Zhenzhen He

Bio: Zhenzhen He is an academic researcher from South China Normal University. The author has contributed to research in topics: Blended learning & Lesson plan. The author has co-authored 2 publications.

Developing eighth-grade students' computational thinking with critical reflection

Abstract: As computer science has become a vital power in facilitating the rapid and sustainable development of various fields, equipping everyone with computational thinking (CT) has been recognized as one of the core pillars supporting the sustainable development of individuals and our digital world. However, it remains challenging for secondary school students to assimilate CT. Recently, critical reflection has been proposed as a useful metacognitive strategy for regulating students’ thinking to solve current and future problems. In this study, a quasi-experiment was conducted to investigate the role of critical reflection in advancing eighth-grade students’ CT. The participants were 95 eighth-grade students, comprising an experimental group (n = 49) and a control group (n = 46). The students’ CT was evaluated based on their learning performance in computational concepts, computational practices, and computational perspectives. The results showed that critical reflection, compared with traditional instruction from teachers, could significantly advance eighth-grade students’ CT. Interestingly, the two groups showed significantly different learning performance in computational practices during the learning process. Furthermore, interaction with peers and instructors played an essential role in helping students engage as active agents in critical reflection. The results of this study emphasize the need to develop students’ CT by practicing critical reflection in eighth-grade education.

Applying an Intelligent Learning Partner in Teacher Education for Improving CT-Related TPACK

Abstract: Developing effective learning strategies and tools to improve teachers’ computational thinking-related teaching ability is becoming an increasingly important issue in the digital age. In this study, an intelligent learning partner was designed and developed under the guidance of the framework of technological pedagogical content knowledge and peer-assisted learning strategy. Moreover, a quasi-experiment has been conducted in a blended learning community to evaluate the effect of the intelligent learning partner on improving teachers’ computational thinking-related technological pedagogical content knowledge. The participants were 32 pre-service teachers, comprising an experimental group (n = 16) and a control group (n = 16). The experimental results showed that the intelligent learning partner enabled the teachers to apply more knowledge of computational thinking-related technological pedagogical content knowledge into their lesson plans. Besides, it was found that the intelligent learning partner not only facilitated the teachers to think about students’ learning process, but also helped the teachers recognize the advantages of specific technology and pedagogy. Besides, participants’ feedback on improving the design of intelligent learning partners indicated that emotional interaction and explanations were needed.

Exploring the Development of Student Teachers’ Knowledge Construction in Peer Assessment: A Quantitative Ethnography

Abstract: Peer assessment (PA) is a formative assessment tool that can effectively monitor the development process of knowledge construction. In comment-based PA, comments contain the evidence of how the assessors construct knowledge to conduct professional assessments, which initiates a research perspective to explore the dynamic knowledge construction of the assessors. Quantitative ethnography is both a method for the quantitative analysis of qualitative data and a technique for the network modelling of professional competencies, providing a new way of thinking about the analysis and evaluation of knowledge construction processes. In this paper, quantitative ethnography was used to mine the comments generated from comment-based PA activities to reveal the characteristics of student teachers’ knowledge construction and the developmental trajectories of knowledge structure at different learning stages. The experimental results show that the student teachers’ knowledge structures and knowledge levels evolve in the PA environment, and the cognitive network gradually tends to become more complex and balanced. The student teachers showed stage and gender differences in the level of knowledge progression during the learning process. The second PA was a turning point in knowledge progression. The knowledge structures of the male and female groups are biased towards different kinds of knowledge elements.

Abstractive-Based Programming Approach to Computational Thinking: Discover, Extract, Create, and Assemble

Abstract: Most studies suggest that students develop computational thinking (CT) through learning programming. However, when the target of CT is decoupled from programming, emerging evidence challenges the assertion of CT transferability from programming. In this study, CT was operationalized in everyday problem-solving contexts in a learning experiment (n = 59) that investigated whether learning programming enhances students’ CT skills. Specifically, this study examined the influence of a novel, systematic and micro instructional strategy that is grounded in abstraction and comprised of four independent but related processes – discover, extract, create, and assemble (DECA) towards simplification of problem-solving. Subsidiary questions explored the effects of students’ age, gender, computer proficiency, and prior programming experience on the development of CT. No significant difference was found between the CT skill and programming knowledge of the groups at the posttest. However, within-group paired t-tests showed that the experimental group that integrated DECA had significant improvement in CT but not in the control group across the pretest-posttest axis. Implications of the inconclusive finding about the transfer of programming skills to CT are emphasized and the arguments for disentangling CT from programming are highlighted.

Effects of the Fundamental Concepts of Computational Thinking on Students’ Anxiety and Motivation toward K-12 English Writing

Abstract: Computational thinking (CT) skills are now a key part of everyday life and work, and CT has been incorporated into K-12 curricula worldwide. Combining the fundamental concepts of CT with English writing constitutes an innovative and sustainable learning strategy. However, few academic studies have examined the incorporation of CT into English writing. English writing frequently generates excessive stress and anxiety among students, yet motivation can mitigate the negative effect of anxiety. This study investigated the effects of the fundamental concepts of CT on reducing writing anxiety and increasing motivation toward English writing. A quasi-experimental design was applied, and data were collected from experimental and control groups through writing anxiety and motivation questionnaires. The results indicated that the fundamental concepts of CT exerted a more significant influence on the dimensions of writing anxiety and motivation than did a conventional learning method. In conclusion, the fundamental concepts of CT promoted organized and structured English writing, increased students’ writing motivation, and reduced their writing anxiety.