Kai-Yi Chin

Department of Data Science, Soochow University, Taiwan (R.O.C.) // kychin.scholar@gmail.com

Tzu-Chi Kuo

Department of Data Science, Soochow University, Taiwan (R.O.C.) // kuotzuchi7@gmail.com

Kai-Hsiang Yang

Department of Mathematics and Information Education, National Taipei University of Education, Taiwan (R.O.C.) // khyang@mail.ntue.edu.tw

ABSTRACT: 

Many studies have integrated concept mapping mechanisms into Digital Game-Based Learning (DGBL), showing the potential for helping students memorize knowledge and grasp complex concepts during gaming. While DGBL approaches with concept maps have promising outcomes, their effectiveness depends on proper integration within instructional and game design. Consequently, this study proposed a DGBL system incorporating distinct mapping mechanisms to assess their efficacy in enhancing financial literacy education. A trial experiment was conducted to investigate the impact of employing different concept mapping mechanisms on students’ learning outcomes, motivation, and self-efficacy. Firstly, the experimental results regarding learning outcomes revealed that the DGBL system with handwriting concept maps produced the most beneficial students’ learning outcomes than those a who used the other two methods. Secondly, the experimental results of self-efficacy demonstrated that the DGBL system with handwriting concept maps leads to more positive self-efficacy perceptions among students in comparison to other methods. Lastly, the experimental results of learning motivation showed that the DGBL system with handwriting concept maps outperformed the other groups. Our findings also suggested that while the DGBL system could indeed stimulate students’ curiosity and motivation, the approach of utilizing handwriting concept maps for learning facilitated a heightened focus and improved cognitive processing capabilities during the process of concept mapping. As a result, this approach fostered a more pronounced sense of the learning content’s significance for the students.

Keywords: 

Games, Post-secondary education, Mobile learning, Concept map  

Zehui Zhan

School of Information Technology in Education, South China Normal University, China // Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China // zhanzehui@m.scnu.edu.cn 

Xuanxuan Zou 

School of Information Technology in Education, South China Normal University, China // zxx200010@163.com

Guangwei Zhang

School of Information Technology in Education, South China Normal University, China // dreamgoway@qq.com

Qianyi Wu

Shenzhen Longhua Senior High School, China // 15521442392@163.com  

Jiajing Yao

Shenzhen Yantian Experimental Middle School, China // 350971224@qq.com

ABSTRACT: 

This study examined the effects of two performance assessment approaches on students’ learning achievement, attitude, computational thinking, and 4C key competencies in an introductory robotics course. An experimental study was conducted with 72 students in a middle school for 8 weeks. A performance assessment given at the end of the course was used as a reflection tool in one group (RPA), while given during the learning process as a guidance tool in another group (GPA). Results indicated that GPA showed a significantly better effect than RPA on students’ learning attitude and computational thinking, although the improvement in learning performance and 4C key competencies was not evident. Within the 4C sub-dimensions, GPA outperformed RPA in promoting students’ creativity and collaboration. However, the RPA group performed significantly better in improving critical thinking. The results confirmed that both GPA and RPA played an important role in robotics education. GPA provides a structured learning experience with clear goals and expectations, allowing students to effectively adjust their learning strategies through immediate feedback and a greater sense of control over their learning process with a positive attitude. In contrast, RPA encourages students to critically reflect on their learning journey from a holistic perspective, which increases student autonomy and deep learning during the process, while requiring a level of self-awareness and introspection that may be challenging for some students. The advantages and disadvantages of both approaches were discussed. The findings highlight the importance of adopting an appropriate approach to performance assessment and avoiding over-guidance in robotics courses.

Keywords: 

Performance assessment, Robotics course, Computational thinking, 4C key competencies  

Ting-Chia Hsu

Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taiwan // ckhsu@ntnu.edu.tw

Yi-Ting Lin

Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taiwan // i79432ytl@gmail.com

ABSTRACT: 

This study developed an artificial intelligence (AI) image recognition application integrated with a robot-based game-based learning (GBL) approach to enhance undergraduates’ understanding of computational thinking (CT) and AI concepts. The learning process was guided by experiential learning theory (ELT) or subject-based learning (SBL), combined with interactive gameplay. Sequential behavior pattern analysis and paired sample t-tests were conducted to evaluate the effects of ELT and SBL on learning outcomes, including improvements in learning achievement of CT and AI concepts, computer programming self-efficacy (CPSE), supervised machine learning self-efficacy (MLSE), and reductions in AI anxiety. Analysis of covariance (ANCOVA) and the Johnson-Neyman technique were further applied to compare differences in CPSE and MLSE between the ELT and SBL approaches. The research results show that both learning approaches can increase students’ CT and AI concepts in the robot GBL. The ELT is more suitable for students who initially have lower CT ability and lower self-efficacy. The stage of reflection, abstraction and operation process of ELT in implementing the AI application can enable students to generate discussion, cooperation, and direct manipulation behaviors. Those behavior patterns can enhance their CT and self-efficacy in particular for those who with low CPSE. Conversely, the SBL is more suitable for students who have advanced CT ability and high self-efficacy initially and this study revealed further discussion and suggestions for future studies.

Keywords: 

Computational thinking, Artificial intelligence anxiety, Game-based learning, Self-efficacy, Behavioral patterns

Chohui Lee

Ewha Womans University, South Korea // etdev97@ewha.ac.kr 

Hyo-Jeong So

Ewha Womans University, South Korea // hyojeongso@ewha.ac.kr

ABSTRACT: 

As STEM education evolves to meet the demands of the 21st century, fostering creativity and problem-solving skills has become a critical objective. This study examines how learners approach STEM problems requiring creative thinking with generative AI (GenAI). The research explores two key questions: (a) How do students engage in the creative problem solving (CPS) process using GenAI across social and scientific problem domains? (b) What differences exist between high- and low-performing students in using prompts during CPS with GenAI? The study involved 38 middle school students (aged 15) in Korea, who solved social and scientific problems from the PISA 2022 Creative Thinking Assessment using ChatGPT. The findings revealed distinct patterns in students’ use of GenAI, varying by problem domain and performance level. In the social problem domain, students primarily relied on ChatGPT’s suggestions, frequently using prompts to request fully developed solutions or elaboration on ChatGPT’s ideas. In contrast, in the scientific problem domain, students developed problem solving strategies around their ideas and integrated ChatGPT’s suggestions in a more balanced way. A detailed analysis of prompt strategies revealed prominent differences between high- and low-performing students. High-performing students used diverse prompt types and strategically combined them with their own insights. In contrast, low-performing students relied heavily on ChatGPT’s suggestions with minimal modification or originality. This study contributes to understanding how learners engage with GenAI for CPS across different domains and provides implications for designing educational strategies that equip learners to utilize GenAI effectively for CPS in STEM education.

Keywords: 

Creativity, Creative problem solving, STEM, Generative AI, PISA