Research Project Summaries

This is an ongoing project in which I seek to understand the career goals of workers on Amazon Mechanical Turk, and how the online on-demand labor platform contributes to unique challenges in developing workers’ careers overtime. I’m interested in understanding how these challenges may create difficulties for workers who want to transition careers into specialized online freelance work or white-collar jobs in more traditional brick-and-mortar organizations. To better understand career development more broadly, I began by interviewing 5 individuals who transitioned jobs into a completely new industry–from nontechnical jobs into various engineering and development roles. Through these interviews I found that close interpersonal relationships were essential to successfully learning new skills and landing a job in a new area. I used these findings to frame surveys with ~120 workers on Amazon Mechanical Turk and interviews with 6 of those workers. Currently I am working on using thematic analysis to analyze the survey and interview data, and writing a research paper based on these findings.
Methods: semi-structured interviews, qualitative analysis (thematic analysis)

In this project I am working on evaluating two different interfaces for Causeway, a web development learning platform. Both interfaces model doing by learning, the act of novices achieving real-world goals while learning and building their skill set. However, the interfaces differ in the amount of guidance and scaffolding of instructions they provide users. I am interested in 1) evaluating how doing by learning is better for learning and motivation than learning by doing, in which novices are not engaged in accomplishing real-world tasks and projects and 2) determining the optimal way to model doing by learning in online educational platforms. To do this, I am working on conducting usability studies of the two interfaces and am in the early stages of planning large-scale learning experiments to evaluate users’ learning gains and motivation around web development and computer science education.
Methods: usability testing, semi-structured interviews, data logging, data analytics