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Qin Ni; Yifei Mi; Yonghe Wu; Liang He; Yuhui Xu; Bo Zhang – IEEE Transactions on Learning Technologies, 2024
Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…
Descriptors: Cognitive Style, Electronic Learning, Prediction, Identification
Azzi, Ibtissam; Jeghal, Adil; Radouane, Abdelhay; Yahyaouy, Ali; Tairi, Hamid – Education and Information Technologies, 2020
In E-Learning Systems, the automatic detection of the learners' learning styles provides a concrete way for instructors to personalize the learning to be made available to learners. The classification techniques are the most used techniques to automatically detect the learning styles by processing data coming from learner interactions with the…
Descriptors: Classification, Prediction, Identification, Cognitive Style
Mohammed Jebbari; Bouchaib Cherradi; Soufiane Hamida; Abdelhadi Raihani – Education and Information Technologies, 2024
With the advancements in technology and the growing demand for online education, Virtual Learning Environments (VLEs) have experienced rapid development in recent years. This demand was especially evident during the COVID-19 pandemic. The incorporation of new technologies in VLEs provides new opportunities to better understand the behaviors of…
Descriptors: MOOCs, Algorithms, Computer Simulation, COVID-19
Papadatou-Pastou, Marietta; Touloumakos, Anna K.; Koutouveli, Christina; Barrable, Alexia – European Journal of Psychology of Education, 2021
Although learning styles (LS) have been recognised as a neuromyth, they remain a virtual truism within education. A point of concern is that the term LS has been used within theories that describe them using completely different notions and categorisations. This is the first empirical study to investigate education professionals'…
Descriptors: Cognitive Style, Teacher Attitudes, Misconceptions, Learning Theories
Joseph, Lumy; Abraham, Sajimon; Mani, Biju P.; N., Rajesh – Journal of Educational Computing Research, 2022
A fixed learning path for all learners is a major drawback of virtual learning systems. An online learning path recommendation system has the advantage of offering flexibility to select appropriate learning content. Learning Analytics Intervention (LAI) provides several educational benefits, particularly for low-performing students. Researchers…
Descriptors: Cognitive Style, Learning Analytics, Educational Benefits, Integrated Learning Systems
Lertnattee, Verayuth; Wangwattana, Bunyapa – Interactive Learning Environments, 2021
In the academic year of 2019, the designed personalized learning and assessment was applied to the fourth-year pharmacy students who registered for the Pharmacognosy Laboratory in the Faculty of Pharmacy, Silpakorn University. We allowed all students to do the experiment as they preferred. We created a personalized assessment that allowed the…
Descriptors: Individualized Instruction, Pharmaceutical Education, Laboratory Equipment, Identification
Hasibuan, Muhammad Said; Nugroho, Lukito Edi; Santosa, Paulus Insap – Journal of Technology and Science Education, 2019
Currently the detection of learning styles from the external aspect has not produced optimal results. This research tries to solve the problem by using an internal approach. The internal approach is one that derives from the personality of the learner. One of the personality traits that each learner possesses is prior knowledge. This research…
Descriptors: Cognitive Style, Artificial Intelligence, Prior Learning, Identification
Aleksandra Maslennikova; Daniela Rotelli; Anna Monreale – Journal of Learning Analytics, 2023
Students organize and manage their own learning time, choosing when, what, and how to study due to the flexibility of online learning. Each person has unique learning habits that define their behaviours and distinguish them from others. To investigate the temporal behaviour of students in online learning environments, we seek to identify suitable…
Descriptors: Learning Analytics, Online Courses, Time Management, Self Management
Luo, Zhanni; O'Steen, Billy; Brown, Cheryl – Interactive Technology and Smart Education, 2020
Purpose: To build adaptive learning systems for a better learning experience, designers need to identify users' behaviour patterns and provide adaptive learning materials accordingly. This study involved a quasi-experiment and also this paper aims to investigate the accuracy of eye-tracking technology in identifying visualisers and verbalisers and…
Descriptors: Eye Movements, Computer Assisted Testing, Educational Technology, Cognitive Style
Caceffo, Ricardo; Valle, Eduardo; Mesquita, Rickson; Azevedo, Rodolfo – European Journal of Physics Education, 2019
According to the Felder and Silverman Learning Styles Model (FSM), students have learning preferences regarding how information is obtained, processed, perceived and understood. The Index of Learning Styles (ILS) is an online questionnaire created by Felder and Soloman to classify students according to their learning styles. With a priori…
Descriptors: Prediction, Cognitive Style, Models, Science Achievement
Anna Y. Q. Huang; Jei Wei Chang; Albert C. M. Yang; Hiroaki Ogata; Shun Ting Li; Ruo Xuan Yen; Stephen J. H. Yang – Educational Technology & Society, 2023
To improve students' learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from…
Descriptors: Academic Achievement, Tutoring, Artificial Intelligence, Individualized Instruction
Cavojová, Vladimíra; Secara, Eugen-Calin; Jurkovic, Marek; Šrol, Jakub – Applied Cognitive Psychology, 2019
Propensity to judge randomly generated, syntactically correct (i.e., bullshit) statements as profound is associated with a variety of conceptually relevant variables (e.g., intuitive cognitive style and supernatural beliefs). Besides generalizing these findings to a different cultural setting, we examined the relationships to sharing the bullshit…
Descriptors: Syntax, Accuracy, Identification, Deception
Kubat, Ulas – International Journal of Research in Education and Science, 2018
It is important for teachers to know variables such as physical characteristics, intelligence, perception, gender, ability, learning styles, which are individual differences of the learners. An effective and productive learning-teaching process can be planned by considering these individual differences of the students. Since the learners' own…
Descriptors: Individual Differences, Science Teachers, Science Instruction, Teaching Methods
Kauffman, James M.; Schumaker, Jean B.; Badar, Jeanmarie; Hallenbeck, Betty A. – Exceptionality, 2019
We suggest that special education could die among common myths about it. That is, special education could cease to exist, at least as we know it, because its true nature and requirements for its functioning are misunderstood. We discuss only 12 common myths about special education, recognizing that there are many more myths and that the ones we…
Descriptors: Special Education, Misconceptions, Educational Change, School Restructuring
Dorça, Fabiano Azevedo; Lima, Luciano Vieira; Fernandes, Márcia Aparecida; Lopes, Carlos Roberto – Informatics in Education, 2012
Considering learning and how to improve students' performances, an adaptive educational system must know how an individual learns best. In this context, this work presents an innovative approach for student modeling through probabilistic learning styles combination. Experiments have shown that our approach is able to automatically detect and…
Descriptors: Cognitive Style, Models, Automation, Probability

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