Myers-Briggs Research Articles

Analisis kemampuan pemahaman konsep matematis siswa berdasarkan tipe kepribadian thinking dan feeling dalam menyelesaikan soal aljabar

This research aims to explore the ability to understand mathematical concepts between students with Feeling and Thinking personality types. The qualitative descriptive method with a case study design was applied to four students of SMA N 3 Sungai Full who were selected based on the results of the Myers Briggs Type Indicator (MBTI) test. The instruments used were personality type questionnaires, tests of ability to understand mathematical concepts, and unstructured interviews. Data analysis was carried out using data triangulation to ensure the accuracy and validity of the results. Students' understanding of mathematical concepts in the feeling and thinking aspects on the seven indicators that have been evaluated shows relatively low results, even though their approaches to learning are different. Students with the Feeling type tend to consider more emotional aspects in their understanding, while students with the Thinking type focus more on logic and analysis. This research underscores the importance of approaches tailored to students' personality types to improve their understanding of mathematical concepts.

A Comparison of MBTI and Big Five Personality

This article explores the two most common measurements in the world today, the Myers-Briggs Type Indicator (MBTI) and the Big Five Personality (Five-Factor Model). Furthermore, the article casts a critical eye on the MBTI, dissecting its methodology, reliability, and validity. It highlights measurements’ strengths, such as its ability to offer a framework for self-understanding and its user-friendly nature, while also addressing the common criticisms levied against it. These include concerns about its scientific rigor, the potential for misinterpretation of results, and the static nature of the personality types it proposes. The discussion then pivots to the Big Five Personality Inventory, exploring its origins and the evolution of its use in contemporary psychological research and practice. The overarching goal of this paragraph is to present a comprehensive study that not only illuminates the advantages and disadvantages of these personality assessments but also opens the door to considering alternative psychological instruments. By doing so, the article aims to provide readers with a broader perspective on how these tools can be utilized to enhance various aspects of life, including academic study, the recruitment process in organizations, and the promotion of mental well-being. This holistic approach encourages a more informed selection of psychological resources, ultimately leading to more effective strategies in these areas.

Sugeno Fuzzy Personality Prediction System: An Approach to Overcoming Psychological Measurement Uncertainty

Personality prediction is a significant field in psychological measurement, yet it faces challenges due to psychological data's ambiguous and uncertain nature. This study aims to develop a Sugeno-based fuzzy logic system for predicting personality types according to the Myers-Briggs Type Indicator (MBTI). The dataset includes synthetic personality data, incorporating age, introversion, sensing, thinking, and judging. The fuzzification process converts crisp input values into fuzzy variables, which are then processed using predefined fuzzy rules to generate personality predictions. The defuzzification step yields crisp outputs corresponding to MBTI types, demonstrating the system's ability to handle uncertainty and ambiguity effectively. Implementation and evaluation were conducted using Python and LabVIEW, revealing a satisfactory performance with a low error rate of 0.445. This study highlights the potential of fuzzy logic, particularly the Sugeno method, in enhancing accuracy and adaptability in personality prediction, contributing to applications in education, human resource management, and personalized digital services.

Personality Types of Medical Students in Terms of Their Choice of Medical Specialty: Cross-Sectional Study.

Research on personality types among doctors reveals its impact on medical specialty choices, suggesting that considering personality in career planning may enhance work satisfaction and reduce burnout risks. This study, encompassing 2104 medical students, explores how personality types, traits, and gender relate to specialty preferences. Participants of this study were medical students from various universities in Poland. The study surveyed 2104 participants. Each participant completed a general questionnaire and a NERIS Type Explorer personality test, based on the Myers-Briggs Type Indicator inventory and the "Big Five" personality traits concept. The questionnaire was distributed on social media groups for medical students from all Polish universities. An exploratory statistical analysis was performed to find relationships. For each tested relationship a Fisher exact test was conducted and the significance level was P<.05. Each test resulted in a P value and odds ratio (OR) with a CI. To ensure we included undecided students and obtained meaningful data, we allowed participants to select up to three medical specialties from the 77 available in Poland at the time of the study. The findings unveil significant relationships between gender, personality types, traits, and specialty preferences. Women tended to favor Neonatology (OR 9.15, 95% CI 3.02-45.46), while men leaned toward Orthopedics and traumatology of the locomotor system (OR 7.53, 95% CI 4.87-11.94). Extroverted, Intuitive, Feeling, Prospecting, and Turbulent students showed a heightened interest in Psychiatry (OR 2.23, 95% CI 1.64-3.01), whereas Introverted, Observant, Feeling, Judging, and Turbulent types favored Family Medicine (OR 2.98, 95% CI 2.08-4.24) and Pediatrics (OR 2.13, 95% CI 1.51-2.99). In conclusion, this research establishes a link between personality and medical specialty selection. Taking into account the significant role of personality traits, it should be considered to integrate them into the process of selecting a medical career or designing a medical curriculum. This approach may allow for the customization of programs to match students' traits, thereby cultivating improved clinical communication skills, fostering interprofessional collaboration and ultimately enhancing treatment outcomes and professional fulfillment among physicians. The main limitation of this study is that it was conducted on medical students, who lack the full knowledge of the work as a specific specialist. A study surveying medical doctors with longer internships across different wards could be conducted to check for any variabilities. Moreover, there are other significant factors that influence one's medical specialty choice. Certainly, this area could be further explored.

A Comparative Analysis of Introvert and Extrovert Dental Students toward Clinical Performance.

This study aims to identify introvert and extrovert personality types among level-6 dental students in the College of Dentistry, Jazan University, Jazan, Saudi Arabia, and analyze their clinical performance. In this causal-comparative research, 72 level-6 dental students (29 males and 43 females) of the College of Dentistry of Jazan University for the school year 2023-2024 were chosen to be the respondents. Myers-Briggs type indicator (MBTI), a personality test designed to differentiate introversion and extroversion, was used. Two independent variables were compared in accordance with the average grades in their clinical performance. The average grade of introvert dental students was 83.54 ± 7.57, with a median of 85.06, and that of extrovert dental students was 84.03 ± 7.01, with a median of 86.9. The p-value was calculated to be 0.917, and a significance level of 0.05 was used for comparison. Clinical performance was not significantly different between introvert and extrovert dental students. Awareness of personality differences of members of a dental team is vital to help translate every weakness into a workable strength so that the treatment plan for each patient will be delivered with utmost quality and excellence. How to cite this article: Ramos RB, Belza MSL, Coronado GB, et al. A Comparative Analysis of Introvert and Extrovert Dental Students toward Clinical Performance. J Contemp Dent Pract 2024;25(9):830-835.

EmoMBTI-Net: introducing and leveraging a novel emoji dataset for personality profiling with large language models

Emojis, integral to digital communication, often encapsulate complex emotional layers that enhance text beyond mere words. This research leverages the expressive power of emojis to predict Myers-Briggs Type Indicator (MBTI) personalities, diverging from conventional text-based approaches. We developed a unique dataset, EmoMBTI, by mapping emojis to specific MBTI traits using diverse posts scraped from Reddit. This dataset enabled the integration of Natural Language Processing (NLP) techniques tailored for emoji analysis. Large Language Models (LLMs) such as FlanT5, BART, and PEGASUS were trained to generate contextual linkages between text and emojis, further correlating these emojis with MBTI personalities. Following the creation of this dataset, these LLMs were applied to understand the context conveyed by emojis and were subsequently fine-tuned. Additionally, transformer models like RoBERTa, DeBERTa, and BART were specifically fine-tuned to predict MBTI personalities based on emoji mappings from MBTI dataset posts. Our methodology significantly enhances the capability of personality assessments, with the fine-tuned BART model achieving an impressive accuracy of 0.875 in predicting MBTI types, which notably exceeds the performances of RoBERTa and DeBERTa, at 0.82 and 0.84 respectively. By leveraging the nuanced communication potential of emojis, this approach not only advances personality profiling techniques but also deepens insights into digital behaviour, highlighting the substantial impact of emotive icons in online interactions.

Personality Prediction System Using Machine Learning

A personality prediction system leverages advanced data analysis techniques to assess and predict an individual's personality traits based on diverse inputs such as text, voice, behaviour, and biometric data. These systems typically utilize psychological models, like the Big Five Personality Traits or Myers-Briggs Type Indicator (MBTI), to derive insights from patterns in the data. Text-based prediction systems use natural language processing (NLP) to analyze written or spoken communication, while voice- based systems analyze vocal attributes such as tone and pitch. Behavioral and biometric data further enhance the accuracy of these systems, with applications spanning areas like human- computer interaction, marketing, recruitment, and mental health. With the aid of artificial intelligence (AI) and machine learning, these systems are increasingly capable of delivering personalized insights, offering the potential for a wide range of applications from customer engagement to psychological well-being. This abstract outline the core mechanisms, applications, and future directions of personality prediction systems, highlighting their potential to transform how individuals are understood and interacted with in both digital and real-world contexts.

Personality Prediction System Using Machine Learning

A personality prediction system leverages advanced data analysis techniques to assess and predict an individual's personality traits based on diverse inputs such as text, voice, behaviour, and biometric data. These systems typically utilize psychological models, like the Big Five Personality Traits or Myers-Briggs Type Indicator (MBTI), to derive insights from patterns in the data. Text-based prediction systems use natural language processing (NLP) to analyze written or spoken communication, while voice-based systems analyze vocal attributes such as tone and pitch. Behavioral and biometric data further enhance the accuracy of these systems, with applications spanning areas like human-computer interaction, marketing, recruitment, and mental health. With the aid of artificial intelligence (AI) and machine learning, these systems are increasingly capable of delivering personalized insights, offering the potential for a wide range of applications from customer engagement to psychological well-being. This abstract outline the core mechanisms, applications, and future directions of personality prediction systems, highlighting their potential to transform how individuals are understood and interacted with in both digital and real-world contexts.

Exploring the Influence of Parental Personality Types on Children’s Academic Choices

The academic decisions that children make are frequently influenced by several different circumstances. One of the most important yet understudied aspects of this is the influence of the personality types of their parents. The objective of this report is to understand how parental personalities may impact a child's inclination towards science or art subjects. The Myers-Briggs Type Indicator (MBTI) Test was used for parents to indicate the relationship between parental personality type and the academic choices made by their children, particularly in the field of science and art. The results indicated that children from the INTJ, INFJ, and ENFJ parents’ personality types, are likely to choose science subjects, while children from ENTJ parents’ personality types choose art subjects. This sheds light on the association between parental personality types and children's academic choices; aiding educators and policymakers better tailor educational approaches to accommodate diverse student needs and aspirations.

Transforming Personalized Travel Recommendations: Integrating Generative AI with Personality Models

Over the past few years, the incorporation of generative Artificial Intelligence (AI) techniques, particularly the Retrieval-Augmented Generator (RAG) framework, has opened up revolutionary opportunities for improving personalized travel recommendation systems. The RAG framework seamlessly combines the capabilities of large-scale language models with retriever models, facilitating the generation of diverse and contextually relevant recommendations tailored to individual preferences and interests, all of which are based on natural language queries. These systems iteratively learn and adapt to user feedback, thereby continuously refining and improving recommendation quality over time. This dynamic learning process enables the system to dynamically adjust to changes in user preferences, emerging travel trends, and contextual factors, ensuring that the recommendations remain pertinent and personalized. Furthermore, we explore the incorporation of personality models like the Myers–Briggs Type Indicator (MBTI) and the Big Five (BF) personality traits into personalized travel recommendation systems. By incorporating these personality models, our research aims to enrich the understanding of user preferences and behavior, allowing for even more precise and tailored recommendations. We explore the potential synergies between personality psychology and advanced AI techniques, specifically the RAG framework with a personality model, in revolutionizing personalized travel recommendations. Additionally, we conduct an in-depth examination of the underlying principles, methodologies, and technical intricacies of these advanced AI techniques, emphasizing their ability to understand natural language queries, retrieve relevant information from vast knowledge bases, and generate contextually rich recommendations tailored to individual personalities. In our personalized travel recommendation system model, results are achieved such as user satisfaction (78%), system accuracy (82%), and the performance rate based on user personality traits (85% for extraversion and 75% for introversion).

Interpretation of Myers–Briggs Type Indicator personality profiles based on ambivert continuum scale

Interpretation of Myers–Briggs Type Indicator personality profiles based on ambivert continuum scale

Logistic Regression Model to Personality Type Prediction Based on the Myers–Briggs Type Indicator

The Myers-Briggs Type Indicator (MBTI) is a widely-used tool in psychology for determining personality types, playing a crucial role in fields like team building, communication, and personalized marketing. Despite its popularity, accurately classifying MBTI types using machine learning remains a significant challenge. This study focuses on addressing this challenge by exploring the effectiveness of logistic regression in MBTI classification tasks. Two approaches are used: four-times binary classification and multi-class classification. The findings show that while logistic regression performs exceptionally well in binary classification tasks but the accuracy is not good in multi-class classification. Additionally, combining binary classification results yields an overall accuracy that is lower than the direct multi-class classification. These results highlight the limitations of logistic regression in multi-class tasks and suggest the necessity for more advanced models. Future research should focus on improving multi-class classification accuracy, potentially through more complex architectures or hybrid models combining binary and multi-class approaches.

Do you love traveling to the beach or mountain? Predicting personality traits and choice behaviour

ABSTRACT Several personality-predicting tools are being used by managers in their strategic decision-making process. The most common are the Big Five model and the Myers – Myers-Briggs Type Indicator model, respectively. This study deviates and tests a novel personality prediction model. Specifically, it utilizes the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm as a feature extraction method to map personality traits based on consumers’ textual data from the Kaggle MBTI Personality Type dataset. This study uses realistic data gathered from traveller reviews and comments on hotel stays from TripAdvisor’s widely recognised platform to predict different personality traits and their travel destination preferences, i.e. beach or mountain. The contribution of this study lies in the identification of multiple dimensions of peoples’ personality traits and their relationship with their preferences for travel locations. This proposed trait classification mechanism can assist marketing managers in effectively carrying out customer segmentation, setting realistic goals, and optimizing marketing strategies.

The Myers-Briggs type indicator association with United States medical student performance, demographics, and career values

BackgroundThe Myers-Briggs Type Indicator (MBTI) is a self-reported personality test that has continued to gain popularity and traction across industries. The MBTI assesses individuals within four dichotomous categories corresponding to ways to, ultimately, assign one of sixteen personality types. Prior studies indicate that physicians trended towards certain personality traits and medical students with a particular trait performed lower academically. Due to the recency of these studies, however, there is a substantial knowledge gap in the relationship between medical student MBTIs and academic performance, demographics, and career interests. Therefore, the purpose of this study is to investigate this relationship amongst 4-year allopathic U.S. medical students and determine if there is substantial correlation to implement MBTI results in creating personalized student support services in school curriculum.MethodsThis study was a single institution, cross-sectional study of 83 medical students. A survey was emailed to all students with the MBTI personality test paper edition, in addition to questions regarding specialty interest, learning style preference, and career values. Each student’s responses were de-identified after pairing with the respective students’ academic performance data and demographic information. Chi-square tests were used to compare MBTI data with categorical data such as demographic characteristics while independent sample t-tests were used to compare MBTI data with numerical variables like academic performance.ResultsOur findings suggest statistically significant associations between Introversion (I) with older age and Thinking (T) with male students. Additionally, Thinking (T) students performed better academically, valued variety in patient care (breadth of patients and pathologies seen), and the specific type of patient contact (longitudinal vs. short-term, etc.). Conversely, Feeling (F) students trended towards people-oriented specialties. Regarding learning styles and study environments, Extroverted (E) and Judging (J) types preferred hands-on learning, though Judging (J) types also preferred a quiet environment. No other statistically significant differences were found amongst the remaining variables.ConclusionMedical schools should utilize student MBTI results to individualize student academic and career support as our study indicates various significant associations between some of the personality types. While not a “one-size-fits-all,” the MBTI can be used as a potential starting point for specialty exploration and learning styles.

The Application of the Myers-Briggs Type Indicator to Human Resource Management System in China

Abstract: The Myers-Briggs Type Indicator has long been an important personality assessment instrument in human resource management. It helps managers to recruit employees, assign tasks, build employee relations and measure performance. With the economic reform and opening-up policy that accelerated worldwide coordination, it was introduced in China. The study, taking a literature review approach and comparative approach, aims to explore how the Myers-Briggs Type Indicator is used in Chinas human resource management system. And through the analysis of the development and status of Chinas human resource management, and the comparison with Self-directed Search and Big Five Personality Theory, to see if there is room for improvement in its use in the future. The result shows that most of the research and use of the Myers-Briggs Type Indicator in China is about recruiting. It can be developed in team building and performance evaluation in the future, like The Big Five Personality Theory. And also in assigning tasks.

How to avoid gifts from your patients after the Christmas holidays?

Receiving gifts from patients could generate an ethical dilemma for physicians and refusing a present may be perceived as an offence. To differentiate the particular physician characteristics that are associated with receiving gifts from outpatients. To propose a strategy to avoid gifts and thus ethical dilemma. A prospective and multicentric study in France during the month of January 2024. Public hospitals, private hospitals and family practices across 20 cities in France. Physicians from 21 medical and surgical specialities. Setting Public hospitals, private hospitals and family practices across 20 cities in France. Each physician completed a questionnaire that highlighted their habitual practices during consultations and recorded the gifts they received on a standardized gift calendar. Receiving at least one gift during January 2024. In total, 655 physicians were contacted before December 31st, 2023, 109 (16.6%) positively responded, of them 76 (70%) declared receiving at least one gift from patients. An experience>10 years in the current job (P=0.04), being the "Explorer" Myers Briggs Type Indicator (MBTI) personality type (P=0.034), and a frequent delay of over 30minutes at the end of the day of consultations (P=0.048) were statistically associated with a higher likelihood of receiving at least one gift from patients in multivariate analysis. The physicians who received the most gifts per 100 consultations were those with over 10 years experience and those who fell under the MBTI personality role "Explorer": 2.72 (IQR [1.56-7.55], P=0.029) and 3.33 (IQR [2.16-6.43], P=0.028), respectively. After analysing the factors that increase the risk of receiving a gift from a patient, we have surmised the following advice for physicians who do not wish to find themselves in the midst of an ethical dilemma. They should avoid staying in the same service for more than 5 years, try to adopt the MBTI personality type "Analyst" and be on time in their consultations.

The Personality of the Intelligent Cockpit? Exploring the Personality Traits of In-Vehicle LLMs with Psychometrics

The development of large language models (LLMs) has promoted a transformation of human–computer interaction (HCI) models and has attracted the attention of scholars to the evaluation of personality traits of LLMs. As an important interface for the HCI and human–machine interface (HMI) in the future, the intelligent cockpit has become one of LLM’s most important application scenarios. When in-vehicle intelligent systems based on in-vehicle LLMs begin to become human assistants or even partners, it has become important to study the “personality” of in-vehicle LLMs. Referring to the relevant research on personality traits of LLMs, this study selected the psychological scales Big Five Inventory-2 (BFI-2), Myers–Briggs Type Indicator (MBTI), and Short Dark Triad (SD-3) to establish a personality traits evaluation framework for in-vehicle LLMs. Then, we used this framework to evaluate the personality of three in-vehicle LLMs. The results showed that psychological scales can be used to measure the personality traits of in-vehicle LLMs. In-vehicle LLMs showed commonalities in extroversion, agreeableness, conscientiousness, and action patterns, yet differences in openness, perception, decision-making, information acquisition methods, and psychopathy. According to the results, we established anthropomorphic personality personas of different in-vehicle LLMs. This study represents a novel attempt to evaluate the personalities of in-vehicle LLMs. The experimental results deepen our understanding of in-vehicle LLMs and contribute to the further exploration of personalized fine-tuning of in-vehicle LLMs and the improvement in the user experience of the automobile in the future.

A Review on Personalization of Gamified Learning Environments*

This study explores the personalization of gamification in educational contexts, particularly adaptive approaches to meet diverse learner needs. The study examines terminologies and offer definitions for adaptive, tailored, personalized, and customized gamification. Personalization serves as an umbrella term covering any changes in the process. Customization is the changes performed by the user/learner; tailored gamification involves adjustments made at the beginning and adaptive gamification involves system-driven changes as things progress. The analysis of adaptive gamification studies is structured around adaptation criteria, adaptation process, adaptation data, and adaptive intervention. Adaptation criteria includes player types, personality models, learning styles, and hybrid profiles. Adaptation process includes user-controlled and system-controlled adaptation. User-controlled adaptation refers to customization; system-controlled adaptation is examined under static adaptation, dynamic adaptation, and combination of these two. Adaptation data is categorized under explicit and implicit information collection. Lastly, recommendations, and adjusting game elements and mechanics are discussed under adaptation intervention. The review highlights the commonly used player typologies, including Bartle, Hexad, and BrainHex, and personality models such as Big Five and Myers-Briggs Type Indicator. The review identifies limitations in current frameworks, stressing the importance of standardized models and guidelines to implement adaptive gamification and incorporating gamification analytics to sustain adaptation and automation.

The Relationship between the Myers-Briggs Type Indicator (MBTI) Types and Psychological Well-being among College Students in China

Aim: This study explores the relationship between Myers-Briggs Type Indicator (MBTI) personality types and psychological well-being among Chinese college students, examining how cultural factors influence this relationship and identifying key factors impacting students' mental health. Methodology: A systematic literature review was conducted, focusing on peer-reviewed articles from the past decade. Databases such as PubMed, PsycINFO, and Google Scholar were searched using keywords like "MBTI," "psychological well-being," "college students," and "China." Inclusion criteria included studies on MBTI and psychological well-being among Chinese college students. Results: The review found that 64% of extraverted students reported high life satisfaction, compared to 42% of introverted students. Sensing students showed 58% academic satisfaction, while only 45% of intuitive students reported similar satisfaction levels. Thinkers (60%) and feelers (55%) displayed high psychological well-being linked to their respective environments. Judging types reported 70% life satisfaction, whereas only 45% of perceiving types expressed similar satisfaction. These findings underscore the significant impact of MBTI personality types on the psychological well-being of Chinese college students. Conclusion: MBTI types significantly influence psychological well-being among Chinese college students, moderated by cultural, social, and educational factors. Tailored mental health interventions and supportive environments are essential to address the diverse needs of students. Recommendations: Colleges should integrate MBTI-based counseling services, provide flexible learning options, and promote social support networks. These measures will accommodate varying personality types and improve overall student well-being. Implications: Understanding the interplay between MBTI types and psychological well-being within the Chinese cultural context offers valuable insights for educators and mental health professionals. These insights can inform the development of culturally sensitive interventions and support systems, enhancing the mental health and academic success of college students.

The Relationships between the Myers-Briggs Type Indicator (MBTI), Job satisfaction and Well-being among Working College Students in China

The phenomenon of working college students is on the rise in China. Evidence elsewhere suggests that working while studying can be problematic for students' well-being. This research sought to assess whether personality types and job satisfaction can be used to prevent the decline of well-being among working college students in the country. It specifically examined the effects of Myers-Briggs Type Indicator (MBTI) types on job satisfaction and well-being, relationship between job satisfaction and well-being wellbeing, and the moderating roles of field of study and emotional state in these relationships. Based on the findings, these objectives have been successfully met. The findings show that MBTI personality types significantly shape both job satisfaction and well-being. Scoring highly on Extraversion, Sensing, Thinking, and Judging predicts high job satisfaction and better well-being among the students. Job satisfaction was also found to positively impact well-being for working students. Additionally, the findings also show that the field of study and emotional state moderate the stated relationships. The findings of this study are sound as they generally align with what other researchers have established. The findings imply that Myers-Briggs Type Indicator (MBTI) types and job satisfaction can be applied and targeted to enhance the well-being of working college students in China as the phenomenon continues to grow.