No abstract

Game-based learning (GBL) can allow learners to acquire and construct knowledge in a fun and focused learning atmosphere. A systematic literature review of 42 papers from 2010 to 2020 in this study showed that the current difficulties in implementing GBL in classrooms could be classified into the following categories: infrastructure, resources, theoretical guidance, teacher’s capabilities and acceptance of GBL. In order to solve the above problems, the study constructs a technology enhanced GBL model, from the four parts of learning objective, learning process, learning evaluation, and smart classroom. In addition, this study adopted the Delphi method, inviting a total of 29 scholars, experts, teachers and school managers to explore how to implement GBL in smart classrooms. Finally, the technology enhanced GBL model was validated and the utilization approaches were provided at the conclusion part.

Kurt Lewin proposed the field theory which stated that our behaviour was a result of both our personality and our environment. Based on this theory, it could be deduced that teacher's teaching behavior was a result of both teacher's personality and classroom environment. Considering the challenges of pedagogy transformation and the modest use of technologies in classroom, we hypothesize that designing and using technology-rich classroom (TRC) is one of the methods for changing the classroom from teacher-centered learning to more student-centered learning that encompasses replacing lectures with active learning, integrating self-paced learning programs and/or cooperative group situations, ultimately holding the student responsible for his own advances. In order to test our hypotheses, a TRC was designed according to the adapted SMATE model, and the differences of students' perceptions, learning and teaching behaviour in TRC and in multimedia classroom (MMC) were analyzed. SMATE model referred to the framework for equipping classroom, including showing content, managing facilitates, accessing technologies, tracking process, and enhancing learning. We conducted an experimental research in a primary school with 143 students from 4 classes. The experimental group comprised of two classes in a TRC environment. The environment was equipped with Wi-Fi, wireless display, dual screens, and site facilitators. Additionally, an iPad was made available for every student in the class. The other two classes were the control group and had a MMC environment, in which a computer and a projector were equipped. The experiment lasted for one full semester with 12 weeks. The results indicated that the scores of students' perceptions in TRC were significantly higher than scores in MMC, and students spend more time engaged in individual learning and collaborative learning in the TRC than in the MMC.

Interaction in the classroom plays the key role for cultivating students’ 21st century skills. Insufficient breadth of interaction, uneven interaction opportunities, and chaotic interaction existed in many classrooms. With the integration of technology into education, many smart classrooms were built, with one of the aims being to promote interaction. However, the differences of interaction behaviors and engagement in a smart class versus a traditional class could rarely be found in literature, especially with the same teacher lecturing in both classes. In this study, a quasi-experiment was conducted by one experienced English teacher lecturing in a smart classroom with students and a traditional classroom with students for one semester. Research data were obtained by coding the 8 class videos with the proposed “Classroom Interaction Analysis Framework” and the adapted engagement questionnaire, and the data were analyzed using SPSS 24. Results showed that there were no significant differences in either interpersonal interaction or human–technology interaction; however students experienced significantly more engagement in the smart classroom. The reasons were analyzed and interaction patterns in smart classroom were discussed. Finally, a smart classroom interaction model was proposed to promote classroom interaction by considering the interplay of pedagogy, space, and technology.

Students’ active learning behavior determines learning performance. In post-COVID-19 period, Online Merging Offline (OMO) method become a common way of university students’ learning. However, at present, there are few studies in active learning behavior in the OMO mode. Combined with learning satisfaction and Technology Acceptance Model (TAM), this paper proposes an Online Active Learning (OAL) Model to predict the influencing factors of college students’ active learning behavior and then analyzes the differences between OMO model and pure online model by multi-group analysis (MGA) based on the model. The designed questionnaire was distributed, and a total of 498 valid questionnaires were collected. Using SmartPLS to analyze partial least squares structural equation modeling (PLS-SEM) and MGA, it is found that: (1) there are differences in the influencing factors of active learning between OMO and pure online model; the moderating effect of learning complaint in OMO mode is not established, and social isolation and age does not affect active learning in OMO mode; (2) learning quality, perceived ease of use, expectation, perceived usefulness, and social isolation indirectly affect active learning through learning satisfaction in both OMO model and pure online model; (3) learning satisfaction is an important mediating variable affecting active learning; and (4) learning complaints will negatively regulate the relationship between learning satisfaction and active learning only in pure online model. According to these findings, the paper provides theoretical and practical implementation suggestions implications for OMO teaching and OAL to ensure the expected learning outcome.