Smart learning environment, measure online student satisfaction: a case study in the context of higher education in Morocco

Safsouf, Yassine; Mansouri, Khalifa; Poirier, Franck

doi:10.1109/iceit48248.2020.9113189

Cited by 22 publications

(26 citation statements)

References 12 publications

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“…These online surveys consisted of 9 constructs applied to learners to test "system performance" in terms of student academic outcomes and sustainability during COVID-19 closure periods. The constructs were chosen to be consistent with previous evaluations of online learning that have been conducted [22,25] and some during the COVID-19 pandemic period [20,23]. Other objectives are to identify their attitude towards the learning framework, the level of understanding of the relationship between the assessments and future real-world mechanical engineering tasks [22].…”

Section: Structure and Conceptsmentioning

confidence: 99%

“…─ Student demographics: This construct includes gender, age, parents' educational background, travel time to the university, age, gender, activities and hobbies [22]. ─ The emotional state of the student during the pandemic containment period, and the impact on their academic progress, after having to take all courses online [20,23]. ─ Technical difficulties [20,23].…”

Section: Structure and Conceptsmentioning

confidence: 99%

“…─ The emotional state of the student during the pandemic containment period, and the impact on their academic progress, after having to take all courses online [20,23]. ─ Technical difficulties [20,23]. ─ Work on homework, including study time and homework results [20,23].…”

Section: Structure and Conceptsmentioning

confidence: 99%

“…Multiple publications examine technical difficulties, stating that stronger the learning effects can be achieved by the high quality and reliability of the technology used. [22,23].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

et al. 2021

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The COVID-19 pandemic has challenged many educational institutions around the world in 2020 and 2021 as traditional education has been interrupted to prevent the spread of the virus. This forced the transition from traditional education to fully distance learning envi-ronments for all levels of education. The widespread adoption of distance learning has led instructors to form new digital learning environments and methods. In response to this unexpected situation, data regarding engineering students and their interaction with the learning environment was accumulated and processed, generating a matrix of 129 × 165 variables. The motivation for this research is to identify new variables that impact student performance during the disorientation of the educational process due to the COVID-19 pandemic. Statistical analysis was performed and discussed in this paper including correla-tion analysis, factor analysis, and clustering. Reliability analysis was also performed and ANOVA (analysis of variance) was applied to clusters. The novelty of this work is to use student performance data and statistical analysis of online surveys to reveal patterns that can help reduce dropout rates and transform the educational process, under extenuating and imposed distance learning circumstances. A major finding is that by applying innovative teaching methods, thereby meeting the challenge of an imposed distance learning environ-ment, students' spatial conceptions improve, overcoming the absence of a physical learning space. Deep insights for individual students were discovered, as well as significant relation-ships between students' transition from secondary to higher education and their understand-ing of geometric features. Evidence of the effectiveness of the online learning framework that was integrated showed that it positively influenced students' learning styles.

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Section: Structure and Conceptsmentioning

confidence: 99%

Section: Structure and Conceptsmentioning

confidence: 99%

Section: Structure and Conceptsmentioning

confidence: 99%

“…Multiple publications examine technical difficulties, stating that stronger the learning effects can be achieved by the high quality and reliability of the technology used. [22,23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

et al. 2021

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“…In einer zweiten Literaturrecherche wurde gezielt nach konkreten, innovativen Technologien für den Distanzunterricht aus den Bereichen Smart Classroom und Smart Learning Environments gesucht, um diese auf die bereits umgesetzten Funktionen hin zu überprüfen und die erste Literaturrecherche zu ergänzen. Ein Smart Learning Environment dient der digitalen Erhebung und Auswertung von Schülerdaten sowie der kontextsensitiven Bereitstellung von Lerninhalten (Safsouf et al 2020). Ein Smart Classroom dient der digitalen Vernetzung zwischen Schülern untereinander sowie zwischen Lehrern und Schülern (Uskov et al 2015).…”

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Smart Cooperative Distance Learning: Ein multiagentenbasierter Ansatz zur Unterstützung des kooperativen Lernens im Distanzunterricht

Gösling

Dreesbach

Brinke

et al. 2021

HMD

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ZusammenfassungLehrkräfte können auf eine Vielzahl von etablierten Unterrichtsmethoden zurückgreifen, um kooperatives Lernen in den Präsenzunterricht zu integrieren. Im Distanzunterricht während der Corona-Pandemie werden diese Methoden jedoch häufig nicht eingesetzt. Dadurch gehen das gemeinschaftliche, strukturierte Lernen und die damit einhergehenden positiven Effekte auf die Schülerinnen und Schüler verloren. Eine State-of-the-Art-Analyse zeigt, dass kaum technische Lösungen existieren, die methodenbasiertes, kooperatives Lernen in einem Distanzunterricht unterstützen – und falls doch, handelt es sich um sehr spezielle Lösungen für die Umsetzung einer konkreten Unterrichtsmethode. Wir schlagen daher das Konzept eines Smart Cooperative Distance Learning Systems vor. Dabei handelt es sich um ein Multiagentensystem in einer Cloud, welches insbesondere die methodenbasierte, kooperative Ausführung von Lerneinheiten unterstützt. Ein Szenario dient zur Veranschaulichung des Smart Cooperative Distance Learning Systems.

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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

Self Cite

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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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Smart learning environment, measure online student satisfaction: a case study in the context of higher education in Morocco

Cited by 22 publications

References 12 publications

Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

Smart Cooperative Distance Learning: Ein multiagentenbasierter Ansatz zur Unterstützung des kooperativen Lernens im Distanzunterricht

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

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