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IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)

作者： Jawed, Soyiba Amin, Hafeez Ullah Malik, Aamir Saeed Faye, Ibrahima Univ Teknol PETRONAS Ctr Intelligent Signal & Imaging Res Dept Elect & Elect Engn Tronoh 31750 Perak Malaysia

ISBN: (纸本)9781538624715

The purpose of this study is to distinguish the visual learners from non-visual learners while learning, having no background knowledge of the contents. The learners are distinguished analysing their brain patterns. EEG data were recorded during learning and memory tasks using 128 channels machine from a sample of thirty - four healthy university students. The students were shown the animated learning content in video format for eight minutes. The brain waves were measured during learning task. The study characterizes and distinguishes between the visual learners and non-visual learners considering the extracted brain patterns. The wavelet features are computed for the recorded EEG and are filtered into alpha and beta sub bands. These features are then given as an input to the Long-Short Term Memory (LSTM) Recurrent neural network (RNN). Feature classification using LSTM Recurrent neural network has attained training accuracy of 89% and 85% for beta and alpha bands for Learning session 1(Learning 1), 86% and 87% for Learning session 2(Learning 2).

关键词： EEG Feature extraction LSTM RNN learning style visual learner

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Deep Learning-Based Assessment Model for Real-Time Identification of visual learners Using Raw EEG

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 378-390页

作者： Jawed, Soyiba Faye, Ibrahima Malik, Aamir Saeed Brno Univ Technol Fac Informat Technol Brno 60167 Czech Republic Univ Teknol PETRONAS Ctr Intelligent Signal & Imaging Res CISIR Dept Fundamental & Appl Sci Seri Iskandar 32610 Malaysia Brno Univ Technol Fac Informat Technol Dept Comp Syst Brno 60167 Czech Republic

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-level feature representation for EEG visual learning identification. Existing computer-aided systems that use electroencephalograms and machine learning can reasonably assess learning styles. Despite their potential, offline processing is often necessary to eliminate artifacts and extract features, making these methods unsuitable for real-time applications. The dataset was chosen with 34 healthy subjects to measure their EEG signals during resting states (eyes open and eyes closed) and while performing learning tasks. The subjects displayed no prior knowledge of the animated educational content presented in video format. The paper presents an analysis of EEG signals measured during a resting state with closed eyes using three deep learning techniques: Long-term, short-term memory (LSTM), Long-term, short-term memory-convolutional neural network (LSTM-CNN), and Long-term, short-term memory-Fully convolutional neural network (LSTM-FCNN). The chosen techniques were based on their suitability for real-time applications with varying data lengths and the need for less computational time. The optimization of hypertuning parameters has enabled the identification of visual learners through the implementation of three techniques. LSTM-CNN technique has the highest average accuracy of 94%, a sensitivity of 80%, a specificity of 92%, and an F1 score of 94% when identifying the visual learning style of the student out of all three techniques. This research has shown that the most effective method is the deep learning-based LSTM-CNN technique, which accurately identifies a student's visual learning style.

关键词： Raw-electroencephalogram deep learning machine learning visual learner classification learning styles

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Requirement Analysis of E-Content for visual learners

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2014年第1期9卷 78-81页

作者： Shaffiei, Z. A. Hamidi, S. R. Jauhari, N. T. Osman, N. Univ Teknol MARA UiTM Shah Alam Selangor Malaysia

This paper studies on IT Project Management (ITS510) subject which is taught by using textbook and simple PowerPoint slides. Due to ineffectiveness of teaching materials, this scenario becomes one of the reasons that affect the students' result. Therefore, this study had been proposed specifically for visual learners to attract their attention in class. The objective of this study is to identify the user requirements of visual learners for E-content. ADDIE model which is derived from ISD model had been selected to complete this project. This model consists of analysis, design, development, implementation and evaluation phases. In this study, the researchers only focused on analysis phase while the next phases are planned to be continued in future research. The questionnaire had been distributed to get the requirements from visual learner users. It is hoped that the requirement analysis can be used as an idea to design and develop future E-content for visual learners. At the end, it can give a lot of benefits to learners where they can access the content everywhere and anytime without time constraint. While, for educators, the E-content can help them to improve their teaching styles and it can be used as an aided tool to teach students in class.

关键词： E-content E-learning requirement analysis visual learner

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Differentiating between visual and non-visual learners using EEG power spectrum entropy 7

Differentiating between visual and non-visual learners using...

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World Engineering, Science and Technology Congress (ESTCON) / 7th International Conference on Intelligent and Advanced System (ICIAS)

作者： Jawed, Soyiba Amin, Hafeez Ullah Malik, Aamir Saeed Faye, Ibrahima Univ Teknol PETRONAS Dept Elect & Elect Engn Ctr Intelligent Signal & Imaging Res Tronoh 31750 Perak Malaysia

ISBN: (纸本)9781538672693

The purpose of this study is to distinguish the visual learners from visual non-learners while learning, having no background knowledge of the contents. The learners are distinguished analysing their brain patterns. EEG data were recorded during learning and memory tasks using 128 channels machine from a sample of thirty-four healthy university students in two sessions. The students were shown the animated learning content in video format for eight minutes. The brain waves were measured during leaning task. The study characterizes and distinguish between the visual learners and non-visual learners considering the extracted brain patterns. The power spectral entropy features are computed for the recorded EEG and is filtered into alpha and beta sub bands. The most suitable features are selected using principal component analysis (PCA). These features are than given as an input to the k-nearest neighbour(k-NN) classifier. Feature classification using k-nearest neighbour has attained testing accuracy of 96% accuracy for alpha and 95% beta bands for first learning session and 97% and 94% for second learning session. The results show's that the alpha and beta power spectral entropy represent distinct and stable EEG signatures for visual learners and non-visual learners while performing the learning tasks.

关键词： EEG Feature extraction k-NN learning style visual learner

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Ergonomic visualization of Logistical Control Parameters for Flexible Production Planning and Control in Future Manufacturing Systems 17th

Ergonomic Visualization of Logistical Control Parameters for...

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2nd International Conference on Learning and Collaboration Technologies / 17th International Conference on Human-Computer Interaction

作者： Nelles, Jochen Kuz, Sinem Schlick, Christopher M. Rhein Westfal TH Aachen Inst Ind Engn & Ergon Aachen Germany

ISBN: (纸本)9783319213835;9783319213828

The research presented in this paper focuses on modeling and visualization of logistical control parameters. In the developed application, the internal logistical control parameters cycle time, timeliness, inventory, and capacity were considered. A novel concept to visualize these parameters was developed on the basis of the well-known funnel model. The original model was enhanced by and compared to a further developed model. Our approach to visualize the internal logistical control parameters is based on a paddle-wheel model. An online survey with both models and different visualizations of the internal logistical control parameters was carried out to compare both models and the different visualizations. Both models are investigated with regard to comprehension and visual learning versus verbal learning. In the poster session, we will present further development of the paddle-wheel model to show the cause-effect relationship between the individual internal logistical control parameters and draw a comparison with the original funnel model.

关键词： Mental model Situation awareness Verbal learner visual learner Production planning and control Internal logistical control parameters

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Moving beyond text, embracing the visual: the Virtual Land Law Field Trip Project @ Sussex

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LAW TEACHER 2021年第2期55卷 186-197页

作者： Ni Drisceoil, Verona Univ Sussex Law Brighton E Sussex England

Set against the pedagogic literature outlining the benefits of the "visual" in instruction, this article charts the conceptualisation, development and impact of the Virtual Land Law Field Trip Project @ Sussex. Drawing on the findings from a student survey and focus group evaluation, the article outlines the strengths and weaknesses of the project. Notwithstanding limitations, and indeed the inherent challenges and obstacles to incorporating "innovative" approaches in teaching, the article concludes by strongly advocating a greater commitment to moving beyond the dominance of text in legal education and embracing the visual where possible. Utilising more dynamic visual representations such as video enhances instruction, supports cognitive memory and is an excellent way to provide meaningful practical insight examples. Moreover, embracing visual representations offers an alternative space within which to include counter voices and narratives to challenge, the curriculums we teach.

关键词： Video visual learner land law multimedia legal education

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Effect of Program visualization to Teach Computer Programming in a Resource Constrained Classroom

Effect of Program Visualization to Teach Computer Programmin...

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IEEE 5th International Conference on Technology for Education (T4E)

作者： Awasekar, Dipali D. Walchand Inst Technol Dept Informat Technol Solapur India

ISBN: (纸本)9780769551418

Acquisition of programming concepts and skills is a key goal of computer science and engineering curricula. It has been shown that students find programming to be a difficult and complex cognitive task. Program visualizations have been shown to improve students' understanding, but most studies have been conducted in resource equipped classrooms, for example, in which students have personalized access to laptops. In this paper, we report findings from a two-group experimental study (N=60) in a resource constrained classroom with a single computer and mounted LCD. One group was taught with an interactive program visualization tool and the other with traditional chalk and blackboard approach. Analysis showed a statistically significant higher performance on a post-test for the program visualization group compared to the traditional group. This difference was seen regardless of students' visual / verbal learning style, leading to the conclusion that use of program visualization in large resource constrained classroom is an effective strategy to teach computer programming.

关键词： Program visualization resource constrained classroom learningobject visual learner verbal learner

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Perceptions of Students Who Are Blind or visually Impaired Regarding Accessibility, Usability, and Navigation of Online University Coursework

Perceptions of Students Who Are Blind or Visually Impaired R...

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作者： Mohanna A. Almohanna Texas Tech University

学位级别：博士

Given the importance of multiplicity, annexation, multimodal culture of learning, and innovation in technology, Universal Design for Learning, also known as UDL, has all the potential to address and mitigate most of higher education s greatest unrelenting concerns and issues regarding access for all students. Specifically, UDL can address the obstinately low rate of perseverance, success, and degree accomplishment manifested at some of the colleges and universities of the present day. The purpose of this study was to investigate how students who are blind or visually impaired (BVI) perceive the implementation and efficacy of UDL in online university coursework. The population of this study was students who are visually impaired and who have used a computer in college coursework with the assistance of any screen reader program.

关键词： Legal blindness students with low vision students with total blindness students with no or limited functional vision tactual learner visual learner auditory learner universal design for learning online coursework

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Generative artificial intelligence tools for diverse learning styles in design education

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INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING 2024年

作者： Chandrasekera, Tilanka Hosseini, Zahrasadat Perera, Ubhaya Hyscher, Anna Bazhaw Oklahoma State Univ Dept Design & Merchandising Stillwater OK USA

The integration of Artificial Intelligence (AI) is transforming design education by automating processes and enhancing learning. Early exposure to AI offers students a competitive edge in critical thinking and problem-solving. AI tools can inspire space planning, improve workflow, reduce cognitive load, and foster creativity. This study explores how the VARK model, which categorizes learning styles into visual, Auditory, Reading/Writing, and Kinesthetic, influences student engagement with AI tools. In an interior design classroom study involving 32 sophomore students, AI tools were used to create floor plans and generate 3D images. The study assessed the correlation between learning styles and technology acceptance. Results revealed no strong correlations between individual learning styles and perceived ease of use, perceived usefulness, or actual use of AI tools. However, visual and kinesthetic learners displayed higher engagement, suggesting that these modalities may benefit more from AI-enhanced design education. The study also confirmed that perceived ease of use and usefulness are critical factors in technology acceptance, aligning with the Technology Acceptance Model (TAM). The findings suggest that AI tools can be broadly effective across diverse learning styles, with a universal perception of their utility in design education. This research highlights the potential of AI to support creative problem-solving and prepare students for future challenges in a digital design landscape. While learning styles influence engagement, AI tools' usability and educational benefits are consistently recognized, demonstrating their transformative potential in design education.

关键词： Artificial intelligence design education learning styles visual learner design creativity

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Multimodal Approach to Identify Learning Strategies of visual and Verbal learners

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2017年第10期12卷 76-94页

作者： Sethi, Mahendra Ambadas Lomte, Santosh S. Shinde, Ulhas B. Dr BAM Univ Aurangabad Maharashtra India VDF Sch Engn & Technol Latur India Dr BAM Univ Fac Engn & Technol Aurangabad Maharashtra India

This paper presents a multimodal approach to identify visual or verbal learning style and learning strategies of visual and verbal learner. A web based tool is developed to deliver the V-Square test and LST Test. V-Square identifies visual and verbal learners. It is based on visual/verbal dimension of Felder Silverman Model. Learning strategy test (LST) identifies the learning strategies of the learners. LST test uses questions associated with aural and readiwrite dimensions of VARK model. This paper summarizes implementation of method, algorithms and data analysis of participants collected from web based tool in an eLearning environment and manual test.

关键词： learner Style Learning Strategies Learning assessment strategies visual learner verbal learner FSLSM VARK

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