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Tessa Charles; Carl Gwilliam – Journal for STEM Education Research, 2023
STEM fields, such as physics, increasingly rely on complex programs to analyse large datasets, thus teaching students the required programming skills is an important component of all STEM curricula. Since undergraduate students often have no prior coding experience, they are reliant on error messages as the primary diagnostic tool to identify and…
Descriptors: Automation, Feedback (Response), Error Correction, Physics
Phung, Tung; Cambronero, José; Gulwani, Sumit; Kohn, Tobias; Majumdarm, Rupak; Singla, Adish; Soares, Gustavo – International Educational Data Mining Society, 2023
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is…
Descriptors: Computational Linguistics, Feedback (Response), Programming, Computer Science Education
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)
Smitha S. Kumar; Michael A. Lones; Manuel Maarek; Hind Zantout – ACM Transactions on Computing Education, 2025
Programming demands a variety of cognitive skills, and mastering these competencies is essential for success in computer science education. The importance of formative feedback is well acknowledged in programming education, and thus, a diverse range of techniques has been proposed to generate and enhance formative feedback for programming…
Descriptors: Automation, Computer Science Education, Programming, Feedback (Response)
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
Chung, Cheng-Yu; Hsiao, I-Han; Lin, Yi-Ling – Journal of Research on Technology in Education, 2023
Creating practice questions for programming learning is not an easy job. It requires the instructor to diligently organize heterogeneous learning resources. Although educational technologies have been adopted across levels of programming learning, programming question generation (PQG) is still predominantly performed by instructors without…
Descriptors: Artificial Intelligence, Programming, Questioning Techniques, Heterogeneous Grouping
Dorottya Demszky; Jing Liu; Heather C. Hill; Dan Jurafsky; Chris Piech – Educational Evaluation and Policy Analysis, 2024
Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource-intensive in most educational contexts. We develop M-Powering Teachers, an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage…
Descriptors: Online Courses, Automation, Feedback (Response), Large Group Instruction
Maria Jesús Marco-Galindo; Julià Minguillón; David García-Solórzano; Teresa Sancho-Vinuesa – ACM Transactions on Computing Education, 2025
Objectives: This study addresses the challenges faced by students repeating an introductory programming course (CS1) at an online university, where dropout and failure rates remain high. While programming education has been widely studied, targeted interventions for repeating students remain scarce. Our research aims to analyze the experiences of…
Descriptors: Computer Science Education, Online Courses, Repetition, Introductory Courses
Meier, Heidi; Lepp, Marina – Journal of Educational Computing Research, 2023
Especially in large courses, feedback is often given only on the final results; less attention is paid to the programming process. Today, however, some programming environments, e.g., Thonny, log activities during programming and have the functionality of replaying the programming process. This information can be used to provide feedback, and this…
Descriptors: Programming, Introductory Courses, Computer Science Education, Teaching Methods
Kaitlyn Storm; Jerry Zhang; Eileen Haase – Biomedical Engineering Education, 2022
Our first year biomedical engineering course exposes students to multiple engineering and design techniques within an overarching theme of understanding health inequity. Currently, the semester-long curriculum excludes computational methods such as Python programming and Machine Learning, which are usually not introduced until more advanced BME…
Descriptors: Artificial Intelligence, Programming Languages, Learning Modules, Introductory Courses
Qian, Yizhou – ProQuest LLC, 2018
With the expansion of computer science (CS) education, CS teachers in K-12 schools should be cognizant of student misconceptions and be prepared to help students establish accurate understanding of computer science and programming. This exploratory design-based research (DBR) study implemented a data-driven approach to identify secondary school…
Descriptors: Misconceptions, Data, Decision Making, Computer Science Education
Neyhart, Jeffrey L.; Watkins, Eric – Natural Sciences Education, 2020
Basic quantitative and population genetics topics are typically taught in introductory plant breeding courses and are critical for success in upper-level study. Active learning, including simulations and games, may be useful for instruction of these concepts, which rely heavily on theory and may be more challenging for students. The statistical…
Descriptors: Genetics, Active Learning, Teaching Methods, Plants (Botany)
Riese, Emma; Stenbom, Stefan – IEEE Transactions on Education, 2023
Contribution: This study evaluates the generalizability of previously identified perceptions among engineering students of assessments in introductory programming (CS1). The students' perceptions of their instructors' and teaching assistants' (TAs) roles in these assessments are also studied, and differences based on prior programming experience,…
Descriptors: Student Attitudes, Educational Experience, Computer Science Education, Student Evaluation
Enhancement of the Command-Line Environment for Use in the Introductory Statistics Course and Beyond
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
Leo C. Ureel II – ProQuest LLC, 2020
Students in introductory computer science courses, are learning to program. Indeed, most students perceive that learning to code is the central topic explored in the courses. Students spend an enormous amount of time struggling to learn the syntax and understand semantics of a particular language. Instructors spend a similar amount of time reading…
Descriptors: Coding, Programming, Computer Science Education, Novices

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