VARK learning styles Research Topics

VARK learning style research topic is now extensively employed to improve the learning. Varies technologies and parameters are examined in this research to get a best result. Below we have providing some VARK learning style concepts, applications, technologies and parameters.

  1. Define VARK learning style?

At the start we first take a look at the definition of VARK learning, in this method is very famous that sensorial approaches can classify learning styles. The full form of VARK is Visual, Auditory, Reading/Writing, and Kinesthetic. When learners learn in this kind, we can analyze how they best absorb and process data. This learning style is how they best consume and process data for the individuals’ information as maintained by this method. Reading and writing students excel when using taking notes and written documents. Realize one’s learning kind may improve educational efficacy and maintenance.

  1. What is VARK learning style?

Next to the definition of VARK learning style we see the detailed explanation of VARK. It consider four various learning kinds. Individuals are classified by VARK corresponding to how they like to process information and get it, developed by Neil Fleming. Diagrams spatial and images illustrations help visual learners recognize things more perfectly. Auditory learners like to have discussions and explanations for listening. Reading and writing learners often prefer to read or write to understand information, and they excel in written content. Kinesthetic learners like practical experience and physical actions. By recognizing one’s preferred learning kinds, teachers and students may adapt their teaching to increase understanding and retention.

  1. Where VARK learning style used?

After the explanation of VARK learning style, we discuss where to incorporate VARK learning style. It is widely used to Educational Institutions, Training Programs, Online Learning Platforms, Study Skills Workshops, Personal Development Programs, Educational Consulting, and Teacher Training.

  1. Why VARK learning style technology proposed? , previous technology issues

AHP learning style based systems for flexible e-learning that include VARK learning styles face more problems, including the inability to update course material and learner inclination, cannot provide the learner understanding accuracy. The limitation can be reducing to use of training semesters size and learners feedback cannot be providing.  The main issues can be Inconsistent update, Imperfection in prediction accuracy, Performance and overfitting issues, and Feedback constraints.

  1. Algorithms/ protocols

The VARK learning style is proposed in this work and it overcomes the difficulties in this work, here we offer some methods or techniques to be employed for VARK learning are Analytic Hierarchy Process (AHP), Takagi Sugeno Fuzzy Inference Engine (TSFIE), Multi-Criteria Decision-Making based Analytic Hierarchy Process with a Link-based Collaborative Filtering (MCDM-AHP-LCF), Felder-Silverman Learning Style Model (FSLSM), Fuzzy Cognitive Map (FCM) of the methodologies used in VARK learning.

  1. Comparative study / Analysis

Following the algorithms or protocols to be utilized in our work, we have to compare several techniques to analyze the corresponding outcomes; here we have provided some technologies to be compared are mentioned below:

  • For VARK questionnaire-based AHP ranking we have introduce Analytic Hierarchy Process (AHP). The VARK-based ranking is successful in learner learning style environment, and every learner uses a register or login, website for learning scenarios. All the learners enter based on questions like audio, write, kinaesthetic, read, and video questions. For VARK questionnaires on Kinesthetic “K” Aural “A”, Visual “V”, and Read/Write “R” used for checking learners understanding based. VARK questionnaire used to find learners‟s learning style, VARK test results ranking preferred for learners.
  • For the list of topics is based on the software, computer, and hardware. These topics differ based on the learner’s performance in the VARK test. That VARK exam mentions every topic.
  • For pre-learning assessment we suggest Takagi Sugeno Fuzzy Inference Engine (TSFIE) algorithm. Every learner completed the pre-learning assessment exam after that exam outcomes updated on the learner record.
  • For recommendation system we introduce Multi-Criteria Decision-Making based Analytic Hierarchy Process with a Link-based Collaborative Filtering(MCDM-AHP-LCF) algorithm. Several determination-making in learning scenarios using the MCDM method. The learners to use a subject suggestion system based on rankings used by AHP technology. The course straining used by LCF. We have suggested different document types like audio lectures, video lectures, textual lectures, etc… And it is used to improve the learner’s learning performances. The Takagi sugeno fuzzy inference engine (TSFIE) algorithm used for every suggested document can updated for the learners.
  • For post learning assessment we recommend Felder-Silverman Learning Style ModelFSLSM algorithm. It is used to improve the exam outcomes accuracy and successfully categorize exam results on both sides. This exam established on the suggested topics and also post learning exam questions same as the pre-learning exam
  • For student feedback we introduce Fuzzy Cognitive Map (FCM) algorithm. This method used for find the student’s condition on encourage every students and appearing exam outcomes. Learners feedback collected successfully based on the pre and post-exam outcomes. We have use five set of questions for calculating learner’s feedback.
  1. Simulation results / Parameters

After the comparison study, to get the relevant result for the VARK learning style, we must compare many parameters.

For VARK learning style we compare the parameter like accuracy, precision,recall,F-F1-source,RMSE and MAE  these all parameters were compared with learning model, prediction performance, and iteration time these are the parameters that we compared to find the best outcomes.

  1. Dataset LINKS / Important URL

In this the parameters we selected are compared to obtain the best outcomes, and then afterwards here we provide some important links that is useful to overview the VARK learning style learners, application and some additional references for any clarification we go through the following links:

  1. VARK learning style Applications

We have provide some application for VARK learning style such as

Visual Learners: It is used to Graphs and Charts, Videos and Images, Mind Maps, and Color Coding.

Auditory Learners: Lectures and Discussions, Podcasts and Audiobooks, Verbal Instruction, and Group Discussions.

Reading/Writing Learners: Textbooks and Articles, Note-taking, Essays and Reports, Lists and Bullet Points.

Kinesthetic Learners: Hands-on Activities, Role-Playing, Physical Models, and Field Trips.

  1. Topology for VARK learning style

VARK learning style also provide some topology like VARK questionnaire-based AHP ranking, List of Topics, Pre-learning assessment, Recommendation system (MCDM-AHP ranking), Post-learning assessment, and Student feedback.

  1. Environment in VARK learning style

The VARK learning style structure classifies four major types like reading/writing, kinesthetic, visual, and auditory. It is dependent on the some specific process and collecting information. Diagrams, charts, and other visual help learners recollect knowledge better. Preferring spoken explanations, verbal communication, discussions, lectures, and other auditory help learners successfully, after being given written documents, textbooks, and note-taking workouts, writing and reading learners do well. And, kinaesthetic learners find their best learning conditions in which they may cooperate with the documents via hands-on practices. The learners prefer these types of VARK learning, recognizing one’s the learning style should be adapt learning scenario and approaches to improve the exam results.

  1. Simulation tools

The proposed system needs the subsequent software requirements. We require that the VARK learning style to implement this work by it is developed by the tool Python 3.11.4. The operating system that is required for the work is Windows-10 (64-bit). These are all the software requirements that we employed for VARK learning style.

  1. Results

VARK learning style is utilized improve accuracy and overall performance in allover learner conditions; we have suggested in this research to overcome the previous problems or issues. In this we compared different approaches to analyze and utilize different parameters to find the perfect output for this research. The software requirements that need to be implemented the research the tool is Python 3.11.4.

VARK learning styles Research Ideas:

  1. Methods to assess adults’ learning styles and factors affecting learning in health education: A scoping review
  2. Utilizing Clustering Algorithms to Provide Vark Learning Style Recommendations
  3. Implications of understanding the undergraduate nursing students’ learning styles: A discussion paper
  4. Utilizing Clustering Algorithms to Provide Vark Learning Style Recommendations
  5. Prediction and Classification of Learning Styles using Machine Learning Approach
  6. Effects of learning styles on disciplines digital careers in Thailand
  7. Systematic Literature Review Detection Learning Style
  8. Electrician Career Guidance Based on Integration Multi-modal Learning and Recreational Rctivities for Grade 8 Student
  9. A Systematic Literature Review Enhanced Felder Silverman Learning Style Models (FSLSM)
  10. The Use of Immersive Technologies to Implement a Multimodal Approach in the Educational Process
  11. Deep learning-based assessment model for Real-time identification of visual learners using Raw EEG
  12. A general model for an instructional video-based personalized programming learning environment and its practical implications
  13. A Measurement of Online-learning Style Based on a Modified Model
  14. Exploring the learning styles of hearing-impaired sign language users and non-sign language users
  15. A Smart Testing Model Based on Mining Semantic Relations
  16. Predicting Learning Styles Using Machine Learning Classifiers
  17. Understanding Relationships among Learning Styles, Learning Activities and Academic Performance: From a Computer Programming Course Perspective
  18. Implementing Gamified Learning in University Environment
  19. Personalised Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review
  20. Step Into My Mind Palace: Exploration of a Collaborative Paragogy Tool in VR
  21. An evaluation of virtual reality maintenance training for industrial hydraulic machines
  22. A Preliminary Study on Learners’ Personal Traits for Modelling Learner Profiles in ITS: A Sensor-free Approach
  23. Modeling Learner Profiles using Ontologies and Machine Learning
  24. A Personalised Support Centre for Mathematics in Engineering Adapted to Students’ Preferences, Knowledge and Needs
  25. Implications of understanding the undergraduate nursing students’ learning styles: A discussion paper
  26. An adaptive and interactive learning toolkit (iLearn)
  27. Review and classification of content recommenders in E-learning environment
  28. Leveraging different learning styles for improved knowledge distillation in biomedical imaging
  29. GRL-LS: A learning style detection in online education using graph representation learning
  30. Effects of flipped classroom on nursing psychomotor skill instruction for active and passive learners: A mixed methods study
  31. Assessing the effect of flight information presentation styles on the usability of airline web booking interface
  32. Adaptive talent journey: Optimization of talents’ growth path within a company via Deep Q-Learning
  33. Technology-Assisted Language Learning Adaptive Systems: A Comprehensive Review
  34. 3-D Printed Fracture Models Improve Resident Performance and Clinical Outcomes in Operative Fracture Management
  35. Automatic prediction of presentation style and student engagement from videos
  36. An Evolving Learning Style Detection Approach for Online Education Using Bipartite Graph Embedding
  37. Review and classification of content recommenders in E-learning environment
  38. Innovative Digital Pedagogy: Adaptive Learning Platform Integration in Nurse Practitioner Curriculum
  39. Online teaching and learning of a pharmacy curriculum designed for active learning and professional skills development – A report of students’ perceptions and learning experience in two international campuses
  40. Virtual and Hybrid Classes as The Challenge Assumed During The SARS-CoV-2 Pandemic: An Interdisciplinary Qualitative Approach
  41. Optimized RB-RNN: Development of hybrid deep learning for analyzing student’s behaviours in online-learning using brain waves and chatbots
  42. Prediction and Classification of Learning Styles using Machine Learning Approach
  43. Onboarding orientation for novice nurse faculty: A quality improvement pilot project
  44. Achieving trust in health-behavior-change artificial intelligence apps (HBC-AIApp) development: a multi-perspective guide
  45. Validating a Model of Smart Service System, Supporting Teachers to Create Educational Maze Video Games
  46. An Evolving Learning Style Detection Approach for Online Education Using Bipartite Graph Embedding
  47. MI brain-computer interfaces: A concise overview
  48. The design and implementation of intelligent ubiquitous learning Multi-Agent Context-Aware System
  49. Dynamic analysis of grain quality during drying in fluidised beds