Principal Investigator: Carolyn Rose
CoPrincipal Investigator(s): Majd Sakr, Michael Hilton
Organization: Carnegie-Mellon University
Abstract:
The World Economic Forum estimates that up to 5 million jobs may be lost to disruptive labor changes by 2020, a development that leads to concerns regarding unemployment, underemployment, and the need for workers to reskill. This project explores how to address this problem by developing methods for embedding learning opportunities within collaborative work tasks in the area of software development. The research aims to gain insight into the differing characteristics of how work is structured and managed that affect trade-offs in terms of effort, time, learning benefit, and productivity both in the long and short term. These insights can then be used by managers to make more strategic choices about prioritization of learning versus productivity within specific work episodes. This project specifically focuses on embedding learning opportunities within team-based work, leveraging a new industry practice for team-based software development to create a new paradigm for shared cognition in software development. In doing this work, the project will provide additional educational opportunities for teams in technical courses who will use the developed tools, as well as releasing the tools for public use.
The experimental work in this project will be conducted within an online software development course that connects learners on campuses on multiple continents as well as in offerings of the course content to industry professionals attempting to retrain. This specific collaborative work paradigm adopted in this research and further developed under this research will afford unique opportunities for embedding learning during work and the ability to adjust the level of priority attached to learning vs productivity and thus creates the ideal social structure in which to conduct the scientific investigation. The research goals of the planned work are: (1) Discover how to develop technology for effectively manipulating the level of interdependence within team-based work, and in so doing to manipulate the trade-off between learning and productivity, and (2) Quantify the long term positive impact of learning in the short term on productivity in the long term so that it is possible to maximize productivity overall in the face of decreases in short term productivity in favor of learning.
This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.
