Search

In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand and to use as guidance, both for implementation and maturity measurement. This research proposes a simple model of SLE as a guideline that is easy to understand and to be used for developing learning environments that support effective, efficient, and engaging learning processes. The proposed model adopted the intelligent tutoring system architecture as a foundation. The components of the model were generated by mapping of 12 existing SLE models, frameworks, and best practices. Model formation and validation followed the system dynamics (SD) modeling process. In addition, indirect model validation process was also performed by interviewing educational technology practitioners and experts, based on assessment results of two newly developed SLE instances. For measuring maturity of existing SLE instances, this research also derived SLE maturity model. It is concluded that the simple model is usable for easy understanding, for establishing new SLE instances, and also for assessing the maturity level of existing SLE instances.

The increasing popularity of mobile devices as alternative tools for learning has led to the adoption of virtual laboratory simulation (VLab) to enhance practical learning experiences. This paper introduces an interactive mobile learning application designed to facilitate medical students in learning ear anatomy using a three-dimensional model. Furthermore, this study examines students' perceptions of VLab and its impact on the learning process, as evidenced by their performance. The research involved a total of 40 medical and engineering students who were enrolled in anatomy and physiology courses. A cross-over repeated measures design was conducted to compare students' experiences with both types of learning methods: conventional and VLab. To analyze students' perceptions of VLab, a five-point Likert scale comprising 22 question items was utilized based on the hedonic-motivation system adoption model, which includes seven variables: heightened enjoyment, behavioral intention to use, perceived usefulness, curiosity, control, perceived ease of use, and focused immersion (FI). Additionally, students' performance data were collected through the pre-test, mid-test, and post-test. The results of quantitative statistical analysis indicated that VLab generally fulfilled students' perceptions. Moreover, the findings revealed that the combination of conventional learning approaches and VLab positively enhanced students' learning performance, as demonstrated by improvements in their scores. [ABSTRACT FROM AUTHOR]

Computer-supported collaborative learning (CSCL) is a digital environment specifically designed for collaborative learning services. Computing metrics are used to measure the computational effectiveness of learning services at CSCL. User attributes, learning domains, group attributes, and learning service technologies are among the current computational metrics. Computing learning services' effectiveness can be evaluated using only one metric. Systemic learning service computing based on these four groups of metrics has received minimal discussion. The next problem is the lack of explanation of learning and service analysis methods for these metrics. There are difficulties in measuring and analyzing the effectiveness of computing learning services at CSCL. Research problem question “how is an effective a learning service computing model in CSCL”. Developing a learning service computing model in CSCL is the objective of this research. Service computing models and evaluation methods are included in the outputs to assess the effectiveness of learning services at CSCL. This research applies Design Research Methodology (DRM) and a model development strategy based on the dynamic system modeling process. This research applies a CSCL environment with the name “smart learning environment”. Model testing results demonstrate the effectiveness of computing learning services on CSCL. [ABSTRACT FROM AUTHOR]

Aim/Purpose: This research proposed a 3D multiuser virtual learning environment (3DMUVLE) educational game design framework by combining ATMSG, ADDIE, E-Simulation, and 3D Open Simulator Technology Architecture. This paper focused on a case study of online training for food crops productivity data surveys. Background: The conventional online training still lacks engagement, immersion, and curiosity aspects, which decreases learners' learning seriousness because the instructors and participants do not meet directly. Integration of 3DMUVLE and gamification in online training has a good potential to tackle the issue. Methodology: This research applied the Design Research Method (DRM) to propose a 3DMUVLE educational game design framework. The proposed framework was applied in training that involved 30 participants (first group), and the result was compared with that of 30 other participants (second group) who studied using the conventional method, which was an e-book and web-based learning. Authors compared the perceived usefulness and heightened enjoyment in using the proposed 3DMUVLE using linear regression analysis on HMSAM model. Contribution: Through statistical tests on the case study data, this research indicated that the 3DMUVLE resulted in better knowledge gain. Findings: Some important findings in this paper include (1) the development steps of a 3DMUVLE educational game design framework for online training of food crops productivity data survey; (2) statistical analysis result that the proposed 3DMUVLE lead to better knowledge gain, enjoyment, curiosity, immersion, and usefulness aspects; and (3) the statistical analytic also showed that enjoyment and perceived of usefulness factors represented the strongest variables that influenced behavioral intention to use. Recommendations for Practitioners: The 3DMUVLE is suggested to produce better knowledge gain, yet it still has to be proven further through similar statistical analysis in real field survey scenarios. Recommendation for Researchers: The proposed 3DMUVLE can be adapted to other domains. Pleasing features in the game can be improved, such as quality of instruction in the simulation, in the hope that these will increase engagement and knowledge gain. Voice communication among users and instructors to improve interactivity may also be introduced. Impact on Society: 3DMUVLE potentially offers better knowledge gain that can be applied in various fields of online training. Future Research: An immediate future research includes a development targeted for smartphone platform, in Virtual Reality (VR) or non-VR mode. VR improves immersion aspect further but is more complicated to perform. Smartphone is relatively cheaper than a computer and more accessible by more people. Training using a smartphone-based 3DMUVLE can be carried out in wider scenarios.

Feedback system is critical for educational game engagement. This system promotes educational game participation and motivation. The feedback system also helps players learn by expanding their knowledge. However, there is a scarcity of comprehensive research on feedback systems in educational games, making it difficult to develop a dependable feedback system capable of better achieving educational game objectives. Educational games often scatter feedback systems, making it challenging to identify which games utilize them. This study improves comprehension of educational game feedback systems while also filling a gap in the literature. This study conducts a comprehensive and systematic review of the educational game feedback system literature. A total of 36 relevant journal articles spanning from 2019 to 2023 were reviewed. This study investigates trends in educational game feedback systems, including the technologies used to build them, the educational domains studied, the products developed, feedback system design methods, and various feedback and algorithm implementations. This study also discusses selecting feedback components for player feedback and how feedback systems in educational games impact players. This study greatly contributes to the progress of learning conducted through educational games. [ABSTRACT FROM AUTHOR]