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Showing 1 to 15 of 81 results Save | Export
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Atharva Naik; Jessica Ruhan Yin; Anusha Kamath; Qianou Ma; Sherry Tongshuang Wu; R. Charles Murray; Christopher Bogart; Majd Sakr; Carolyn P. Rose – British Journal of Educational Technology, 2025
The relative effectiveness of reflection either through student generation of contrasting cases or through provided contrasting cases is not well-established for adult learners. This paper presents a classroom study to investigate this comparison in a college level Computer Science (CS) course where groups of students worked collaboratively to…
Descriptors: Cooperative Learning, Reflection, College Students, Computer Science Education
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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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Wiegand, R. Paul; Bucci, Anthony; Kumar, Amruth N.; Albert, Jennifer; Gaspar, Alessio – ACM Transactions on Computing Education, 2022
In this article, we leverage ideas from the theory of coevolutionary computation to analyze interactions of students with problems. We introduce the idea of "informatively" easy or hard concepts. Our approach is different from more traditional analyses of problem difficulty such as item analysis in the sense that we consider Pareto…
Descriptors: Concept Formation, Difficulty Level, Computer Science Education, Problem Solving
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Roland Kiraly; Sandor Kiraly; Martin Palotai – Education and Information Technologies, 2024
Deep learning is a very popular topic in computer sciences courses despite the fact that it is often challenging for beginners to take their first step due to the complexity of understanding and applying Artificial Neural Networks (ANN). Thus, the need to both understand and use neural networks is appearing at an ever-increasing rate across all…
Descriptors: Artificial Intelligence, Computer Science Education, Problem Solving, College Faculty
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Xuanyan Zhong; Zehui Zhan – Interactive Technology and Smart Education, 2025
Purpose: The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners' computational thinking. Design/methodology/approach: By…
Descriptors: Intelligent Tutoring Systems, Computer Science Education, Programming, Independent Study
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Strömbäck, Filip; Mannila, Linda; Kamkar, Mariam – Informatics in Education, 2021
Concurrency is often perceived as difficult by students. One reason for this may be due to the fact that abstractions used in concurrent programs leave more situations undefined compared to sequential programs (e.g., in what order statements are executed), which makes it harder to create a proper mental model of the execution environment. Students…
Descriptors: College Students, Programming, Programming Languages, Concept Formation
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Ragonis, Noa; Shmallo, Ronit – Informatics in Education, 2022
Object-oriented programming distinguishes between instance attributes and methods and class attributes and methods, annotated by the "static" modifier. Novices encounter difficulty understanding the means and implications of "static" attributes and methods. The paper has two outcomes: (a) a detailed classification of aspects of…
Descriptors: Programming, Computer Science Education, Concept Formation, Thinking Skills
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Iserte, Sergio; Tomas, Vicente R.; Perez, Miguel; Castillo, Maribel; Boronat, Pablo; Garcia, Luis A. – IEEE Transactions on Education, 2023
Team project-based learning (TPBL) combines two learning techniques: 1) project-based learning (PBL) and 2) teamwork. This combination leverages the learning outcomes of both methods and places students in a real work situation where they must develop and solve a real project while working as a team. TPBL has been used in two advanced database…
Descriptors: Cooperative Learning, Student Projects, Active Learning, Teamwork
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Wang, Weitian; Coutras, Constantine; Zhu, Michelle – Smart Learning Environments, 2021
With the increasing employment of robots in multiple areas such as smart manufacturing and intelligent transportation, both undergraduate and graduate students from computing related majors (e.g., computer science and information technology) demonstrated strong interests in learning robotics technology to broaden their career opportunities.…
Descriptors: Computer Science Education, College Students, Situated Learning, Robotics
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Ramírez-Donoso, Luis; Pérez-Sanagustín, Mar; Neyem, Andrés; Alario-Hoyos, Carlos; Hilliger, Isabel; Rojos, Felipe – Interactive Learning Environments, 2023
Over the past years, higher education institutions have been exploring different mechanisms to adapt their learning and teaching practices to increase students' engagement. One of the proposals has been to reuse Massive Online Open Courses (MOOCs) as Small Online Private Courses (SPOCs), or as complementary resources in traditional courses through…
Descriptors: Technology Uses in Education, Electronic Learning, Cooperative Learning, Gamification
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Mangaroska, Katerina; Sharma, Kshitij; Gaševic, Dragan; Giannakos, Michail – Journal of Computer Assisted Learning, 2022
Background: Problem-solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE-log data).…
Descriptors: Cognitive Processes, Psychological Patterns, Problem Solving, Programming
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Lai, Ying-Hsun; Chen, Shih-Yeh; Lai, Chin-Feng; Chang, Yao-Chung; Su, Yu-Sheng – Interactive Learning Environments, 2021
Due to their applications on varied and complex issues, Artificial Intelligence (AI) and Internet of Things (IoT) (collectively, AIoT) have become popular new-generation courses, but the learning of such courses needs to consider actual situations and to analyze complicated problems, making it difficult for students to improve their academic…
Descriptors: Artificial Intelligence, Internet, Computation, Thinking Skills
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Rashkovits, Rami; Lavy, Ilana – Journal of Information Technology Education: Innovations in Practice, 2020
Aim/Purpose: Multi-threaded software design is considered to be difficult, especially to novice programmers. In this study, we explored how students cope with a task that its solution requires a multi-threaded architecture to achieve optimal runtime. Background: An efficient exploit of multicore processors architecture requires computer programs…
Descriptors: Computer Software, Novices, Programming, Difficulty Level
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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
Chamidy, Totok; Degeng, I. Nyoman Sudana; Ulfa, Saida – Online Submission, 2020
This study aims to examine the effect of problem-based learning and tacit knowledge on problem-solving skills when students study in the laboratory. The method employed in this research was Quasi-Experimental Design. Data collection techniques were questionnaires and tests. Seventy-seven students were taken as the research participant and divide…
Descriptors: Problem Based Learning, Instructional Effectiveness, Knowledge Level, Problem Solving
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