Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 4 |
| Since 2007 (last 20 years) | 10 |
Descriptor
| Novices | 10 |
| Programming | 10 |
| Statistical Analysis | 10 |
| Computer Science Education | 9 |
| Foreign Countries | 7 |
| Comparative Analysis | 6 |
| Control Groups | 5 |
| Experimental Groups | 5 |
| Programming Languages | 5 |
| College Students | 4 |
| Introductory Courses | 4 |
| More ▼ | |
Source
Author
| Ahmad, Rodina Binti | 1 |
| Becker, Brett A. | 1 |
| Chang, Chih-Kai | 1 |
| Cui, Wen | 1 |
| D'Souza, Daryl | 1 |
| Fathi, Moein | 1 |
| Fukazawa, Yoshiaki | 1 |
| Glanville, Graham | 1 |
| Goslin, Kyle | 1 |
| Hooshyar, Danial | 1 |
| Horng, Shi-Jinn | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 10 |
| Reports - Research | 10 |
| Tests/Questionnaires | 1 |
Education Level
| Higher Education | 8 |
| Postsecondary Education | 6 |
| Secondary Education | 2 |
| Elementary Education | 1 |
| High Schools | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
| Group Embedded Figures Test | 1 |
What Works Clearinghouse Rating
Shi, Nianfeng; Cui, Wen; Zhang, Ping; Sun, Ximing – Journal of Educational Computing Research, 2018
This research applies the roles of variables to the novice programmers in the C language programming. The results are evaluated using the Structure of Observed Learning Outcomes (SOLO) taxonomy. The participants were divided into an experimental group and a control group. The students from the control group learned programming in the traditional…
Descriptors: Computer Science Education, Programming, Programming Languages, Novices
Saito, Daisuke; Washizaki, Hironori; Fukazawa, Yoshiaki – Journal of Information Technology Education: Research, 2017
Aim/Purpose: When learning to program, both text-based and visual-based input methods are common. However, it is unclear which method is more appropriate for first-time learners (first learners). Background: The differences in the learning effect between text-based and visual-based input methods for first learners are compared the using a…
Descriptors: Programming, Computer Science Education, Comparative Analysis, Questionnaires
Hooshyar, Danial; Ahmad, Rodina Binti; Yousefi, Moslem; Fathi, Moein; Horng, Shi-Jinn; Lim, Heuiseok – Innovations in Education and Teaching International, 2018
In learning systems and environment research, intelligent tutoring and personalisation are considered the two most important factors. An Intelligent Tutoring System can serve as an effective tool to improve problem-solving skills by simulating a human tutor's actions in implementing one-to-one adaptive and personalised teaching. Thus, in this…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Skill Development, Programming
Becker, Brett A.; Glanville, Graham; Iwashima, Ricardo; McDonnell, Claire; Goslin, Kyle; Mooney, Catherine – Computer Science Education, 2016
Programming is an essential skill that many computing students are expected to master. However, programming can be difficult to learn. Successfully interpreting compiler error messages (CEMs) is crucial for correcting errors and progressing toward success in programming. Yet these messages are often difficult to understand and pose a barrier to…
Descriptors: Computer Science Education, Programming, Novices, Error Patterns
Hsu, Ting-Chia; Hwang, Gwo-Jen – Educational Technology & Society, 2017
Programming concepts are important and challenging to novices who are beginning to study computer programming skills. In addition to the textbook content, students usually learn the concepts of programming from the web; however, it could be difficult for novice learners to effectively derive helpful information from such non-structured open…
Descriptors: Web Sites, Teaching Methods, Computer Science Education, Information Sources
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
Chang, Chih-Kai – Journal of Educational Computing Research, 2014
Scratch, a visual programming language, was used in many studies in computer science education. Most of them reported positive results by integrating Scratch into K-12 computer courses. However, the object-oriented concept, one of the important computational thinking skills, is not represented well in Scratch. Alice, another visual programming…
Descriptors: Foreign Countries, College Freshmen, Information Technology, Computer Science Education
Uysal, Murat Pasa – Contemporary Educational Technology, 2014
The introductory computer programming (CP) course has been taught for three decades in the faculty. Besides pursuing CP technology, one major goal has been enhancing learners' problem-solving (PS) skills. However, the current situation has implied that this might not be the case. Therefore, a research was conducted to investigate the effects of a…
Descriptors: Introductory Courses, Programming, Problem Solving, Skill Development
Wei, Liew Tze; Sazilah, Salam – Journal of Interactive Learning Research, 2012
This study investigated the effects of visual cues in multiple external representations (MER) environment on the learning performance of novices' program comprehension. Program codes and flowchart diagrams were used as dual representations in multimedia environment to deliver lessons on C-Programming. 17 field independent participants and 16 field…
Descriptors: Programming, Multimedia Materials, Computer Assisted Instruction, Computer Science Education
Hung, Y.-C. – IEEE Transactions on Education, 2012
This paper investigates the impact of combining self explaining (SE) with computer architecture diagrams to help novice students learn assembly language programming. Pre- and post-test scores for the experimental and control groups were compared and subjected to covariance (ANCOVA) statistical analysis. Results indicate that the SE-plus-diagram…
Descriptors: Foreign Countries, Control Groups, Experimental Groups, Web Sites

Peer reviewed
Direct link
