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Dan Sun; Chee-Kit Looi; Yan Li; Chengcong Zhu; Caifeng Zhu; Miaoting Cheng – Educational Technology Research and Development, 2024
In the current era where computational literacy holds significant relevance, a growing number of schools across the globe have placed emphasis on K-12 programming education. This field of education primarily comprises two distinct modalities--the block-based programming modality (BPM) and the text-based programming modality (TPM). Previous…
Descriptors: Programming, Student Behavior, Thinking Skills, Computation
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Dan Sun; Chengcong Zhu; Fan Xu; Yan Li; Fan Ouyang; Miaoting Cheng – Journal of Educational Computing Research, 2024
Although previous research has provided some insights into the effects of block-based and text-based programming modalities, there is a dearth of a detailed, multi-dimensional analysis of the transition process from different introductory programming modalities to professional programming learning. This study employed a quasi-experimental design…
Descriptors: Programming, Secondary School Students, Computation, Thinking Skills
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Yu Lei; Xin Fu; Jingjie Zhao; Baolin Yi – Education and Information Technologies, 2025
Grouping students according to their abilities and promoting deeper interaction and moderation are key issues in improving computational thinking in collaborative programming. However, the distribution characteristics and evolving pathways of computational thinking in different groups have not been deeply explored. During the course of a…
Descriptors: Ability Grouping, Computation, Programming, Cooperative Learning
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Kristina Litherland; Anders Kluge – Computer Science Education, 2024
Background and Context: We explore the potential for understanding the processes involved in students' programming based on studying their behaviour and dialogue with each other and "conversations" with their programs. Objective: Our aim is to explore how a perspective of inquiry can be used as a point of departure for insights into how…
Descriptors: Programming, Programming Languages, Secondary School Students, Computer Science Education
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Chih-Yueh Chou; Wei-Han Chen – Educational Technology & Society, 2025
Studies have shown that students have different help-seeking behavior patterns and tendencies and furthermore, that students with certain help-seeking behavior patterns and tendencies may have poor performance (i.e., at-risk students). This study applied an educational data mining approach, including clustering and classification, to analyze…
Descriptors: Student Behavior, Help Seeking, Problem Solving, Information Retrieval
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Shao-Heng Ko; Kristin Stephens-Martinez – ACM Transactions on Computing Education, 2025
Background: Academic help-seeking benefits students' achievement, but existing literature either studies important factors in students' selection of all help resources via self-reported surveys or studies their help-seeking behavior in one or two separate help resources via actual help-seeking records. Little is known about whether computing…
Descriptors: Computer Science Education, College Students, Help Seeking, Student Behavior
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Seongyune Choi; Hyeoncheol Kim – Education and Information Technologies, 2025
Attention to programming education from K-12 to higher education has been growing with the aim of fostering students' programming ability. This ability involves employing appropriate algorithms and computer codes to solve problems and can be enhanced through practical learning. However, in a formal educational setting, it is challenging to provide…
Descriptors: Foreign Countries, High School Freshmen, Programming, Artificial Intelligence
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Melina Verger; Chunyang Fan; Sébastien Lallé; François Bouchet; Vanda Luengo – Journal of Educational Data Mining, 2024
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term…
Descriptors: Algorithms, Prediction, Bias, Classification
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Yin-Rong Zhang; Zhong-Mei Han; Tao He; Chang-Qin Huang; Fan Jiang; Gang Yang; Xue-Mei Wu – Journal of Computer Assisted Learning, 2025
Background: Collaborative programming is important and challenging for K12 students. Scaffolding is a vital method to support students' collaborative programming learning. However, conventional scaffolding that does not fade may lead students to become overly dependent, resulting in unsatisfactory programming performance. Objectives: This study…
Descriptors: Middle School Students, Grade 8, Scaffolding (Teaching Technique), Programming
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Abdullahi Yusuf; Norah Md Noor; Shamsudeen Bello – Education and Information Technologies, 2024
Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In…
Descriptors: Learning Analytics, Student Behavior, Video Technology, Classification
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Qian Fu; Wenjing Tang; Yafeng Zheng; Haotian Ma; Tianlong Zhong – Interactive Learning Environments, 2024
In this study, a predictive model is constructed to analyze learners' performance in programming tasks using data of programming behavioral events and behavioral sequences. First, this study identifies behavioral events from log data and applies lag sequence analysis to extract behavioral sequences that reflect learners' programming strategies.…
Descriptors: Predictor Variables, Psychological Patterns, Programming, Self Management
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Denis Zhidkikh; Ville Heilala; Charlotte Van Petegem; Peter Dawyndt; Miitta Jarvinen; Sami Viitanen; Bram De Wever; Bart Mesuere; Vesa Lappalainen; Lauri Kettunen; Raija Hämäläinen – Journal of Learning Analytics, 2024
Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first…
Descriptors: Learning Analytics, Prediction, School Holding Power, Academic Achievement
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Arjan J. F. Kok; Lex Bijlsma; Cornelis Huizing; Ruurd Kuiper; Harrie Passier – Informatics in Education, 2024
This paper presents the first experiences of the use of an online open-source repository with programming exercises. The repository is independent of any specific teaching approach. Students can search for and select an exercise that trains the programming concepts that they want to train and that only uses the programming concepts they already…
Descriptors: Programming Languages, Computer Science Education, Open Source Technology, Teaching Methods
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Anael Kuperwajs Cohen; Alannah Oleson; Amy J. Ko – ACM Transactions on Computing Education, 2024
Collaboration is an important aspect of computing. In a classroom setting, working with others can increase a student's motivation to attempt more challenges, reduce the difficulty of complicated concepts, and bring about greater overall success. Despite extensive research in other domains, there has been minimal exploration within computing on…
Descriptors: College Students, Help Seeking, Student Behavior, Programming
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I-Fan Liu; Hui-Chun Hung; Che-Tien Liang – Interactive Learning Environments, 2024
With the rise of big data, artificial intelligence, and other emerging information technologies, an increasing number of students without computer science (CS) backgrounds have begun to learn programming. Programming is considered a complex task for beginners, and instructors find it difficult to quickly address all the problems that students…
Descriptors: Programming, Student Attitudes, Blended Learning, Video Technology