmentioning

confidence: 99%

Detecting communities using social network analysis in online learning environments: Systematic literature review

Yassine¹,

Kadry²,

Sicilia³

2021

WIREs Data Min & Knowl

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“…However, personalized learning is not only enabled by the technology, but it also has to be accompanied by an adaptive pedagogy approach. According to Bendou, Megder, & Cherkaoui (2017), there are several pedagogy principles in the adaptive online learning environment that need to be taken into account in consideration of the need to consider the diverse profiles of students, continuous presences, assistance, and feedback as well as adaptation.…”

Section: Resultsmentioning

confidence: 99%

Critical Factors in E-Learning Influencing Student Motivation and Collaboration in Indonesian Higher Education Institution

Magdalena

Napitupulu

2018

The Winners

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The purpose of this research was to determine the effect of e-learning and its elements to student motivation and collaboration. This research was conducted in one of Indonesian higher education institutions. A questionnaire was distributed to collect data from students. Data were obtained from 145 active undergraduate students. The statistical method of multiple regression analysis was used for data analysis, using software Statistical Product and Service Solution (SPSS) version 22. Overall, the results of this research indicate that e-learning has a positive impact on student motivation and collaboration. However, it is found that not all elements of e-learning have positive impacts on student motivation and collaboration.

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“…Students enrolled in LMS's programs or courses can communicate with each other and receive individualized attention from instructors. Therefore, in MOOCs, students need to encourage and motivate themselves to complete their courses, while in LMSs, students are attempt to complete the course but either fail or withdraw due to their lack of understanding or for other causes Bendou et al (2017); Mihaescu and Popescu (2021).…”

Section: Dataset Used In Predictive Modelsmentioning

confidence: 99%

Deep Learning Techniques for Predicting Student’s Academic Performance on Virtual Learning Environments: A Review

Alnasyan,

Basheri,

Alassafi

2024

Preprint

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With advances in Artificial Intelligence (AI) and the increasing volume of online educational data, Deep Learning techniques have played a critical role in predicting student’s academic performance. Recent developments have assisted instructors in determining the strengths and weaknesses of student’s academic achievement. This understanding will benefit from adopting the necessary interventions to assist students in improving their performance, helping at-risk students, and preventing dropout rates. In this review, 46 studies between 2019 and 2023 that apply one or more Deep Learning (DL) techniques, either alone or in combination with Machine Learning (ML) or Ensemble Learning techniques, have been reviewed and analyzed. Moreover, the review has examined utilizing datasets from public (MOOCs), private (LMSs), and other platforms. Four categories were used to group the features: demographic, previous and current academic performance, and learning behavior/activity features. Therefore, the analysis has demonstrated that the DNNs and CNN-LSTM models were the most commonly used techniques. Moreover, it has been noted that the studies that have used DL techniques such as CNNs, DNNs, and LSTMs, performed well by achieving high prediction accuracy above 90%; other studies achieved accuracy ranging between 60% and 90%. For datasets used within the reviewed studies, even though 44% of the studies have used LMSs datasets, it is found that OULAD was the most used dataset from MOOCs. For the grouped features, the results of the analysis indicate that the best features for prediction are the learning behavior and activity features, which outperform other feature categories.

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Animated Pedagogical Agents to Assist Learners and to keep them motivated on Online Learning Environments (LMS or MOOC)

Cited by 7 publications

References 10 publications

Detecting communities using social network analysis in online learning environments: Systematic literature review

Detecting communities using social network analysis in online learning environments: Systematic literature review

Critical Factors in E-Learning Influencing Student Motivation and Collaboration in Indonesian Higher Education Institution

Deep Learning Techniques for Predicting Student’s Academic Performance on Virtual Learning Environments: A Review

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