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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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Jeremie Regnier; Ethan Shafer; Edward Sobiesk; Nicholas Stave; Malcolm Haynes – Education and Information Technologies, 2024
In our post-pandemic world, where the majority of higher education institutions have transitioned back to in-person classes, this paper argues that we must not return to pre-COVID teaching practices. Instead, we have the obligation and opportunity to create an educational experience and environment that better facilitates learning and instruction.…
Descriptors: COVID-19, Pandemics, Educational Technology, Best Practices
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C. Florou; G. Stamoulis; A. Xenakis; A. Plageras – Education and Information Technologies, 2025
This study focuses on students' self-assessment during their learning process related to computer programming concepts, taking into account challenges and obstacles both teachers and students face, with the aim to contribute to the development of guiding principles and practices and enhance the teaching process of computer programming in primary…
Descriptors: Teacher Role, Self Evaluation (Individuals), Computer Science Education, Programming
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Gutiérrez, Luz E.; Guerrero, Carlos A.; López-Ospina, Héctor A. – Education and Information Technologies, 2022
This study describes the most relevant problems and solutions found in the literature on teaching and learning of object-oriented programming (OOP). The identification of the problem was based on tertiary studies from the IEEE Xplore, Scopus, ACM Digital Library and Science Direct repositories. The problems and solutions identified were ranked…
Descriptors: Programming, Comprehension, Computer Science Education, Computer Software
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Ezeamuzie, Ndudi O. – Education and Information Technologies, 2023
Several instructional approaches have been advanced for learning programming. However, effective ways of engaging beginners in programming in K-12 are still unclear, especially among low socioeconomic status learners in technology-deprived learning environments. Understanding the learning path of novice programmers will bridge this gap and explain…
Descriptors: Programming, Constructivism (Learning), Programming Languages, Computer Science Education
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Marowka, Ami – Education and Information Technologies, 2018
Python is gaining popularity in academia as the preferred language to teach novices serial programming. The syntax of Python is clean, easy, and simple to understand. At the same time, it is a high-level programming language that supports multi programming paradigms such as imperative, functional, and object-oriented. Therefore, by default, it is…
Descriptors: Computer Science Education, Programming Languages, Computer Software, Engineering Education
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Malik, Sohail Iqbal; Coldwell-Neilson, Jo – Education and Information Technologies, 2017
High failure and drop-out rates from introductory programming courses continue to be of significant concern to computer science disciplines despite extensive research attempting to address the issue. In this study, we include the three entities of the didactic triangle, instructors, students and curriculum, to explore the learning difficulties…
Descriptors: Programming, Barriers, Introductory Courses, Student Attitudes
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Giannakos, Michail N.; Pappas, Ilias O.; Jaccheri, Letizia; Sampson, Demetrios G. – Education and Information Technologies, 2017
Researchers have been working to understand the high dropout rates in computer science (CS) education. Despite the great demand for CS professionals, little is known about what influences individuals to complete their CS studies. We identify gains of studying CS, the (learning) environment, degree's usefulness, and barriers as important predictors…
Descriptors: College Students, School Holding Power, Computer Science Education, Environmental Influences