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教育背景下软件工程团队合作评估数据集

教育背景下软件工程团队合作评估数据集

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Computer Classification

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

数据结构 ? 399M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    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
    #  ===========================================================

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