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Miedema, Daphne; Fletcher, George; Aivaloglou, Efthimia – ACM Transactions on Computing Education, 2023
Prior studies in the Computer Science education literature have illustrated that novices make many mistakes in composing SQL queries. Query formulation proves to be difficult for students. Only recently, some headway was made towards understanding why SQL leads to so many mistakes, by uncovering student misconceptions. In this article, we shed new…
Descriptors: Computer Science Education, Novices, Misconceptions, Programming Languages
Dan Sun; Fan Ouyang; Yan Li; Chengcong Zhu; Yang Zhou – Journal of Computer Assisted Learning, 2024
Background: With the development of computational literacy, there has been a surge in both research and practice application of text-based and block-based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major…
Descriptors: Computer Science Education, Programming, Computer Literacy, Comparative Analysis
Kristina Litherland; Anders Kluge – Computer Science Education, 2024
Background and Context: We explore the potential for understanding the processes involved in students' programming based on studying their behaviour and dialogue with each other and "conversations" with their programs. Objective: Our aim is to explore how a perspective of inquiry can be used as a point of departure for insights into how…
Descriptors: Programming, Programming Languages, Secondary School Students, Computer Science Education
Qian, Yizhou; Lehman, James – Journal of Research on Technology in Education, 2022
This study investigated common student errors and underlying difficulties of two groups of Chinese middle school students in an introductory Python programming course using data in the automated assessment tool (AAT) Mulberry. One group of students was from a typical middle school while the other group was from a high-ability middle school. By…
Descriptors: Middle School Students, Programming, Computer Science Education, Error Patterns
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
Damar Rais; Zhao Xuezhi – Journal on Mathematics Education, 2024
Python programming is widely employed in educational institutions worldwide. Within the "Merdeka Belajar" curriculum context, this programming is recognized as a suitable vehicle for mathematics instruction, significantly influencing students' motivation and learning outcomes, particularly following periods of educational hiatus. This…
Descriptors: Student Motivation, Learning Motivation, Programming Languages, Student Attitudes
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
Veerasamy, Ashok Kumar; D'Souza, Daryl; Laakso, Mikko-Jussi – Journal of Educational Technology Systems, 2016
This article presents a study aimed at examining the novice student answers in an introductory programming final e-exam to identify misconceptions and types of errors. Our study used the Delphi concept inventory to identify student misconceptions and skill, rule, and knowledge-based errors approach to identify the types of errors made by novices…
Descriptors: Computer Science Education, Programming, Novices, Misconceptions
Bringula, Rex P.; Manabat, Geecee Maybelline A.; Tolentino, Miguel Angelo A.; Torres, Edmon L. – World Journal of Education, 2012
This descriptive study determined which of the sources of errors would predict the errors committed by novice Java programmers. Descriptive statistics revealed that the respondents perceived that they committed the identified eighteen errors infrequently. Thought error was perceived to be the main source of error during the laboratory programming…
Descriptors: Error Patterns, Programming, Programming Languages, Predictor Variables
Hughes, Michael C.; Jadud, Matthew C.; Rodrigo, Ma. Mercedes T. – Computer Science Education, 2010
In Java, "System.out.printf" and "String.format" consume a specialised kind of string commonly known as a format string. In our study of first-year students at the Ateneo de Manila University, we discovered that format strings present a substantial challenge for novice programmers. Focusing on their first laboratory we found…
Descriptors: Foreign Countries, Computer Science Education, Programming Languages, Introductory Courses