Personal Learning Style Research Articles

Thinking on your feet: potentially enhancing phylogenetic tree learning accessibility through a kinaesthetic approach.

Phylogenetics is one of the main methodologies to understand cross-cutting principles of evolution, such as common ancestry and speciation. Phylogenetic trees, however, are reportedly challenging to teach and learn. Furthermore, phylogenetics teaching methods traditionally rely solely on visual information, creating inaccessibility for people with visual impairment. Sensory learning style models advocate for tailoring teaching to individual preferred sensory learning style. However, recent research suggests that optimal learning, independently of preferred learning style, depends on the types of transmitted information and learning tasks. The lack of empirically-supported education into the effectiveness of teaching phylogenetics through alternative sensory modalities potentially hinders learning. The aim of this study was to determine whether phylogenetic trees could be better understood if presented in kinaesthetic or multisensory teaching modalities. Participants (N = 52) self-assessed personal learning style and were randomly assigned to: visual, kinaesthetic or multisensory learning conditions. Phylogeny reading performance was better for both kinaesthetic and multisensory teaching conditions, compared to the visual teaching condition. There was no main effect and no interaction effect of personal learning style. This study establishes a baseline for further research by suggesting that easy-to-implement kinaesthetic teaching modalities might support phylogenetic tree learning and reading. This has practical implications for evolution education and accessibility for students with visual impairment, underscoring the need to shift from vision-centric teaching paradigms towards evidence-based instructional strategies that accommodate sensory diversity. The online version contains supplementary material available at 10.1186/s12052-024-00215-y.

Research on Education and Teaching of Artificial Intelligence Enabling Intelligent Construction

This study is committed to in-depth analysis of the practical application of artificial intelligence in intelligent construction teaching and its empowerment effect. An efficient and intelligent teaching platform is built, which can intelligently recommend relevant learning resources according to students’ learning progress and feedback, and track students’ learning situation in real time. This personalized learning style not only improves the learning efficiency of students, but also enables them to better adapt and master the knowledge and skills in the field of intelligent construction. The introduction of artificial intelligence technology has innovated the method of intelligent construction teaching. Based on the learning analysis technology of big data, we can conduct in-depth analysis of students’ learning behaviors and achievements, so as to grasp students’ learning status and needs more accurately. These achievements not only show the great potential of artificial intelligence technology in the field of education, but also provide new ideas and methods for the reform and innovation of intelligent construction teaching.

Exploration correlates with dietary choosiness and avoidance learning style in a generalist predator

Abstract The hypothesis of slow-fast syndromes predicts a correlation between personality type and learning style; fast explorers would have a more proactive (fast but inflexible) learning style whereas slow explorers would be more reactive (slow but flexible) learners. Empirical evidence for this personality-cognition coupling remains inconclusive and heavily biased towards birds. Moreover, most studies did not examine the personality-cognition correlation when the cognitive task is discerning food quality, a scenario directly related to energy acquisition that underpins the evolution of slow-fast syndromes. In this study, we examined the exploration-cognition correlation in the context of avoidance learning in an opportunistic predator - the common sun skink Eutropis multifasciata. We quantified exploration tendencies of individuals in an unfamiliar environment and compared foraging behaviors when lizards associated prey color and taste during the initial learning trials and subsequent reverse learning trials, where the color-taste associations were switched. We found that fast explorers were less choosy and modified their foraging behaviors less with experience, conforming to a more proactive cognitive style. In contrast, slow explorers were reactive learners and were able to change foraging behaviors in both learning and reverse learning phases, even though the ability to do so depended on the color-taste treatment. Contrary to conventional predictions, the proactive-reactive learning styles in our focal species was not differentiated by a learning speed-flexibility trade-off. Our findings offer nuanced support to the slow-fast syndromes and suggest that the two types of exploration-cognition correlations could be different responses to fast-changing environmental predictability.

Developing an intelligent and sustainable model to improve E-learning satisfaction based on the learner’s personality type: data mining approach in high education systems

PurposeAlthough E-learning has been in use for over two decades, running parallel to traditional learning systems, it has gained increased attention due to its vital role in universities in the wake of the COVID-19 pandemic. The primary challenge within E-learning pertains to the maintenance of sustainable effectiveness and the assurance of stakeholders' satisfaction. This research focuses on an intelligence-driven solution to recommend the most effective approach to education policymakers by considering the unique characteristics of all components within the educational system (course type, student and teacher characteristics, and technological features) to achieve a sustainable E-learning system.Design/methodology/approachThrough a systematic literature review and qualitative content analysis, a conceptual model of the critical components influencing E-learning quality and satisfaction has been developed. The proposed model comprises six main dimensions: usage, service quality, learning system quality, content quality, perceived usefulness, and individual characteristics. These dimensions are further divided into 15 components and 114 sub-components. A data mining process encompassing two scenarios has been designed to prioritize the components impacting E-learning success.FindingsIn the first scenario, data mining techniques identified the most influential components based on the features outlined in the conceptual model. According to the results, the components affecting E-learning quality enhancement in the studied case are “usage purpose, system loyalty, technical and supportive system quality, and student characteristics.” The second scenario examines the impact of individuals' personality types and learning styles on E-learning satisfaction across various aspects (Average System Satisfaction, Overall System Satisfaction, Efficiency and Effectiveness, Skill Enhancement, and Personal Enhancement). The findings reveal that, with an accuracy of over 70%, E-learning satisfaction for diplomat and guard learners is influenced by the alignment between “course learning style” and “student-suggested course learning style.” Conversely, for analyzer and researcher types, satisfaction levels are impacted by the “learning style compatible with their personality type.”Originality/valueImplementing a dynamic model founded on data mining enables educational institutions to personalize the E-learning experience for each individual as much as possible. The study’s findings indicate that “achieving higher satisfaction levels in the E-learning process is not necessarily contingent upon providing a learning style congruent with learners' personality types.” Rather, perceived and suggested learning styles exert a more profound influence. Consequently, providing prescriptive principles for higher education institutions seeking to enhance E-learning quality is inadvisable. Instead, adopting a dynamic, knowledge-based process that furnishes recommendations to policymakers for each course—tailored to the specific course type, teaching records, current processes and technology, and student type—is highly recommended.

Exploring EFL Teachers’ Teaching Styles at a Junior Secondary School

The study aimed to find out the teachers’ teaching styles applied at SMP Telkom Makassar, students’ perceptions, and the teachers’ obstacles faced during teaching. This study was categorized as qualitative research, and the data was collected through observation, TSI questionnaire, and interview. There were six participants in this study, consisting of three teachers, and three students. The findings of this research showed that: (1) Teachers respectively applied various teaching styles in teaching English in the classroom and no one teacher applied only one style in teaching, they applied as a facilitator, delegator, formal authority, personal, expert, academic style, audiolingual, communicative style, and task-based learning style. The dominant teaching styles applied by the participants are facilitator and delegator styles. (2) The students’ perceptions, they feel comfortable learning English with their teachers because their teachers applied various styles to teach not only focusing on the book, but their teachers providing the material using interesting PPT, audio-video conversation, ice-breaking, and games to make their class fun. Another activity that the students enjoyed learning English like discussion with pairs or groups and the atmosphere that is built by the teachers such as interaction with other students, never judging, and yelling at students when they make a mistake. (3) The obstacle that the participants faced during teaching they were exhausted from teaching in large classes that consisted of many students with different characteristics and abilities. Sometimes, they are confused about the method or strategy, and media that are suitable for their students that can increase their ability in English.

Improving Critical Thinking by Using Technology in the Learning Process

Improving Critical Thinking by Using Technology in the Learning Process

Professional and Ethical, Development of Teacher

Teaching relationships are interactive, caring and ethical. As such they take into account both the importance and the uniqueness of the individual; in particular, personal history, developmental (social, emotional, physical, cognitive) level, personal learning style. Teaching relationships with peers are based on professional qualifications, ethical conduct, trust, equality and collaboration. Communication, interpersonal skills and decision-making in all professional interactions are based on the Encouragement values of trust, mutual respect, equality, acceptance and co-operation. Community building involves developing a sense of belonging and honoring the uniqueness and diversity of each individual. Teaching as a planned process is based upon defined standards and practice. Teachers believe that it is through the knowledge, skills and example of teachers that public school education nurtures the growth of students, intellectually, socially, physically and emotionally, so that they may be and become self-reliant, self-disciplined, participating members with a sense of environmental and social responsibility within a democratic society. Teaching, as a planned process, is based upon a set of Standards of Professional Practice which incorporate principles of pedagogy, social responsibility, ethical practice, and collaborative relationships.

Effectiveness of Gamification Strategy in Teaching Science

Today’s generation is dominated by science and technology. It is essential in many industries, including education, health care, agriculture, transportation, and communication. Technology is utilized in education to inspire students to learn and to propel innovative methods of knowledge acquisition, information that acts as a catalyst for change. To achieve optimal learning results, students should be motivated, and their learning styles should be modified as necessary. Learning style refers to the personal preference that a student brings to bear in a given learning situation; such preferences were the variable learning processes and strategies affecting personal learning outcomes. For this reason, personal learning styles were a consideration in the teacher’s teaching strategy and course activity design. Teaching strategy, the motivation of curriculum design, played a critical role in the learning process. Meaningful learning was a teaching strategy, which allowed traditional curriculum design to be tuned to the learner’s willingness to actively and positively face learning in order to enhance learning benefits and learning achievement.

Evaluation of teaching effect of online education courses based on random forest

At present, online education platforms are very popular. However, these platforms also have some drawbacks, including that they cannot be completely based on the needs of students, many students ignore the needs of students in order to adapt to the platform, do not adopt a more personalized learning style, and do not fully realize the individualized teaching. There are many factors at play, such as shifts in learning and teaching patterns, online platforms, hardware limitations of the network, and more. However, whether this model can finally satisfy teachers and students, and how the teaching effect is, has become a problem of concern. We propose a random forest-based method to evaluate college online courses. This method not only focuses on a single influencing factor, but also takes into account the special needs of students and teachers, and formulates different evaluation criteria, so that the system has not only universal applicability. The experimental results show that this method can improve the proficiency and level of teachers' online teaching, and can effectively combine several courses taught by students to form a personalized teaching method.

Association of Learning Styles with Academic Achievements in First-year Professional MBBS Students of a Medical College in Eastern India: A Cross-sectional Study

Introduction: Research has shown that learning may be optimised by synchronising the learning environment with the learning style preferences of students. First-year medical students face immense stress as they adapt to a new learning environment and curriculum at the onset of their medical career. The simultaneous use of two supplementary learning styles questionnaires, namely, the Visual-Aural/Auditory-Read/WriteKinesthetic (VARK) questionnaire and the Vermunt Inventory of Learning Styles (Vermunt ILS), would provide detailed knowledge of their instructional preferences, information processing, and cognitive personality learning styles. Judicious use of such information at this stage may guide them towards improved learning and higher academic achievement. Aim: To study the association between learning styles and academic achievements in first-year professional MBBS students of a medical college. Materials and Methods: A cross-sectional study was conducted at Medical College, Kolkata, West Bengal, India over the duration of 10 months from August 2021 to June 2022. Online surveys of 250 first-year MBBS students’ learning styles were conducted using the VARK questionnaire and Vermunt ILS, and the marks of three internal assessment examinations were collected. The data was entered into Microsoft Excel. Group as well as individual scores were analysed, and Pearson’s Chisquare test was used to determine the association between the students’ learning styles and their academic achievement. A p-value of <0.05 was considered statistically significant. Results: Out of the total of 160 submitted questionnaires, 139 (86.8%) students were visual learners, while the rest were multimodal learners. The factor loading of the ILS scales revealed higher alpha coefficients for learning orientations and mental models of learning, with the ‘use of knowledge’ being awarded high scores by the highest number of students, 104 (83.56%). A total 134 learners gave lower scores to processing and regulation strategies, but a significant association was found between 126 (94%) of them and their academic scores of ≥50% (p-value=0.024). Conclusion: Although no positive association was found between learning styles and academic achievement of firstyear medical students, the integration of two learning style tests provided educators with comprehensive insight into the learning preferences of their students, enabling them to develop an adaptive curriculum. Students might also utilise knowledge of their learning styles to guide themselves towards self-directed learning, lifelong learning, and higher academic achievement.

Personality Types and Academic Achievement of Students in Obio-Akpor Local Government Area of Rivers State

The study investigated the Personality types and academic achievement of students in Obio/Akpor Local Government Area of Rivers State. Two research questions and two corresponding null hypotheses guided this study. The study adopted the correlational research design. 300 secondary school students were used for the study and were drawn through a combination of simple random sampling technique and stratified random sampling technique. Type A and B personalities are the independent variables while academic achievement are the dependent variables of this study. The instrument used for the study is the Personality Type Assessment Scale (PTAS,) and student scores for their last term examination was used as their achievement score. The data collected were analyzed with simple regressions as statistical tool. The results obtained indicated that the type A and type B personalities do not statistically predict academic achievement. Based on the findings, it was recommended among others that Similar studies should be conducted using volunteer and non volunteer participants. This would remove the extraneous variable that volunteer participants may have similar personality traits or learning styles thereby reducing the potential of skewing the sample population

LEARNING STYLES AND LEARNING STRATEGIES IN LEARNING ENGLISH USED BY NON-ENGLISH STUDENTS

Non-English students of Nursing and Economics need to learn English effectively by using appropriate learning strategies that reflect their learning styles in which to achieve their learning goals. This study investigated learning styles and learning strategies in learning used by non-English students. An explanatory mixed-methods design was used involving both quantitative and qualitative data. The findings revealed that out of listening, speaking, and writing strategies, there was a statistical difference (p> 0.05) in the language learning strategies used in reading by different sensory learning styles (visual, auditory, and tactile). For personal learning styles, the use of strategies in speaking and reading employed by Extroverted-Random-Open and Introverted-Concrete-Closure of Nursing and Economics students also had statistical differences (p< 0.05). On the contrary, strategies in listening and writing used by both groups of Extroverted-Random-Open and Introverted-Concrete-Closure were no statistical differences. Meanwhile, in cognitive learning styles, there was a statistical difference (p> 0.05) in the use of strategies in speaking and reading between synoptic and etanic Science and Social Science students. These findings showed that both Nursing and Economics students used the strategies for receptive skills (listening and reading) and productive skills (speaking) in high-frequency usage except for writing strategies in the moderate range. It means that they can be categorized as language learners and users

Continuous Student Improvement Using Machine Learning Techniques

Abstract: Student Improvement model aims in identifying student behavior traits and patterns using multiple psychometrictests followed by suggesting ways to improve areas of their interest along with overall psychological balance by continuous testing at constant intervals of time. To identify the subject’s strengths, weaknesses, and behavioral interests, assessments are carried out on the principle of psychological assessments which assess the students by making them imagine their reactions in various psychological situations. We use different classification methods to predict psychological factors and the efficiency ismeasured by performance factors like recall, precision, and F1 score. It is mainly based on 3 Psychometric tests that are Big Five Personality, Benziger Thinking style assessment, and KOLBlearning preferences. These tests have a huge role to play in the fields of career guidance and employability. Our model evaluates an individual’s personality traits and learning styles and provides a descriptive analysis which helps in identifying students’ overall improvement.

Survey on different Machine Learning Techniques for Student Improvement using Psychometric Data

Abstract: Student improvement aims in identifying student behavior traits and patterns using multiple psychometric tests followed by suggesting ways to improve areas of the subject’s interest along with overall psychological balance by continuous testing at constant intervals of time and suggesting the same. To identify the subject’s strengths, weaknesses, and behavioral interests, assessments are carried out on the principle of psychological assessments which aims to assess the students by making them imagine their reactions in various psychological situations. Each response to the question is evaluated with pre-decided scores. Psychometric tests are aimed at measuring a person’s traits, behavior, guidance, and placements/employment to match the person’s capabilities and features like cognitive ability, EQ, IQ, and the tendency to react to predefined situations. Psychometric tests have a huge role to play in the fields of career guidance and employability. These tests suggest your strengths, weaknesses, and imaginative abilities and hint if they suit the particular role. This paper provides a survey on different machine learning algorithms used over the years to recognize personality traits and learning styles which would further contribute to students’ overall improvement.

비대면 원격수업에 대한 MBTI 선호지표별 학습환경 만족도와 학습양식 분석

Objectives This study aimed to analyze learners' reactions to non-face-to-face classes using the MBTI personality type test and learning style test as tools for students who entered high school during the COVID-19 crisis. In detail, the focus was on revealing the differences in perception, satisfaction, and learning styles for non-face- to-face classes according to the intensity of each preference index of the MBTI personality type. Ultimately, in designing non-face-to-face remote classes, the purpose was to be used as a material to improve the learning effect of learners by considering individual personality differences.
 Methods The MBTI personality type test was conducted by sampling 207 second graders of K High School located in Guro-gu, Seoul, who experienced remote and school classes at the same time after entering high school. Afterwards, the recognition and satisfaction of non-face-to-face remote classes and learning style tests were conducted, and the results of a total of 140 students, 61 boys and 79 girls, who agreed to this study, were analyzed. The results derived through factor analysis and variance analysis (ANOVA) using the statistical program SPSS 22 are as follows.
 Results First, there was no significant difference in perception of the learning environment by 16 types of MBTI, but there was a significant difference in perception function among preference indicators. Depending on the strength of the recognition function, differences were recognized for the face-to-face and non-face-to-face learning environments in the order of obvious intuition (N), slight sense (S), and slight intuition (N). Second, there was no significant difference in satisfaction with the non-face-to-face learning environment by strength of both MBTI personality type and preference index. Third, cooperative, participatory, and avoidance types showed significant differences in learning styles by 16 personality types of MBTI, but competitive types did not show significant differences. ENFJ for cooperative type, INTJ for participatory type, and ENTP for avoidance type showed the greatest tendency. Fourth, there was a significant difference in the learning styles of cooperative, participatory, and avoidance according to the strength of the MBTI preference index. Cooperative types were clearly extroverted (E), clearly intuitive (N), clearly emotional (F), clearly recognized (P), participating types were clearly/slightly introverted (I), slightly intuitive (N), slightly judged (T), slightly avoided (P), slightly extroverted (E), clearly sensed (S), and clearly recognized (P).
 Conclusions This study suggested that there is a clear difference in the learning environment and learning style according to the strength of the 16 MBTI personality types and preference indicators. Accordingly, it suggests that it is important to prepare conditions to effectively increase the learning effect by providing a learning environment and learning type suitable for the learner's personality type and tendency.

The relationship between personality traits and learning styles in medical education students at Iran University of Medical Sciences: A cross-sectional study.

The relationship between personality traits and learning styles is an attractive subject for researchers. "Learning Style" indicates on a method of education for effective studying. The Kolb's Learning Style is a known theory in the field of learning style. The Kolb's four learning styles include: Divergent, convergent, assimilating, and accommodating. Furthermore, personality, known as relatively fixed and durable characteristics that distinguish people from each other, and personality contains five broad identified traits of extraversion, agreeableness, openness, conscientiousness, and neuroticism. In this study, we intended to evaluate the relationship between personality traits and learning styles in medical education students. This research was an observational analytic cross-sectional study that was conducted on medical education students at Iran University of Medical Sciences, between 2018 and 2019, and 31 students were participated as volunteers. NEO Five-Factor Inventory and the Kolb's learning style inventory (version 3.1) were used to evaluate the students' personality traits and learning styles, respectively. The simple and multiple analysis of multinomial logistic regression were used for statistical analysis. The results indicated that if one unit would be added to the score of the "Extraversion" personality trait, the estimated odds, by which the student's learning style would become "Accommodating" rather than "Assimilating," is 1.3 times more than the basic condition, in which "Extraversion" score would be remained unchanged (P = 0.035). It seems that "Accommodating" learning style might be related to "Extraversion" personality trait in medical education students. However, researches with a larger population are suggested.

ED-Sense, platformă tehnologică pentru educație personalizată în era digitală bazată pe inteligență artificială și conținut interactiv

The article analyses the current platforms for digital education, identifying the key needs of participants in the educational process and presenting innovative solutions, based on recent research and well-founded theories, to respond to these needs effectively. The EdSense platform addresses the challenges of digitizing education with a creative approach, integrating artificial intelligence and augmented reality to create a ubiquitous, accessible and personalized learning environment. Gamification elements and augmented reality enrich the learning experience, turning it into an engaging and collaborative adventure. Adapting flexibly to the school curriculum and individual student needs through its artificial intelligence component, it offers an adaptive, interactive and deeply personalized learning style. The analysis of similar approaches, as well as the integrative, complex, but well-modularized architecture of the system, profiles EdSense as a reference model for the future of digital education.

A framework for utilizing unexplored game elements in designing learning systems

Gamification is defined as the use of game elements and designs in non-gaming areas and applications such as education, marketing, and healthcare. That is to facilitate and develop the engagement of users with a product or service. Researchers have found that gamified learning has the ability to improve student success, interaction, and enjoyment of courses. Recently, researchers suggested that one way to achieve that is through the personalization of students’ experiences. However, research has been focusing on a narrow group of game elements which does not ensure equal consideration when designing for different personality types. In this paper, our aim is threefold that is: to identify the unexplored game elements in the learning domain to ensure equal experiences for different personality traits; to study the utilization of the identified unexplored elements and how they can be used in relation with different personality types and learning styles to make learning tasks more desirable and enjoyable; to inform and enrich the design of gamified e-learning systems. To achieve that aim, we analyzed the literature to identify the unexplored game elements, conducted a focus group study to examine the utilization of unexplored game elements along with other contextual aspects. Then to formalize the results reached from our study and provide more systematic means for software engineers to extract useful information that can inform their designs, an ontology was implemented for that purpose. Finally, an existing gamified e-learning framework was adapted to illustrate how the formed artifacts and models interrelate to realize the research aim.

Teaching Design of English Writing Based on UMU

With the development of time, traditional teaching methods cannot meet the needs of education and society for innovative talents. Blended learning conforms to the reform of education and teaching and is becoming the new normal of future education. Blended learning is a kind of teaching mode that integrates face-to-face teaching and online learning after people reflect on online learning to improve the learning effect. This kind of teaching mode that transcends the limitation of time and space brings great convenience to students and teachers. Blended learning can not only satisfy students’ personalized learning style and the independent construction of personalized knowledge but also fully reflect the characteristics of students as cognitive subjects in the learning process. Compared with traditional education, blended learning pays more attention to the perspective of students and creates a truly highly participatory and personalized learning experience for students by using the Internet, mobile technology, and face-to-face teaching, which is the promotion and improvement of classroom teaching. In mixed learning in the Internet age, various online teaching platforms can provide students with diversified learning resources and learning methods. Supported by information technology, the UMU interactive learning platform provides independent learning methods, open learning space, and two-way interaction. The platform has the basic functions of online discussion, uploading and downloading learning resources, and submitting assignments. At the same time, it can be used on the Web and mobile phones to organize classes and interactions at any time. Teachers can create a variety of activities and courses on the platform and can also add interaction in the content link to improve the quality and experience of traditional education through mobile Internet technology.

Language Learning Styles of Students at the People’s Police College II

This paper presents the results of an investigation into the language learning styles of students at the People’s Police College II. The participants in this study consisted of 105 students (male = 96 and female = 9) who are studying at the People’s Police College II. They all are first-year students. Subjects completed some types of questionnaires, that was a background questionnaire used to collect the students’ information, Joy Reid’s Perceptual Learning Style Preference Questionnaire (PLSPQ) in their formal classes. The research finding displayed three learning style preferences used by the first-year students at the People’s Police College II, namely auditory, group, and visual learning styles. From the survey results, the author found that teachers' teaching methods should also vary accordingly. If teachers know students' preferred learning styles, they will design appropriate activities for their students; moreover, if the students understand their personal learning styles, they may heighten their learning ability and adapt their learning preferences to different teaching situations.