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From Blocks to Text: Bridging programming misconceptions

JOURNAL OF EDUCATIONAL COMPUTING RESEARCH 2024年第5期62卷 1302-1326页

作者： Mladenovic, Monika Zanko, Zana Zaharija, Goran Univ Split Fac Sci Split Croatia Elementary Sch Mejasi Split Croatia Univ Split Fac Sci Dept Comp Sci Rudera Boskovica 33 Split 21000 Croatia

The use of a pedagogical approach mediated transfer with the bridging method has been successful in facilitating the transitions from block-based to text-based programming languages. Nevertheless, there is a lack of research addressing the impact of this transfer on programming misconceptions during the transition. The way programming concepts are taught to K-12 learners can later result in misconceptions for adult learners. The main objective was to examine the impact of mediated transfer using the bridging method pedagogical approach on the prevalence of programming misconceptions. We conducted a quasi-experimental study in school settings during informatics (computer science) classes among 163 sixth-grade students. The control group received traditional programming lectures using the text-based programming language, Python. Conversely, the experimental group utilized a mediated transfer pedagogical approach by starting with the block-based programming language MakeCode for micro:bit before transitioning to the text-based Python. Our findings indicate that the experimental group significantly reduced programming misconceptions in fundamental programming concepts: variables, sequencing, selection, and loops - compared to the control group. This suggests that the use of block-based programming language as an initial step in programming education, followed by a structured transition to text-based programming language, can effectively mitigate common misconceptions among K-12 learners.

关键词： programming misconceptions programming novices Python MakeCode for micro:bit K-12

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Prevalence of programming misconceptions in Primary School Students 24

Prevalence of Programming Misconceptions in Primary School S...

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24th Koli Calling

作者： Hartmann, Marco Hielscher, Michael Marinus, Eva Schwyz Univ Teacher Educ Goldau Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798400710384

This study investigates the prevalence of programming misconceptions among primary school students using the programming Misconception Assessment Tool (ProMAT). The ProMAT was designed to measure programming misconceptions in two educational programming environments: Scratch and xLogo. We analyzed data from 366 Grade 5 and 6 children in German-speaking Switzerland to identify common misconceptions about sequences, loops, conditionals and to find out if they believed that there is a hidden mind in the programming environment that has intelligent interpretive powers (the so-called superbug misconception). In addition, we compared response patterns across the two programming environments. We found two misconceptions related to loops to be most common in Scratch, namely the belief that loops produce the exact same output in every iteration and that each command inside a loop is repeated separately. For xLogo, the most common misconception was from the sequences category, namely relating to the order of subprogram execution. Furthermore, variations of the superbug misconception were more prevalent among xLogo than among Scratch learners. We discuss how our results compare and add to the outcomes of earlier work, including the seminal study by Swidan and colleagues (2018). Finally, we explain how programming-environment-specific features might influence the formation or prevention of misconceptions in primary school students.

关键词： Computer Science Education K-12 programming misconceptions Assessment

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Towards Automated Interactive Tutoring - Focussing on misconceptions and Adaptive Level-Specific Feedback 23

Towards Automated Interactive Tutoring - Focussing on Miscon...

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5th European Conference on Software Engineering Education (ECSEE)

作者： Jell, Lea List, Corinna Kipp, Michael Augsburg Tech Univ Appl Sci Augsburg Germany

ISBN: (纸本)9781450399562

programming is an essential cross-disciplinary skill, yet teaching it effectively in large classes can be challenging due to the need for close feedback loops. Identifying and addressing common misconceptions is particularly important during the initial stages of learning to program. While automated interactive tutoring systems have the potential to offer personalized tutoring at scale, current systems tend to emphasize errors and predefined solutions rather than focusing on common misconceptions. In this study, we introduce a novel platform centered on addressing misconceptions in programming education. We describe methods for detecting misconceptions using Abstract Syntax Trees (AST) and providing tailored, level-specific feedback to emulate human-like tutoring. As an empirical basis for this project, we gathered data from various introductory programming courses. Additionally, we advocate for the establishment of a repository of common misconceptions, offering examples derived from both the literature and our own data. Investigating misconceptions can ultimately enhance the teaching strategies of both human educators and AI agents, such as GPT, in guiding learners effectively.

关键词： CS in higher education intelligent tutoring programming misconceptions

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