Learning Style Classification Research Articles

Learning Styles and Second Language Acquisition: A Review of Influences, Factors, and Educational Implication

Academia and the public have focused on studies of learning styles in second language acquisition in recent years. Students’ cultural backgrounds can reveal how education and culture affect learning. This paper summarizes the definition and the classification of learning styles and lists a brief review of influence, factors, and educational implications related to learning styles in the field of second language acquisition. The researchers analyzed learning styles are variable and numerous factors affect academic success. Studies show the beneficial correlation between learning styles and academic achievement within an appropriate teaching environment. A strong association exists between an effective learning style and favorable language learning results in students. Simultaneously, variables such as the learners' native language background and their personal traits will impact the selection of their specific learning style. There is a correlation between the variety of learning styles among students and the teaching techniques and aims of teachers in the field of education. An in-depth understanding of students' learning patterns will facilitate the advancement of the teaching profession. Since current evaluations and methods for evaluating students' learning types are questioned, more trustworthy learning style assessments must be developed in future studies.

Exploring the Teaching Reform Path of Ideological and Political Education in Colleges and Universities in the Context of Deep Learning

Abstract Deep learning significantly enhances the comprehension of ideological and political education (IPE) concepts and facilitates the advancement of IPE teaching in colleges and universities through bilinear blended teaching reforms. This innovative teaching approach employs association rules from big data and an enhanced Apriori algorithm to analyze students’ online learning data. An analytical model of student learning behaviors in IPE, utilizing a Support Vector Machine (SVM) for learner classification, forms the basis for developing an online learner profile. This profile integrates assessments of learning outcomes, group identification, and style classification to tailor learning activities to individual needs. The efficacy of this reform was tested over one year, comparing the impacts of IPE across four different regions. Results indicate that students in the experimental group significantly outperformed their counterparts in the control group. For instance, in Shanghai, scores from the experimental group averaged 89.63 compared to 83.25 in the control group, yielding a significant difference of 6.38 points. Statistical analysis showed a t-value of 6.21 and a p-value of 0.000, affirming the positive impact of the bilinear blended teaching reform on enhancing student ideological engagement and promoting educational reform in colleges.

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.

Design and Application of Hybrid Personalized Effective Teaching Strategy based on Learning Style Classification

With the continuous advancement of information technology in vocational education, personalized learning teaching models are deeply integrated with mobile teaching platforms. The online and offline blended teaching model has become a new perspective for higher education teaching reform. However, blended teaching has not yet achieved the expected teaching results in practical applications. How to further improve the effectiveness of blended teaching has become a problem to be solved. After analyzing the necessity of integrating learning styles into effective blended personalized teaching, this article draws on the Felder-Silverman learning style model to guide blended personalized teaching strategies. From four aspects: learning content organization, learning tool support, learning resource organization, and learning evaluation organization, teaching support strategies are designed for different types of learning styles, Finally, taking the task of calculating internal rate of return of investment management project in the course of financial management as an example, this paper introduces the specific application of this teaching strategy in hybrid teaching.

Analysis of Students' Mathematical Problem Solving Ability in Lesson Study-Based Learning for Learning Community Based on Student Learning Style

Data analysis techniques include data reduction, data presentation, and drawing conclusions. The results of this study indicate that students who are classified into the Accommodator learning style are 7 students (19.4%). Students classified in the Divergent learning style amounted to 14 people (38.8%). There are 10 students classified into Assimilation learning style (27.7%). There are 5 students who are classified into Convergent learning styles (13.8%). From the results of the classification of learning styles, learning styles diverge more than other learning styles. However, the divergent learning style is not the learning style that gets the highest score in the problem-solving ability test, but the convergent learning style that gets the highest score in the mathematical problem-solving ability test. and drawing conclusions. However, the divergent learning style is not the learning style that gets the highest score in the problem-solving ability test, but the convergent learning style that gets the highest score in the mathematical problem-solving ability test. From the results of the classification of learning styles, learning styles diverge more than other learning styles. Keywords: Problem Solving Ability; Learning Style; Lesson Study for Learning Community. DOI: 10.7176/JEP/12-35-03 Publication date: December 31 st 2021

ДОСЛІДЖЕННЯ СТИЛЬОВИХ ОСОБЛИВОСТЕЙ НАВЧАЛЬНОЇ ДІЯЛЬНОСТІ СТУДЕНТІВ В УМОВАХ ОНЛАЙН НАВЧАННЯ

The article discusses the style features of the educational activities of students in the conditions of forced distance learning. The concept of learning style is analyzed, by which the authors understand a certain psychophysiological system of mastering knowledge and skills, which to a certain extent determines the success of a student as a future professional. Based on the results of the analysis of special literature, a theoretical model is presented that systematizes the most famous classifications of learning styles according to the areas of educational activity of the individual (cognitive, emotional-volitional, behavioral spheres of the individual). It was determined that the differentiation of learning styles based on the motivational characteristics of the individual remains insufficiently developed. The results of the study of the motivation of students’ learning indicate that the majority of them (41.1 %) are motivated by the motivation to receive a diploma. The authors have developed a classification of learning styles based on the motivational sphere of the student’s personality (solid, strategic, superficial, and affiliation learning style). It was revealed that the strategic style of learning prevails among students (41.9 %), the solid style is presented in 24.8 % of the respondents, the affiliationary style – in 18.6 %, the superficial one is less pronounced (14.7 %). The authors have developed recommendations for organizing the educational process. students depending on their style. For students with a dominant strategic style of teaching, the teacher is recommended to determine the goals of the lesson, as well as design a promising goal for the entire period of studying the discipline. It is advisable to organize pedagogical interaction with students with the dominance of the affiliationary style based on their communication needs. In order to attract students with a dominanceof a superficial style of learning activity, it is recommended to provide them with the opportunity to take initiative, to ensure freedom of expression. As a universal, it is proposed to establish feedback and create a favorable learning environment.

Process of building a dataset and classification of vark learning styles with machine learning and predictive analytics models

Process of building a dataset and classification of vark learning styles with machine learning and predictive analytics models

Identifying Driver behaviors in learning style classification Using Machine Learning Technique

Identifying Driver behaviors in learning style classification Using Machine Learning Technique

A learning style classification approach based on deep belief network for large-scale online education

With the rapidly growing demand for large-scale online education and the advent of big data, numerous research works have been performed to enhance learning quality in e-learning environments. Among these studies, adaptive learning has become an increasingly important issue. The traditional classification approaches analyze only the surface characteristics of students but fail to classify students accurately in terms of deep learning features. Meanwhile, these approaches are unable to analyze these high-dimensional learning behaviors in massive amounts of data. Hence, we propose a learning style classification approach based on the deep belief network (DBN) for large-scale online education to identify students’ learning styles and classify them. The first step is to build a learning style model and identify indicators of learning style based on the experiences of experts; then, relate the indicators to the different learning styles. We improve the DBN model and identify a student’s learning style by analyzing each individual’s learning style features using the improved DBN. Finally, we verify the DBN result by conducting practical experiments on an actual educational dataset. The various learning styles are determined by soliciting questionnaires from students based on the ILS theory by Felder and Soloman (1996) and the Readiness for Education At a Distance Indicator. Then, we utilized those data to train our DBNLS model. The experimental results indicate that the proposed DBNLS method has better accuracy than do the traditional approaches.

Analyzing the Relevance of Peer Relationship, Learning Motivation, and Learning Effectiveness—Design Students as an Example

In a design department’s practice course there are often group exercises that include intensive interactions between students in the classroom or in the internship factory. In addition, students will deepen the interaction between peers due to course groupings or borrowing of model tools, etc. This study intended to carry out a differential analysis and discussion of the differences among design students from different backgrounds under the three factors of peer relationships, learning motivation, and learning effectiveness. The research method was based on literature analysis and a questionnaire survey, and the research objects were sophomores and seniors in four classes. Statistical analysis methods included the independent sample T-test, one-way ANOVA, and factor and cluster analysis, which were used to summarize different learning styles. The results showed that the students had significant differences of varying degrees in the three factor dimensions. Regarding gender, “care about classmates’ lives” in peer relationships scored higher for the females than the males, and the rest had no effect. Regarding educational system, “care about the classmates’ life” and “sharing life trivia” was included in peer relationships. “keep the enthusiasm in the course of learning” was included in the learning motivation. “recognition for self-directed learning” and “ability improvement” was included in learning effectiveness. The three factors all had significant differences, and the differences for full-time students were higher than for night school students. Regarding grade, there were significant differences in “friends will value my comments” and “sharing life trivia” in peer relationships, “understand course content” in learning motivation, and “data collection ability” and “understanding team member expertise” in learning effectiveness, and seniors scored higher than sophomores in these areas. In addition, the ANOVA and post-hoc tests revealed significant differences in learning the processes between different groups. In peer relationships, full-time seniors scored higher than the other groups; in learning motivation and learning effectiveness, full-time seniors scored higher than night school sophomores. In addition, the overall factors of the full-time seniors were higher than those of the other groups. In the analysis of different learning factors, under the premise of the variation of 58.975%, three factors were extracted by principal axis for analysis with Promax rotation. The different learning factors can be summarized in “emphasizing ability improvement”, “care about peer friendship”, and “careful and active learning”. Classification of learning styles under the three factor dimensions was based on two-stage cluster analysis to obtain two clustering results, including “enthusiastic and friendly” and “active and autonomous”. The results showed that the mastery of self-learning time and the learning experience performance have a key influence on the learning motivation and learning effectiveness of design students from different backgrounds. In addition, the results also showed a new opportunity for course improvement and teaching innovation at night schools. The final results of this study could be used as an important reference for research on peer relationships, learning motivation, and learning effectiveness in design education.

VARK Learning Style Classification Using Decision Tree with Physiological Signals

Learning style is deemed crucial for different types of age groups. It is essential, especially for individual learning achievement. Learning is a part of cognitive processes affecting the human central nervous system, which can be monitored by using the physiological signals. In this study, physiological signals thus are proposed as key attributes for the classification of learning styles to avoid biased data from completing the questionnaire and promote the real-time response in the classroom environment. More specifically, heart rate and blood pressure signals are chosen for this study. Following the VARK model, the physiological signals of learners are classified with the decision tree into four different types, including visual, aural, read and write, and kinesthetic learners. There are 40 primary school children and 30 university students involved in the whole study. The results show that the proposed factors obtain 85% and 90% classification accuracy for children and university students, respectively. Both heart rate and blood pressure are thus reasonably impacted as the classification attributes.

Honey-Mumford's learning styles of medical laboratory students: An observational study with implications for laboratory efficiency

Background and Aim: Learning styles are subconscious stable traits influencing the perception and response of learners to their learning environment. Honey–Mumford classification of learning styles has been utilized and evaluated in a number of studies among students across different professional courses. Though learning style preferences of medical students have been assessed, we did not come across any study evaluating the paramedical students of Medical Laboratory Technology (MLT). Because laboratory technicians form the backbone of clinical laboratories in today's evidence-based medicine, it seems imperative to understand the basic learning styles of these students. Thus, this study aimed to identify the learning styles among students pursuing the bachelor degree of medical laboratory technology at a tertiary-level hospital. Materials and Methods: A cross-sectional observational study was undertaken among 98 students pursuing Bachelor of MLT at a tertiary-level hospital and medical college. The Honey–Mumford's learning style questionnaire was administered to each student after taking informed consent. Individual students' preference for learning style was categorized as per Mumford classification, and students were categorized according to their preferred learning style. Results: Majority of the students (60%) showed very strong preference for the activist learning style followed by reflector style. Prevalence of activist and reflector styles was found to be equal among our students followed by the theorist style. No difference was noted between male and female students. Conclusion: We demonstrated, for the first time, the learning styles of medical laboratory technology students or the future laboratory technicians. Activist and reflector styles were the most frequent among our study group. Future studies at other institutions providing similar training shall help in refining these results. Such studies are likely to help in adopting appropriate teaching methods and assessment strategies leading to better learning and consequent improvement in laboratory efficiency.

VARK learning style and cooperative learning implementation on impulse and momentum

Individuals have various preferences in learning and processing information. These variances in individuals learning are clarified by learning styles. A recent classification of learning style associates state that individual learning styles can be categorized into Visual, Aural, Read or Write and Kinesthetics (VARK). Current learning concepts focus more on the intellectual development features through visual/verbal approach. This visual/verbal approach seems to incorporate frequent scopes from other learning style preference. This research aims to determine student achievement on impulse momentum subject using cooperative learning which instruction is based on learning style and cooperative learning which instruction is mismatched on learning style. Research was designed according to quasi experimental with non-equivalent control group pre-test post-test design. The experimental group were formed into cooperative groups which instructions is based on learning styles for each student and the control group formed into cooperative groups which instruction was mismatched with students learning styles. The result shows that there is significant difference between control group and experimental group. In addition, it was determined that students in experimental group were more successful rather than students in control group.

Artificial Intelligent in Education: The Development of ‘Disabel’ System to Analyze Student Learning Styles

The industrial revolution 4.0 is Characterized by automation and digitalization in various sectors. This of course has an immediate impact on the development of education. The Science of Technology has been integrated into the education sector so that the modern era of education can Utilize technological developments to improve the quality of education. The quality of education is related to the learning process. Learners must maximize the learning process to improve learning outcomes. Learning style plays an important role in the learning process, learners must therefore Recognize the learning style they have. This study aims to analyze learning styles based on the type of learning style classification (visual, auditory and kinesthetic). The research uses waterfall method and developed based on the website by applying the principle of artificial intelligence by applying the forward chaining algorithm as an inference engine. The forward chaining is designed based on the rules of the system knowledge and collaborated with knowledge of learning styles. To assess the system, the program was tested twice by involving two program experts. The results of the first learning style analysis system test program obtained an average score of 3.68 on a scale of 4 with the criteria of "Very Good". Second program expert assessed this system with an average score of 3.89on a scale of 4 with criteria for "Very Good". Then the overall score for the learning style analysis system at a score of 3.87 with the criteria of "Very Good".

Learners’ attention preferences of information in online learning

PurposeThis study aims to use eye-tracking technology to conduct an empirical study about online learning process analysis, thus aiming to understand the attentional preferences and learning paths in online learners.Design/methodology/approachWith eye movement tracking and data analysing technology, the Tobii X120 eye-tracking instrument, Tobii studio and online learning platform are used to record and visualise data of eye moving and learning steps during the real task-based online learning processes of 14 online learners. According to Barbara A. Soloman’s learning style classification framework, these learners’ learning style was presented in four dimensions. Based on data of eye moving, leaning style and operation in online course, the correlation about learners’ preferences of learning content, online learning paths and learning style were analysed based on according data.FindingsThe paper provides empirical insights about how change is brought about during online learning. It is found that there is no significant difference in attention preference between the students with the difference on the learning style of visual-verbal, although each person has a different attention preference on the learning content.Research limitations/implicationsThe limitation of this study is that only one common type of video learning process is studied. The learning process of various types of instructional videos in online learning will be done in future research.Practical implicationsIn this study, eye-movement tracking technology is used to understand students’ learning path and learning preference in the online learning process, which is helpful to optimise the online learning process and improve the efficiency of online learning.Social implicationsThis research findings have been approved by relevant experts and have won the first prize in the school-level competition of South China Normal University in China.Originality/valueIn this study, the technology of psychology (eye-tracking technology) is introduced into the study of real task-based online learning process in the subject of educational technology, realising the integration of multi-disciplinary research techniques and methods.

Learning Style Classification with Weighted Distance Grey Wolf Optimization

Learning Style Classification with Weighted Distance Grey Wolf Optimization

Enriching behavior patterns with learning styles using peripheral devices

The human being is currently under an increased demand for attention, a result of a society that is moving faster. In most of the so-called developed countries, workers have nowadays increasingly busier activities. This makes them stretch their limits to find time for children, sports activities and other. This necessary extra time is frequently obtained at the expense of shorter periods of sleep or rest and with a cost in terms of pressure and stress. In this paper, we present a non-intrusive distributed system for enriching behavior patterns applying learning styles in a class of students which was considered as a team. It is especially suited for teams working at the computer. The presented system is able to provide real-time information about each individual as well as information about the team. It can be very useful for teachers or managers to identify potentially distracting events or individuals, and to detect the onset of mental fatigue or boredom, which significantly influence attention. In summary, this tool can be very useful for the implementation of better human resource management strategies, namely in the classification of learning style and the monitoring of the level of attention of each user.

Personalized Wearable Guides or Audio Guides: An Evaluation of Personalized Museum Guides for Improving Learning Achievement and Cognitive Load

ABSTRACTThis study aimed to investigate the effects of incorporating personalized learning based on students’ learning styles (visual or verbal) into wearable or audio museum guides. Personalized learning is achieved by matching participants’ learning styles with either a wearable or an audio guide. The participants in this study consisted of 79 university students divided into four groups: audio guide-visual, audio guide-verbal, wearable guide-visual, and wearable guide-verbal. The students’ cognitive load (intrinsic, extraneous, and germane) and learning achievement (remembering, understanding, and analyzing) were assessed and compared. In this study, it was found that personalized learning helps to reduce students’ intrinsic cognitive load and extraneous cognitive load, while increasing their germane cognitive load and improving their ability to memorize. It is suggested that museum educators refer to our classification of learning styles, provide visitors with personalized devices, and incorporate wearable guide devices into their museums’ learning processes, in order to improve visitors’ learning achievement.

Elman Neural Network Trained by using Artificial Bee Colony for the Classification of Learning Style based on Students Preferences

Elman Neural Network Trained by using Artificial Bee Colony for the Classification of Learning Style based on Students Preferences

Rule extraction from autoencoder‐based connectionist computational models

SummaryMapping problems are typical research topics related to natural language learning, and they include not only classification mappings but also nonclassification mappings, such as verbs and their past tenses. Connectionist computational models are one of the most popular approaches for simulating those mapping problems; however, their lack of explanatory ability has prevented them from being further used to understand the language learning process. Therefore, the work of extracting rational rules from a connectionist model is as important as simulating the mapping behaviors. Unfortunately, there is no available technique that can be directly applied in those computational models to simulate nonclassification problems. In this paper, an autoencoder‐based connectionist computational model is proposed to derive a rule extraction method that can construct “If‐Then” rational relations with high fidelity for nonclassification mapping problems. To demonstrate its generalizability, this computational model is extended to a modified version to address a multi‐label classification mapping problem related to cognitive style prediction. Experiments prove this computational model's simulation ability and its explanatory ability on nonclassification problems by comparing its fidelity performances with those of the classical connectionist computational model (multilayer perceptron artificial neural network), and its similar ability on a multi‐label classification problem (Felder‐Silverman learning style classification) by comparing its prediction accuracy with those of other rule induction techniques.