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Many studies have confirmed that digital game-based learning can effectively improve students' learning motivation and that properly integrating learning strategies into digital games can effectively improve students' learning achievement. However, rare research has been considered the long-term effect of learning retention. In the current study, a game-based learning system that integrated a "Prediction-Observation-Explanation" learning strategy was developed to provide learners with appropriate guidance and feedback during the learning process to improve the learning retention. A quasi-experiment was conducted in an elementary school mathematics course to evaluate the effectiveness of the method. The experimental result shows that the proposed approach can significantly improve students' learning achievement and learning retention. According to the analysis of students' behavioral pattern, the proposed approach can help students focus on the learning material of the game. Therefore, the proposed approach is a potential and effective way to help students with better long-term learning performance.

Artificial intelligence (AI) in higher education has proven to be a useful learning technology; it can help learners achieve positive learning outcomes in the learning environment and can also enable teachers to better understand learners' learning status and further improve their teaching strategies. This study reviewed the top 50 AI in higher education studies in the Web of Science database from the perspective of highly cited papers and based on a technology-based learning model. The results show that predictions of learners' learning status (including dropout and retention, student models, and academic achievement) are most frequently discussed in the AI in higher education studies; AI technology is most commonly applied in engineering (including computer courses); AI technologies most often play the role of profiling and prediction in higher education, followed by intelligent tutoring systems and assessment and evaluation. In terms of research issues, the most frequently discussed issues are learning behaviour, accuracy, sensitivity and precision, cognition and affect. Learners' higher order thinking skills, collaboration or communication, self-efficacy or confidence and skills are less frequently discussed in AI in higher education studies. Accordingly, we propose potential research issues and practitioner notes for AI in higher education as a reference for researchers, educators and decision-makers.

In digital microfluidics, actuation voltage is critical in droplet manipulation using electrowetting-on-dielectric. Compared to parallel electrode devices, the droplet manipulation on coplanar electrodes requires a higher actuation voltage due to its structure. This study derives an equation related to the threshold actuation voltage and many critical parameters (applied frequency, surface tension, fluid conductivity, droplet volume, dielectric thickness, etc.). Our developed model has three innovations: (1) liquid and device parameters related to the threshold actuation voltage are integrated into the model, (2) the absolute value of the complex permittivity of the fluid is added into our model to clarify the conductivity-dependent and frequency-dependent threshold actuation voltage, and (3) the extrand model is used to derive the effects of the surface tension, contact angles, and droplet radius on the threshold actuation voltage. Additionally, we used many solutions for biochemical and biomedical assays—DI (Deionized) water, DMEM (Dulbecco's Modified Eagle Medium), PBS (phosphate buffered saline), glycerol–water solutions, ethanol–water solutions, potassium chloride solutions, and sodium chloride solutions—to verify the developed Vth in our model. By using the model and conducting an experiment, we investigated the Vth of different liquids on coplanar electrodes. Our model can successfully explain the difference of Vth between these solutions on coplanar electrode devices. The model and experimental data can be utilized to predict the actuation voltage for a broad range of buffers and organic solvents on coplanar electrodes. [ABSTRACT FROM AUTHOR]

It is widely accepted that the digital game-based learning approach has the advantage of stimulating students' learning motivation, but simply using digital games in the classroom does not guarantee satisfactory learning achievement, especially in the case of the absence of a teacher. Integrating appropriate learning strategies into a game can better enhance the learning performance. Therefore, in this study, a mastery theory-based digital game with different feedback models was developed to compare the differences in the learning behavior of students using the two feedback models. Lag sequential analysis was then applied to identify the sequential behaviors that are statistically proven to have impact. The results of the experiments and behavior analysis show that, with proper design of the game, students in both feedback methods can achieve the same learning performance as that in the conventional learning method with a teacher involved. Moreover, students in the Regular Feedback Group reviewed the learning material more times than those in the Corrective Feedback Group, which seemed to mitigate the drawbacks of the regular feedback. This result suggests that a proper game design will be able to achieve effective learning and is robust in terms of feedback models.

Peer-assessment and video comment-sharing are effective learning strategies for students to receive feedback on their learning. Researchers have emphasized the need for well-designed peer involvement in order to improve students' abilities in the cognitive and affective domains. Although student perceptions of peer-assessment have been studied extensively in higher education, few studies have focused on the effects of the peer-assessment strategy on students' performance from the affective perspective, especially in Physical Education courses. Therefore, in this study, a peer-assessment mobile physical education approach is proposed for developing a mobile learning system for a Kung Fu Tai-Chi physical education course. To evaluate the effectiveness of the proposed approach, an experiment was conducted by assigning 42 college students to participate in this learning activity. The experimental results show that the proposed approach not only promoted the students' learning interest and motivation, but also improved their learning self-efficacy and socialization.

Concept maps have been recognized as an effective tool for students to organize their knowledge; however, in history courses, it is important for students to learn and organize historical events according to the time of their occurrence. Therefore, in this study, a time sequence-oriented concept map approach is proposed for developing a game-based environment to facilitate students' learning of historical events and their organization during the gaming process. With this approach, students can easily learn the precedence relationships among the historical events that occurred in different time periods with the time sequence-oriented concept map. To evaluate the effectiveness of the proposed approach, a historical role-playing game has been developed for an elementary school history course to examine the students' performance in terms of learning motivation, self-efficacy and learning achievements. A subject unit, the "Siege of Fort Zeelandia by Zheng Cheng-Gong," was chosen as the history topic. The results show that the proposed approach can significantly enhance the students' learning achievement, but did not affect their learning motivation or self-efficacy for the history course. As a consequence, it is concluded that students can benefit from concept maps in terms of enhancing their learning achievement, but they do not necessarily enjoy using concept maps in game-based learning activities.