Dr. Min Kyu Kim is a postdoctoral researcher at the Research Laboratory for Digital Learning at the Ohio State University. He earned his Ph.D. in the learning, design, and technology program at the University of Georgia in 2012 and worked at the University of Southern California before joining OSU. His research focuses on advancing personalized learning environments that address ill-structured and complex problem domains and often emphasizes technology-enhanced formative assessment and feedback.
His scholarly work is grounded in his belief that most students can master learning goals if they are “provided with authentic learning experiences with integrating personalized learning that can be advanced with formative assessment and feedback methods deepening learning engagement.”
His belief about better education has led a series of research achievements. For example, he currently published a research, entitled “Models of learning progress in solving complex problems: expertise development in teaching and learning,” to top-ranking journal, Contemporary Educational Psychology,
Importantly, the study’s approach was cleverly designed to address two major limitations. The first is that traditional studies tend to contrast via extremes, in other words, comparing experts with beginners. This inevitably results in what Dr. Kim refers to as a ‘lack of developmental focus’ that does not account for the middle stages of development between novice to expert levels of knowledge. Interestingly, the study shifts the focus of expertise development to problem-solving situations in the classroom and examines how incremental short-term changes can lead to expertise. Specifically, participants were given asked to provide written responses to a poorly-structured and complex problem scenario, which was then analyzed using Exploratory Factor Analysis and Log-linear Cognitive Diagnostic Model (LCDM) methodologies. The result of the analysis demonstrated and validated staged learning progress when using an appropriate model, which would allow a more focused insight of an individual’s progress. The validation analysis using C-LCDM demonstrated that the data contained three latent classes that exactly corresponded to the three-stage model: (a) acclimation, (b) competence, and (c) proficiency, which further evidences stage-sequential modeling as a justified method of measuring knowledge. This in turn, can be applied towards a multitude of technology-enhanced assessment applications such as analyzing in-class responses in order to determine a students’ level of understanding. In particular, it provides the following functions and methods for future research including a set of parameters quantifying the attributes of knowledge structure; a set of similarity measures applicable to the study of cognitive changes; and a statistical approach to diagnosing the stages of learning progress. Ultimately these models and methods for understanding a student’s level of understanding should help teachers develop learning environments which cater toward every student regardless of ability. Such technology would provide instructional support and feedback to students in ways that are otherwise impossible for one teacher alone.
Kim, M. (2015). Models of learning progress in solving complex problems: expertise development in teaching and learning. Contemporary Educational Psychology, 42, 1-16. doi: 10.1016/j.cedpsych.2015.03.005