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The difficulty of designing intrinsically integrated game-based learning systems has led to alternative design strategies based on extrinsic integration. This study extends prior work on extrinsic integration design by examining the effectiveness of alternative reward structures in integrating learning and game. Specifically, a performance-contingent reward is proposed as a new integration mechanism and its effects on learning, motivation, engagement, and system perception are assessed, vis-à-vis a completion-contingent reward. A group of university students (N = 64) were involved in an empirical experiment designed to determine the effectiveness of the new reward structure in the context of English vocabulary learning and arrow-shooting gaming. The results from the experiment show that the proposed reward structure produces a statistically significant increase in the level of learning, motivation, and engagement. The results are highly encouraging for game-based learning research as the proposed approach is easily extendable, with design implications that are widely applicable. • We examine the effectiveness of alternative reward structures in integrating learning and game for game-based learning. • Performance-contingent rewards produce higher levels of learning and motivation relative to completion-contingent rewards. • Our proposed integration strategy is easily extendable to many other learning domains. [ABSTRACT FROM AUTHOR]

Computational thinking (CT), a way to think about solving problems, is recognized as a basic skill for the 21st century. Although game-based learning (GBL) has been shown to improve student's CT skills, students may fail to self-evaluate or debate as they play games, thus failing to develop higher-order thinking. Also not widely explored is how teachers are to practice the CT core competencies during the learning process to cultivate students' problem-solving thinking. As the adoption of student-generated questions (SGQ) is an instructional strategy to promote higher-order thinking, we develop a GBL platform that uses this strategy to enhance students' CT skills, learning motivation, and learning confidence. For this purpose, fifty-three primary school students were recruited for this study, and data were collected during four classes of experimental CT activities. This study shows that the experimental group using the SGQ strategy with a GBL platform exhibited significantly higher CT skills than the control group. In addition, with respect to learning motivation and learning confidence it is found that the experimental group also outperformed the control group. This confirms that integrating the SGQ strategy and CT core competencies fosters higher-order cognitive thinking in students in problem-solving through gamification of the learning environment, resulting in higher motivation and confidence to complete experimental activities. • This study used student-generated questions (SGQ) in game-based learning platform. • This study integrated five core competencies of computational thinking into SGQ. • This result indicated that SGQ enhanced student's computational thinking skills. • It was studied that SGQ could improve student's learning motivation and confidence. • This study confirmed the contribution of SGQ to computational thinking skills. [ABSTRACT FROM AUTHOR]

Digital game-based math learning environments (math DGBLE) are promising platforms that provide students with opportunities to master conceptual understanding and cultivate mathematical thinking, on which the contemporary mathematics education places an emphasis. Literature on learning support in digital game-based learning (DGBL) rarely investigate learners' support-use behaviors and interaction patterns in relation to math learning. We addressed this research gap in this exploratory mixed-methods study. We designed and developed a packet of learning supports (i.e., Task Planner and Math Story) in a math DGBLE. Task Planner is designed to assist learners' systematic and planned efforts for math problem solving whereas Math Story features historical stories and real-life applications of math concepts. With the data collected via mixed-methods approach, we extracted six clusters of learning-support-use behaviors via unsupervised machine learning technique (i.e., Gaussian Mixture Model), including 1) skills development and application of mathematical problem decomposition, 2) conceptual knowledge development, 3) metacognitive mathematical connections, 4) metacognitive regulation, 5) information selection using cognitive aids, and 6) sustained motivation for necessary aversive practices. Qualitative multi-cases study revealed nuanced details regarding learners' interactions with the learning supports in DGBLE. Results showed that the designed in-game learning supports facilitated individual meaningful and mindful math problem-solving experiences. The findings suggested that a mixed-methods research design integrating machine learning with multi-cases study could act as a tool for learner behavior and interaction pattern research that informs the design of adaptive and effective DGBL. • A packet of two learning supports was designed for and integrated into digital game-based math learning. • Six clusters of students' learning support use for cognition, metacognition, and intrinsic motivation. • A mixed-methods approach integrating machine learning with multi-cases study is useful for learner behavior research. [ABSTRACT FROM AUTHOR]

The literature of games in education has a rich and multidisciplinary content. Due to the large number of studies in the field, it is not easy to analyze all relevant studies. There are few studies exploring the big picture of research trends in the field. For this reason, the purpose of this study is to examine longitudinal trends of game-based research in education using text mining techniques. 4980 publications were retrieved as an experimental dataset indexed by the SCOPUS database in the period 1968 to mid-2021. The results include descriptive statistics of game-based research, trends of the research topics, and trends in the frequency of each topic over time. They show that the number of studies focusing on the use of games in education has increased, particularly since the 2000s when Internet use accelerated and became widespread. Approximately 70% of all the studies were conducted in the last 10 years. One third of the studies is related to the main topic of game-based learning. It is significant that in the last three decades the topic of serious games has been among the top three trends. Considering usage acceleration of the topics, the highest values belong to game-based learning, serious games and student science games, in that order. The findings of this study are expected to guide the field by providing a better understanding of the trends of games in education and offer a direction for future research. • Increase in the number of game researches in the last two decades was explored. • Transition to Serious Educational Games and Game-based Learning Terms was seen. • Increase in the number of games developed in science and physical education was explored. • Insufficient number of robust and longitudinal researches was obtained. • The key purpose of the study is to reveal the trends and changes in game in education. [ABSTRACT FROM AUTHOR]

Research has shown that difficulty adaptation is a promising instructional technique in digital game-based learning. Nevertheless, although the strength and direction of difficulty adaptation can affect motivational outcomes, these effects remain insufficiently examined in game-based learning. This within-subject study examined how the strength and direction of difficulty adaptation affected motivational outcomes in digital game-based math learning. The participants were 167 Finnish fifth-graders who studied fractions with the Number Trace game. The game included 144 tasks, half were adapted according to participants' playing performance. Situational interest and perceived difficulty were measured several times with in-game self-report items during the intervention. The manipulation check confirmed that difficulty adaptation was implemented successfully as task correctness and perceived difficulty changed according to the strength and direction of adaptation. Regarding motivational outcomes, two-way repeated-measures ANOVAs showed that the difficulty adaptation increased situational interest, but only when the task difficulty was substantially adapted downwards. Contrary to our expectations, a substantial upwards adaptation of the task difficulty significantly decreased situational interest. Minor adaptation of difficulty did not affect situational interest. The current study contributes to the field of adaptive digital learning environments by highlighting the effects of the strength and direction of difficulty adaptation on motivational outcomes. Theoretical, practical, and methodological implications of the findings are discussed. • Repeated in-game measurements captured temporal changes of situational interest. • Difficulty adaptation affected perceived difficulty as expected. • The strength and direction of difficulty adaptation affected situational interest. • Only substantial adaptation of difficulty affected situational interest. • Changes in task difficulty led to inverse changes in situational interest. [ABSTRACT FROM AUTHOR]