Collaborative learning services in the smart university environment

Akhrif, Ouidad; Benfares, Chaymae; Idrissi, Younès El Bouzekri El; Hmina, Nabil

doi:10.1145/3368756.3369020

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…A weight that expresses the learner's preference and difficulty will be associated with each learning content. The next step foresees the possibility of using Bayesian inference to implement an algorithm to automatically refine personalized learning paths [25], [26], [27].…”

Section: Further Developmentsmentioning

confidence: 99%

Improving Adaptive Learning in a Smart Learning Environment

Marzano

Abuže

Nur³

2021

ETR

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It has been broadly argued that, in the near future, the demand for skilled labor will increase whilst that for routine activities will decrease. In this regard, the need for making greater investments in education to re-skill workers and support continuous learning has been invoked as an essential requirement for preserving people’s employability.Digital technology is deemed increasingly necessary to sustain the educational endeavor, for the possibilities it offers to make more accessible and low-cost educational interventions. It allows for the creation of personalized learning paths and customized digital learning solutions, for courses to be available to a large attendance of learners, and for teaching-learning activities to be offered at significantly reduced cost.In this article, a learning unit structure designed to improve adaptive learning is proposed, and mechanisms for adaptive learning in a smart learning environment are discussed.The implemented teaching-learning solution is also illustrated. This is a preliminary application based on an approach that combines the teacher experience with learning analytics.

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Section: Further Developmentsmentioning

confidence: 99%

Improving Adaptive Learning in a Smart Learning Environment

Marzano

Abuže

Nur³

2021

ETR

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“…In terms of application for recommending in university, the authors of the paper [25] concluded with "smart collaborative learning" as a relevant concept that adopts smart interactions to promotes modern methods of collaboration between teams of smart learners. Summarily, in this work, we propose an approach to gather the relationships of the courses (e.g., knowledge/skills) and use them for integrating into the Matrix Factorization for solving the PSP problem in the ITS.…”

Section: Related Workmentioning

confidence: 99%

Integrating courses' relationship into predicting student performance

Huynh-Ly¹

2020

IJATCSE

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In Intelligent Tutoring System (ITS) as well as the E-learning system at the university, predicting student learning performance to suggest courses is an essential task of an academic advisor. Many kinds of research address to solve this problem with diverse approaches such as classification, regression, association rules, and recommender systems. Recently, it was a measurable success in using collaborative filtering in the recommender system, especially the matrix factorization technique, to build the courses' recommendation system. There are many advances to improve the accuracy of the prediction, such as using student profiles, course properties, or course relationships; however, they have not been mined. This study proposes an approach which integrates the course relationships into the courses' recommendation system to improve the prediction accuracy. Experimental results of the proposed approach are positive when we validate the published educational datasets.

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Re-imagining the Futures of University: Foresight for Higher Education Using Innovation Labs Learning Space

Al-Jayyousi

Aldhmour

2022

Explore Business, Technology Opportunities and Challenges ‎After the Covid-19 Pandemic

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Collaborative learning services in the smart university environment

Cited by 3 publications

References 19 publications

Improving Adaptive Learning in a Smart Learning Environment

Improving Adaptive Learning in a Smart Learning Environment

Integrating courses' relationship into predicting student performance

Re-imagining the Futures of University: Foresight for Higher Education Using Innovation Labs Learning Space

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