Prof. D. Petkovic (SFSU) Petkovic '@' sfsu.edu; Prof. Rainer Todtenhoefer (Fulda University, Germany); Prof. Shihong Huang (FAU)

Data Set Information:

The data can be used to try to predict student learning in SE teamwork based on observation of their team activity

**** README FILE from the submitted data ZIP ****

#  San Francisco State University
#  Software Engineering Team Assessment and Prediction (SETAP) Project
#  Machine Learning Training Data File Version 0.7
#  ====================================================================
#
#  Copyright 2000-2017 by San Francisco State University, Dragutin
#  Petkovic, and Marc Sosnick-Perez.
#
#  CONTACT
#  -------
#  Professor Dragutin Petkovic:  petkovic '@' sfsu.edu
#
#  LICENSE
#  -------
#  This data is released under the Creative Commons Attribution-
#  NonCommercial 4.0 International license.  For more information,
#  please see
#  [Web link].
#
#  The research that has made this data possible has been funded in
#  part by NSF grant NSF-TUES1140172.
#
#  YOUR FEEDBACK IS WELCOME
#  ------------------------
#  We are interested in how this data is being used.  If you use it in
#  a research project, we would like to know how you are using the
#  data.  Please contact us at petkovic '@' sfsu.edu.
#
#
#  FILES INCLUDED IN DISTRIBUTION PACKAGE
#  ======================================
#  This archive contains the data collected by the SETAP Project.
#
#
#  More data about the SETAP project, data collection, and description
#  and use of machine learning to analyze the data can be found in the
#  following paper:
#
#  D. Petkovic, M. Sosnick-Perez, K. Okada, R. Todtenhoefer, S. Huang,
#  N. Miglani, A. Vigil: 'Using the Random Forest Classifier to Assess
#  and Predict Student Learning of Software Engineering Teamwork'.
#  Frontiers in Education FIE 2016, Erie, PA, 2016
#
#
#
#  See DATA DEscriptION below for more information about the data.  The
#  README file (which you are reading) contains project information
#  such as data collection techniques, data organization and field
#  naming convention.  In addition to the README file, the archive
#  contains a number of .csv files.  Each of these CSV files contains
#  data aggregated by team from the project (see below), paired with
#  that team's outcome for either the process or product component of
#  the team's evaluation.  The files are named using the following
#  convention:
#
#                  setap[Process|Product]T[1-11].csv
#
#  For example, the file setapProcessT5.csv contains the data for all
#  teams for time interval 5, paired with the outcome data for the
#  Process component of the team's evaluation.
#
#  Detailed information about the exact format of the .csv file may be
#  found in the csv files themselves.
#
#
#  DATA DEscriptION
#  ====================================================================
#  The following is a detailed description of the data contained in the
#  accompanying files.
#
#  INTRODUCTION
#  ------------
#
#  The data contained in these files were collected over a period of
#  several semesters from students engaged in software engineering
#  classes at San Francisco State University (class sections of CSC
#  640, CSC 648 and CSC 848).  All students consented to this data
#  being shared for research purposes provided no uniquely identifiable
#  information was contained in the distributed files.  The information
#  was collected through various means, with emphasis being placed on
#  the collection of objective, quantifiable information.  For more
#  information on the data collection procedures, please see the paper
#  referenced above.
#
#
#  PRIVACY
#  -------
#  The data contained in this file does not contain any information
#  which may be individually traced to a particular student who
#  participated in the study.
#
#
#  BRIEF DEscriptION OF DATA SOURCES AND DERIVATIONS
#  -------------------------------------------------
#  SAMs (Student Activity Measure) are collected for each student team
#  member during their participation in a software engineering class.
#  Student teams work together on a final class project, and comprise
#  5-6 students.  Teams that are made up of students from only one
#  school are labeled local teams.  Teams made up of students from more
#  than one school are labeled global teams.  SAMs are collected from:
#  weekly timecards, instructor observations, and software engineering
#  tool usage logs.  SAMs are then aggregated by team and time interval
#  (see next section) into TAMs (Team Activity Measure).  Outcomes are
#  determined at the end of the semester through evaluation of student
#  team work in two categories:  software engineering process (how well
#  the team applied best software engineering practices), and software
#  engineering product (the quality of the finished product the team
#  produced).  Thus for each team, two outcomes are determined, process
#  and product, respectively.  Outcomes are classified into two class
#  grades, A or F.  A represents teams that are at or above
#  expectations, F represents teams that are below expectations or need
#  attention.  For more information, please see the paper referenced
#  above.
#
#  The SE process and SE product outcomes represent ML training classes
#  and are to be considered separately, e.g. one should train ML for SE
#  process separately from training for SE product.
#
#  TIME INTERVALS FOR WHICH DATA IS COLLECTED
#  ------------------------------------------
#  Data collected continuously throughout the semester are aggregated
#  into different time intervals for the semester's project reflecting
#  different dynamics of teamwork during the class.  Time intervals
#  represent time periods in which a milestone was developed by each
#  team.  A milestone represents a major deliverable point in the class
#  for all student teams.  The milestones are roughly divided into the
#  following topics:
#
#            M1 - high level requirements and specs
#            M2 - more detailed requirements and specs
#            M3 - first prototype
#            M4 - beta release
#            M5 - final delivery
#
#  Time intervals are combinations of the time in which milestones are
#  being produced.  Time intervals are used in research only.
#
#  In addition to time intervals corresponding to milestones, a number
#  of time intervals combining multiple T1-T5 time intervals have been
#  calculated.  This was done to group student activities into design
#  vs. implementation phases which have different dynamics.
#
#  These time intervals are defined as follows:
#
#      Time Interval        Corresponding Milestone Periods in Class
#    -----------------    --------------------------------------------
#           0               Milestone 0
#           1               Milestone 1
#           2               Milestone 2
#           3               Milestone 3
#           4               Milestone 4
#           5               Milestone 5
#           6               Milestone 1 - Milestone 2 inclusive
#           7               Milestone 1 - Milestone 3 inclusive
#           8               Milestone 1 - Milestone 4 inclusive
#           9               Milestone 1 - Milestone 5 inclusive
#          10               Milestone 4 - Milestone 5 inclusive
#          11               Milestone 3 - Milestone 5 inclusive
#
#
#
#  SETAP PROJECT OVERALL DATA STATISTICS
#  ===========================================================