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