A Study on the Influence of Learning Styles on the Preference to Learning Support IoTs

Abstract

The Internet of Things (IoT) is a new paradigm that is revolutionizing computing. It is intended that all objects around us will be connected to the network, providing “anytime, anywhere” access to information. This study introduces four imaginary IoT devices with Kolb’s learning style in order to enhance the learning experience for university students. The research emphasizes the role of learning styles with IoT as a discovery process that incorporates the characteristics of learning. Moreover, IoT architecture with five levels: hardware, software, network, cloud and application layers are clearly explained in this study. All the artificial IoT devices were designed based on this structure from the student, data, technology and security perspectives. Kolb’s Learning Style was chosen as the students’ indicator to prefer specific IoT device, as it is widely used in research and in practical information systems applications. Kolb's learning theory sets out four distinct learning styles, which are based on a four-stage learning cycle with two continuums the east-west axis as the Processing Continuum and the north-south axis as the Perception Continuum. Everyone’s learning style is a product of these two choice decisions. Each learning style represents a combination of two continuums: diverging, assimilating, and converging, accommodating.

The target population was college students with a sample of 150 participants. Experiment questionnaire with two parts, Design of Imaginary IoT devices and Kolb’s Learning Style questionnaire, was used to collect data on the preferences to specific IoT devices. Data on students’ dominant learning styles and preferences to imaginary IoT devices were collected online by google docs form from students, who study in Korean universities, in English or Korean languages. The major independent variable in this study was students’ learning styles, which had four categories: accommodators, divergers, assimilators and convergers while the dependent variable was students’ preferences to the imaginary IoT devices. The importance of learning styles on preference of imaginary internet of things devices was investigated and analyzed by Chi-Square Test of Independences (4 x 4 Table) and Multinomial Logistic Regression Analysis. The chi-square test was used to test a variety of sizes of contingency tables, as well as more than one type of null and alternative hypotheses having contingency tables that are referred to as 4 x 4 contingency tables, and tested whether there was an association between the two variables measured at the nominal level. While multinomial logistic was a classification method that generalized logistic multi class problems, which in that case were four artificial devices. The goal of the multinomial logistic regression was to predict the probability or the odds that a specific IoT device was preferred by a certain learning style. In the multinomial regression a reference category was chosen for the dependent variable first, and then several binomial regressions were run. Thus, this study was carried out to test the null hypothesis that there was no relationship between college students’ learning styles and their preferences to IoT devices. Therefore, research results indicated the statistical significance of the estimated model and the impacts of each category over the model. This study revealed the importance of considering the students’ learning styles before inventing learning support IoT devices. Results of this research showed that both chi square test of independence and multinomial regression model gave accurate estimates for our research variables and all four hypotheses were supported.