Since the beginning of the development of massive open online courses (MOOCs), these and other online learning environments have been considered as potential partial solutions to some persistent problems in higher education. These learning environments, while they have great educational value, have not been as effective as they could be, because they have largely been built with little or no foundation in the cognitive processes (e.g., the conversion of items from short-term to long-term memory) required for effective and efficient online learning. Many innovative online learning approaches are in development, such as personalized learning (learning experiences tailored to address particular information that students need) using adaptive learning systems (machine learning techniques used by computers to recommend materials). However, these approaches would also benefit from being grounded in cognitive theory to better reveal how learning occurs in these systems. Furthermore, crucial features of interventions in online learning, such as supplementary elements designed to fill in gaps or reinforce knowledge, have not been thoroughly examined in conjunction with the insights of cognitive theory and the concept of desirable difficulty (i.e., the notion that the addition of difficulty to a task can improve learning and increase retention).
In this exploratory work, I experimentally examine five different types of interventions and their effects on undergraduate engineering students’ learning gains and experience. This study presents quantitative research along with detailed qualitative thematic analysis. Its objective is to provide critical insights into how to better design online learning environments and how we can create more effective interventions that promote students’ online learning gains. The research questions for this work are: (1) What factors in online learning environments affect learning gains (i.e., measured difference between post- and pre-test scores) for undergraduate engineering students?; (2) What factors in online learning environments affect the learning experience for undergraduate engineering students, and, specifically, what factors produce desirable difficulty?; and (3) What factors in online learning affect undergraduate engineering students’ self-reported memory?
The experimental results, examined within the framework of cognitive theory, showed quantitatively that levels of frustration with interventions were correlated with learning gains while qualitative analysis results revealed instances that both confirmed and contradicted aspects of the quantitative results. A number of practical design guidelines emerged from the analysis: for example, in specific circumstances, one type of intervention is likely to be more effective than another, or that particular sorts of additional difficulties should be avoided. These recommendations may provide researchers with a better understanding of how to challenge students in more efficient and productive ways in online learning environments.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162900/1/seokjook_1.pd
