Social learning analytics for determining learning styles in a smart classroom

Aguilar, José; Buendía, Omar; Pinto, Angel; Gutiérrez, José Antonio

doi:10.1080/10494820.2019.1651745

Cited by 32 publications

(24 citation statements)

References 19 publications

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“…The identification of learning styles has been approached in different ways, among which the following stand out: using behavioral features and twin support vector machine [25], social learning analitycs [26], SVM and PCA based learning feature classification [27], dynamic modeling of student profiles, learning patterns and feature extraction [22], fuzzy C means [28], machine learning approaches and learning analitycs [29], among others.…”

Section: B Learning Stylesmentioning

confidence: 99%

Using Project-based Learning in a Hybrid e-Learning System Model

Alfaro¹,

Rivera²,

Luna-Urquizo

2019

IJACSA

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After conducting the historical review and establishing the state of the art, the authors of this paper focus on the incorporation of Project Based Learning (PBL), in an adaptive e-Learning environment, a novel and emerging perspective, which allows the application of what today constitutes one of the most effective strategies for the process of teaching learning. In PBL, each project is defined as a complex task or problem of reality, for which resolution, the student must develop research activities, planning, design, development, validation, testing, etc. For the proposal of the Hybrid Architecture of the e-Learning system model, the authors use artificial intelligence techniques, which make it possible to identify the Learning Styles (LS), with the purpose of automatically assigning the projects, according to the characteristics, interests, expectations and demands of the student, who will interact with an e-Learning environment, with a high capacity of adaptation to each individual. Finally, the conclusions and recommendations of the research work are established.

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Section: B Learning Stylesmentioning

confidence: 99%

Using Project-based Learning in a Hybrid e-Learning System Model

Alfaro¹,

Rivera²,

Luna-Urquizo

2019

IJACSA

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“…These techniques range from the most traditional approaches as the psychological questionnaires or tests, up to the methods of automatic detection or prediction, based on Artificial Intelligence and data mining techniques, such as the proposals by [2], [3], [4], [5], [6]. Within the review of related works, carried out for the present work, the methods proposed by Alfaro et al [7], Aguilar et al [8], and Seyal et al [9], have been particularly considered, and will be briefly described in the following paragraphs.…”

Section: Related Workmentioning

confidence: 99%

“…In Aguilar et al [8], Social Learning Analytics (SLA) is used, wich "focuses mainly on the analysis of social networks (SNA) and the WWW, to obtain hidden information in large amounts of data (Big Data), and discover patterns of interaction and behavior of educational social actors", with the objective of determining the particular LS of each student. The importance of this approach is that it allows obtaining a point of view outside the learning environment, considering the interactions of the subject in "situations with greater freedom of action and greater diversity of resources" [8]. This work involves concepts such as Big Data, Semantic Mining, Text Mining, Data Mining, among others domains.…”

Section: Related Workmentioning

confidence: 99%

“…While there are a large number of case studies and published articles about the application and efficiency of LS in the learning process, such as those presented in [2], [3], [4], [5], [6], [7], [8], [10], [9], [11], [12], [13], [14], their efficiency has been highly questioned. The most recent studies seem to indicate that there is insufficient evidence to consider that the LS concept, by themselves, can have a significant impact on the learning process or in the knowledge retention process, as mentioned in [15], [16], [17], [18], [19], [20], [21].…”

Section: Related Workmentioning

confidence: 99%

“…Stores the LS of each student, identified from their development in social networks and websites, obtained through Social Learning Analytics techniques according to the proposal of Aguilar et al [8], which is done on large amounts of data, taking as reference the Felder and Silverman model.…”

Section: Social Learning Stylementioning

confidence: 99%

See 2 more Smart Citations

Learning Management System Personalization based on Multi-Attribute Decision Making Techniques and Intuitionistic Fuzzy Numbers

Luna-Urquizo¹

2019

IJACSA

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The personalization of Learning Management Systems is a fundamental task in the current context of e-Learning and the WWW. However, there are many controversies around the criteria used to make the selection and presentation of the most appropriate content for each user. The most used approaches in the last decade were the identification of learning styles, the analysis of the history and navigational behavior, and the classification of user profiles, without finding conclusive evidence to determine a method that can be adopted universally, considering the complexity of the cognitive processes involved. This paper proposes an approach based on multi-attribute decision making techniques, which allows considering and combining the criteria most effectively used in the area, according to particular contexts, as a new approach to the content personalization and appropriate learning objects selection. The application of this approach aims to maximize the effectiveness and efficiency of the teaching process and enrich the user experience.

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Towards a Social Learning Analysis Using Learning Management System and Learning Experience to Predict Learners’ Success

Gharbaoui

Mansouri

Poirier

2023

Lecture Notes in Computer Science

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Online learning is an alternative to the traditional (face-to-face) educational system. Contrary to traditional teaching (classroom), distance learning is characterized by the lack of physical contact between instructors and students and also between students within a classroom. Unfortunately, some learners may fail or even stop learning online quite quickly. In addition, learners find it more difficult to learn, not because there is not enough content, but because there is too much and they cannot find what is useful and up-to-date. This study aims to prove the importance of using social and adaptive learning to overcome the problems that learners face in online learning platforms, we propose a model to determine the effects of using the two types of platforms in online learning, namely Learning Management System (LMS) and Learning Experience Platform (LXP) for two groups of learners in Moroccan higher education applying social learning analysis (SLA). Specifically, in this proposal we attempt to analyze data from the LMS and LXP platforms to build knowledge models about students and determine their learning styles based on their interactions on these platforms, improve their learning experience and predict the conditions that favor their progress and success in order to recreate the conditions for quality social learning and help learners find possible paths to success in the online learning platforms in Morocco.

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Social learning analytics for determining learning styles in a smart classroom

Cited by 32 publications

References 19 publications

Using Project-based Learning in a Hybrid e-Learning System Model

Using Project-based Learning in a Hybrid e-Learning System Model

Learning Management System Personalization based on Multi-Attribute Decision Making Techniques and Intuitionistic Fuzzy Numbers

Towards a Social Learning Analysis Using Learning Management System and Learning Experience to Predict Learners’ Success

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