Learning Style Dynamics

Knowledge of an individual’s learning style dynamics might be used to further improve personalized learning, instruction, or educational materials. Previous contributions to learning style theories have assumed that an individual’s learning style preference is invariant. However, the findings in this study suggest that an individual’s learning style preference can dynamically change depending on the circumstances in which the learning is taking place. A learning style is the type of training method an individual prefers to use in developing working knowledge. We define learning style dynamics as the natural change in preferred learning style as a function of one or more circumstances. Such circumstances might include: type of material being studied, mode of delivery, educational level, motivational level, etc. The particular circumstances that the present study focused on was the type of subject matter. Related work in this area includes the Kolb Learning Style Inventory (KLSI), which measures learning style and flexibility. Flexibility is the ability of an individual to use a different style than their preferred style of learning. However, one’s ability to change learning styles is not the same as one’s natural change in learning style preference due to circumstances. Since the KLSI survey does not include questions relating to learning circumstances, learning style dynamics does not appear to be measurable by the current KLSI survey. Based on the apparent assumption made in prior studies, that one’s learning style is invariant to learning circumstances, we chose to test that assumption in this study. We chose the circumstances to be class subjects that all of our survey participants have studied. Our research question was: Can an individual dynamically change learning styles between subject matters (mathematics and English)? To investigate our research question, we created a Dynamic Learning Style Inventory (DLSI) and analyzed the significance of the results provided by 185 university students. The wording of each survey question was strategically chosen to apply to both mathematics and English to help ensure that differences in learning styles between the disparate subjects were fairly measured. To automate the investigation, we administered our DLSI online and we developed computer algorithms to statistically analyze the survey data. This enables other researchers to verify our findings, perform a DLSI on a different set of individuals, or using a different set of learning circumstances. Our results showed that 36 percent of the students had used a different learning style between studying mathematics and English. These results were shown to be statistically significant (tave = 3.39, tstd = 1.17, p < 0.05), and therefore appear to support the existence of dynamics in learning styles.