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Jin Wei-Kocsis; Moein Sabounchi; Gihan J. Mendis; Praveen Fernando; Baijian Yang; Tonglin Zhang – IEEE Transactions on Education, 2024
Contribution: A novel proactive and collaborative learning paradigm was proposed to engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary artificial intelligence (AI)-cybersecurity knowledge. Specifically, the proposed learning paradigm contains: 1) an immersive learning environment to…
Descriptors: Computer Security, Artificial Intelligence, Interdisciplinary Approach, Models
K. G. Srinivasa; Aman Singh; Kshitij Kumar Singh Chauhan – IEEE Transactions on Education, 2024
Contribution: This article investigates the impact of gamified learning on high school students (grades 9-12) in computer science, emphasizing learner engagement, knowledge improvement, and overall satisfaction. It contributes insights into the effectiveness of gamification in enhancing educational outcomes. Background: Gamification in education…
Descriptors: High School Students, Gamification, Computer Science Education, Critical Thinking
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)
Mike, Koby; Hazzan, Orit – IEEE Transactions on Education, 2023
Contribution: This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect…
Descriptors: Engineering Education, Computer Science Education, Data Science, Introductory Courses
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
Alexander Tobias Neumann; Yue Yin; Sulayman Sowe; Stefan Decker; Matthias Jarke – IEEE Transactions on Education, 2025
Contribution: This research explores the benefits and challenges of developing, deploying, and evaluating a large language model (LLM) chatbot, MoodleBot, in computer science classroom settings. It highlights the potential of integrating LLMs into LMSs like Moodle to support self-regulated learning (SRL) and help-seeking behavior. Background:…
Descriptors: Computer Science Education, Databases, Information Systems, Classroom Environment
Precup, Radu-Emil; Hedrea, Elena-Lorena; Roman, Raul-Cristian; Petriu, Emil M.; Szedlak-Stinean, Alexandra-Iulia; Bojan-Dragos, Claudia-Adina – IEEE Transactions on Education, 2021
This article proposes an approach based on experiments to teach optimization technique (OT) courses in the Systems Engineering curricula at undergraduate level. Artificial intelligence techniques in terms of nature-inspired optimization algorithms and neural networks are inserted in the lecture and laboratory parts of the syllabus. The experiments…
Descriptors: Engineering Education, Teaching Methods, Systems Approach, Undergraduate Students
Corno, Fulvio; De Russis, Luigi – IEEE Transactions on Education, 2017
The increasing complexity of the new breed of distributed intelligent systems, such as the Internet of Things, which require a diversity of languages and protocols, can only be tamed with design and programming best practices. Interest is also growing for including the human factor, as advocated by the "ambient intelligence" (AmI)…
Descriptors: Programming, Best Practices, Artificial Intelligence, Student Projects
Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen – IEEE Transactions on Education, 2012
An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…
Descriptors: Puzzles, Artificial Intelligence, Mathematics, Computer Science Education
Lavesson, N. – IEEE Transactions on Education, 2010
This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning…
Descriptors: Artificial Intelligence, Diagnostic Tests, Foreign Countries, Self Evaluation (Individuals)
Gaudioso, E.; Hernandez-del-Olmo, F.; Montero, M. – IEEE Transactions on Education, 2009
Teachers in e-learning play a crucial role as facilitators of the students' learning experiences. To this end, a teacher needs to monitor, understand and evaluate the activity of the students in the course. What is more, e-learning can be enhanced if tools for supporting teachers in this task are provided. In this paper, two experiences are…
Descriptors: Foreign Countries, Physics, Teacher Effectiveness, Teacher Role