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Showing 1 to 15 of 19 results Save | Export
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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
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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
Villamor, Maureen M. – Research and Practice in Technology Enhanced Learning, 2020
High attrition and dropout rates are common in introductory programming courses. One of the reasons students drop out is loss of motivation due to the lack of feedback and proper assessment of their progress. Hence, a process-oriented approach is needed in assessing programming progress, which entails examining and measuring students' compilation…
Descriptors: Novices, Problem Solving, Computer Science Education, Introductory Courses
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Lokkila, Erno; Christopoulos, Athanasios; Laakso, Mikko-Jussi – Journal of Information Systems Education, 2023
Educators who teach programming subjects are often wondering "which programming language should I teach first?" The debate behind this question has a long history and coming up with a definite answer to this question would be farfetched. Nonetheless, several efforts can be identified in the literature wherein pros and cons of mainstream…
Descriptors: Comparative Analysis, Programming Languages, Probability, Error Patterns
Velez, Martin – ProQuest LLC, 2019
Software is an integral part of our lives. It controls the cars we drive every day, the ships we send into space, and even our toasters. It is everywhere and we can easily download more. Software solves many real-world problems and satisfies many needs. Thus, unsurprisingly, there is a rising demand for software engineers to maintain existing…
Descriptors: Computer Science Education, Programming, Introductory Courses, Computer Software
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Miller, Craig S.; Settle, Amber – ACM Transactions on Computing Education, 2019
We investigate conditions in which novices make some reference errors when programming. We asked students from introductory programming courses to perform a simple code-writing task that required constructing references to objects and their attributes. By experimentally manipulating the nature of the attributes in the tasks, from identifying…
Descriptors: Error Patterns, Novices, Programming, Introductory Courses
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Kenney, Rachael; An, Tuyin; Kim, Sung-Hee; Uhan, Nelson A.; Yi, Ji Soo; Shamsul, Aiman – International Journal of Science and Mathematics Education, 2020
In linear programming, many students find it difficult to translate a verbal description of a problem into a valid mathematical model. To better understand this, we examine the existing characteristics of college engineering students' errors across linear programming (LP) problems. We examined textbooks to identify the types of problems typically…
Descriptors: Programming, Error Patterns, Engineering Education, Word Problems (Mathematics)
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Gerbing, David W. – Journal of Statistics and Data Science Education, 2021
R and Python are commonly used software languages for data analytics. Using these languages as the course software for the introductory course gives students practical skills for applying statistical concepts to data analysis. However, the reliance upon the command line is perceived by the typical nontechnical introductory student as sufficiently…
Descriptors: Statistics Education, Teaching Methods, Introductory Courses, Programming Languages
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Xie, Benjamin; Loksa, Dastyni; Nelson, Greg L.; Davidson, Matthew J.; Dong, Dongsheng; Kwik, Harrison; Tan, Alex Hui; Hwa, Leanne; Li, Min; Ko, Andrew J. – Computer Science Education, 2019
Background and Context: Current introductory instruction fails to identify, structure, and sequence the many skills involved in programming. Objective: We proposed a theory which identifies four distinct skills that novices learn incrementally. These skills are tracing, writing syntax, comprehending templates (reusable abstractions of programming…
Descriptors: Programming, Skill Development, Computer Science Education, Instructional Design
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Qian, Yizhou; Lehman, James – Journal of Educational Computing Research, 2020
This study implemented a data-driven approach to identify Chinese high school students' common errors in a Java-based introductory programming course using the data in an automated assessment tool called the Mulberry. Students' error-related behaviors were also analyzed, and their relationships to success in introductory programming were…
Descriptors: High School Students, Error Patterns, Introductory Courses, Computer Science Education
Rebecca Smith – ProQuest LLC, 2019
In recent years, computer science has become a cornerstone of modern society. As a result, enrollment in undergraduate computer science programs has expanded rapidly. While the influx of talent into the field will undoubtedly lead to countless technological developments, this growth also brings new pedagogical challenges. Educational resources,…
Descriptors: Computer Science Education, Individualized Instruction, Interaction, Learning Experience
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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
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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
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Park, Thomas H.; Dorn, Brian; Forte, Andrea – ACM Transactions on Computing Education, 2015
Many people are first exposed to code through web development, yet little is known about the barriers beginners face in these formative experiences. In this article, we describe a study of undergraduate students enrolled in an introductory web development course taken by both computing majors and general education students. Using data collected…
Descriptors: Programming Languages, Computer Science Education, Hypermedia, Web Sites
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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
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