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Athitaya Nitchot; Lester Gilbert – Education and Information Technologies, 2025
Learning programming is a complex process that requires understanding abstract concepts and solving problems efficiently. To support and motivate students, educators can use technology-enhanced learning (TEL) in the form of visual tools for knowledge mapping. Mytelemap, a prototype tool, uses TEL to organize and visualize information, enhancing…
Descriptors: Learning Motivation, Concept Mapping, Programming, Computer Science Education
Melissa T. A. Simarmata; Gwo-Guang Lee; Hoky Ajicahyadi; Kung-Jeng Wang – Education and Information Technologies, 2024
Teaching computer programming language remotely presents particular difficulties due to its requirement for abstract and logical thinking. There is a dearth of research specifically examining the potential factors that determine student performance when distance self-learning is conducted for programming language education. This study aims to…
Descriptors: Distance Education, Independent Study, Computer Science Education, Programming
Using Analytics to Predict Students' Interactions with Learning Management Systems in Online Courses
Ali Alshammari – Education and Information Technologies, 2024
In online education, it is widely recognized that interaction and engagement have an impact on students' academic performance. While previous research has extensively explored interactions between students, instructors, and content, there has been limited exploration of course design elements that promote the fourth type of interaction:…
Descriptors: Learning Analytics, Learning Management Systems, Academic Achievement, Correlation
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
Ko, Chia-Yin; Leu, Fang-Yie – IEEE Transactions on Education, 2021
Contribution: This study applies supervised and unsupervised machine learning (ML) techniques to discover which significant attributes that a successful learner often demonstrated in a computer course. Background: Students often experienced difficulties in learning an introduction to computers course. This research attempts to investigate how…
Descriptors: Undergraduate Students, Student Characteristics, Academic Achievement, Predictor Variables
Varga, Erika B.; Sátán, Ádám – Hungarian Educational Research Journal, 2021
The purpose of this paper is to investigate the pre-enrollment attributes of first-year students at Computer Science BSc programs of the University of Miskolc, Hungary in order to find those that mostly contribute to failure on the Programming Basics first-semester course and, consequently to dropout. Our aim is to detect at-risk students early,…
Descriptors: Identification, At Risk Students, Computer Science Education, Undergraduate Students

Sarah K. Mason; Matt Hancock; Izzy Thornton – Grantee Submission, 2024
Rural Mississippi schools often face challenges in providing equitable access to Advanced Placement (AP) courses, particularly in STEM fields. This lack of access limits opportunities for high-achieving students to pursue rigorous STEM coursework and related careers. The AP STEM program aims to improve access to AP STEM courses for high-achieving…
Descriptors: Rural Schools, STEM Education, Advanced Placement Programs, Access to Education
Mohammed Alzaid – ProQuest LLC, 2022
Distributed self-assessments and reflections empower learners to take the lead on their knowledge gaining evaluation. Both provide essential elements for practice and self-regulation in learning settings. Nowadays, many sources for practice opportunities are made available to the learners, especially in the Computer Science (CS) and programming…
Descriptors: Learning Analytics, Self Evaluation (Individuals), Programming, Problem Solving
Chen, Chen; Jeckel, Stuart; Sonnert, Gerhard; Sadler, Philip M. – International Journal of Computer Science Education in Schools, 2019
This study examines the relationship between students' pre-college experience with computers and their later success in introductory computer science classes in college. Data were drawn from a nationally representative sample of 10,197 students enrolled in computer science at 118 colleges and universities in the United States. We found that…
Descriptors: Computer Science Education, Programming, Academic Achievement, College Students
Schwarzenberg, Pablo; Navon, Jaime; Pérez-Sanagustín, Mar – Journal of Computing in Higher Education, 2020
The flipped classroom gives students the flexibility to organize their learning, while teachers can monitor their progress analyzing their online activity. In massive courses where there are a variety of activities, automated analysis techniques are required in order to process the large volume of information that is generated, to help teachers…
Descriptors: Models, Blended Learning, Teaching Methods, Electronic Learning
Chen, Chen; Haduong, Paulina; Brennan, Karen; Sonnert, Gerhard; Sadler, Philip – Computer Science Education, 2019
Background and Context: The relationship between novices' first programming language and their future achievement has drawn increasing interest owing to recent efforts to expand K-12 computing education. This article contributes to this topic by analyzing data from a retrospective study of more than 10,000 undergraduates enrolled in introductory…
Descriptors: Computer Science Education, Programming Languages, College Students, Computer Attitudes
Kokoç, Mehmet; Kara, Mehmet – Educational Technology & Society, 2021
The purposes of the two studies reported in this research are to adapt and validate the instrument of the Evaluation Framework for Learning Analytics (EFLA) for learners into the Turkish context, and to examine how metacognitive and behavioral factors predict learner performance. Study 1 was conducted with 83 online learners enrolled in a 16-week…
Descriptors: Learning Analytics, Electronic Learning, Measures (Individuals), Test Validity
Ruiz, Samara; Urretavizcaya, Maite; Rodríguez, Clemente; Fernández-Castro, Isabel – Interactive Learning Environments, 2020
A positive emotional state of students has proved to be essential for favouring student learning, so this paper explores the possibility of obtaining student feedback about the emotions they feel in class in order to discover emotion patterns that anticipate learning failures. From previous studies about emotions relating to learning processes, we…
Descriptors: College Students, Computer Science Education, Emotional Response, Student Reaction
Quille, Keith; Bergin, Susan – Computer Science Education, 2019
Background and Context: Computer Science attrition rates (in the western world) are very concerning, with a large number of students failing to progress each year. It is well acknowledged that a significant factor of this attrition, is the students' difficulty to master the introductory programming module, often referred to as CS1. Objective: The…
Descriptors: Computer Science Education, Introductory Courses, Programming, Student Attrition
Hao, Qiang; Barnes, Brad; Wright, Ewan; Branch, Robert Maribe – British Journal of Educational Technology, 2017
This study investigated the online help-seeking behaviors of computer science students with a focus on the effect of achievement goals. The online help-seeking behaviors investigated were online searching, asking teachers online for help, and asking peers or unknown people online for help. One hundred and sixty-five students studying computer…
Descriptors: College Students, Computer Science Education, Computer Mediated Communication, Help Seeking