Our paper, Predicting Individual Performance in Student Project Teams, will be presented Tuesday May 24 in the 1:30-3:00 PM time slot as part of the Student performance evaluation and assessment (SPEA) portion of the CSEE&T conference.
SEREBRO as courseware provides a broad set of features targeted toward studying software engineering. Tied to these features are indicators that cue the instructor and team members as to their performance with respect to their collaboration, contribution, and progress toward stated milestones. We show that these indicators correlate to SME ratings of content and contribution of an individual in idea networks and to instructor project grades on work products associated with milestones. Thus, automatic SEREBRO assessment mechanisms are able to predict an individual’s grade and contribution to a project team.
We are currently pursuing additional types of analysis to examine an individual’s performance at filling particular roles on the team, such as lead, analyst, and programmer, and team dynamics over the project milestone period, such as how “bursty” vs. consistent communication relates to milestone success. We are interested in how performance indicators might be combined to yield real time predictions of impending build or challenge shortcomings. Our goal is to derive a weighted product which can be used to calculate an individual’s likelihood of failure given their progress towards milestone completion and mitigate that failure before it manifests itself. While we are several steps away from this, deriving performance metrics from existing real time system data lends support to our continued pursuit of performance classification.
Program information at:
For a paper preview go here: