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Wang, Tingting; Li, Shan; Huang, Xiaoshan; Pan, Zexuan; Lajoie, Susanne P. – Education and Information Technologies, 2023
Students process qualitatively and quantitatively different information during the dynamic self-regulated learning (SRL) process, and thus they may experience varying cognitive load in different SRL behaviors. However, there is limited research on the role of cognitive load in SRL. This study examined students' cognitive load in micro-level SRL…
Descriptors: Cognitive Processes, Difficulty Level, Learning Strategies, Self Efficacy
Johns, Carolyn; Mills, Melissa; Ryals, Megan – International Journal of Research in Undergraduate Mathematics Education, 2023
Despite the prevalence of undergraduate drop-in mathematics tutoring, little is known about the behaviors of this specific group of tutors. This study serves as a starting place for identifying their behaviors by addressing the research question: what observable behaviors do undergraduate drop-in mathematics tutors exhibit as they interact with…
Descriptors: Undergraduate Students, Tutors, Tutoring, Student Behavior
Wang, Tingting; Li, Shan; Huang, Xiaoshan; Lajoie, Susanne P. – Educational Technology Research and Development, 2023
This study examined how task complexity affected the temporal characteristics of self-regulated learning (SRL) behaviours in clinical reasoning. Eight-eight (N = 88) medical students participated in this study. They were required to diagnose two virtual patients of varying complexity in BioWorld, an intelligent tutoring system (ITS) designed to…
Descriptors: Medical Students, Difficulty Level, Independent Study, Student Behavior
Huang, Xiaoshan; Li, Shan; Wang, Tingting; Pan, Zexuan; Lajoie, Susanne P. – Journal of Computer Assisted Learning, 2023
Background: Medical students use a variety of self-regulated learning (SRL) strategies in different medical reasoning (MR) processes to solve patient cases of varying complexity. However, the interplay between SRL and MR processes is still unclear. Objectives: This study investigates how self-regulated learning (SRL) and medical reasoning (MR)…
Descriptors: Medical Students, Self Management, Problem Solving, Logical Thinking
Yaras, Zübeyde – Journal of Educational Technology and Online Learning, 2021
In the study, it is aimed to investigate the academic procrastination behaviors of teacher candidates in the management of personal learning environments within intelligent tutoring systems. In the study, which was structured in the phenomenological pattern, included in the qualitative research method, the participants were formed from 52 teacher…
Descriptors: Time Management, Student Behavior, Individualized Instruction, Intelligent Tutoring Systems
Feifei Han – Australasian Journal of Educational Technology, 2024
This study examined Chinese undergraduate medical students' acceptance and adoption of intelligent tutoring systems (ITSs) using the general extended technology acceptance model for e-learning via a Likert-scale questionnaire. Specifically, it examined the relations between the five antecedents and the four core components in the model (i.e.,…
Descriptors: Foreign Countries, Influences, Undergraduate Students, Undergraduate Study
Brigitta Septarini Rahmasari; Ahmad Munir; Him'mawan Adi Nugroho – Cogent Education, 2024
Peer Tutoring is a widely used method of teaching English. Peer tutoring, a pedagogical strategy that has the potential to assist Indonesian advanced students in developing inferential understanding by combining it with KWL charts, has, however, received little attention in the context of Indonesian EFL (English as a Foreign Language). The aims…
Descriptors: Peer Teaching, Tutoring, Skill Development, Inferences
Fang Xu – ProQuest LLC, 2023
Measuring reading engagement is critical for monitoring student involvement in academic tasks, as it predicts student reading achievement and further academic success (Anderson et al., 2021; Guthrie et al., 2012). As a multidimensional construct, researchers have employed various methods to assess reading engagement (Gill & Remedios, 2013; Lee…
Descriptors: Learner Engagement, Reading Achievement, College Students, Tutoring
Pfuurai Chimbunde; Godfrey Jakachira – Technology, Pedagogy and Education, 2024
Informed by Charles Wright Mills' sociological imagination and the Technology Acceptance Model, this qualitative study was undertaken to report the emergence of Shadow Education (SE) in teacher education in Zimbabwe amid COVID-19. WhatsApp discussions and Google interviews generated data from 12 lecturers and 12 students, selected using snowball…
Descriptors: Foreign Countries, Teacher Education Programs, COVID-19, Pandemics
Maniktala, Mehak; Cody, Christa; Isvik, Amy; Lytle, Nicholas; Chi, Min; Barnes, Tiffany – Journal of Educational Data Mining, 2020
Determining "when" and "whether" to provide personalized support is a well-known challenge called the assistance dilemma. A core problem in solving the assistance dilemma is the need to discover when students are unproductive so that the tutor can intervene. Such a task is particularly challenging for open-ended domains, even…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Helping Relationship, Prediction
Xu Li; Wee Hoe Tan; Yu Bin; Peng Yang; Qiancheng Yang; Taukim Xu – Education and Information Technologies, 2025
Globally, physical education curricula are progressively integrating intelligent physical education systems, a breakthrough in physical technology. These systems utilise advanced data analytic and sensing technologies, significantly enhancing the interactivity and personalisation of physical activity, thus improving students' athletic performance…
Descriptors: Undergraduate Students, Intelligent Tutoring Systems, Physical Education, Curriculum
Maziriri, Eugine Tafadzwa; Gapa, Parson; Chuchu, Tinashe – International Journal of Instruction, 2020
YouTube as an educational tool has been recently receiving a great deal of attention from researchers and teachers. The study in question therefore investigates this phenomenon. To this end, a modified conceptual model based on the technology acceptance model (TAM) was proposed to test student perceptions, attitudes and intentions to adopt YouTube…
Descriptors: Student Attitudes, Social Media, Web Sites, Usability
Tempelaar, Dirk – International Association for Development of the Information Society, 2022
E-tutorial learning aids as worked examples and hints have been established as effective instructional formats in problem-solving practices. However, less is known about variations in the use of learning aids across individuals at different stages in their learning process in student-centred learning contexts. This study investigates different…
Descriptors: Learning Analytics, Student Centered Learning, Learning Processes, Student Behavior
Makhlouf, Jihed; Mine, Tsunenori – Journal of Educational Data Mining, 2020
In recent years, we have seen the continuous and rapid increase of job openings in Science, Technology, Engineering and Math (STEM)-related fields. Unfortunately, these positions are not met with an equal number of workers ready to fill them. Efforts are being made to find durable solutions for this phenomena, and they start by encouraging young…
Descriptors: Learning Analytics, STEM Education, Science Careers, Career Choice
Šaric-Grgic, Ines; Grubišic, Ani; Šeric, Ljiljana; Robinson, Timothy J. – International Journal of Distance Education Technologies, 2020
The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning…
Descriptors: Learning Analytics, Intelligent Tutoring Systems, Grouping (Instructional Purposes), Undergraduate Students