A Machine Learning Approach For Assessment And Prediction Of Teamwork Effectiveness In Software Engineering Education

Dragutin Petkovic - San Francisco State University

Stevenson Hall 1300
12:00 PM - 12:50 PM

One of the challenges in effective software engineering (SE) education is the lack of objective assessment methods of how well student teams learn the critically needed teamwork practices, defined as the ability: (i) to learn and effectively apply SE processes in a teamwork setting, and (ii) to work as a team to develop satisfactory software (SW) products. In this talk we present a novel approach to address the assessment and prediction of student learning of teamwork effectiveness in software engineering education based on extracting only objective and quantitative student team activity data during their team class project together paired with grading of their teamwork proficiency, then applying a machine learning (ML) approach for assessment and prediction of student learning achievements. The work is joint work between San Francisco State University (SFSU), Florida Atlantic University (FAU) and Fulda University, Germany (Fulda).