Data Collection and Management for Assessment of Teamwork in Software Engineering Education


Aishwarya Iyer

Oral Defence Date: 



TH 434


Professors Dragutin Petkovic and Marguerite Murphy


Surveys suggest that 10% of Software projects are abandoned, about one third fail and over half exceed their cost and schedules. One of the key reasons for this poor success rate is inadequate teamwork, communication and project organization. Therefore, there is urgent need for educators in SE to address this issue, which includes not only teaching but also assessment of students. ability to learn and apply SE teamwork which we define as their ability to adhere to SE process and produce SE products in team settings. Assessment of students. ability to learn SE teamwork has been a challenge and has been done using subjective surveys and very primitive data analysis. Faculty at San Francisco State University, jointly with Florida Atlantic University and Fulda University, Germany have been doing research in novel ways of assessment of teamwork using Machine learning (ML) to analyze extracted objective and quantitative team behavior data from joint SE classes offered at these universities. Our project contributes to this research by collecting, organizing and providing access to teamwork activity data collected in 2008 and 2010 in the form of a ML training database. This data can then be used for ML analysis to provide teamwork learning assessment. Our contributions include: collecting the teamwork behavior data from various original sources and surveys from SE classes in 2008 and 2010; designing and creating a relational DB to store the data; using SQL for extracting proper teamwork measurements; organizing the data in the form of a training DB for ML analysis; and providing WWW site for accessing the data by other researchers. In addition, we sanitized the data to ensure students. privacy.


Software Engineering education, teamwork assessment, training database, machine learning


Aishwarya Iyer