NotesFAQContact Us
Collection
Advanced
Search Tips
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing 1 to 15 of 91 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Chengliang Wang; Xiaojiao Chen; Yifei Li; Pengju Wang; Haoming Wang; Yuanyuan Li – Journal of Educational Computing Research, 2025
This study explored the impact of MetaClassroom, a virtual immersive programming learning environment designed based on the three-dimensional learning progression (3DLP) concept, on students' multidimensional development. Utilizing a quasi-experimental research design, this study compared students' programming learning achievements (PLA),…
Descriptors: Programming, Computer Science Education, Metacognition, Computer Simulation
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Shi, Yang; Schmucker, Robin; Chi, Min; Barnes, Tiffany; Price, Thomas – International Educational Data Mining Society, 2023
Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…
Descriptors: Computer Science Education, Data Analysis, Programming, Coding
Peer reviewed Peer reviewed
Direct linkDirect link
Erkan Er; Gökhan Akçapinar; Alper Bayazit; Omid Noroozi; Seyyed Kazem Banihashem – British Journal of Educational Technology, 2025
Despite the growing research interest in the use of large language models for feedback provision, it still remains unknown how students perceive and use AI-generated feedback compared to instructor feedback in authentic settings. To address this gap, this study compared instructor and AI-generated feedback in a Java programming course through an…
Descriptors: Student Evaluation, Student Attitudes, Feedback (Response), Artificial Intelligence
Peer reviewed Peer reviewed
Direct linkDirect link
Ellie Lovellette; Dennis J. Bouvier; John Matta – ACM Transactions on Computing Education, 2024
In recent years, computing education researchers have investigated the impact of problem context on students' learning and programming performance. This work continues the investigation motivated, in part, by cognitive load theory and educational research in computer science and other disciplines. The results of this study could help inform…
Descriptors: Computer Science Education, Student Evaluation, Context Effect, Problem Solving
Peer reviewed Peer reviewed
Direct linkDirect link
Grethe Sandstrak; Bjorn Klefstad; Arne Styve; Kiran Raja – IEEE Transactions on Education, 2024
Teaching programming efficiently to students in the first year of computer science education is challenging. It is especially cumbersome to retain the interest of both groups, when the student group consists of novice (i.e., those who have never programmed before) and expert programmers in the same crowd. Thus, individualized teaching cannot be…
Descriptors: Computer Science Education, Programming, Teaching Methods, College Freshmen
Peer reviewed Peer reviewed
Direct linkDirect link
Tavares, Paula Correia; Gomes, Elsa Ferreira; Henriques, Pedro Rangel; Vieira, Diogo Manuel – Open Education Studies, 2022
Computer Programming Learners usually fail to get approved in introductory courses because solving problems using computers is a complex task. The most important reason for that failure is concerned with motivation; motivation strongly impacts on the learning process. In this paper we discuss how techniques like program animation, and automatic…
Descriptors: Learner Engagement, Programming, Computer Science Education, Problem Solving
Peer reviewed Peer reviewed
Direct linkDirect link
Guozhu Ding; Xiangyi Shi; Shan Li – Education and Information Technologies, 2024
In this study, we developed a classification system of programming errors based on the historical data of 680,540 programming records collected on the Online Judge platform. The classification system described six types of programming errors (i.e., syntax, logical, type, writing, misunderstanding, and runtime errors) and their connections with…
Descriptors: Programming, Computer Science Education, Classification, Graphs
Peer reviewed Peer reviewed
Direct linkDirect link
Chen, Peggy P. – New Directions for Teaching and Learning, 2023
Many introductory computer science (CS) courses are intended to address the increased demand for computer literacy and the development of cross-cutting concepts and practices of computational thinking (CT). Colleges and universities offer introductory CS courses every semester toward this end. The issue is centered on how to support CT learning in…
Descriptors: Introductory Courses, Computer Science Education, Computer Literacy, Thinking Skills
Peer reviewed Peer reviewed
Direct linkDirect link
Vesin, Boban; Mangaroska, Katerina; Akhuseyinoglu, Kamil; Giannakos, Michail – ACM Transactions on Computing Education, 2022
Online learning systems should support students preparedness for professional practice by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students' development and engagement with programming is a challenging process. Early career computer…
Descriptors: Adaptive Testing, Online Courses, Programming, Computer Science Education
Peer reviewed Peer reviewed
Direct linkDirect link
Kakavas, Panagiotis; Ugolini, Francesco C. – Research on Education and Media, 2019
This study presents a 13-year (2006-2018) systematic literature review related to the way that computational thinking (CT) has grown in elementary level education students (K-6) with the intention to: (a) present an overview of the educational context/setting where CT has been implemented, (b) identify the learning context that CT is used in…
Descriptors: Computation, Thinking Skills, Elementary School Students, Programming
Peer reviewed Peer reviewed
Direct linkDirect link
Yang, Fan; Akanbi, Temitope; Chong, Oscar Wong; Zhang, Jiansong; Debs, Luciana; Chen, Yunfeng; Hubbard, Bryan J. – Journal of Civil Engineering Education, 2024
Computing technology is reshaping the way in which professionals in the architecture, engineering, and construction industries conduct their business. The execution of construction tasks is changing from traditional 2D to 3D building information modeling (BIM)-based concepts. The use of BIM is expanded and enriched by the introduction of advanced…
Descriptors: Civil Engineering, Engineering Education, Programming Languages, Construction Management
Peer reviewed Peer reviewed
Direct linkDirect link
Jui-Hung Chang; Chi-Jane Wang; Hua-Xu Zhong; Hsiu-Chen Weng; Yu-Kai Zhou; Hoe-Yuan Ong; Chin-Feng Lai – Educational Technology Research and Development, 2024
Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students' learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study…
Descriptors: Artificial Intelligence, Technological Advancement, Student Evaluation, Models
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Mukasheva, Manargul; Omirzakova, Aisara – World Journal on Educational Technology: Current Issues, 2021
The study was carried out from 2018 to 2020 with the challenge - how to assess the level of computational thinking. The research design is mixed since the disclosure of mutual influence of the components of the chain 'learning programming -- computational thinking -- evaluating computational thinking' requires the use of both qualitative and…
Descriptors: Computation, Thinking Skills, Student Evaluation, Cognitive Measurement
Peer reviewed Peer reviewed
Direct linkDirect link
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
Jun Rao – ProQuest LLC, 2021
In recent years, not only has there been a dramatic drop in the number of students enrolling in computer science courses, and attrition from computer science courses continues to be significant. Traditionally, computer programming courses have high failure rates, and as they tend to be core to computer science courses can be a roadblock for many…
Descriptors: Self Efficacy, Student Evaluation, Grading, Computer Science Education
Previous Page | Next Page »
Pages: 1  |  2  |  3  |  4  |  5  |  6  |  7