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Jan Delcker; Joana Heil; Dirk Ifenthaler; Sabine Seufert; Lukas Spirgi – International Journal of Educational Technology in Higher Education, 2024
The influence of Artificial Intelligence on higher education is increasing. As important drivers for student retention and learning success, generative AI-tools like translators, paraphrasers and most lately chatbots can support students in their learning processes. The perceptions and expectations of first-years students related to AI-tools have…
Descriptors: Artificial Intelligence, Learning Processes, Higher Education, College Freshmen
Isaac N. Treves; Jonathan Cannon; Eren Shin; Cindy E. Li; Lindsay Bungert; Amanda O'Brien; Annie Cardinaux; Pawan Sinha; John D. E. Gabrieli – Journal of Autism and Developmental Disorders, 2024
Some theories have proposed that autistic individuals have difficulty learning predictive relationships. We tested this hypothesis using a serial reaction time task in which participants learned to predict the locations of a repeating sequence of target locations. We conducted a large-sample online study with 61 autistic and 71 neurotypical…
Descriptors: Autism Spectrum Disorders, Adults, Learning Processes, Visual Perception
Nicole M. Hutchins; Gautam Biswas – British Journal of Educational Technology, 2024
This paper provides an experience report on a co-design approach with teachers to co-create learning analytics-based technology to support problem-based learning in middle school science classrooms. We have mapped out a workflow for such applications and developed design narratives to investigate the implementation, modifications and temporal…
Descriptors: Problem Based Learning, Teaching Methods, Science Instruction, Learning Analytics
Hoa-Huy Nguyen; Kien Do Trung; Loc Nguyen Duc; Long Dang Hoang; Phong Tran Ba; Viet Anh Nguyen – Education and Information Technologies, 2024
This article presents the results of an experiment in personalizing course content and learning activity model tailored for online courses based on students' learning styles. The main research objectives are to design and pilot a model to determine students' learning styles to create personalized online courses. The study also addressed an…
Descriptors: Models, Online Courses, Cognitive Style, Classification
Milagros Castilllo-Montoya; Kari B. Taylor – International Journal of Qualitative Studies in Education (QSE), 2024
In higher education, educators establish complex learning outcomes for students to achieve during their collegiate studies. These outcomes, given their complexity, necessitate an advanced approach to studying the teaching and learning process as it unfolds in real time. We brought together extant literature and our own research experience studying…
Descriptors: Higher Education, Learning Processes, Teaching Methods, Classroom Environment
Allison S. Theobold; Megan H. Wickstrom; Stacey A. Hancock – Journal of Statistics and Data Science Education, 2024
Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these…
Descriptors: Computer Science Education, Coding, Data Science, Statistics Education
Abdullahi Yusuf; Norah Md Noor; Shamsudeen Bello – Education and Information Technologies, 2024
Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In…
Descriptors: Learning Analytics, Student Behavior, Video Technology, Classification
Patricia Domínguez-Gómez; Flavio Celis d’Amico – Informatics in Education, 2024
The creative programming language Processing can be used as a generative architectural design tool, which allows the designer to write design instructions (algorithms) and compute them, obtaining graphical outputs of great interest. This contribution addresses the inclusion of this language in the architecture curriculum, within the context of…
Descriptors: Undergraduate Students, Architectural Education, Architecture, Courseware
Mariyam Abdulhadi; Fred Awaah; Deborah Agbanimu; Emmanuel Okyere Ekwam; Emmanuella Sefiamor Heloo – Journal of Research in Innovative Teaching & Learning, 2024
Purpose: The lecture method has been compared with teaching methods such as flip learning, cooperative learning and simulations to establish which holds the key to students' understanding of concepts. What is bereft in the education literature is its comparative efficiency with the culturo-techno contextual approach (CTCA) in the teaching of…
Descriptors: Foreign Countries, Developing Nations, Computer Science Education, Cultural Context
Felicia Meusel; Nadine Scheller; Günter Daniel Rey; Sascha Schneider – Education and Information Technologies, 2024
Color has been investigated as a signaling cue in multimedia learning environments, guiding the learner's attention and as an emotional design element, increasing the learner's motivation and, thus, improving learning outcomes. Retrieval cues (e.g., visual cues, odor, sound) facilitating memory retrieval have been primarily investigated in…
Descriptors: Foreign Countries, Color, Student Motivation, Cues
Ghada ElSayad – Education and Information Technologies, 2024
In student-centric learning environments, such as blended learning, students' metacognitive self-regulation is required to plan, monitor, and control their learning processes and achieve positive learning outcomes. The lack of metacognitive self-regulation may lead students to encounter difficulties that, eventually, affect their learning…
Descriptors: Undergraduate Students, Student Attitudes, Blended Learning, Metacognition
Seyedahmad Rahimi; Valerie J. Shute – Educational Technology Research and Development, 2024
Research fields related to learning (e.g., educational technology and learning sciences) have historically focused on what questions using traditional methods (e.g., comparing different learning tools and methods). New methodologies that are grounded in learning, engagement, and motivational theories are needed to additionally address the how…
Descriptors: Evaluation Methods, Psychometrics, Learning Processes, Educational Technology
Shiyan Jiang; Joey Huang; Hollylynne S. Lee – Educational Technology Research and Development, 2024
Analyzing qualitative data from learning processes is considered "messy" and time consuming (Chi in J Learn Sci 6(3):271-315, 1997). It is often challenging to summarize and synthesize such data in a manner that conveys the richness and complexity of learning processes in a clear and concise manner. Moreover, qualitative data often…
Descriptors: Learning Processes, Data Analysis, Qualitative Research, Visual Aids
Bhagya Maheshi; Wei Dai; Roberto Martinez-Maldonado; Yi-Shan Tsai – Journal of Computer Assisted Learning, 2024
Background: Feedback is central to formative assessments but aligns with a one-way information transmission perspective obstructing students' effective engagement with feedback. Previous research has shown that a responsive, dialogic feedback process that requires educators and students to engage in ongoing conversations can encourage student…
Descriptors: Feedback (Response), Learning Analytics, Dialogs (Language), Learner Engagement
William A. Bergeron; Christopher M. Parfitt; Ellen Hahn – Alabama Journal of Educational Leadership, 2024
The modern era of accountability and standards movement was ushered in by publishing "A Nation at Risk: The Imperative for Educational Reform" (1983). Accountability measures and other educational changes have increasingly consumed the principal's time, leading to the heightened importance of teacher leaders (Bryant et al., 2017).…
Descriptors: Instructional Leadership, Teaching Methods, Learning Processes, Rural Schools