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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
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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
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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
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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
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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
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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
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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
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Asmaa Bengueddach; Djamila Hamdadou – International Society for Technology, Education, and Science, 2024
The COVID-19 pandemic, an unprecedented global health crisis, has not only significantly impacted public health but has also caused substantial disruptions to conventional education systems. In response to these challenges, our institution has undertaken innovative measures within the realm of education. A pivotal aspect of our response involves…
Descriptors: Personal Autonomy, Online Courses, Educational Change, Coding
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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
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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
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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
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Dominguez, Cesar; Garcia-Izquierdo, Francisco J.; Jaime, Arturo; Perez, Beatriz; Rubio, Angel Luis; Zapata, Maria A. – IEEE Transactions on Learning Technologies, 2021
The study of the relationships between self-regulated learning and formative assessment is an active line of research in the educational community. A recent review of the literature highlights that the study of these connections has been mainly unidirectional, focusing on how formative assessment helps students to self-regulate their learning,…
Descriptors: Learning Analytics, Time Factors (Learning), Self Evaluation (Individuals), Formative Evaluation
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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
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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
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Myers, Matthew C.; Wilson, Joshua – International Journal of Artificial Intelligence in Education, 2023
This study evaluated the construct validity of six scoring traits of an automated writing evaluation (AWE) system called "MI Write." Persuasive essays (N = 100) written by students in grades 7 and 8 were randomized at the sentence-level using a script written with Python's NLTK module. Each persuasive essay was randomized 30 times (n =…
Descriptors: Construct Validity, Automation, Writing Evaluation, Algorithms
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