Collaborative Optimization of Learning Team Formation Based on Multidimensional Characteristics and Constraints Modeling: A Team Leader-Centered Approach via E-CARGO

Ma, Hua; Li, Jingze; Zhu, Haibin; Tang, Wensheng; Huang, Zhuoxuan; Tang, Yuqi

doi:10.1109/tcss.2022.3224762

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…Even today, India continues to uphold its legacy of academic excellence through its various educational institutions and programs. The root of the problem was caused by some difficulties, including an outdated curriculum, a dearth of hands-on learning opportunities, a paucity of the best tutors and others [4]. However, during the last five to ten years it has been clear that the use of digital tools has advanced instructional helpfulness.…”

Section: Introductionmentioning

confidence: 99%

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

G. M.,

Goudar,

Kulkarni

et al. 2024

IEEE Access

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This review delves into using e-learning technology and personalized recommendation systems in education. It examines 60 articles from prominent databases and identifies the different methods used in recommendation systems, such as collaborative and content-based approaches with a recent shift towards machine learning. However, the current personalized recommendation system faces challenges such as a lack of understanding of the content, student discontinuity, language barriers, confusion in selecting study materials, inadequate infrastructure and funding. The review proposes using new digital technologies to address these issues, including Fluxy AI, Twin technology, AI-powered virtual proctoring, and Alter Ego. These technologies can create a dynamic and interactive learning environment, providing tailored learning experiences for students and insights for educators to provide targeted support and guidance. The integration of these technologies can improve individualized learning, increase understanding capacity and enhance the learning experience for students with speech disorders.

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Section: Introductionmentioning

confidence: 99%

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

G. M.,

Goudar,

Kulkarni

et al. 2024

IEEE Access

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“…The measurements of learners' skill proficiencies are helpful in predicting examinee performance in a future test (Ma et al 2020;Song et al 2022;Yao et al 2019). This insight enables the possibility of personalized recommendation (Wan and Niu 2020;Yang et al 2023;Yun et al 2022), school precaution (Bernacki et al 2020Niyogisubizo et al 2022), examination paper generation (Wu et al 2020), collaborative learning team formation (Ma et al 2023(Ma et al , 2022 and learning path recommendation (Jiang et al 2022;Liu and Li 2020;Liu et al 2019b).…”

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confidence: 99%

Predicting examinee performance based on a fuzzy cloud cognitive diagnosis framework in e-learning environment

Huang

Zhu

et al. 2023

Soft Comput

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The score profiles could be used to measure learners’ skills proficiency via cognitive diagnosis models (CDMs) for predicting their performance in the future examination. The prediction results could provide important decision-making supports for personalized e-learning instruction. However, facing the possible complexity of skills, the uncertainty of learners’ skill proficiency and the large-scale volume of score profiles, the existing CDMs have limitations in the measurement mechanisms and diagnostic efficiency. In this paper, we proposed an approach based on a fuzzy cloud cognitive diagnosis framework (FC-CDF) to predicting examinees’ performance in e-learning environment. In this approach, the normal cloud models (NCMs) are utilized innovatively to measure the expectation, degree of variation and variation frequency of learners’ skill proficiency, and each NCM is transformed into an interval fuzzy number to characterize the uncertainty of the skill proficiency for every learner. Combining the educational psychology hypothesis with the parameter estimation method, we could obtain the learners’ skill proficiency level and the slip and guess factors relevant to each test item, based on which the learners’ scores could be predicted in a future test. Finally, the experiments demonstrate that the proposed approach provides good accuracy and significantly reduces execution time for predicting examinee performance, compared with the existing methods.

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Comparing computational algorithms for team formation in the classroom: a classroom experience

et al. 2023

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Throughout recent years, several researchers have proposed computational tools and algorithms to support team formation in the classroom. The result is that team formation algorithms have been widely applied in classroom environments to create well-balanced teams. One of the challenges in designing algorithms for automatic team formation is designing an appropriate function to estimate team performance, which is used as part of the optimization algorithm that divides students into teams. This function (referred to as a team evaluation heuristic) serves as an approximation to team performance, which is a complex phenomenon that is difficult to quantitatively assess in many settings and that cannot be accurately calculated prior to the task at hand. Despite showing their relative success compared to traditional and manual team formation strategies (manually employed by lecturers and teachers), there is a lack of research comparing team evaluation heuristics in a real classroom setting. Such a comparison would help teachers, practitioners, and system designers to appropriately select the most suitable team formation algorithms. In this article, we present an experimental evaluation that was carried out in a Bachelor’s Degree Program in Tourism that compares two team evaluation heuristics based on Belbin and Myer-Briggs. The experimental evaluation was carried out by means of an intelligent, extensible team formation tool whose optimization is based on an integer linear model that can be extended to support different team evaluation heuristics.

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Collaborative Optimization of Learning Team Formation Based on Multidimensional Characteristics and Constraints Modeling: A Team Leader-Centered Approach via E-CARGO

Cited by 7 publications

References 37 publications

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

Predicting examinee performance based on a fuzzy cloud cognitive diagnosis framework in e-learning environment

Comparing computational algorithms for team formation in the classroom: a classroom experience

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