Microgenetic Learning Analytics

Principal Investigator: 
Project Overview
Background & Purpose: 

The purpose of this work is first 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. Second, our research focus will investigate aspects of student's computational thinking while working in a robotics environment as well as explore girls' feelings about their own efficacy in working with robotics.

Setting: 

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.

Research Design: 

The project uses a comparative research design and will generate evidence that is descriptive [case study, observational, Beliefs and Efficacy Surveys] and associative/correlational [analytic essay, quasi-experimental]. 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 and sixth grade students of varying ethnic identities using observation [personal observation, videography, Web logs] and survey research [self-completion questionnaire, structured interviewer-administered questionnaire]. 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, including text and sentiment analyses. We will correlate these findings with responses on the beliefs and efficacy surveys. 

Findings: 

Findings will be posted as they become available.

Other Products: 

A new research methodology.