Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model

Research studies show that different personality type students tend to have their own learning styles. Personality traits and learning styles have played a significant role in the academic success of students. However, most of the studies used a more popularized learning styles instrument such as Kolb's, VARK, or Felder-Silverman's learning styles, for data collection. This study examined the relationships among the Big Five, learning styles, and academic performance of G12 students. A multivariate analysis of variance (MANOVA) statistical technique was chosen to investigate two dependent variables that were continuous (GPA and QPT scores), whereas the independent variables and the confounding variables, gender and school were all categorial. The IPIP Big Five personality markers, the Learning Styles Indicator (LSI) scales, and the Quick Placement Test (QPT) were employed to collect the data. Students' grade point averages (GPAs) were also used. Purposive sampling was employed, comprising 1,358 students from the three largest government secondary schools in Chiang Rai, Thailand: one school from each of the three districts. Overall, the results confirmed that there were significant relationships among the Big Five, learning styles, and academic performance, and there was a significant relationship between personality types and learning styles. Agreeableness and conscientiousness were found to be the first two dominant personality types, whereas GAO (group activity orientation) and PO (project orientation) were preferable to IAO (individual activity orientation). The multivariate results reveal that GPA and QPT were significantly related to school, gender, and learning styles, but were related to personality types non-significantly. The LSI scales have been proved to be practical and appropriate to determine EFL G12 students' learning styles. The study has contributed to the body of knowledge about personality types and learning styles of G12 students in Thailand. The findings suggest that tailoring teaching methods to students' learning styles could improve academic performance, especially in English proficiency.

This study investigated the correlation among personality types, learning styles and writing ability to the eleventh grade students at Senior High School Number 1 Sekayu. The aims of this study were to measure the correlation between personality types and writing ability, the correlation between learning styles and writing ability, the correlation between personality types and learning styles, and the correlation among personality types, learning styles, and writing ability. In this study, the writer used purposive technique sampling. The data were collected by using questionnaire and test. The data were analyzed by using r-product moment and multiple regressions. The findings of this study are: 1. There was not any significant correlation between personality types and writing ability of the eleventh grade students at Senior High at Senior High School 1 Sekayu. 2. There was not any significant correlation between learning styles and writing ability of the eleventh grade students at Senior High School 1 Sekayu. 3. There was significant correlation between learning styles and writing ability of the eleventh grade students at at Senior High School 1 Sekayu. 4. There were not any significant correlations among personality types, learning styles, and writing ability of the eleventh grade students at Senior High School Number 1 Sekayu. It could be concluded that personality types and learning styles were not correlated significantly with students’ writing ability.

The aim of the present study was to investigate the relationship between male and female EFL learners' learning styles and their personality types.To this end, one-hundred English majors took part in the study.The participants consisted of 78 females and 22 males.The Group Embedded Figures Test (GEFT) was used to assess the participants' field dependent/independent (FD/FI) learning styles.The Personality Style Inventory (PSI) was also administered to assess the personality types of the participants.The data obtained from the PSI showed that the most frequent personality types were ISTJ (introvert, sensing, thinking, judging), ESTJ (extrovert, sensing, thinking, judging), and ESTP (extrovert, sensing, thinking, perceiving) personality types, and the least frequent types were ENFJ (extrovert, intuitive, feeling, judging), and INFJ (introvert, intuitive, feeling, judging).Moreover, the results of the GEFT showed that 52% of the subjects were of FI learning style type and 48% of them had a FD learning style.The results of Chi-Square test and Fisher's Exact Test also showed that there was no statistically significant relationship between males and females' learning styles and personality types.The EFL instructors' awareness of these differences in personality types and learning styles can enable them to create a more dynamic atmosphere in the classroom through designing activities that meet the learning preferences of different types of learners.

Objective: Medical student profile is changing on campuses today and there is a much greater variation in the range of personality type and learning style preferences to be considered. In this study it is aimed to determine the learning styles of medical school students at Gazi University and to find out whether there is any relationship between students’ personality types, learning style preferences and their medical specialty choices. Methods: The study was conducted on 170 final year students (96.6%) at the Gazi University School of Medicine in the 2011-2012 academic year . The authors used Myers-Briggs Type Indicator (MBTI) to determine the personality traits and Grasha Riechmann Student Learning Style Scale (GRSLSS) to establish the learning styles. Results: During the study, 91.8% of the students declared that they wanted to be a specialist and 4.1% wanted to be a general practitioner in the near future. Most preferred specialty appeared to be dermatology (11.2%) in whole group. Choices of female students who want to be a specialist were dermatology, ophthalmology and obstetrics and gynecology and their distributions were 22.8%, 13.0% and 9.8%, respectively (p<0.05). The most common personality type in all preferred specialty areas was Introverted Sensing Thinking Judging (ISTJ). The students with ISTJ personality type had a higher score on the collaborative and competitive learning style. Conclusion: Last-year medical students are characterized by a ISTJ personality type in most of the medical specialty preferences. Furthermore, these students have collaborative and competitive learning styles.

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.

rpose: This study aims to develop and evaluate an adaptive e-learning model tailored to the diverse learning styles of students, particularly within the context of distance learning at STIE Krakatau. The objective is to overcome the limitations of conventional e-learning systems that deliver uniform content to all users, regardless of their individual learning needs. Methodology: The study employed a development-based approach using an engineering model consisting of analysis, design, implementation, and evaluation phases. The adaptive e-learning system was created using multimedia authoring tools and tested in a seminar and training session lasting three hours. Results/Findings: The results demonstrated that the adaptive e-learning system effectively tailored instructional materials to the students' preferred learning styles (visual, auditory, and kinesthetic). The system successfully identified students’ learning preferences through a questionnaire and adjusted content presentation accordingly. Ongoing evaluations confirmed that the system functioned as intended and aligned with the designed functionalities. Conclusions: The implementation of an adaptive e-learning model significantly enhances the personalization and relevance of instructional materials, leading to better engagement and potentially improved learning outcomes in distance education. The seminar and training program proved effective in introducing and applying this adaptive model in a higher education setting. Limitations: The study was limited to formative evaluations and alpha testing. Further testing, including beta testing and broader implementation, is needed to determine user acceptance and the system's effectiveness in real classroom settings. Additionally, the current version relies on self-reported learning styles which may affect the accuracy of adaptation. Contributions: This research contributes to the advancement of personalized e-learning technologies by proposing a dual-level adaptation model incorporating both adaptivity (system-driven) and adaptability (user-driven).

This study investigates the correlation between personality traits and learning styles among university students, with the aim of identifying patterns that can enhance personalized educational strategies.Drawing on the Myers-Briggs Type Indicator (MBTI) framework and VARK (Visual, Auditory, Reading/Writing, Kinesthetic) learning styles, the study analyzes data from 77 students to understand how their personalities align with specific learning preferences.Findings reveal a predominance of visual learners and highlight recurring personality types such as INFJ (Advocate) and ENTJ (Commander).The analysis emphasizes the need for diversified teaching approaches to foster engagement and learning outcomes across different learner profiles.This research not only fills the existing gap in educational psychology but also offers actionable recommendations for educators, providing a foundation for future studies on personalized learning environments.

Intelligent Tutoring Systems (ITS) offer adaptivity to user abilities, personal character trait, learning styles and preferences. The user modelling is one of the major factors that can influence an adaptivity. The content and structure of user profile should allow to recommend learning material suitable for student's needs. In this work a user profile is designed and a method for predicting learner's abilities is proposed. We use a Naive Bayes classifier in order to predict student's learning results. A prediction of user's abilities could be very useful for determining an initial learning scenario or assigning a student to a suitable collaborative learning group.

Abstract  The purposes of this study were to find: (1) the most personality type was employed by the fourth semester students of English Education Study program at PGRI University of Palembang (2) the most learning style was employed by the fourth semester students’ of English Education Study Program at PGRI University of Palembang, (3) whether or not the fourth semester students’ personality types of correlate significantly with their writing achievement, (4) whether or not the fourth semester students’ learning styles correlate significantly with their writing achievement, (5) whether or not the fourth semester students’ learning styles correlate significantly with their writing achievement, and (6) whether or not the fourth semester students’ personality types and learning styles contribute to their writing achievement; The population of this study was the Fourth semester students of English Education Study Program of PGRI University of Palembang in academic year of 2013/2014 consisting 231 students. From the population, 75 students were taken as his research sample. Then, the writer distributed two questionnaires for knowing students personality types and learning styles, and a writing achievement test for knowing students writing achievement. Statistical analysis of Pearson Product Moment Correlation Coefficient was used to investigate whether there was a significant correlation between students’ personality types on students’ writing achievement, then learning styles on students’ writing achievement. Key Words: Correlation, Personality Types, Learning Styles, Writing                  Achievement.

Currently, e-learning is becoming an option as it can save the cost of education, time, and more flexible in its implementation. The main problem that arises is how to create e-learning content that is interesting and really fit the needs of the users. One way that can be done to optimize the content of e-learning is to analyze the user behavior. This study aims to analyze user (student) behavior in KALAM UMP, based on logs report (activity history), which is often called as behavioral tracking. First, the learning style of the students is determined based on Honey and Mumford Learning Styles Model by using Learning Styles Questionnaire. The analysis is done using SPSS 16.0 for Windows. The results shows that student with Reflector and Theorist learning styles access e-learning materials the most. From Spearman Correlation analysis, the relationship between learning styles and students’ behavior in e-learning is found to be very weak (r&lt;sub&gt;s&lt;/sub&gt;=.276, p=.000), but statistically significant (p&amp;lt;0.05). In other words, students’ learning styles and behavior in e-learning have significant impacts on the improvement or degradation of students’ performance. Therefore, from the results of this study, an adaptive KALAM e-learning system which can suits the learning styles of UMP students is proposed. In adaptive e-learning system, students can access learning materials that match the students' learning needs and preferences.

The personality traits and learning styles of students are important influencing factors that affect their understanding, mastery, and application of knowledge. With the increasing emphasis on personalized education in higher education, exploring the interactions between these factors has become increasingly important for optimizing teaching strategies. However, existing research has rarely integrated MBTI personality types and KOLB learning style scales to investigate their correlation with academic performance. The aim of this study is to investigate the relationship between MBTI personality traits, KOLB learning styles, and CET-4 scores of non English major college students, in order to provide reference for English teaching strategies. The correlation between CET-4 scores was explored using descriptive statistics and comparative analysis methods. The study found that assimilative learning styles are the most common, while aggregative learning styles perform the highest on the CET-4 average score. Although no statistically significant differences were observed, the overall trend suggests that there are differences in performance across different MBTI dimensions, such as higher average scores for the thinking (T) type and more concentrated average scores for the outward oriented (E) type. These findings provide empirical support for designing targeted teaching strategies, which can help develop personalized English teaching models and improve the efficiency of college English education.

The importance of effective training in ensuring the success of End-User Computing (EUC) has been emphasized by several researchers in MIS. A vast amount of evidence from research in such related areas as educational psychology suggest that individual differences, such as a novice end user's learning style may effect his/her learning of a new EUC software. This paper reports the findings of a series of studies that examined the influence of a novice's learning style in learning of typical EUC tools such as spreadsheets and electronic mail. A consistent pattern of findings emerged that indicate that learning style is an important predictor of learning performance, both by itself and in interaction with training methods. The findings suggest that in the design of training, it is essential to match training methods to individual difference variables. Based on these findings, guidelines are suggested for IS professional involved in EUC training.

This study aimed to develop a Structural Equation Model (SEM) that captures the influence of learning style, personality traits, and emotional intelligence on the academic performance of Grade 4 learners in the Division of Butuan City. Employing a descriptive-predictive quantitative research design, data were collected from 341 learners using standardized survey instruments. The study found that most learners exhibited a kinesthetic learning style and displayed high levels of openness and agreeableness, alongside average levels of neuroticism, extraversion, and conscientiousness. Emotional intelligence dimensions such as self-awareness, motivation, and social skills were strongly manifested, while empathy and emotion regulation appeared moderately. In terms of academic outcomes, most learners achieved “outstanding” or “very satisfactory” general weighted averages (GWA), with none performing below satisfactory levels. Statistical analyses revealed significant positive correlations between academic performance and each of the independent variables. A multiple linear regression model showed that 75.9% of the variance in GWA could be explained by the combined effects of learning style, personality traits, and emotional intelligence. To address potential multicollinearity, a Partial Least Squares (PLS)-based SEM was developed, demonstrating strong reliability, convergent validity, and discriminant validity. The model accounted for 66% of the variance in academic performance and confirmed that these psychosocial and cognitive variables are significant predictors of student success. The study concludes that the interplay among learning preferences, personality, and emotional intelligence offers valuable insights into learner achievement and provides a robust framework for future educational planning and intervention.

The major requirement of present e-learning system is to provide a personalized interface with adaptiveness. This is possible to provide by analyzing the learning behaviors of the learners in the e-learning portal through Web Usage Mining (WUM). In this paper, a method is proposed where the learning behavior of the learner is captured using web logs and the learning styles are categorized according to Felder-Silverman Learning Style Model (FSLSM). Each category of FSLSM learner is provided with the respective content and interface that is required for the learner to learn. Fuzzy C Means (FCM) algorithm is used to cluster the captured data into FSLSM categories. Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the new learner. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm that calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN. Based on the identified learning style, the adaptive contents and interface can be provided to the learner.

Adaptive e-learning systems are considered one of the interesting research areas in technology-based learning strategies. The main goal of these systems is to offer learns a personal and a unique learning experience based on their preferences, needs, educational background, learning style, etc. The objective of this research is to identify the learning style of the learner. The identification is based on using web Log Mining data which contain learning behavior of the learner, and then the learning styles are mapped to Felder-Silverman Learning Style Model categories using Fuzzy C means Algorithm. The learning style can be changed over a period of time therefore the system has to adapt to the changes. For this, an Artificial Neural Network Algorithm is used to predict the learning style of a learner.