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Showing 1 to 15 of 39 results Save | Export
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Zachary M. Savelson; Kasia Muldner – Computer Science Education, 2024
Background and Context: Productive failure (PF) is a learning paradigm that flips the order of instruction: students work on a problem, then receive a lesson. PF increases learning, but less is known about student emotions and collaboration during PF, particularly in a computer science context. Objective: To provide insight on students' emotions…
Descriptors: Student Attitudes, Psychological Patterns, Fear, Failure
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Almabrok Musa Almdahem – International Journal of Computer Science Education in Schools, 2024
A national curriculum for the study of computing became compulsory in English secondary schools in September 2014, replacing the study of information and communications technology with computer science (CS). This posed difficulties for teachers and students who did not have knowledge or experience of programming. This study was designed to…
Descriptors: Secondary School Students, Computer Science Education, Programming, Student Attitudes
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Rahel Schmid; Robbert Smit; Nicolas Robin; Alexander Strahl – British Journal of Educational Psychology, 2025
Background: Students make many errors in visual programming. In order to learn from these, it is important that students regulate their emotions and view errors as learning opportunities. Aims: This study aimed to explore to what extent momentary emotions, specifically enjoyment, anxiety and boredom, as well as the error learning orientation of…
Descriptors: Psychological Patterns, Emotional Response, Learning Processes, Error Patterns
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Jinbo Tan; Lei Wu; Shanshan Ma – British Journal of Educational Technology, 2024
The purpose of this study was to investigate the collaborative dialogue patterns of pair programming and their impact on programming self-efficacy and coding performance for both slow- and fast-paced students. Forty-six postgraduate students participated in the study. The students were asked to solve programming problems in pairs; those pairs'…
Descriptors: Coding, Programming, Computer Science Education, Self Efficacy
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Chih-Ming Chen; Ming-Yan Huang – International Journal of STEM Education, 2024
Background: Computational thinking (CT) is crucial to fostering critical thinking and problem-solving skills. Many elementary schools have been cultivating students' CT through block-based programming languages such as Scratch using traditional teacher-centered teaching methods. However, the approach excessively relies on teacher lectures, so the…
Descriptors: Computation, Thinking Skills, Programming, Learning Processes
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David Roldan-Alvarez; Francisco J. Mesa – IEEE Transactions on Education, 2024
Artificial intelligence (AI) in programming teaching is something that still has to be explored, since in this area assessment tools that allow grading the students work are the most common ones, but there are not many tools aimed toward providing feedback to the students in the process of creating their program. In this work a small sized…
Descriptors: Intelligent Tutoring Systems, Grading, Artificial Intelligence, Feedback (Response)
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Paola Iannone; Athina Thoma – International Journal of Mathematical Education in Science and Technology, 2024
Programming is becoming increasingly common in mathematics degrees as it is a desirable skill for new graduates. However, research shows that its use is mostly restricted to computational or modelling tasks. This paper reports a study on students' perceptions of and difficulties with Lean, an interactive theorem prover introduced as part of a…
Descriptors: Programming, Mathematics Instruction, Computer Science Education, Student Attitudes
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Amaya, Edna Johanna Chaparro; Restrepo-Calle, Felipe; Ramírez-Echeverry, Jhon J. – Journal of Information Technology Education: Research, 2023
Aim/Purpose: This article proposes a framework based on a sequential explanatory mixed-methods design in the learning analytics domain to enhance the models used to support the success of the learning process and the learner. The framework consists of three main phases: (1) quantitative data analysis; (2) qualitative data analysis; and (3)…
Descriptors: Learning Analytics, Guidelines, Student Attitudes, Learning Processes
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Imre Bende – Acta Didactica Napocensia, 2024
The continuous development of artificial intelligence-based tools makes their emergence inevitable in education as well as other fields of life. This article presents findings of a mixed method study aimed at investigating the current perceptions and potential applications of AI in Hungarian educational settings. Through interviews with high…
Descriptors: Readiness, Artificial Intelligence, Technology Uses in Education, Foreign Countries
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Menon, Pratibha – Information Systems Education Journal, 2022
The autonomy and flexibility that online learning contents provide students in a traditional face-to-face course require them to pick up newer strategies for regulating their learning process. This study focuses on identifying how students' self-reported traits of self-regulated learning may relate to the task value of the learning contents of an…
Descriptors: Learning Strategies, Programming, Computer Science Education, Metacognition
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Chou, Te-Lien; Tang, Kai-Yu; Tsai, Chin-Chung – Journal of Educational Computing Research, 2021
Programming learning has become an essential literacy for computer science (CS) and non-CS students in the digital age. Researchers have addressed that students' conceptions of learning influence their approaches to learning, and thus impact their learning outcomes. Therefore, we aimed to uncover students' conceptions of programming learning…
Descriptors: Foreign Countries, College Students, Student Attitudes, Computer Attitudes
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Li, Jiansheng; Lin, Yuyu; Sun, Mingzhu; Shadiev, Rustam – Interactive Learning Environments, 2023
This study examined whether socially shared regulation of learning (SSRL) enhances students' algorithmic thinking performance, promotes learning participation and improves students' learning attitudes through game-based collaborative learning. The students learned algorithmic knowledge and completed programing tasks using Kodu, a new visual…
Descriptors: Cooperative Learning, Game Based Learning, Educational Environment, Algorithms
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Joel B. Jalon Jr.; Goodwin A. Chua; Myrla de Luna Torres – International Journal of Education in Mathematics, Science and Technology, 2024
ChatGPT is largely acknowledged for its substantial capacity to enhance the teaching and learning process despite some concerns. Based on the available literature, no study compares groups of students using ChatGPT and those who did not, more so in programming. Therefore, the main goal of this study was to examine how ChatGPT affects SHS students'…
Descriptors: Artificial Intelligence, Computer Software, Synchronous Communication, Learning Processes
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Stone, Jeffrey A.; Cruz, Laura – Teaching & Learning Inquiry, 2021
Higher education has embraced integrative learning as a means of enabling students to tackle so-called "wicked" problems, i.e. problems that are sufficiently complex, contested, and ambiguous that conventional, disciplinary specific approaches are inadequate to address. However, challenges remain in defining integrative learning…
Descriptors: Introductory Courses, Computer Science Education, Interdisciplinary Approach, Integrated Activities
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Loksa, Dastyni; Margulieux, Lauren; Becker, Brett A.; Craig, Michelle; Denny, Paul; Pettit, Raymond; Prather, James – ACM Transactions on Computing Education, 2022
Metacognition and self-regulation are important skills for successful learning and have been discussed and researched extensively in the general education literature for several decades. More recently, there has been growing interest in understanding how metacognitive and self-regulatory skills contribute to student success in the context of…
Descriptors: Metacognition, Programming, Computer Science Education, Learning Processes
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