Enhancing Learners’ Experience Through Extending Learning Systems

Tran, Tich Phuoc; Meacheam, David

doi:10.1109/tlt.2020.2989333

Cited by 24 publications

(11 citation statements)

References 15 publications

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“…Existing LMSs such as Moodle 3 , Canvas and Open edX provide a rich set of features including multimedia integration, mobile support and assessments, etc. However, they are unable to offer adequate support for most advanced innovative technologies meant to enhance learning experience, for example, augmented reality (AR), virtual reality (VR), mix reality (MR) [21], multi-sensory media, fabrication and virtual laboratories (Fab Labs/Virtual Labs), artificial intelligencebased visualisation [22]. Moreover, although some of them do have gamification features, their gamification functionalities are rather basic and offer no support for heterogeneous technologies.…”

Section: Gamification and The Newtelp Platformmentioning

confidence: 99%

An Innovative Multi-Layer Gamification Framework for Improved STEM Learning Experience

Zhao

Playfoot²,

Nicola³

et al. 2022

IEEE Access

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Lately, gamification (i.e., employing game-design elements and game principles in nongame contexts) has gained massive popularity and widespread usage in various areas, including education. However, gamification deployment in education in general and remote education in particular still faces many challenges that mainly influence user quality of experience. These challenges include lack of dedicated communication-based systems, potential additional load on teachers, absence of customization and personalization for users, and no support for advanced technology-enhanced learning (TEL). This paper investigates the use of gamification for networked delivery of science, technology, engineering and mathematics (STEM) subjects. It proposes an innovative gamification framework, the NEWTONenhanced gamification model (N-EGM), which was designed as part of the European Horizon 2020 project NEWTON. The paper also describes the proposed N-EGM model deployment in the gamification engine of a real learning management system and its associated communication and networking solution. The communication support provides easy-to-use gamification configuration functionality and efficient data collection and processing in a heterogeneous technology context. Finally, the paper evaluates the proposed N-EGM model as part of a NEWTON project pilot deployed in a Romanian school. The results demonstrate the effectiveness of the proposed gamification solution in improving both students' learning experience and their engagement, while also increasing student knowledge gain.

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Section: Gamification and The Newtelp Platformmentioning

confidence: 99%

An Innovative Multi-Layer Gamification Framework for Improved STEM Learning Experience

Zhao

Playfoot²,

Nicola³

et al. 2022

IEEE Access

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“…This involves understanding and analyzing real-world educational data to provide useful support for improving learning and teaching. Tran et al [48] used LA for a learning management system (LMS). The experimental results showed that LA plays an important role in improving productivity, learning, and support for LMS user.…”

Section: Educational Data Mining and Learning Analyticsmentioning

confidence: 99%

Impact of Practical Skills on Academic Performance: A Data-Driven Analysis

Rahman¹,

Watanobe

Kiran

et al. 2021

IEEE Access

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Most academic courses in information and communication technology (ICT) or engineering disciplines are designed to improve practical skills; however, practical skills and theoretical knowledge are equally important to achieve high academic performance. This research aims to explore how practical skills are influential in improving students' academic performance by collecting real-world data from a computer programming course in the ICT discipline. Today, computer programming has become an indispensable skill for its wide range of applications and significance across the world. In this paper, a novel framework to extract hidden features and related association rules using a real-world dataset is proposed. An unsupervised k-means clustering algorithm is applied for data clustering, and then the frequent pattern-growth algorithm is used for association rule mining. We leverage students' programming logs and academic scores as an experimental dataset. The programming logs are collected from an online judge (OJ) system, as OJs play a key role in conducting programming practices, competitions, assignments, and tests. To explore the correlation between practical (e.g., programming, logical implementations, etc.) skills and overall academic performance, the statistical features of students are analyzed and the related results are presented. A number of useful recommendations are provided for students in each cluster based on the identified hidden features. In addition, the analytical results of this paper can help teachers prepare effective lesson plans, evaluate programs with special arrangements, and identify the academic weaknesses of students. Moreover, a prototype of the proposed approach and data-driven analytical results can be applied to other practical courses in ICT or engineering disciplines.

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“…Extended learning systems [7] was built with four different zones. Learner's behavior was assessed .Integrated curriculum design was focused.…”

Section: Literature Surveymentioning

confidence: 99%

“…Different types of learner modeling [2,6] could be estimated through the proposed parametric and outcome based models. Self regulation parameters [3,7] could be correlated with the proposed parameters and feature extraction could be done. Emotional modeling [4,8] could be enhanced with the proposed parametric modeling.…”

Section: Experimental Investigationmentioning

confidence: 99%

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A Deep Learning Model to predict the Industry Readiness of Engineering Students Community

Murugesan¹,

Ramasamy

Ashok³

et al. 2021

ITM Web Conf.

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Industry readiness of Engineering students community is a big challenge in the recent campus recruitments. 21st century skills are completely mapped with the technical and non – technical knowledge background of the engineering graduates. In this paper the work narrated the process of identifying the parameters for skill assessment of the candidates and derived a learner model using deep learning framework. Further the model can be used to predict the employability readiness of candidates.

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Enhancing Learners’ Experience Through Extending Learning Systems

Cited by 24 publications

References 15 publications

An Innovative Multi-Layer Gamification Framework for Improved STEM Learning Experience

An Innovative Multi-Layer Gamification Framework for Improved STEM Learning Experience

Impact of Practical Skills on Academic Performance: A Data-Driven Analysis

A Deep Learning Model to predict the Industry Readiness of Engineering Students Community

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