Learning Styles Of Learners Research Articles

The effect of educational situations according to the dimension (sequential - total) in learning the net lift

The aim of the research is to identify the learners’ preferred educational style according to the dimension (sequential and total), by designing educational situations according to the dimension (sequential and total) in learning the deadlift. The researcher assumed that there were statistically significant differences between the results of the pre- and post-tests for the two experimental research groups in learning the deadlift. For the sake of post-testing for students, the researcher used the experimental method to suit the nature of the research. The research population was intentionally selected from first-level students in the College of Physical Education and Sports Sciences - Samarra University for the academic year (2023-2024), as it reached (97) male and female students distributed into three divisions. Study section: Section (A) numbered (33), Section (B) numbered (32), and Section (C) numbered (32). As for the research sample, it was identified and chosen randomly. The first experimental group was represented by Section (B), which adopted the dimension ( Sequential) and its number is (15) students who tend to the sequential style according to the Felder and Silverman scale. As for the second experimental group, it was represented by Section (C) and its number is (15) students, which adopted the (total) dimension of those who tend to the holistic style according to the Felder and Silverman scale. Thus, it reached The proportion of the research sample was (30.927%) from members of the overall population, and the researcher used the assessment of the apparent form of performance as a means of collecting data. The main research experiment lasted (5) weeks with two educational units during one week for each group, and the statistical means were (arithmetic mean, standard deviation, Percentage, mode, skewness coefficient, simple correlation coefficient, T-test for related samples - for independent samples. The results showed that the educational situations prepared according to the dimension (sequential - total) have a positive impact on learning the movement of the net for the two experimental research groups under study, and it was recommended The researcher uses educational situations that take into account the different learning styles of learners because this has a positive impact on learning outcomes.

Analysis of Students' Mathematical Concept Understanding Ability on Circle Material Based on Learning Style

This research aims to analyze students' mathematical concept understanding ability in solving circle material problems by considering learning styles. The method used in this research is qualitative method with descriptive approach. This research was conducted on students of class XI Culinary 1 at SMKN 6 Semarang which amounted to 36 students. Data were collected through mathematical concept understanding test and student learning style questionnaire. The subjects in this study were three students who represented each student's learning style. The techniques used to collect data were observation, mathematical concept understanding ability test, interview and documentation. The results showed variations in learning styles and variations in students' mathematical concept understanding abilities in solving circle material problems. Learners with visual and auditory learning styles tend to have better understanding abilities in solving the problem, while learners with kinesthetic styles show different patterns. So it can be concluded that the learning style of learners can affect students' mathematical understanding ability in solving circle material problems, therefore the importance of recognizing the learning style of students in the mathematics learning process. Each learner with a different learning style has a different mathematical concept understanding ability, so an approach that is in accordance with the characteristics of learners is needed to improve students' mathematical concept understanding ability.

Learning path recommendation with multi-behavior user modeling and cascading deep Q networks

An online learning platform has become an important channel for learners to obtain knowledge due to its easy access and rich resources. In order to meet online learners' short-term needs with frequent changes and long-term learning goals during learning process, this paper focuses on user modeling and learning path recommendation, and we propose a new method for learning path recommendation through multi-behavior user modeling and cascading deep Q networks (cDQN-PathRec). Our model uses a knowledge graph-based multi-behavior transformer architecture for users’ state modeling, in which a learner's knowledge background, learning styles, learning settings, and learning preferences are taken into consideration. We use a cascading DQN with a two-level reward function to help an agent converge towards a balanced overall and local optima and to generate a learning path recommendation. Comprehensive experiments on two real-world online learning datasets demonstrate effectiveness of the proposed cDQN-PathRec method.

UTILIZING ARTIFICIAL INTELLIGENCE IN EDUCATION TO ENHANCE TEACHING EFFECTIVENESS

Artificial Intelligence in Education (AIEd) has been evolving for some time, and the advent of GPT chat at the end of December 2022 has opened up new opportunities, potentials, and challenges in educational practice. Advances in computational technology and information processing have led to widespread applications of Artificial Intelligence (AI) in the field of education. Over the last 20 years, the number of papers on AIED has been steadily increasing, with a dramatic rise since 2015 until the present. In its brief history, AIEd has undergone several paradigm shifts. This research aims to explore the use of AI in education by examining the publication trends sourced from metadata from Google Scholar, PubMed, CrossRef, OpenAlex, and Scopus. The development and application of Artificial Intelligence (AI) technology, particularly in education, significantly supports educational reform and profoundly influences the learning styles of learners. Artificial Intelligence in Education (AIED) can assist teachers in preparing teaching materials, presentation media, and accurate evaluations. Furthermore, AIED can help students adapt their traditional learning styles according to their differences, thus realizing intelligent teaching that meets students' learning needs. Teachers' positive perceptions of educational technology (ET) are beneficial for using AI technology to aid teaching positively, which in turn can enhance teaching effectiveness. Overall, the trend of AIEd development has successfully empowered learner personalization, enabling learners to think critically and innovatively, and fostering personalized learning.

Implementasi Pendekatan Teaching at the Right Level (TaRL) melalui Pembelajaran Berdiferensiasi untuk Siswa SD Kelas II

The diverse learning needs and understanding levels of students require teachers to be creative in crafting learning experiences. The realization of student-centered and empowering learning is one solution to the diverse needs and levels of understanding of students. Differentiated learning is a learning effort based on the needs, characteristics and learning styles of learners. Learning by implementing the Teaching at The Right Level approach can accommodate the learning needs of diverse students. This study aims to provide an overview of the implementation of the Teaching at The Right Level (TaRL) approach in differentiated learning in grade II of SD Negeri Kalicari 01. The research methodology used is descriptive qualitative. The data sources used are the implementation of learning itself and field notes obtained during the research. Data collection techniques include interviews, observations, and document analysis. The results of the study show that the application of the TaRL approach in differentiated learning can be done by grouping students based on their level of understanding, designing learning plans, and implementing learning according to the plan that has been prepared. Differentiated learning with the TaRL approach is a positive good practice because it can accommodate the needs and skills of students in grade II SD Negeri Kalicari 01.

Spectral clustering algorithm based web mining and quadratic support vector machine for learning style prediction in E-learning platform

A learning system, which is composed of a computer and the internet as the major elements, is termed an e-learning platform. It also promotes the education standard with the utilization of modern technology and equipment. Meanwhile, to enhance the standard of education significantly, it is important to predict the learning style of the users with the assistants of feedback and supervision. Nevertheless, it will avert the inherent correlation among e-learning behaviors. Hence, to predict the learning style automatically we propose a novel Spectral Clustering algorithm based Quadratic Support Vector Machine (E-SVM) approach. Our proposed approach employs two phases: (i) Utilizing the Web usage mining approach the learning secrets are extracted from the log files of learners. (ii) The classification of learning styles of learners is effectuated with the proposed approach. Experiments are demonstrated with Python package and analyzed the performance. For simulation, we have utilized real-time dataset and compared the results with the state-of-art approaches. Our approach surpasses all the other approaches.

Potential Risks of Artificial Intelligence Integration into School Education: A Systematic Review

Currently, artificial intelligence (AI) is being rapidly incorporated into K-12 education because of its increasing social and pedagogical importance. The integration of AI in K-12 education is likely to have a profound influence on the lives and learning styles of learners, teaching approaches of teachers, and the whole mechanism of school management systems. As AI technologies are new to K-12 school curriculum, the research on AI for K-12 classrooms is under explored. In this study, the current state of AI integration in school education and risks associated with it were explored. The study using a systematic review method attempted to explore the findings, observations, and results of recent research regarding the possible risk factors of AI integration in K-12 education. At initial search using predefined key terms, 390 articles were recorded. Using inclusion and exclusion criteria, 71 articles covering 34 journals and other publications were selected for final analysis. Selected 71 articles reported that AI innovation incorporation into K-12 education is associated with certain risks and challenges. Through a systematic review technique, we categorized them into major 6 risk areas namely Privacy and Autonomy Risks, AI Biases, Accuracy and Functional Risks, Deepfakes and FATE Risks, Social Knowledge and Skill Building Risks, and Risk in Shifting Teacher's Role. The study also explored these risk areas to provide an overview of how these are connected with teaching and learning process of K-12 education.

Analisis dan Solusi Kesulitan Belajar dalam Pendidikan Islam

This research aims to identify the various forms of learning difficulties experienced by learners and present solutions inspired by Qur'anic teachings to overcome these problems. This research used a literature study which involved collecting data through searching and reviewing relevant literature. Literature sources were selected based on inclusion and exclusion criteria. The data analysis process involved summarizing the data, presenting relevant data, and drawing conclusions. The results of the study suggest: 1) Learning difficulties in the Islamic view are influenced by internal (physical and spiritual aspects) and external (environmental situations and conditions) factors. Learning difficulties are classified based on criteria such as the type of difficulty, the field of study affected, the nature of the difficulty, and the factors causing it. 2) To overcome learning difficulties, the solutions exemplified in the Qur'an include increasing the motivation of learners, selecting appropriate learning methods, services that focus on the differences of learners, providing examples of stories and teachings that show the importance of the process of seeking knowledge. In addition, educators are expected to recognize the characteristics and learning styles of learners, provide services that are appropriate to differences, and be good role models. Thus, learning difficulties in the Islamic context are not only an individual problem, but also include external factors and require a holistic approach.

Context and Learning Style Aware Recommender System for Improving the E-Learning Environment

The learning management system (LMS) is an e-learning software that raised the interest of disparate learners’ groups. However, learners have difficulties in finding learning resources tailored to their preferences in the best way at the right time. Making the learning process more efficient and pleasant for learners can be achieved by using context and learning styles such as customizing aspects. This study proposes a new data-driven approach to retrieve learners' characteristics using traces of their activities based on the Felder-Silverman Learning Style Model (FSLSM). In this research, the traces of 714 learners who enrolled in three agronomy courses taught at IAV HASSAN II (winter session 2019, 2020, and 2021) were analyzed. Learners are categorized into clusters by their preference level for global/sequential learning styles, using an unsupervised clustering method. Then a classifier model tailored to our requirements was trained and based on the learner's learning style and their current context, a learning object recommendation list is proposed for them. The results revealed that the k-means algorithm performed well in identifying learning styles (LS) and the use of context features defined from the learners' adaptive close environments improved learning performance with an accuracy of over 96% given that most of the learners preferred a global learning style.

Analysis of Student Learning Styles Using Fleming's VARK Model in Science Subject

The implementation of high-quality learning is something teachers must ensure. Consequently, teachers need to be able to design engaging learning procedures that draw students' attention to the subjects they are teaching. Understanding a student's preferred learning style is one of the necessary teachers. People with learning styles closely tied to their environments and personalities are adjusted. The process of receiving knowledge will be improved by defining the learning material to the learner's preferred learning style. One of the most used diagnostic tools for determining learning type is the VARK model. The visual, Auditory, Read/Write, and kinesthetic learning styles are divided into four divisions in the VARK paradigm. This research aims to identify the dominant learning style among science students. A qualitative descriptive methodology used one hundred junior high school students as research participants. Diagnostic exams and interviews were the tools used. Data analysis was carried out qualitatively by reducing data, presenting data, and making conclusions. According to the findings, 35% of students are kinesthetic learners, with 30% being visual, 21% being Auditory, and 14%t being Read/Write. Identifying learning tools, such as choosing learning models, creating and choosing materials, and learning media and assessment approaches that are more effective in accordance with student learning styles, can be done by developing one's understanding of learning styles.

Children on The Spectrum: Their Odyssey of Language Learning

Language learning is considered as one of the essentials in the life development and growth of an individual. As for typical learners, English language acquisition process varies depending on the learners’ preferences and styles. Whereas, children on the Autism Spectrum Disorder apparently has different styles and routines of learning. There have been many past literatures on the language learning styles of normal learners but not much on the journey of Autism children’s language learning. Due to that, this study describes a reflective journey of a fieldwork in two Autism Centres. Observations were conducted to investigate the learning style of learners on the spectrum, or also known as children with Autism. This study highlights the environment condition of the centres, the facilities provided and the journey of their learning process. Further interview session was also conducted with the Operational Therapists at the centre for depth understanding of the children on the spectrum’s life and journey.

Understanding the pedagogical content knowledge of teachers in teaching isiZulu reading: a case study of two rural primary schools in KwaZulu-Natal

Teaching reading in the Foundation Phase is a challenging issue since most South African learners struggle to read at their grade level. Learners in rural areas suffer the most as they are from disadvantaged homes that do not have sufficient resources for the promotion of a literate environment. The study aimed to understand the teachers’ pedagogical content knowledge in teaching reading in isiZulu. A qualitative approach framed within an interpretive paradigm was undertaken. Seventeen Foundation Phase teachers in King Cetshwayo District, KwaZulu-Natal, participated in two focus group discussions. The data obtained from transcripts were analysed using a thematic approach. The findings revealed that teachers seemed to have misconceptions and inadequate knowledge of how to develop basic foundational reading skills. It was evident that the teachers lacked the capacity to choose appropriate strategies for teaching reading that suited the learners’ individual needs. Thus, the study recommends multisensory and multimodal approaches to teaching reading to accommodate the different learning styles of learners.

The Use of AI in E-Learning Recommender Systems: A Comprehensive Survey

Nowadays, Artificial Intelligence (AI) technology, especially, machine learning has received significant attention from many researchers and professionals in several domains. For instance, this technology has revolutionized industrials sectors, such as e-commerce, health, manufacturing, and entertainment, as well as, the educational sector. In this context, during the pandemic, educational institutions around the globe recognized the benefits of adopting remote learning and teaching based on many e-learning platforms. Hence, deploying virtual classrooms is not that easy, due to many constraints, such as time, technological cost, lack of interactivity of learners, and the unsuitability of the learner's cognitive abilities and learning style. To this end, understanding the learner's style and providing adapted and personalized content are the main concerns of today's e-learning systems. To this end, integrating efficient recommender systems into online learning environment is required in order to consider student's behaviors and preferences when recommending various learning materials. Thus, intelligent recommender systems are able to provide a personalized learning path in which the system adapts to the student's learning requirements and abilities. This review discusses the different applications, methods, and challenges of AI-based recommendation systems used to assist different stockholders in an online learning environment.

A Survey on Learning Styles of Undergraduate Medical Students and its Associated Factors

Background: Global education curriculum is gradually shifting from teacher centric to learner centric approaches. Each of the learners is different in his/her preference of the style of learning. Aims: The present study aims to decipher the learning styles of learners of 1st year MBBS students studying in Pt. JNM Medical College, Raipur, and also to find its association with gender and past academic achievement. Patients and Methods: The present cross sectional descriptive study was conducted by self administered visual aural read kinaesthetic (VARK) questionnaire version 8.01. Test of association was done to illicit any significant difference between learning style preferences with gender or percentage in Class 12th. Results: Amongst 188, 184 students participated in the study, and thus, response rate was 97.9%. Amongst these participants, 41.3% were females and mean Class 12th percentage was 85.4 ± 8.66. Seventy one percent of the subjects were found to have multimodal learning preferences whereas 29% were unimodal learners. Kinaesthetic components were maximum whereas read/write learning components were minimum. The frequency of learners was maximum for K (Kinaesthetic) followed by VARK (Visual Aural Read Kinaesthetic) and AK (Aural Kinaesthetic) type of learners. There was no significant association between type of learners with gender or academic performance in Class 12th with P > 0.05 by non parametric Mann–Whitney U-test. Conclusion: Most of the learners had kinaesthetic components, and thus, teaching–learning methods should be accordingly adapted to suit the learning styles of the subjects.

Influences of Artificial Intelligence in Education on Teaching Effectiveness

The continuous development and application of artificial intelligence (AI) technology greatly support education reform and profoundly influence the learning styles of learners. Artificial intelligence in education (AIED) can help teachers recognize teaching tasks explicitly and teach content accurately. Moreover, AIED can help students change the traditional learning styles according to their differences, thereby realizing intelligent teaching and meeting the learning needs of students. A good teachers’ perception of educational technology (ET) is beneficial for using AI technology to positively assist all teaching links, which in turn improves teaching effectiveness. In this study, five hypotheses concerning the influences of AIED on teaching effectiveness were verified. The teachers’ perception of ET was introduced as a mediating variable, and the mediating effect of AIED on the improvement of teaching effectiveness was analyzed. The influences of AI using the period of learners on teaching effectiveness were estimated. Results showed that the overall Cronbach α and Kaiser–Meyer–Olkin of the designed questionnaire were 0.907 and 0.878, respectively. Moreover, the χ2 of Bartlett’s test of sphericity reached the 0.01 significance level, indicating the considerable good reliability and validity of the questionnaire. All four aspects, namely, AI-assisted teaching, exercise, exam, and assessment, had significantly positive influences on teaching effectiveness. The teachers’ perceptions of ET played a partial mediating effect for AIED on the improvement of teaching effectiveness. Samples with different AI-using periods had significant influences on teaching (p < 0.01). Research conclusions can provide important references to teachers for making scientific use of AIED and propose more accurate teaching strategies according to the learning states of learners.

Learning Behavior Analysis to Identify Learner’s Learning Style based on Machine Learning Techniques

Learning styles cover various attributes related to the attitude and the learning behavior of individuals. Research and educational theories confirm that considering learning styles in distance learning environments can improve academic performance and learner satisfaction. The traditional approach to identify learning styles is based on asking students to fill out a questionnaire. This approach is considerably less accurate due to the learners’ lack of awareness of their own preferences. Furthermore, learners’ learning styles are defined only once. In this study, we propose an automatic approach to identify learners’ learning styles based on patterns of learning behavior with respect to Felder and Silverman Learning Style Model (FSLSM), in an online learning environment. Patterns of behavior were analysed based on a data-driven approach. This approach exploits different Machine Learning (ML) techniques to detect the learning styles of learners. To validate our proposals, experiments were carried out in a higher education institution with 73 students enrolled in online courses on the ADLS (Automatic Detection of Learning Styles) system that we implemented. A 9 runs cross-validation was used to evaluate the selected ML techniques. Detection accuracy, recall, precision, and F measure were observed. The findings show the possibility of detecting learning styles automatically based on learning behavior with high performances. Different levels of accuracy were found for the different dimensions of FSLSM. However, Support Vector Machines (SVM) have exhibited great ability in predicting learning styles for all dimensions of FSLSM with an accuracy average of 88%.

Evaluation of Learning Efficiency of Massive Open Online Courses Learners

This study selected 175 massive open online courses (MOOC) learners of the School of Marxism in a university in Henan Province as respondents. A hierarchical clustering analysis was carried out using SPSS22.0, and the learning styles of learners were classified by k-means clustering. The learning efficiency of learners was estimated by data envelopment analysis (DEA), and the differences of learning styles in learning inputs and learning outputs were analyzed through a variance test. Results demonstrated that according to hierarchical clustering analysis, the learning behavioral indicators of MOOC learners could be divided into four classes. According to the results of k-means clustering, learning styles could be divided into four types, namely, high-input-high-output, high-input-low-output, low-input-high-output, and low-input-low-output. Clustering results could explain significant differences in learning inputs well, thus showing significance (P<0.05). A total of 125 respondents were non-DEA effective, accounting for 71.43%. Moreover, 114 respondents had fixed or increasing returns to scale, accounting for 65.14%. The conclusions of this research are of important significance to analyze the progress effectiveness of students, increase the scientificity and rationality of teaching evaluation theory, train teaching managers to control the teaching effects, and make scientific evaluations of the learning efficiency of university students.

The effect of micro-learning on learning and self-efficacy of nursing students: an interventional study

BackgroundIn the present age, e-learning has been playing a good role in educational and clinical settings along with face-to-face training. This study aimed to determine the effect of distance class using micro-learning contents on learning outcomes and self-efficacy in the clinical education of nursing students in 2021.MethodsThis research is a quasi-experimental pre-test-post-test study conducted at Shiraz University of Medical Sciences. The study population consisted of 46 nursing students who were recruited by the full census method. Students were randomly divided into two groups of intervention and control. Before and after educational interventions in both groups, students' learning and self-efficacy were evaluated using a questionnaire. Data Analysis was done using descriptive and analytical statistical methods and with SPSS software version 23.ResultsThe results revealed a statistically significant difference in the mean score of clinical learning level of nursing students between the control and experimental groups after the intervention (p = 0.041). Also, the difference between the mean score of self-efficacy in the intervention group before and after the training was statistically significant (p = 0.001).ConclusionMicro-learning is an effective training method for raising learning outcomes and self-efficacy among nursing students, especially in internship units. This method is recommended since multimedia pays attention to all learning styles of learners and affects the learning outcomes and self-efficacy of learners.

Multiple Intelligence and Learning Styles of Learners in Online Distance Learning: Basis of Intervention Program

This study aims to determine the multiple intelligence and learning styles of the learners among the selected grade six students of Private and Public schools in Manila. The study used the Independent two-tailed test and made use of descriptive-correlational method of research design to determine the significant difference on the learning styles and Multiple Intelligence of the learners. The p-value is greater than the significance level, which means it is not significant and thus fails to reject the null hypothesis. The Pearson r correlation coefficient was used to measure the significant relationship on the Learning styles and Multiple Intelligence of the learners. The p-value is less than the significance level and is significant, and it has enough evidence to reject the null. The Kinesthetic Intelligence needs to be developed based on the result that has the lowest score among the selected grade six students and it is recommended for grade six teachers to implement the Theory of Multiple Intelligence and learning styles during online learning. Teachers should make use of a certain strategy to improve kinesthetic intelligence. For example, the teachers could give their students different project/experiment kits they can use at their respective homes virtually.

Identifying EFL Students Learning Styles in the Context of Afghanistan

In recent years, great attention was paid to the factors of individual differences in EFL context, especially the learner learning styles. The main purpose of this study is to determine the learning styles of Parwan university EFL learners. A mixed method of research (quantitative and qualitative) was considered in order to collect and analyze the data. A total of 110 EFL Parwan university students (90 males) and (20 females) were participated in this study. The results of descriptive statistics indicated that the majority of the participant 81 % positively valued the visual learning styles. In addition, the findings showed that 53 % of the participants preferred auditory learning styles while 66 % of them determined as kinesthetic learners.