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Yingbin Zhang; Yafei Ye; Luc Paquette; Yibo Wang; Xiaoyong Hu – Journal of Computer Assisted Learning, 2024
Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an…
Descriptors: Learning Analytics, Learning Processes, Test Reliability, Psychometrics
Catalina Cortázar; Iñaki Goñi; Andrea Ortiz; Miguel Nussbaum – ACM Transactions on Computing Education, 2024
Integrating graduate education with professional skills development is still a challenge. People's beliefs about learning impact their learning processes. Therefore, we need to understand the mindset of graduates to determine best practices for promoting professional skills development. In this study, we explore the perspective of computing…
Descriptors: Computer Science Education, Graduate Students, Computer Literacy, Job Skills
Ailsa Zayyan Salsabila; R. Yugo Kartono Isal; Harry B. Santoso – Journal of Educators Online, 2025
This study aims to provide recommendations for interaction design to enhance students' task interpretation, as one of the crucial aspects of Self-Regulated Learning (SRL) in online learning environments. Utilizing the Engineering Design Metacognitive Questionnaire (EDMQ), open-ended questions, and in-depth interviews, this study examines the…
Descriptors: Learning Processes, Electronic Learning, College Students, Computer Science Education
O. S. Adewale; O. C. Agbonifo; E. O. Ibam; A. I. Makinde; O. K. Boyinbode; B. A. Ojokoh; O. Olabode; M. S. Omirin; S. O. Olatunji – Interactive Learning Environments, 2024
With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to…
Descriptors: Cognitive Style, Individualized Instruction, Learning Processes, Preferences
Fulya Kula; Nelly Litvak; Tracy S. Craig – IEEE Transactions on Education, 2024
The sample mean in statistics is a concept of great importance, with its properties being extensively utilized in other areas, such as computer science. This research centers on the concept of the sample mean and its characteristics in a cohort of computer engineering students undertaking a required course in statistics at a university in the…
Descriptors: Computer Science Education, Engineering Education, Learning Processes, Statistics
Zachary M. Savelson; Kasia Muldner – Computer Science Education, 2024
Background and Context: Productive failure (PF) is a learning paradigm that flips the order of instruction: students work on a problem, then receive a lesson. PF increases learning, but less is known about student emotions and collaboration during PF, particularly in a computer science context. Objective: To provide insight on students' emotions…
Descriptors: Student Attitudes, Psychological Patterns, Fear, Failure
Xu, Weiqi; Wu, Yajuan; Ouyang, Fan – International Journal of Educational Technology in Higher Education, 2023
Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students' discourses, behaviors, and socio-emotions, it is of critical importance to examine…
Descriptors: Cooperative Learning, Problem Solving, Computer Science Education, Programming
von Hausswolff, Kristina – Computer Science Education, 2022
Background and Context: Research in programming education seems to show that hands-on writing at the keyboard is beneficial for learning, but we lack an explanation of why that is and an underlying theory to anchor that explanation. Objective: The first objective is to lay out a theoretical foundation for understanding the learning situation when…
Descriptors: Programming, Computer Science Education, Novices, Student Experience
Jinbo Tan; Lei Wu; Shanshan Ma – British Journal of Educational Technology, 2024
The purpose of this study was to investigate the collaborative dialogue patterns of pair programming and their impact on programming self-efficacy and coding performance for both slow- and fast-paced students. Forty-six postgraduate students participated in the study. The students were asked to solve programming problems in pairs; those pairs'…
Descriptors: Coding, Programming, Computer Science Education, Self Efficacy
Barua, Antara Mahanta; Bharali, Sruti Sruba – Asian Association of Open Universities Journal, 2023
Purpose: The purpose of the case study is to investigate the perception of computer science learners at Krishna Kanta Handiqui State Open University (KKHSOU) regarding the use of gamification and to identify the challenges in implementing gamified teaching-learning processes in open and distance learning (ODL) institutions.…
Descriptors: Gamification, Electronic Learning, Computer Science Education, College Students
Coto, Mayela; Mora, Sonia; Grass, Beatriz; Murillo-Morera, Juan – Computer Science Education, 2022
Background and context: Emotions are ubiquitous in academic settings and affect learning strategies, motivation to persevere, and academic outcomes, however they have not figured prominently in research on learning to program at the university level. Objective: To summarize the current knowledge available on the effect of emotions on students…
Descriptors: Programming, Computer Science Education, Psychological Patterns, Emotional Response
Embracing Artificial Intelligence to Improve Self-Directed Learning: A Cybersecurity Classroom Study
Jim Marquardson – Information Systems Education Journal, 2024
Generative artificial intelligence (AI) tools were met with a mix of enthusiasm, skepticism, and fear. AI adoption soared as people discovered compelling use cases--developers wrote code, realtors generated narratives for their websites, students wrote essays, and much more. Calls for caution attempted to temper AI enthusiasm. Experts highlighted…
Descriptors: Artificial Intelligence, Capstone Experiences, Computer Security, Information Security
Kasakowskij, Regina; Haake, Joerg M.; Seidel, Niels – International Educational Data Mining Society, 2023
Improving competence requires practicing, e.g. by solving tasks. The Self-Assessment task type is a new form of scalable online task providing immediate feedback, sample solution and iterative improvement within the newly developed SAFRAN plugin. Effective learning not only requires suitable tasks but also their meaningful usage within the…
Descriptors: Self Evaluation (Individuals), Student Behavior, College Students, Learning Processes
David Roldan-Alvarez; Francisco J. Mesa – IEEE Transactions on Education, 2024
Artificial intelligence (AI) in programming teaching is something that still has to be explored, since in this area assessment tools that allow grading the students work are the most common ones, but there are not many tools aimed toward providing feedback to the students in the process of creating their program. In this work a small sized…
Descriptors: Intelligent Tutoring Systems, Grading, Artificial Intelligence, Feedback (Response)
Mingli Han – International Society for Technology, Education, and Science, 2023
Teaching robotics courses online is challenging due to the complexity of the interdisciplinary topics involved. One of the most challenging topics is 3D coordinate transformations. Students often struggle to grasp the concept of 3D coordinate transformations and their relevance to real-world robotic applications. This paper applies the Scholarship…
Descriptors: Self Evaluation (Individuals), Robotics, Assignments, Computer Software