A Relational Study of Personality Traits, Learning Styles, Learning Competencies, Learning Methods, and Assessment Methods for Engineering and Management Students Studying the Internet of Things Related Knowledge Areas

Abstract

According to India's Human Resource Development Ministry, as of 2019, 6000 plus engineering institutions across India enrolled 2.9 million students every year. They also mentioned that nearly 1.5 million students graduated and entered the job market every year. Of these, 30% were employable, and only 7% were skilled. In this era of technology-intensive organizations and automation, the expectations set by the industries on the higher educational institutions have been to have the students quite well-skilled and partially industry-ready. Hence, the government of India suggested that higher educational institutions consider the academic requirements of the workforce developing intelligent technologies and thereby facilitate such required knowledge areas using appropriate learning methodologies.

As per Gartner's Hype Cycle, one such intelligent technology in this industry 4.0 era, considered for my study, is the Internet of Things (IoT). Courses revolving around the IoT are currently offered to engineering and management students as there is a demand for technology developers and managers in the industries. Further, given the learning process, students exhibit different ways of learning with regards to their learning styles and learning methods. It is observed that personality traits play an essential role in the preferred learning styles, and the learning methods are significantly related to the assessment methods of the students. It is also observed that the learning styles are significantly related to the learning methods, and the learning competencies have a significant impact on the learning methods in an educational setting. Hence, a clear understanding of the associations mentioned above is pivotal. By examining these relationships for those students studying the Internet of Things related knowledge areas, the current study is a step forward in engineering and management education as it could potentially lead to better learning outcomes. Subsequently, the following four objectives are framed to address the research study: 1. To assess and relate the personality traits with the learning styles 2. To assess and relate the learning methods with the assessment methods 3. To relate the learning styles and the learning competencies with the learning methods 4. To provide recommendations in the process of learning and assessment methodologies

The objective of the study is to understand the relationship between constructs. The hypotheses are then postulated wherein their validity is accepted or rejected based on the statistical tests. This study has adopted the primary data collection method by using a 5-point Likert scale-based questionnaire as the primary research instrument. The sample respondents selected were final year engineering students pursuing their studies in programs such as computer science, information science, electronics and communications, and final year management students pursuing their studies in programs such as industrial engineering management, data sciences, information systems, and business management. Students for the study were selected from those colleges that offered the IoT related knowledge areas as core courses in their respective engineering and management programs. A total of 2315 respondents studying the IoT related knowledge areas were sampled, of which 1588 were from the engineering program, and 727 were from the management program. Those students selected for the study were from colleges across the state of Karnataka in the southern part of India.

Descriptive statistics facilitate the summarization of the recorded data by describing the association between variables within the sample. The application of the descriptive statistics method assisted in analyzing the constructs items-wise and dimension-wise for engineering and management students. After descriptive statistics, an independent t-test was applied for testing the proposed hypotheses. The t-test for this study assisted in understanding the significant difference in the path of the constructs among engineering and management students. Further, a correlation matrix was developed between the constructs for both engineering and management students. This test in the study assisted in understanding the degree of association between the independent and the dependent variables among engineering and management students. In the end, the application of PLS-SEM was performed to evaluate the relationship frameworks with the constructs under the study. This test for the study assisted in establishing the reliability and the validity of the constructs, outer model loadings of the reflective constructs, explanatory power between the constructs, and the significance of the relationship between the constructs through bootstrapping analysis.

The study shows a significant relationship between the constructs for all the above associations for engineering and management students. The study also shows the impact of learning competencies over learning styles on the learning methods for students. However, the study shows no significant difference in the path of the relationship between the constructs among engineering and management students.

Understanding the linkages between personality traits and learning styles, learning methods and assessment methods, and learning styles and learning competencies with learning methods assist the students in their learning process. This research in the future could help educators grasp the complex nature of student diversity and relate them to different kinds of assessments that could potentially enhance the students' learning outcomes. Instructors who recognize the importance of personality traits, learning styles, and learning competencies and consider them vital for effective learning may design courses, assignments, and other teaching and learning approaches that encourage students to adopt required learning methods.

Later, a forced-choice questionnaire was given to all the students in the sample. The forced-choice questionnaire consisted of preferences that were opted by the students. One set of preferences consisted of existing practices in the process of learning and assessment methodologies, and the other set of preferences consisted of possible changes to the existing practices suggested by the literature and the faculties. Based on the students' preferences and the faculties' feedback, possible changes to the learning and assessment methodologies framework are recommended. With the implementation of this framework, the potential impact on the students towards enabling their learning effectiveness and the possible institutional outcomes towards enhancing the research output are highlighted. Further, universities in India could use this proposed learning and assessment methodologies framework for further validation and studies for other programs. They could have a control group and a test group to see which one of the two, i.e., existing practices or proposed framework for learning and assessment, enables better learning and research outcomes.