The relationship between Kolb's experiential learning styles and Big Five personality traits in international managers

How Personality, Emotions and Situational Characteristics Affect Learning from Social Interactions in the Workplace

The present study examines the effects of social interactions' situational characteristics, emotions, and personality on self-perceived learning from social interactions at work based on diary and survey data. The sample comprises 43 German vocational education and training (VET) trainees in various apprenticeship programs. During the diary period of ten working days, the participants were instructed to record five typical social interactions at work every day. Quantitative data of 1,328 social interactions were analyzed by means of multilevel analysis. Regarding social interactions' characteristics, the analysis revealed the baseline level of instrumentality, an interruption of the social interaction, its instrumentality and questions asked by the trainee during the interaction as positive predictors of self-perceived learning. A trainee's higher speech proportion, however, was a negative predictor. Regarding state emotions, the emotional experiences of bored and motivated were identified as significant positive predictors of learning from social interactions at work. Emotions' baseline level as well as personality traits had no significant influence. The results indicate that social interactions' situational characteristics have the biggest influence on self-perceived learning from social interactions.

Physical Education Classes and Responsibility: The Importance of Being Responsible in Motivational and Psychosocial Variables

The objective of this research work was to analyse the different profiles that can be identified, based on levels of responsibility in relation to Self-Determination Theory, school climate and violence in Physical Education classes. For this, a total of 470 students of Compulsory Secondary Education or Baccalaureate were given a questionnaire where aspects related to motivation, basic psychological needs, responsibility, school social climate and violence were analysed. An analysis of these profiles was conducted, taking into account the variables of "personal responsibility" and "social responsibility", with the results leading to the conclusion that three profiles exist: "low responsibility" (n = 89), "moderate responsibility" (n = 187) and "high responsibility" (n = 194). The results reflected statistically significant differences in all the variables between the three profiles. The high responsibility cluster obtained significantly higher values for the different constructs of motivation (except in external regulation, where there were no differences, compared to the "moderate responsibility" group), basic psychological needs and school social climate. On the other hand, it obtained lower values in amotivation and violence, with no differences based on the sex or age of the participants in the distribution of the clusters. It is concluded that the more responsible profile can have positive results in psychological variables in Physical Education classes and in the general educational field. For this reason, the use of active methodologies, which have been extensively studied to promote responsibility in PE classes, could be an appropriate strategy to achieve a more adaptive psychological profile regardless of the gender or age of the students.

Personality and satisfaction with online courses: The relation between the Big Five personality traits and satisfaction with online learning activities

Online courses have become widespread in higher education. Yet, despite their prevalence, they may not suit all learners. Personality influences learner satisfaction and therefore affects learning experience. This study explores the relation between personality traits (using Costa & McCare's Big-Five model) and student satisfaction with various of learning activities offered in online courses, called Techno-Pedagogical Learning Solutions (TPLS). The tested TPLS were discussion groups, digital books, online assignments, surveys/polls and media. Questionnaires were used to measure personality types and satisfaction of 108 university students enrolled in a credited online academic course. Significant correlations were found between all five personality traits and satisfaction with several TPLS. Cluster analysis method was applied to identify learners with similar personality traits. Four groups were formed and group's satisfaction score was measured. It was found that learners assigned to the "neurotic" group exhibited low satisfaction with all TPLS, contrary to learners assigned to the "non-neurotic" group. The findings clearly indicate that personality plays a significant role in online learner satisfaction. Thus, personality traits should be considered when designing learning activities for online courses. Such personality-based personalization may ensure that no learner is left behind, regardless of his\ her attitude toward online learning.

Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques

In a hybrid university learning environment, the rapid identification of students’ learning styles seems to be essential to achieve complementarity between conventional face-to-face pedagogical strategies and the application of new strategies using virtual technologies. In this context, this research aims to generate a predictive model to detect undergraduates’ learning style profiles quickly. The methodological design consists of applying a k-means clustering algorithm to identify the students’ learning style profiles and a decision tree C4.5 algorithm to predict the student’s membership to the previously identified groups. A cluster sample design was used with Chilean engineering students. The research result is a predictive model that, with few questions, detects students’ profiles with an accuracy of 82.93%; this prediction enables a rapid adjustment of teaching methods in a hybrid learning environment.

An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

For a student, determining how to choose from a set of courses is an important issue prior to learning. An appropriate learning guide can direct students toward an area of interest. The learning results produced by the student in this case are superior due to their strong interest in the subject matter. Although a number of methods have been proposed to address this issue, the effectiveness remains unsatisfactory. To this end, we created an effective system, called the personality-driven course decision assistant, to help students determine the courses they should select by mining and filtering learners’ personality patterns. For learner pattern mining, the relationships between the students’ learning results and the referred personalities are discovered to provide the learners with valuable information before learning. For filtering learner personality patterns, students with similar personality patterns are filtered to predict the potential learning results. Through the actual system, a number of subjective and objective evaluations were conducted, and the evaluation results reveal that the proposed system is highly effective and reliable.

Effects of Noise on English Listening Comprehension among Chinese College Students with Different Learning Styles

This study was intended to determine whether the effects of noise on English listening comprehension would vary among Chinese college students with different learning styles. A total of 89 participants with different learning styles measured using Kolb’s (1985) Learning Style Inventory finished English listening comprehension tests in quiet and in white noise, Chinese two-talker babble, and English two-talker babble respectively. The results showed that the participants in general had significantly poorer performance in the two babble conditions than in quiet and white noise. However, the participants with assimilative and divergent learning styles performed relatively better in Chinese babble, and exhibited stable performance across the three noisy conditions, while the participants with convergent and accommodative learning styles had more impaired performance in both Chinese babble and English babble than in white noise. Moreover, of Kolb’s four learning modes, reflective observation had a facilitative effect on listening performance in Chinese babble and English babble. These findings suggest that differences in learning style might lead to differential performance in foreign language listening comprehension in noise.