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Cristina Cambra; Núria Silvestre – International Journal of Disability, Development and Education, 2024
The main objective of this study was to explore the benefits of the use of educational audio-visual materials in facilitating learning for students with hearing loss. The study analysed whether students with hearing loss had a visual learning preference when they watched an audio-visual and if the images present contributed to the retention of…
Descriptors: Foreign Countries, Spanish Speaking, Language of Instruction, Students with Disabilities
Mainali, Bhesh – International Electronic Journal of Mathematics Education, 2021
Preferences for solution methods have an important implication teaching and learning mathematics and students' mathematical performances. In the domain of learning mathematics, there are two modes of processing mathematical information: verbal logical and visual-pictorial. Learners who process mathematical information using verbal logical and…
Descriptors: Teaching Methods, Mathematics Instruction, Preferences, Mathematics Achievement
Oshra Aloni; Michal Zion; Ornit Spektor-Levy – Instructional Science: An International Journal of the Learning Sciences, 2024
Students' individual characteristics influence the effectiveness of instruction and learning and, therefore, the depth of learning. This study brings forth the voices of middle school students regarding their science learning preferences through four modalities: visual, auditory, sensorimotor, and agency support. We examined the relationship…
Descriptors: Student Attitudes, Preferences, Cognitive Style, Student Characteristics
Emberson, Lauren L.; Misyak, Jennifer B.; Schwade, Jennifer A.; Christiansen, Morten H.; Goldstein, Michael H. – Developmental Science, 2019
Statistical learning (SL), sensitivity to probabilistic regularities in sensory input, has been widely implicated in cognitive and perceptual development. Little is known, however, about the underlying mechanisms of SL and whether they undergo developmental change. One way to approach these questions is to compare SL across perceptual modalities.…
Descriptors: Statistics, Learning Processes, Infants, Learning Modalities
Chetty, Nithya Dewi Subramaniam; Handayani, Lina; Sahabudin, Noor Azida; Ali, Zuraina; Hamzah, Norhasyimah; Rahman, Nur Shamsiah Abdul; Kasim, Shahreen – International Journal of Evaluation and Research in Education, 2019
Individuals learn in different ways using several learning styles, but lecturers may not always share material and learning experiences that match students' learning preferences. Mismatches between learning and teaching styles can lead to disappointment with students are taking, and lead to underperformance among them. The aim of this study is to…
Descriptors: Cognitive Style, Teaching Styles, Academic Achievement, College Students
Rosar, Michaela; Weidlich, Joshua – Research and Practice in Technology Enhanced Learning, 2022
Creativity is an increasingly recognized construct in technology-enhanced learning. However, our understanding of how creativity interacts with the design of online learning environments to affect learning experiences is still limited. For example, do creative students benefit from different learning environment designs than those benefitting…
Descriptors: Online Courses, Creativity, Independent Study, Personality Traits
Pamela Luft; Charlotte Brochu – American Annals of the Deaf, 2023
Online learning environments are challenging for deaf and hard of hearing (DHH) individuals. A major concern is split attention, which occurs when one simultaneously attends to multiple stimuli, a situation that characterizes most multimedia presentations and instruction that combines sound, text, images, graphs or charts, and video. Needing to…
Descriptors: Fatigue (Biology), Visual Learning, Deafness, Electronic Learning
Kurniawan, Citra; Setyosari, Punaji; Kamdi, Waras; Ulfa, Saida – Problems of Education in the 21st Century, 2019
The purpose of this research was to build a classification model and to measure the correlation of self-efficacy with visual-verbal preferences using data mining methods. This research used the J48 classifier and linear projection method as an approach to see patterns of data distribution between self-efficacy and visual-verbal preferences. The…
Descriptors: Engineering Education, Self Efficacy, Preferences, Verbal Learning
Khan, Sher Afgan; Arif, Manzoor Hussain; Yousuf, Muhammad Imran – Bulletin of Education and Research, 2019
Major focus of this study was on visual, auditory and kinesthetic learning preferences of college students as given in Dunn and Dunn model (Dunn & Dunn, 1993) and their relationship with academic achievement of students. The nature of study was quantitative, descriptive and correlational. All (male and female, science and arts) students…
Descriptors: Correlation, Preferences, Academic Achievement, High Achievement
Nja, Cecilia Obi; Umali, Cornelius-Ukpepi Beneddtte; Asuquo, Edoho Emmanuel; Orim, Richard Ekonesi – Educational Research and Reviews, 2019
This research work investigated the influence of learning styles on academic performance among Science Education undergraduates of the University of Calabar, Nigeria. The learning model used for this study comprised, visual, auditory, kindergarten, global analytical impulsive, reflective, individual and group models. Expo facto design was used for…
Descriptors: Cognitive Style, Academic Achievement, Science Education, Undergraduate Students
Wouters, Pieter; van der Meulen, Esmee S. – International Journal of Game-Based Learning, 2020
Adapting learning to the level and preferences of learners and game-based learning have increasingly received much attention. The current study examined whether learning styles based on the Felder-Silverman classification (perception, input, processing and organization of information) influence learning in GBL. Only the input and processing scales…
Descriptors: Cognitive Style, Educational Games, Preferences, Mathematics Education
Lui, Catherine J.; Ferrin, Scott E.; Baum, Donald R.; Randall, Vance E. – Journal of Hispanic Higher Education, 2020
This article addresses the question of whether higher education Hispanic students of different nationalities have different perceptual learning style preferences. Independent samples t test results suggest students of non-Mexican heritage prefer visual learning styles more than students of Mexican heritage. ANOVA results show older students and…
Descriptors: Hispanic American Students, Cognitive Style, Preferences, Visual Learning
Alkooheji, Lamya; Al-Hattami, Abdulghani – International Education Studies, 2018
The purpose of this study was to determine what factors other than individual preferences affect undergraduate students' learning style preferences, if learning style is influenced by gender, age, college affiliation and/or type of activities. A total of 185 students from the University of Bahrain, Bahrain, participated in an online VARK (Visual,…
Descriptors: Cognitive Style, Foreign Countries, College Students, Visual Learning
Okulich-Kazarin, Valery; Zhurba, Mykola; Bokhonkova, Yuliia; Losiyevska, Olga – European Journal of Contemporary Education, 2019
The aim of the study was a verification of statistical hypotheses about the preferences of Ukrainian and Polish Law-students related the method of learning at lectures. There were two groups of Ukrainian Law-students and one group of Polish Law-students. The main research methods were the methods of sociological research. There are statistically…
Descriptors: Law Students, Preferences, Student Attitudes, Cognitive Style
Echeverria, Vanessa; Martinez-Maldonado, Roberto; Shum, Simon Buckingham; Chiluiza, Katherine; Granda, Roger; Conati, Christina – Journal of Learning Analytics, 2018
From a human-centred computing perspective, supporting the interpretation of educational dashboards and visualizations by the people intended to use them exposes critical design challenges that may often be trivialized. Empirical evidence already shows that "usable" visualizations are not necessarily effective from an educational…
Descriptors: Story Telling, Visual Learning, Data Analysis, Visual Aids
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