Yangyang Li

Background: ChatGPT, an AI‐based chatbot, supports learning by accurately interpreting and responding to user inputs. Despite its potential, few empirical studies have examined its influence on college students' mathematical problem‐solving processes.Objectives: This study aimed to introduce a ChatGPT‐facilitated scaffolding to investigate its impact on students' mathematical problem‐solving behaviours, performance and perceptions.Methods: Twenty‐nine undergraduates participated in this study, engaging in mathematical problem‐solving tasks using the scaffolding. A mixed‐method approach was employed, incorporating performance data, interaction analysis and self‐reported surveys to assess both quantitative and qualitative aspects of students' experiences. In particular, lag sequential analysis was applied to explore the undergraduates' problem‐solving behavioural patterns.Results and Conclusions: Results demonstrated that the ChatGPT‐facilitated scaffolding significantly improved students' mathematical problem‐solving performance. The high‐performance group (HPG) exhibited a greater frequency of interpretive and evaluative activities, transitioning from factual to metacognitive representations, while the low‐performance group (LPG) primarily transitioned from prompt selection to procedural representations. Additionally, most participants expressed positive perceptions of the scaffolding experience and reported an improvement in their problem‐solving skills.Major Takeaways: These findings offer valuable insights for the design and implementation of AI‐facilitated learning activities in mathematical problem‐solving contexts.

Computational thinking (CT), an essential competency for comprehending and addressing intricate issues in the digital world, has been incorporated into curriculum planning as a goal for programming education. This study introduced flow design into programming curricula to investigate its impact on undergraduates ’CT skills during pair work. Two types of flow design approaches, construct-by-self flow design (CBS-FD) and construct-on-scaffold flow design (COS-FD), were proposed to determine which approach better enhances students’ CT skills. Seventy-six first-year undergraduates participated, including thirty in the CBS group and thirty-six in the COS group. Evaluations made from the results and processes of programming tasks were employed to describe computational performance and computational practices, respectively. Data gathered from CT skill surveys were thoroughly analyzed to gain a deeper understanding of computational perspectives. Our findings highlighted that COS-FD significantly improved participants' computational performance compared with CBS-FD. The COS groups fostered an engaging, sharing atmosphere, while CBS groups spent more energy on negotiating the manipulation of flow design. Moreover, both COS-FD and CBS-FD proved beneficial in enhancing participants' computational perspectives, with the COS groups better improving their algorithm thinking. The study presents valuable perspectives on the design and implementation of collaborative programming activities within curriculum education.

Purpose: The learning subjective well-being of high school students has significant value for their academic achievement and future life development. A growth mindset is one of the key factors affecting the learning subjective well-being of high school students. However, research on the mechanism by which a growth mindset affects learning subjective well-being is still relatively limited. Therefore, the study aims to investigate the impact of a growth mindset on the learning subjective well-being of high school students, as well as the role that achievement motivation and grit play as serial mediators in this relationship.Methods: This study employed a convenience sampling method to select 708 high school students from Chinese public high schools as participants. The research utilized the Growth Mindset Scale, Achievement Motivation Scale, Grit Scale, and the Learning Subjective Well-being Questionnaire for High School Students to collect data. All data were analyzed using SPSS 26.0, employing Model 6 from Hayes’ SPSS PROCESS macro to test the serial mediation model.Results: Our results found that (1) high school students’ growth mindset positively predicted their learning subjective well-being. (2) Achievement motivation played a mediating role between a growth mindset and learning subjective well-being among high school students. (3) Grit acted as a mediator between learning subjective well-being and growth mindset among high school students. (4) Achievement motivation and grit served as serial mediators between a growth mindset and learning subjective well-being among high school students.Conclusion: A growth mindset can influence the learning subjective well-being of high school students through achievement motivation and grit. Educators can enhance the learning subjective well-being of high school students by implementing intervention strategies that foster a growth mindset, achievement motivation, and grit.

A deep understanding of the learning level of online learners is a critical factor in promoting the success of online learning. Using knowledge structures as a way to understand learning can help analyze online students’ learning levels. The study used concept maps and clustering analysis to investigate online learners’ knowledge structures in a flipped classroom’s online learning environment. Concept maps (n = 359) constructed by 36 students during one semester (11 weeks) through the online learning platform were collected as analysis objects of learners’ knowledge structures. Clustering analysis was used to identify online learners’ knowledge structure patterns and learner types, and a non-parametric test was used to analyze the differences in learning achievement among learner types. The results showed that (1) there were three online learners’ knowledge structure patterns of increasing complexity, namely, spoke, small-network, and large-network patterns. Moreover, online learners with novice status mostly had spoke patterns in the context of flipped classrooms’ online learning. (2) Two types of online learners were found to have different distributions of knowledge structure patterns, and the complex knowledge structure type of learners exhibited better learning achievement. The study explored a new way for educators to analyze knowledge structures by data mining automatically. The findings provide evidence in the online learning context for the relationship between complex knowledge structures and better learning achievement while suggesting the existence of inadequate knowledge preparedness for flipped classroom learners without a special instructional design.