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Karnalim, Oscar; Simon; Chivers, William; Panca, Billy Susanto – ACM Transactions on Computing Education, 2022
To help address programming plagiarism and collusion, students should be informed about acceptable practices and about program similarity, both coincidental and non-coincidental. However, current approaches are usually manual, brief, and delivered well before students are in a situation where they might commit academic misconduct. This article…
Descriptors: Computer Science Education, Programming, Plagiarism, Formative Evaluation
Marwan, Samiha; Akram, Bita; Barnes, Tiffany; Price, Thomas W. – IEEE Transactions on Learning Technologies, 2022
Theories on learning show that formative feedback that is immediate, specific, corrective, and positive is essential to improve novice students' motivation and learning. However, most prior work on programming feedback focuses on highlighting student's mistakes, or detecting failed test cases after they submit a solution. In this article, we…
Descriptors: Feedback (Response), Formative Evaluation, Programming, Coding
Barczak, Andre L. C.; Mathrani, Anuradha; Han, Binglan; Reyes, Napoleon H. – Educational Technology Research and Development, 2023
An important course in the computer science discipline is 'Data Structures and Algorithms' (DSA). "The coursework" lays emphasis on experiential learning for building students' programming and algorithmic reasoning abilities. Teachers set up a repertoire of formative programming exercises to engage students with different programmatic…
Descriptors: Computer Assisted Testing, Automation, Computer Science Education, Programming
Veerasamy, Ashok Kumar; Laakso, Mikko-Jussi; D'Souza, Daryl – Informatics in Education, 2022
Previous studies have proposed many indicators to assess the effect of student engagement in learning and academic achievement but have not yet been clearly articulated. In addition, while student engagement tracking systems have been designed, they rely on the log data but not on performance data. This paper presents results of a non-machine…
Descriptors: Formative Evaluation, Educational Indicators, Learner Engagement, At Risk Students
Barbosa Rocha, Hemilis Joyse; Cabral De Azevedo Restelli Tedesco, Patrícia; De Barros Costa, Evandro – Informatics in Education, 2023
In programming problem solving activities, sometimes, students need feedback to progress in the course, being positively affected by the received feedback. This paper presents an overview of the state of the art and practice of the feedback approaches on introductory programming. To this end, we have carried out a systematic literature mapping to…
Descriptors: Classification, Computer Science Education, Feedback (Response), Problem Solving
Lakshminarayanan, Srinivasan; Rao, N. J. – Higher Education for the Future, 2022
There are many grey areas in the interpretation of academic integrity in the course on Introduction to Programming, commonly known as CS1. Copying, for example, is a method of learning, a method of cheating and a reuse method in professional practice. Many institutions in India publish the code in the lab course manual. The students are expected…
Descriptors: Integrity, Cheating, Duplication, Introductory Courses
Marwan, Samiha; Shi, Yang; Menezes, Ian; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2021
Feedback on how students progress through completing subgoals can improve students' learning and motivation in programming. Detecting subgoal completion is a challenging task, and most learning environments do so either with "expert-authored" models or with "data-driven" models. Both models have advantages that are…
Descriptors: Expertise, Models, Feedback (Response), Identification
Zhizezhang Gao; Haochen Yan; Jiaqi Liu; Xiao Zhang; Yuxiang Lin; Yingzhi Zhang; Xia Sun; Jun Feng – International Journal of STEM Education, 2025
Background: With the increasing interdisciplinarity between computer science (CS) and other fields, a growing number of non-CS students are embracing programming. However, there is a gap in research concerning differences in programming learning between CS and non-CS students. Previous studies predominantly relied on outcome-based assessments,…
Descriptors: Computer Science Education, Mathematics Education, Novices, Programming
Rowlett, Peter; Corner, Alexander S. – International Journal of Mathematical Education in Science and Technology, 2022
During the COVID-19 pandemic, the teaching of programming for undergraduate mathematicians was moved online. This was delivered asynchronously, with students working through notes and exercises and asking for help from staff via online messages as needed. Staff delivery time was redirected from content delivery into a formal system of formative…
Descriptors: COVID-19, Pandemics, Programming, Undergraduate Students
Hao, Qiang; Smith, David H., IV; Ding, Lu; Ko, Amy; Ottaway, Camille; Wilson, Jack; Arakawa, Kai H.; Turcan, Alistair; Poehlman, Timothy; Greer, Tyler – Computer Science Education, 2022
Background and Context: automated feedback for programming assignments has great potential in promoting just-in-time learning, but there has been little work investigating the design of feedback in this context. Objective: to investigate the impacts of different designs of automated feedback on student learning at a fine-grained level, and how…
Descriptors: Computer Science Education, Feedback (Response), Teaching Methods, Comparative Analysis
Whitney, Michael; Lipford, Heather Richter; Chu, Bill; Thomas, Tyler – Journal of Educational Computing Research, 2018
Many of the software security vulnerabilities that people face today can be remediated through secure coding practices. A critical step toward the practice of secure coding is ensuring that our computing students are educated on these practices. We argue that secure coding education needs to be included across a computing curriculum. We are…
Descriptors: Computer Security, Programming, Coding, Computer Science Education
Haldeman, Georgiana; Babes-Vroman Monica; Tjang, Andrew; Nguyen, Thu D. – ACM Transactions on Computing Education, 2021
Autograding systems are being increasingly deployed to meet the challenges of teaching programming at scale. Studies show that formative feedback can greatly help novices learn programming. This work extends an autograder, enabling it to provide formative feedback on programming assignment submissions. Our methodology starts with the design of a…
Descriptors: Student Evaluation, Feedback (Response), Grading, Automation
R. Rosario; T. S. Hopper; A. Huang-Saad – Biomedical Engineering Education, 2022
There are increasing calls for the use of research-based teaching strategies to improve engagement and learning in engineering. In this innovation paper, we detail the application of research-based teaching strategies in a computer programming focused biomedical engineering module. This four-week, one-credit undergraduate biomedical engineering…
Descriptors: Undergraduate Students, Biomedicine, Engineering Education, Programming
Taslibeyaz, Elif; Kursun, Engin; Karaman, Selcuk – Informatics in Education, 2020
The primary purpose of this study is to investigate CT skills development process in learning environments. It is also aimed to determine the conceptual understanding and measurement approaches in the studies. To achieve these aims, a systematic research review methodology was implemented as the research design. Empirical studies on computational…
Descriptors: Thinking Skills, Skill Development, Problem Solving, Programming
Green, Nancy L. – International Journal of Artificial Intelligence in Education, 2017
This paper describes an educational argument modeling system, GAIL (Genetics Argumentation Inquiry Learning). Using GAIL's graphical interface, learners can select from possible argument content elements (hypotheses, data, etc.) displayed on the screen with which to construct argument diagrams. Unlike previous systems, GAIL uses domain-independent…
Descriptors: Persuasive Discourse, Feedback (Response), Inquiry, Computer Assisted Instruction