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Franc Vrbancic; Slavko Kocijancic – Education and Information Technologies, 2024
Microcontroller programming competencies contribute to the sustainable employability of engineering graduates of both higher and secondary education. To develop the required programming skills, one of the challenges for educators is to determine which programming environments should be implemented in introductory programming courses. Conceptually,…
Descriptors: Programming, Competence, Introductory Courses, Secondary Education
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Oscar Karnalim; Hapnes Toba; Meliana Christianti Johan – Education and Information Technologies, 2024
Artificial Intelligence (AI) can foster education but can also be misused to breach academic integrity. Large language models like ChatGPT are able to generate solutions for individual assessments that are expected to be completed independently. There are a number of automated detectors for AI assisted work. However, most of them are not dedicated…
Descriptors: Artificial Intelligence, Academic Achievement, Integrity, Introductory Courses
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Menon, Pratibha – Journal of Information Systems Education, 2023
This paper introduces a teaching process to develop students' problem-solving and programming efficacy in an introductory computer programming course. The proposed teaching practice provides step-by-step guidelines on using worked-out examples of code to demonstrate the applications of programming concepts. These coding demonstrations explicitly…
Descriptors: Introductory Courses, Programming, Computer Science Education, Feedback (Response)
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Hoq, Muntasir; Brusilovsky, Peter; Akram, Bita – International Educational Data Mining Society, 2023
Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…
Descriptors: Academic Achievement, Prediction, Models, Introductory Courses
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Silva, Leonardo; Mendes, Antonio Jose; Gomes, Anabela; Fortes, Gabriel – IEEE Transactions on Education, 2023
Contribution: Students' problem-understanding abilities and their relationship with programming learning were investigated using a methodology little explored in the existing literature. Background: Problem comprehension is an ability used during software development. Current research points to conflicting results on students' ability to interpret…
Descriptors: Programming, Comprehension, Computer Software, Electronic Learning
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Tang, Marc – Teaching Statistics: An International Journal for Teachers, 2020
University students in other disciplines without prior knowledge in statistics and/or programming language are introduced to the statistical method of decision trees in the programming language R during a 45-minute teaching and practice session. Statistics and programming skills are now frequently required within a wide variety of research fields…
Descriptors: Statistics, Teaching Methods, Programming, Programming Languages
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Han Wan; Hongzhen Luo; Mengying Li; Xiaoyan Luo – IEEE Transactions on Learning Technologies, 2024
Automatic program repair (APR) tools are valuable for students to assist them with debugging tasks since program repair captures the code modification to make a buggy program pass the given test-suite. However, the process of manually generating catalogs of code modifications is intricate and time-consuming. This article proposes contextual error…
Descriptors: Programming, Computer Science Education, Introductory Courses, Assignments
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Mahatanankoon, Pruthikrai; Wolf, James – Information Systems Education Journal, 2021
Learning a computer programming language is typically one of the basic requirements of being an information technology (IT) major. While other studies previously investigate computer programming self-efficacy and grit, their relationships between "shallow" and "deep" learning (Miller et al., 1996) have not been thoroughly…
Descriptors: Cognitive Processes, Learning Strategies, Introductory Courses, Computer Science Education
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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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Hsu, Wen-Chin; Gainsburg, Julie – Journal of Educational Computing Research, 2021
Block-based programming languages (BBLs) have been proposed as a way to prepare students for learning to program in more sophisticated, text-based languages, such as Java. Hybrid BBLs add the ability to view and edit the block commands in auto-generated, text-based code. We compared the use of a non-hybrid BBL (Scratch), a hybrid BBL (Pencil…
Descriptors: Computer Science Education, Introductory Courses, Teaching Methods, Student Attitudes
Adrian Salguero – ProQuest LLC, 2024
Introductory computer programming (i.e. CS1) is the entry point into the computer science major at higher education institutions worldwide. It introduces foundational concepts to students that are then built upon in future courses. Computer science as a whole has struggled to attract and retain students in the major, particularly women and…
Descriptors: Computer Science, Programming, Introductory Courses, Disproportionate Representation
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Jon-Paul Paolino – Teaching Statistics: An International Journal for Teachers, 2024
This article presents a novel approach to introducing principal component analysis (PCA), using summary tables and descriptive statistics. Given its applicability across a variety of academic disciplines, this topic offers abundant opportunity for class discussion and activities. However, teaching PCA in an introductory class can be challenging…
Descriptors: Statistics Education, Factor Analysis, Teaching Methods, Introductory Courses
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Shi, Yang; Schmucker, Robin; Chi, Min; Barnes, Tiffany; Price, Thomas – International Educational Data Mining Society, 2023
Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…
Descriptors: Computer Science Education, Data Analysis, Programming, Coding
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Shi, Yang; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2022
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In…
Descriptors: Programming, Knowledge Level, Prediction, Instructional Innovation
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Haglund, Pontus; Strömbäck, Filip; Mannila, Linda – Informatics in Education, 2021
Controlling complexity through the use of abstractions is a critical part of problem solving in programming. Thus, becoming proficient with procedural and data abstraction through the use of user-defined functions is important. Properly using functions for abstraction involves a number of other core concepts, such as parameter passing, scope and…
Descriptors: Computer Science Education, Programming, Programming Languages, Problem Solving
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