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Showing all 15 results Save | Export
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Shindler, Michael; Pinpin, Natalia; Markovic, Mia; Reiber, Frederick; Kim, Jee Hoon; Carlos, Giles Pierre Nunez; Dogucu, Mine; Hong, Mark; Luu, Michael; Anderson, Brian; Cote, Aaron; Ferland, Matthew; Jain, Palak; LaBonte, Tyler; Mathur, Leena; Moreno, Ryan; Sakuma, Ryan – Computer Science Education, 2022
Background and Context: We replicated and expanded on previous work about how well students learn dynamic programming, a difficult topic for students in algorithms class. Their study interviewed a number of students at one university in a single term. We recruited a larger sample size of students, over several terms, in both large public and…
Descriptors: Misconceptions, Programming, Computer Science Education, Replication (Evaluation)
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Esche, Svana; Weihe, Karsten – IEEE Transactions on Education, 2023
Contribution: Most work on languages in computing education currently focuses on non-native speakers. In contrast, to the best of the authors' knowledge, this article is the first response to the call for research on terms that takes into account the terms used by novices in their language. Background: Terms are key factors in communication,…
Descriptors: Programming Languages, Computer Science Education, Misconceptions, Undergraduate Students
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Orly Barzilai; Sofia Sherman; Moshe Leiba; Hadar Spiegel – Journal of Information Systems Education, 2024
Data Structures and Algorithms (DS) is a basic computer science course that is a prerequisite for taking advanced information systems (IS) curriculum courses. The course aims to teach students how to analyze a problem, design a solution, and implement it using pseudocode to construct knowledge and develop the necessary skills for algorithmic…
Descriptors: Statistics Education, Problem Solving, Information Systems, Algorithms
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Daniele Traversaro; Giorgio Delzanno; Giovanna Guerrini – Informatics in Education, 2024
Concurrency is a complex to learn topic that is becoming more and more relevant, such that many undergraduate Computer Science curricula are introducing it in introductory programming courses. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi, a…
Descriptors: Misconceptions, Programming Languages, Computer Science Education, Undergraduate Students
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Christina Kyriakou; Agoritsa Gogoulou; Maria Grigoriadou – Informatics in Education, 2023
This paper presents an educational setting that attempts to enhance students' understanding and facilitate students' linking-inferencing skills. The proposed setting is structured in three stages. The first stage intends to explore students' prior knowledge. The second stage aims to help students tackle their difficulties and misconceptions and…
Descriptors: Thinking Skills, Inferences, Computer Science Education, Computer System Design
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Yun Huang; Christian Dieter Schunn; Julio Guerra; Peter L. Brusilovsky – ACM Transactions on Computing Education, 2024
Programming skills are increasingly important to the current digital economy, yet these skills have long been regarded as challenging to acquire. A central challenge in learning programming skills involves the simultaneous use of multiple component skills. This article investigates why students struggle with integrating component skills--a…
Descriptors: Programming, Computer Science Education, Error Patterns, Classification
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Ioannis, Berdousis; Maria, Kordaki – Education and Information Technologies, 2019
The study of gender differences in Computer Science (CS) has captured the attention of many researchers around the world. Over time, research has revealed that negative stereotypes and 'myths' about the cognitive skills, academic abilities and interests of females in CS do exist, deterring females from entering the field. Thus, this study aims to…
Descriptors: Computer Science Education, Gender Differences, Stereotypes, Misconceptions
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Jegede, Philip Olu; Olajubu, Emmanuel Ajayi; Ejidokun, Adekunle Olugbenga; Elesemoyo, Isaac Oluwafemi – Journal of Information Technology Education: Innovations in Practice, 2019
Aim/Purpose: The study examined types of errors made by novice programmers in different Java concepts with students of different ability levels in programming as well as the perceived causes of such errors. Background: To improve code writing and debugging skills, efforts have been made to taxonomize programming errors and their causes. However,…
Descriptors: Programming Languages, Programming, Low Achievement, High Achievement
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Gal-Ezer, Judith; Trakhtenbrot, Mark – Computer Science Education, 2016
Reduction is one of the key techniques used for problem-solving in computer science. In particular, in the theory of computation and complexity (TCC), mapping and polynomial reductions are used for analysis of decidability and computational complexity of problems, including the core concept of NP-completeness. Reduction is a highly abstract…
Descriptors: Computer Science Education, Problem Solving, Computation, Difficulty Level
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Kwon, Kyungbin – International Journal of Computer Science Education in Schools, 2017
Understanding the students' programming misconceptions is critical in that it identifies the reasons why students make errors in programming and allows instructors to design instructions accordingly. This study investigated the mental models of programming concepts held by pre-service teachers who were novice programmers. In an introductory…
Descriptors: Programming, Novices, Misconceptions, Instructional Design
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Li, Voon Li; Julaihi, Nor Hazizah; Eng, Tang Howe – Asian Journal of University Education, 2017
The paper presents the results of a case study examining students' difficulties in the learning of integral calculus. It sought to address the misconceptions and errors that were encountered in the students' work solution. In quantitative study, the marks obtained by 147 students of Diploma in Computer Science in advanced calculus examinations…
Descriptors: Misconceptions, Calculus, Mathematics Instruction, Case Studies
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Cetin, Ibrahim – Computer Science Education, 2013
The purpose of this study was twofold: to investigate students' concept images about class, object, and their relationship and to help them enhance their learning of these notions with a visualization tool. Fifty-six second-year university students participated in the study. To investigate his/her concept images, the researcher developed a survey…
Descriptors: Computer Science Education, Programming, Visualization, Animation
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Herman, G. L.; Loui, M. C.; Zilles, C. – IEEE Transactions on Education, 2011
To improve instruction in computer engineering and computer science, instructors must better understand how their students learn. Unfortunately, little is known about how students learn the fundamental concepts in computing. To investigate student conceptions and misconceptions about digital logic concepts, the authors conducted a qualitative…
Descriptors: Misconceptions, Equipment, Computer Science Education, Engineering Education
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Sien, Ven Yu – Computer Science Education, 2011
Object-oriented analysis and design (OOAD) is not an easy subject to learn. There are many challenges confronting students when studying OOAD. Students have particular difficulty abstracting real-world problems within the context of OOAD. They are unable to effectively build object-oriented (OO) models from the problem domain because they…
Descriptors: Foreign Countries, Computer Science Education, Undergraduate Students, Computer Software
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Bayman, Piraye; Mayer, Richard E. – Journal of Educational Psychology, 1988
BASIC programing was taught to 95 undergraduates from a manual emphasizing the language's syntax or from a manual that included additional material on the underlying semantics. Both approaches produced equivalent learning of syntactic features of BASIC; however, semantically trained students developed fewer misconceptions and performed better on…
Descriptors: Computer Science Education, Higher Education, Instructional Materials, Misconceptions