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Tamisha Thompson; Jennifer St. John; Siddhartha Pradhan; Erin Ottmar – Journal of Computer Assisted Learning, 2025
Background: Educational technologies typically provide teachers with analytics regarding student proficiency, but few digital tools provide teachers with process-based information about students' variable problem-solving strategies as they solve problems. Utilising design thinking and co-designing with teachers can provide insight to researchers…
Descriptors: Mathematics Instruction, Educational Technology, Problem Solving, Instructional Design
Shai Goldfarb Cohen; Gideon Dishon – Journal of Computer Assisted Learning, 2025
Background: The rising importance of digital technologies in everyday communication has played a key role in processes of political polarization and rising mistrust and intolerance. We suggest this crisis could be productively conceptualized as a crisis in perspective-taking--the tendency and competency to actively consider others' mental and…
Descriptors: Affordances, Barriers, Perspective Taking, Electronic Learning
Yuhui Jing; Chengliang Wang; Zhaoyi Chen; Shusheng Shen; Rustam Shadiev – Journal of Computer Assisted Learning, 2024
Background Study: Technology-supported learning environments, act as significant observational and enabling indicators for evaluating and encouraging the digital revolution of education, are of vital importance in current educational research. Keeping track of the dynamics of technology-supported learning environment research allows for the…
Descriptors: Educational Technology, Technology Uses in Education, Educational Environment, Educational Research
Yanqing Wang; Shaoying Gong; Ning Jia; Ying Liu – Journal of Computer Assisted Learning, 2025
Background: Online learning is becoming increasingly popular among learners. To enhance the effectiveness of online learning, researchers have embedded an affective pedagogical agent (PA) on the computer screen to help regulate learners' emotions and support their learning. However, previous research has paid little attention to the effects of…
Descriptors: Metacognition, Prompting, Electronic Learning, Computer Uses in Education
Yuko Suzuki; Fridolin Wild; Eileen Scanlon – Journal of Computer Assisted Learning, 2024
Background: Cognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non-invasive biometric sensors, unbiased real-time detection of cognitive load in AR is expected. However, a range of sensors and parameters are used in various subject…
Descriptors: Computer Simulation, Cognitive Processes, Difficulty Level, Physiology
Sule Biyik Bayram; Gamze Özener; Nilay Çakici; Handan Eren; Sinan Aydogan; Deniz Öztürk; Emel Gülnar; Nurcan Çaliskan – Journal of Computer Assisted Learning, 2024
Background: There are deficiencies in ensuring the permanence of some theoretical information taught in nursing education and transferring it to practice environment. Mobile-assisted teaching can be useful to eliminate deficiencies. The aim of this study was to determine the effect of mobile-assisted teaching on nursing students' learning…
Descriptors: Foreign Countries, Nursing Students, Human Body, Electronic Learning
Cohen, Anat; Soffer, Tal; Henderson, Michael – Journal of Computer Assisted Learning, 2022
Background: The rapid globalization along with the growing trend of openness and sharing approach enabled widespread of digital technologies all over the world. However, we can still find differences between countries in technology use and perceptions of usefulness for learning. Understanding students' use of educational technology and their…
Descriptors: Technology Uses in Education, Educational Technology, Electronic Learning, COVID-19
Stephanie L. Day; Jin Kyoung Hwang; Tracy Arner; Danielle S. McNamara; Carol M. Connor – Journal of Computer Assisted Learning, 2025
Background: The affordances of technology, such as e-books, offer the opportunity to increase engagement and provide personalised feedback to promote students' learning outcomes. E-books that encourage the use of comprehension monitoring strategies in real time may support stronger outcomes. Objectives: The purpose of this feasibility study was to…
Descriptors: Electronic Books, Reading Comprehension, Word Recognition, Elementary School Students
Zhe Wang; Sara Abercrombie; Rachel Wong; Yuxin Ren; Shiting Dai – Journal of Computer Assisted Learning, 2024
Background: There are two major types of pictures that have been the focus of multimedia learning research, namely, seductive and interpretational pictures. Despite an increasing body of literature documenting the effects of either seductive or interpretational pictures added to text-based materials, there is a paucity of research explicitly…
Descriptors: Electronic Learning, Computers, Computer Assisted Instruction, Visual Aids
Bissonnette, Steve; Boyer, Christian – Journal of Computer Assisted Learning, 2022
Tingir et al. (2017) concluded from their meta-analysis that the subject areas taught through mobile devices had significantly higher achievement scores (d = 0.48) than the ones taught with traditional teaching methods. Given the relatively high positive effect of mobile devices on student achievement, we carefully analysed the selected research…
Descriptors: Meta Analysis, Electronic Learning, Handheld Devices, Academic Achievement
Jiarui Hou; James F. Lee; Stephen Doherty – Journal of Computer Assisted Learning, 2025
Background: Recent research has demonstrated the potential of mobile-assisted learning to enhance learners' learning outcomes. In contrast, the learning processes in this regard are much less explored using eye tracking technology. Objective: This systematic review study aims to synthesise the relevant work to reflect the current state of eye…
Descriptors: State of the Art Reviews, Eye Movements, Electronic Learning, Handheld Devices
Hui-Tzu Hsu; Chih-Cheng Lin – Journal of Computer Assisted Learning, 2024
Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning…
Descriptors: Intention, Vocabulary Development, Handheld Devices, College Students
Mugur V. Geana; Dan Cernusca; Pan Liu – Journal of Computer Assisted Learning, 2024
Background: Education is, after gaming, the second largest sector embracing augmented reality (AR) at an accelerated pace, yet studies on AR's potential as an efficient learning environment had mixed results. Objectives: This study's primary objective is to test students' interaction with graphical 3D elements in AR and its impact on information…
Descriptors: Learner Engagement, Computer Simulation, Technology Uses in Education, Information Dissemination
Weipeng Yang; Xinyun Hu; Ibrahim H. Yeter; Jiahong Su; Yuqin Yang; John Chi-Kin Lee – Journal of Computer Assisted Learning, 2024
Background: Artificial Intelligence (AI) literacy is a crucial part of digital literacy that all individuals should possess in today's technologically advanced world. Despite the potential benefits that AI education offers, little research has been done on how to teach AI literacy to children. Objectives: This study aimed to fill that gap by…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Digital Literacy
Novak, Elena; Mulvey, Bridget K. – Journal of Computer Assisted Learning, 2021
There is growing demand in our society to cultivate creativity and foster innovation. Design thinking has been successfully practiced as an educational framework for supporting innovation in educational and work contexts. However, research on design thinking education that facilitates the acquisition of knowledge related to design process and…
Descriptors: Design, Innovation, Educational Technology, Instructional Design

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