The purpose of this work is to develop a new research methodology that combines microgenetic techniques derived from the field of human development with computational methods derived from the emerging field of learning analytics. The goal is to create a theoretically sound research method that will allow researchers who use microgenetic methods to efficiently include larger amounts of data in their studies. This will improve the generalizability of the findings derived from microgenetic studies.
The method will be piloted on an existing data set. Additional research data to be used in this study will be collected in an out-of-school activity held at UMass, Amherst.
The project uses a comparative research design and will generate evidence that is descriptive [case study, observational, Beliefs and Efficacy Surveys] and associative/correlational. We are piloting our research method with a data set that the PI collected for a different research project. Original data are being collected on adolescent girls of color using observation [personal observation, videography, Web logs] and survey research [paper & pencil]. The intervention being tested is a robotics workshop.
Instruments or measures being used include beliefs and efficacy surveys related to girls and technology. We will collect video data and, through computational means, identify the heuristic strategies, invented algorithms, conditional reasoning and other collaborative problem solving techniques used by students to solve the robotics challenges. We will also collect screen shots of their evolving robotics programs, which will be analyzed in terms of computer science conceptual understanding. We will use our micorgenetic learning analytic approach to analyze the data. We will correlate these findings with responses on the beliefs and efficacy surveys.
Findings will be posted as they become available.
A new research methodology.