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教育环境数据集中软件工程团队合作评估数据

教育环境数据集中软件工程团队合作评估数据

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Action/Event Detection Classification

Data Set Information:The data can be used to try to predict student learning in SE teamwork based on observation of thei......

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    README.md

    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
    #  ==================================================================
    #  The following is a set of statistics about the entire dataset which
    #  may be useful in the configuration of machine learning methods.
    #
    #  This data was collected only from students at SFSU.  Global teams
    #  represent only the data from the SFSU student portion of the team.
    #
    #  GENERAL STATISTICS
    #  ------------------
    #                       Number of semesters: 7
    #                            First semester: Fall 2012
    #                             Last semester: Fall 2015
    #                        Number of students: 383
    #                            Class sections: 18
    #
    #                    Number of TAM features: 115
    #         Number of class labels (outcomes): 2
    #
    #                     Issues closed on time:   202
    #                        Issues closed late: +  53
    #                                            -------
    #                              Total issues:   255
    #
    #  TEAM COMPOSITION STATISTCS
    #  --------------------------
    #      Local Teams:    59
    #     Global Teams: +  15
    #                   ------
    #            Total:    74 Teams
    #
    #  OUTCOME (CLASSIFICATION) STATISTICS
    #  -----------------------------------
    #   Total Outcomes: 74
    #
    #                Proces               Product
    #           ------------------  ------------------
    #  outcome:      A       F           A       F
    #                49      25          42     32
    #
    #  TAM FEATURE NAMING CONVENTION
    #  -----------------------------
    #  A systematic approach to aggregating and naming TAM features was
    #  developed.  By using this systematic approach, TAM feature names are
    #  produced that are human understandable and intuitive and related to
    #  aggregation method.
    #
    #
    #  There are a number of base TAM which are then aggregated into
    #  aggregated TAM.
    #
    #  base TAM
    #  --------
    #
    #  General TAM
    #  -----------
    #  The following TAMs are collected for each team: Year, semester,
    #  timeInterval, teamNumber, semesterId, teamMemberCount,
    #  femaleTeamMembersPercent, teamLeadGender, teamDistribution
    #
    #  Calculated TAM
    #  --------------
    #  For each team, TAM were calculated from SAMs for every time interval
    #  Ti.  The core TAM variables where for each we compute as applicable:
    #  count, average, standard deviation over weeks, over students etc.
    #
    #  TAMs collected by Weekly Time Cards (WTS) TAM
    #  ---------------------------------------------
    #  teamMemberResponseCount, meetingHours, inPersonMeetingHours.
    #  nonCodingDeliverablesHours, codingDeliverablesHours, helpHours,
    #  globalLeadAdminHours, LeadAdminHoursResponseCount,
    #  GlobalLeadAdminHoursResponseCount
    #
    #  TAMs collected  by Tool Logs (TL) TAM
    #  -------------------------------------
    #  commitCount, uniqueCommitMessageCount, uniqueCommitMessagePercent,
    #  CommitMessageLength
    #
    #  Collected by Instructor Observations (IO) TAMs
    #  ------------------------------------------------
    #  issueCount, onTimeIssueCount, lateIssueCount
    #
    #
    #  AGGREGATED TAM
    #  --------------
    #
    #  Several aggregation method and derived variable names for TAMs
    #  reflect how the core TAM variables were aggregated in final TAM
    #  measures for each time interval Ti:
    #
    #  Let VAR be the core TAM variable above. The naming conventions and
    #  aggregation operators to obtain TAMs for each time interval Ti were
    #  as follows:
    #
    #Total - total sum of VAR in the time interval Ti
    #Average - average of VAR in the time interval
    #StandardDeviation - SD of variable in time interval
    #Count - count of events measured by VAR (e.g. missed
    #  checkpoints) in time interval
    #  AverageByWeek - total sum/count of VAR in the time interval
    #  divided by weeks in time interval
    #  StandradDeviationByWeek - the standard devation of the weekly
    #  total of VAR taken over the time interval
    #  AverageByStudent - total count/sum of VAR in time interval,
    #  divided by number of students in the team
    #  StandardDeviationByStudent - standard deviation of  VAR in the
    #  time interval, over students in the team
    #
    #
    #  NULL VALUES
    #  -----------
    #  NULL values are used in the training data to indicate that no SAMs
    #  were recorded in that particular time period, week, or for that
    #  student.
    #
    #  Frequently TAM features involving teamLeadHours or globalTeamLead
    #  hours will result in a NULL for a particular training sample.  For
    #  local team leads, that usually means that the local team lead did
    #  not complete any timecard surveys for the aggregation in quesiton.
    #  While for global team lead TAM features this may also be the case,
    #  the more usual cause of NULLS in global team lead TAM features comes
    #  from the fact that most teams are not global, and therefore this
    #  statistic was not gathered for these teams.
    #
    #  It is left to the individual researcher to decide how to accomodate
    #  NULL values, and the data is included in this file.  Though these
    #  may not be useful for machine learning directly, valuable
    #  information can be obatined with some processing.
    #
    #  TAM FEATURES
    #  ------------
    #  The following is a list of tam features available in the data files.
    #  The TAM feature names are listed in the order in which the data
    #  appear in each training sample, i.e. the first feature corresponds
    #  to the first column, the second feature corresponds to the second
    #  column, etc.
    #
    #  The first sample line in the data section of the data file is not a
    #  true sample, but consists of TAM feature names, which allows for
    #  easy import into spreadsheets and for human readability.
    #
    #  The final two TAM features (columns) are the outcome data for
    #  process and product, and are the last two columns in each sample
    #  row.  The training sample data follow the header comment section.
    #
    #
    #  TAM FEATURE LIST
    #  ----------------
    #  year
    #  semester
    #  timeInterval
    #  teamNumber
    #  semesterId
    #  teamMemberCount
    #  femaleTeamMembersPercent
    #  teamLeadGender
    #  teamDistribution
    #  teamMemberResponseCount
    #  meetingHoursTotal
    #  meetingHoursAverage
    #  meetingHoursStandardDeviation
    #  inPersonMeetingHoursTotal
    #  inPersonMeetingHoursAverage
    #  inPersonMeetingHoursStandardDeviation
    #  nonCodingDeliverablesHoursTotal
    #  nonCodingDeliverablesHoursAverage
    #  nonCodingDeliverablesHoursStandardDeviation
    #  codingDeliverablesHoursTotal
    #  codingDeliverablesHoursAverage
    #  codingDeliverablesHoursStandardDeviation
    #  helpHoursTotal
    #  helpHoursAverage
    #  helpHoursStandardDeviation
    #  leadAdminHoursResponseCount
    #  leadAdminHoursTotal
    #  leadAdminHoursAverage
    #  leadAdminHoursStandardDeviation
    #  globalLeadAdminHoursResponseCount
    #  globalLeadAdminHoursTotal
    #  globalLeadAdminHoursAverage
    #  globalLeadAdminHoursStandardDeviation
    #  averageResponsesByWeek
    #  standardDeviationResponsesByWeek
    #  averageMeetingHoursTotalByWeek
    #  standardDeviationMeetingHoursTotalByWeek
    #  averageMeetingHoursAverageByWeek
    #  standardDeviationMeetingHoursAverageByWeek
    #  averageInPersonMeetingHoursTotalByWeek
    #  standardDeviationInPersonMeetingHoursTotalByWeek
    #  averageInPersonMeetingHoursAverageByWeek
    #  standardDeviationInPersonMeetingHoursAverageByWeek
    #  averageNonCodingDeliverablesHoursTotalByWeek
    #  standardDeviationNonCodingDeliverablesHoursTotalByWeek
    #  averageNonCodingDeliverablesHoursAverageByWeek
    #  standardDeviationNonCodingDeliverablesHoursAverageByWeek
    #  averageCodingDeliverablesHoursTotalByWeek
    #  standardDeviationCodingDeliverablesHoursTotalByWeek
    #  averageCodingDeliverablesHoursAverageByWeek
    #  standardDeviationCodingDeliverablesHoursAverageByWeek
    #  averageHelpHoursTotalByWeek
    #  standardDeviationHelpHoursTotalByWeek
    #  averageHelpHoursAverageByWeek
    #  standardDeviationHelpHoursAverageByWeek
    #  averageLeadAdminHoursResponseCountByWeek
    #  standardDeviationLeadAdminHoursResponseCountByWeek
    #  averageLeadAdminHoursTotalByWeek
    #  standardDeviationLeadAdminHoursTotalByWeek
    #  averageGlobalLeadAdminHoursResponseCountByWeek
    #  standardDeviationGlobalLeadAdminHoursResponseCountByWeek
    #  averageGlobalLeadAdminHoursTotalByWeek
    #  standardDeviationGlobalLeadAdminHoursTotalByWeek
    #  averageGlobalLeadAdminHoursAverageByWeek
    #  standardDeviationGlobalLeadAdminHoursAverageByWeek
    #  averageResponsesByStudent
    #  standardDeviationResponsesByStudent
    #  averageMeetingHoursTotalByStudent
    #  standardDeviationMeetingHoursTotalByStudent
    #  averageMeetingHoursAverageByStudent
    #  standardDeviationMeetingHoursAverageByStudent
    #  averageInPersonMeetingHoursTotalByStudent
    #  standardDeviationInPersonMeetingHoursTotalByStudent
    #  averageInPersonMeetingHoursAverageByStudent
    #  standardDeviationInPersonMeetingHoursAverageByStudent
    #  averageNonCodingDeliverablesHoursTotalByStudent
    #  standardDeviationNonCodingDeliverablesHoursTotalByStudent
    #  averageNonCodingDeliverablesHoursAverageByStudent
    #  standardDeviationNonCodingDeliverablesHoursAverageByStudent
    #  averageCodingDeliverablesHoursTotalByStudent
    #  standardDeviationCodingDeliverablesHoursTotalByStudent
    #  averageCodingDeliverablesHoursAverageByStudent
    #  standardDeviationCodingDeliverablesHoursAverageByStudent
    #  averageHelpHoursTotalByStudent
    #  standardDeviationHelpHoursTotalByStudent
    #  averageHelpHoursAverageByStudent
    #  standardDeviationHelpHoursAverageByStudent
    #  commitCount
    #  uniqueCommitMessageCount
    #  uniqueCommitMessagePercent
    #  commitMessageLengthTotal
    #  commitMessageLengthAverage
    #  commitMessageLengthStandardDeviation
    #  averageCommitCountByWeek
    #  standardDeviationCommitCountByWeek
    #  averageUniqueCommitMessageCountByWeek
    #  standardDeviationUniqueCommitMessageCountByWeek
    #  averageUniqueCommitMessagePercentByWeek
    #  standardDeviationUniqueCommitMessagePercentByWeek
    #  averageCommitMessageLengthTotalByWeek
    #  standardDeviationCommitMessageLengthTotalByWeek
    #  averageCommitCountByStudent
    #  standardDeviationCommitCountByStudent
    #  averageUniqueCommitMessageCountByStudent
    #  standardDeviationUniqueCommitMessageCountByStudent
    #  averageUniqueCommitMessagePercentByStudent
    #  standardDeviationUniqueCommitMessagePercentByStudent
    #  averageCommitMessageLengthTotalByStudent
    #  standardDeviationCommitMessageLengthTotalByStudent
    #  averageCommitMessageLengthAverageByStudent
    #  standardDeviationCommitMessageLengthAverageByStudent
    #  averageCommitMessageLengthStandardDeviationByStudent
    #  issueCount
    #  onTimeIssueCount
    #  lateIssueCount
    #  processLetterGrade
    #  productLetterGrade

    Attribute Information:

    See above


    Relevant Papers:

    D. Petkovic, M. Sosnick-Pérez, 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


    Citation Request:

    Please cite above FIE paper


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