A programming learning system for beginners-a completion strategy approach

Chang, Kuo–En; Chiao, Bea-Chu; Chen, Sei-Wang; Hsiao, Rong-Shue

doi:10.1109/13.848075

Cited by 34 publications

(1 citation statement)

References 8 publications

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“…Thus, the student's programming solution can then be one of four states: (1) syntactically correct and semantically correct, (2) syntactically correct and semantically incorrect, (3) syntactically incorrect and semantically correct, and (4) syntactically incorrect and semantically incorrect [19]. Many programming instruction systems tend to detect syntactic and semantic errors in the programs submitted by students and indicate where and why the errors occur in the programs [11]. One study developed a Web‐based Java Programming Learning Assistant System (JPLAS) that provides novice students with the element fill‐in‐blank problem (EFP), where students learn Java syntax and basic programming skills by filling in the missing elements in the source code [25].…”

Section: Related Workmentioning

confidence: 99%

Exploring the differences and evolution of college students' computational thinking in programming learning through data analysis

Zhang,

Zeng,

et al. 2023

Comp Applic In Engineering

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Programming has received extensive attention for the embodiment of computational thinking (CT) skills. However, the lack of in‐depth research on the characteristics of college students' CT skills in text‐based programming learning has resulted in the unknown distribution characteristics and evolution patterns of their CT skills, which cannot effectively constitute an effective tracking analysis of their CT skills, thus affecting the cultivation of creative thinking and problem‐solving skills for them. Therefore, the purpose of the paper is to explore the characteristics of college students' CT skills through text‐based programming work and discover the overall characteristics, individual differences and evolutionary patterns of their CT skills on the relation between text‐based programming and CT skills through data analysis. It was found that there are roughly five categories of CT difference and four major categories with nine subcategories of CT evolution among college students. The results showed that the Control and Data skills were the students' dominant skills. In contrast, Algorithm skills were relatively weak. Only about 1/3 of the students had the phenomenon of continuous skill improvement, and programming learning did not have a significant impact on the improvement of CT skills for some students. Finally, we propose several suggestions for CT skills development strategies in response to the students' characteristics in CT skills. The study can not only fill the gaps of the current study but also identify the evolutionary characteristics of students' CT skills, which can effectively intervene in college students' programming learning and help improve their CT skills.

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Section: Related Workmentioning

confidence: 99%

Exploring the differences and evolution of college students' computational thinking in programming learning through data analysis

Zhang,

Zeng,

et al. 2023

Comp Applic In Engineering

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An educational tool for visualizing students' program tracing processes

Chou

Sun

2010

Comp Applic In Engineering

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Making Sense of Knowledge Integration Maps

Schwendimann

2013

Digital Knowledge Maps in Education

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Digital knowledge maps are rich sources of information to track students' learning. However, making sense of concept maps has been found challenging. Abstract Digital knowledge maps are rich sources of information to track students' learning. However, making sense of concept maps has been found challenging.Using multiple quantitative and qualitative methods in combination allows triangulating of changes in students' understanding. This chapter introduces a novel form of concept map, called knowledge integration map (KIM), and uses KIMs as examples for an overview of concept map analysis methods. KIMs are a form of digital knowledge maps. KIMs have been implemented in high school science classrooms to facilitate and assess complex science topics, such as evolution. KIM analysis aims to triangulate changes in learners' conceptual understanding through a multilevel analysis strategy, combining quantitative and qualitative methodologies. Quantitative analysis included overall, selected, and weighted propositional analysis using a knowledge integration rubric and network analysis describing changes in network density and prominence of selected concepts. Research suggests that scoring only selected propositions can be more sensitive to measuring conceptual change because it focuses on key concepts of the map. Qualitative analysis of KIMs included topographical analysis methods to describe the overall geometric structure of the map and qualitative analysis of link types. This chapter suggests that a combination of quantitative and qualitative analysis methods can capture different aspects of KIMs and can provide a rich description of changes in students' understanding of complex topics.

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A programming learning system for beginners-a completion strategy approach

Cited by 34 publications

References 8 publications

Exploring the differences and evolution of college students' computational thinking in programming learning through data analysis

Exploring the differences and evolution of college students' computational thinking in programming learning through data analysis

An educational tool for visualizing students' program tracing processes

Making Sense of Knowledge Integration Maps

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