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Educational Data Mining Workbench User Manual V3.53

1. 7 F227 18 Compie dsistudentpro CS21a 3 Class Section Lab id TIMESTAMP DELTA VERSION BJ EXT VERSION SYSUSER HOME OSNAME OSVER os A labi s 20100708 F227_10_CompileData csv 1 1278571102 20060907 2 6 c7929d40971230909becc3748c66e60 C Usersc7929d40971230909becc3748c66e60 Windows 7 6 1 x86 A labis 20100708 F227 10 CompieData csv 2 1278571103 20060907 2 6 7929d40971830909becc3748c66e60 C Usersc79a9d40971830909becc3748c66e60 Windows7 6 1 x86 A Labi S 20100708 F227 10 CompieData csv 3 1278571129 20060907 26 c7929d40971230909becc3748c66e60 C Usersc79a9d4097 1a30909becc3748c66260 Windows7 6 1 x86 la lLab1 5_20100708 F227 10 CompieData csv 4 1278571142 20060907 26 c7939d40971a30909becc3748c66e60 CiUsersc799d40971230909becc3748c66e60 Windows 7 6 1 x86 A abi s 20100708 227 10 CompieData csv 5 1278571149 20060907 2 6 7929d40971830909becc3748c66e60 C Usersc7939d40971a30909becc3748c66e60 Windows 7 6 1 x86 A labis 20100708 F227 1i CompileData csv 1 1278571124 20060907 26 c7939d40971230909becc3748c66e60 C Usersc79a9d40971a30909becc3748c66e60 Windows7 6 1 x86 A labi S 20100708 F227 1i CompileData csv 2 1278571129 20060907 26 c7929d40971230909becc3748c66e60 C Usersc79a9d40971230909becc3748c66e60 Windows 7 6 1 x86 la labi s 20100708 F227 ii CompileData csv 3 1278571132 20060907 2 6 c7929d40971230909becc374
2. Wed Nov 07 08 39 34 CST 2012 INFO Imported C Users Francis Desktop EDM v3 5 latest Distribution Copy Nov 5 Sampl Wed Nov 07 08 39 42 CST 2012 INFO Imported C Users Francis Desktop EDM v3 5 latest Distribution Copy Nov 51Sampl Wed Nov 07 08 40 33 CST 2012 INFO Imported C Users Francis Desktop EDM v3 5 latest Distribution Copy Nov 51Sampl EDM Workbench git 20120227 Figure 24 Clip submission Sampling The data sampling feature of the Workbench allows the user to specify how clips are sampled from the data set It can also be used to sample at the action transaction level The user can specify the sample size and whether the Workbench will randomly take the sample across the entire population or whether the workbench will stratify the sampling based on one or more variables Note that the Workbench allows the user to sample the data at any point of the process after importing after clipping or after labelling depending on the user s analytical goals To start sampling the dataset click Sampling Button located either in the Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Function menu Figure 7 or Toolbar Figure 9 Sampling functionalities involve creating subsets from the dataset using automatic select and grouping options A user may take samples or a subset from the loaded dataset and save as a new dataset Sampling can be
3. Figure 43 Default Or function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not True Value assigned to the result in the Output Column Name if operation returns a true False Value assigned to the result in the Output Column Name if operation returns a false Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MES e Default PercentError Function Name Default PercentError Enabled True O False Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each columns Row v Add Column Name Output Column Names use to seperate each columns Error Values use to seperate each columns Figure 44 Default PercentError function window Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected column Problem Column name of the column corresponding to the problem Skill Column name of the column specifying the skill Error Values used to specify which values constitute an error for use by percent
4. EDM User WERE ucational Data Mining Workbench Manual V3 53 Educational Data Mining Workbench User Manual V3 53 MY Content Revision Historia ti a dde E da ai daa dot 6 Introduction 6 i Definition of Terms osa rita 8 Bm Overall Description ciini E OR Ln added mane comes 8 Overall Use Cases creer re eese nadaa 10 Chapter 1 System OVetviGW nee a AU RE YU n ER US RU ERES RR ciertas 11 Title Bali e 11 7 Men Bat ge 11 A OA 12 o Function Menu cocina eaaa a eaa aR ia ea Eas 12 Or LAGER 12 Tool A e 13 I Load BUTTON ct a downs a ea aa e EES E Aia 13 Ze SAVE BUON ensce 13 3 Import ButtoN sv da ER EN RENE EN ERR REUS 13 4 EXport BUELOl eere rrr Eaa EEE EE aV RRSERA reUs EREKE RENS EERR SRM ER ERN FREE REEF ERERVEARERER 14 5 Add Process BUELON one codecs ec D eee eoe a te 14 6 Clip Button ER 14 7 Sampling Button iiec race idas 14 8 Labelling BUON M ia tia 14 9 Add Featuren an te secs a ec naccan tede ecco ese eames teer ed 14 M DataGrid e 14 a Status BOX isla 15 Loading Animation ccono nccinnccncnonocnncnononnnnnanonononanonononononnncnonnnnnnnono ESSEE ESSEE ESSEE Essen Essen nennen nnns 15 Chapter 2 System Man al nen rrr rr terere eene Poste use repa tee vada ue REPE pda 15 B uj det A ee ee ee 15 HUESCA 19 Sizeas CUP p
5. Row Count 36395 Figure 10 EDM DataGrid The DataGrid displays the logs that are active and are to be processed The down arrow button hides the data grid Row Count 39468 Row Count controls the amount of rows shown in the active tab Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Status Box Vv Wed Nov 07 08 39 34 CST 2012 INFO Imported C Users Francis Desktop EDM v3 5 latest Distribution Copy Nov 5XSample Wed Nov 07 08 39 42 CST 2012 INFO Imported C Users Francis Desktop EDM v3 5 latest Distribution Copy Nov 5MSample Wed Nov 07 08 40 33 CST 2012 INFO Imported C Users Francis Desktop EDM v3 5 latest Distribution Copy Nov 5XSample Figure 11 System Status Box The Status Bar displays feedback information such as status error messages time elapsed and others Loading Animation Loading animation has been added to export import load and save functions to easily identify if the program has either hanged or is still functioning Imp ting File Figure 12 Loading Animation Chapter 2 System Manual Import The EDM Workbench allows users to import logs in DataShop text format and CSV The data is assumed to be stored in a flat file organized in rows and columns The first row of the import file is assumed to contain each column s name Each succeeding row represents one logged transaction usually between the student and tutor bu
6. TIMESTAMP id Figure 62 Labelling Window Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 A Set Up Labelling parameters Note Click Add Label or press Enter to add label name Labeler s Name Name of User Francis Use Template Figure 63 A sample Labelling window 1 Label Textbox Label Textbox is the top most textbox in the image above Fig 67 User will need to input labels for the labelling process later If the system reads a comma the texts next to it will be considered as different label from the previous text from the comma Click Add Label to transfer the labels to the label list the textbox to the right of the Label Textbox 2 Labeller s Name Name of User Here the user will need to input the user s name so that we can keep track to whom did the labelling of the dataset 3 Parameter Sentence Textbox The textbox where the user can create sentences and choose parameters enclosed with from the drop down menu right above the textbox that will change depending on the values of the row currently being labelled in the Labelling Process Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 o Use Template The template area specifies a pretty print of the text replay The user supplies descriptive text
7. E Figure 17 EDM Workbench Data Shop Tab Mon Feb 20 09 46 48 GMT 08 00 2012 INFO Imported C Users Paul Documents DataShop Figure 18 Status bar with timestamp and file directory The Status bar displayed the information of the file imported together with the location C User Paul Documents Datashop and the current time Monday February 20 9 46 AM and 48 seconds Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Clipping The EDM Workbench allows the user to define the set of features by which the data should be grouped so that clips do not contain rows from different groups For example if the data should be grouped by student a single clip will contain data from only one student and not multiple students The Workbench also specifies the clip size either by time or by number of transactions Delineation of clips by beginning and ending events is not yet possible but is a feature planned for future implementation The Workbench then generates the clips for analysis according to a sampling scheme discussed in the next section To clip the dataset click Clip Button located either in the Function menu Figure 7 or Toolbar Figure 9 The system will then display a form with the column names the basis for grouping e g group data with the same Logs of Student in Section A E with the same Anon Student Id and with the same Tim
8. and indicates where the fields should be inserted Select column name Note Click Add Label or press Enter to add label name a Section TIMESTAMP DELTA_VERSION BJ_EXT_VERSION Francis SYSUSER HOME Labeler s Name Name of User 4 Use Template TOTAL COMPILES HOSTNAME LOCATION ID PROJECT ID SESSION ID PROJECT PATH PACKAGE PATH DELTA NAME DELTA SEQ NUMBER Class id TOTAL COMPILES Load Template Save Template Submit Cancel Figure39 Parameter Addition Note The system will automatically select the parameter in the Select Column Name list from the textbox Set up Labelling Parameters e Label Text Box Label Textbox is the top most textbox in figure 39 The user will need to input labels for the labelling process later If the system reads a comma the string after the comma will be considered as a different label from the previous string before the comma Click Add Label to transfer the labels to the label list the textbox on the right of the Label Textbox Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Labeller s Name User Name Here the user will need to input the user s name in order to be able to keep track of the changes and who carried them out e Parameter and sentence textbox This is the textbox where the user can input sentences and choose parame
9. data corpus Automatically distil additional information from log files for use in machine learning Export student behaviour data to tools which enable sophisticated secondary analysis DAT Statistical Packages Tag Helper Sequential Analyzer SAS SPSS R PSLC DataShop CTAT Tutors Text Data e Export mport Analysis Cognitive Export Tutors Data Definition amp Data Analysis amp Multi User Display Distillation Collaborative Functionality Functionality functionality Defining metacog Sampling Techniques Inter rater reliability categories analysis ollabo ive 4 Collaborative Customized Log Action f o jn Collaborative definition Learning System Viewer Vi of labelling manual Database Automatically set up relabeling en metacog categories are edited appropriate cross Streamed Log Files validation Figure 1 EDM Workbench Entity Diagram Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MO Overall Use Cases System Load Template entend extents Save Template Stratified extendsy extends 21 LN a ample specified number of logs K VE d P extends Cr or Group Logs vy Save Template VA with specifications e extends extends x T v extends Clip by Size extends Add Features Load Template Add Sampling Load Features Save Features Add Clipping xport as cvs and or txt file xte
10. each columns Figure 35 Default Copy function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default Duration Function Name Default Duration Enabled 8 True Date Column Year Month Date Time Column Hour Minute Second v Date Time Column Year Month Date Hour Minute Second wv Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each columns Row v Add Column Name Output Column Names use to seperate each columns Figure 36 Default Duration function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Date Column s value should be in the Date Year Month Date format Time Column s value should be in the Time Hour Minute Second format Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected columns e Default FirstAttempt Output Column Names use to seperate each columns Group Columns use to seperate each columns id v Add Column Name Date Column Year Month Date Time
11. stratified or random o Random Sampling To randomly select samples from a selected dataset Select Sampling Method gt Random Indicate the number of samples in the Sample Size textbox Sampling Method Random w Auto generate samples randomly Set Samples Size Maximum Sample Size 1339 100 Figure 25 Sampling method selection Note The size inputted in the textbox should not exceed the indicated maximum sample size If the user specifies a number greater than the maximum the operation returns all the rows in the dataset o Stratified Sampling Stratified sampling randomly selects data from within specified subgroups to produce a stratified sample Select Sampling Method gt Stratified Set the number of samples in the Sample Size textbox In the Strata list click the column names that define the groupings Figure 25 Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Sampling Method Stratified 7 v Divide logs into smaller groups Set Samples Size Maximum Sample Size 1339 10 Select Strata Hold Crtl and dick to select multiple strata evision LI CN ILOCATION ID IPROJECT ID SESSION ID IPROJECT PATH PACKAGE PATH DELTA NAME DELTA SEQ NUMBER IDELTA START TIME IDELTA END TIME Figure 26 Strata selection o Save Button Save Button saves the properties as a template o Load Button The Load button allows the use
12. 29 y 3 2005 10 15 Kc75 3 2005 10 15 KC496 10 2005 10 15 kcs 11 2005 10 15 KC1410 12 2005 10 15 KC1547 13 2005 10 15 KC1330 14 2005 10 15 KC750 is 2005 10 15 Kcsos 16 2005 10 15 KC658 17 2005 10 15 KC1397 18 2005 10 15 Kcees 19 2005 10 15 KC742 20 2005 10 15 KC1143 CMU 21 2005 10 15 Figure 61 Sample distil features Labelling Labelling is an operation that is usually performed after clipping and sampling During labelling the user assigns ground truth labels to clips of data The user first specifies a subset of the clip columns that should be displayed The user also specifies the labels that the observer or expert will use to characterize each clip The expert or observer will have to select between three labels Confused Not Confused or Bad Clip The circumstances under which an expert or observer labels a clip as bad changes depending on the data set but typically indicate cases that should not Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MS Note Click Add Label or press Enter to add label name Labeler s Name Name of User DELTA_VERSION BJ EXT VERSION rends SYSUSER HOME OSNAME Use Template OSVER OSARCH T IPADDR HOSTNAME LOCATION ID PROJECT ID SESSION ID PROJECT PATH PACKAGE PATH DELTA NAME DELTA SEQ NUMBER IDELTA START mme Y
13. 3 53 Tool Bar Di AINE Figure 9 EDM Toolbar with activated buttons The Tool bar is composed of action buttons that are also found in the menu bar for ease of use Load Button Loads log files which were previously saved using the EDM Workbench and stored in an EDM Workbench specific zip file The file contains logs that may have been previously processed clipped sampled or labelled by the user together with some Workbench specific information Note that because of the additional information the zip file may not be opened using archiving software such as WinZip or WinRar Once loaded the user may make further changes to the file Save Button Saves the logs from the active tab in the DataGrid and all its properties such as clipped formats and labels into EDM format Import Button Allows the user to import logs or batches of logs such as Datashop or comma separated value csv files to be processed clipped sample or labelled by the user Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 ME 4 Export Button Exports the final output from the active tab in the DataGrid as a CSV file or in other specified file formats 5 Add Process Button Allows the user to add and possibly save an action to a sequence of actions 6 Clip Button Groups logs from a given batch based on user specified parameters Sampling Button Selects rows from the dataset based on use
14. 3748c66 60 C Usersc79a9d40971a30909becc3748c66e60 Windows 7 6 1 x86 A lab1 5_20100708 F227_12_CompileData csv 10 lo 1278571090 20060907 26 c79a9440971230909becc3748c66e60 C Usersc7929d40971a30909becc3748c66e60 Windows 7 6 1 x86 A labi S 20100708 F227_12_CompileData csv lo 1278571090 20060907 26 c79a9440971a30909becc3748c66e60 C Usersc79a9d40971a30909becc3748c66260 Windows 7 6 1 86 v lt gt Row Count 1339 RUIL NUV S I 31 0J EST ZUIZ INTO Lmpurteu v YUSEIS VFISIUISTUESKCUD VEU V3 J ISCEST UISCIIDULIUI COPY NOV J vsampie Uata IIS iUsV VZUIU ZUII VCSZI amp V VbRUI ZUIUU7UZ T ZZT 18 Mon Nov 19 22 03 00 CST 2012 INFO Imported C Users Francis Desktop EDM 1 v3 5 latest Distribution Copy Nov 5 Sample data files csv 2010 2011 CS21a F Lab1_3 20100702 F227_ 18 CompileData csv Mon Nov 19 22 03 46 CST 2012 WARNING Error in Loading File de schlichtherle truezip fs FsEntryNotFoundException zip file C Users Francis Desktop EDM v3 58201atest amp 20Distribution 20Co Mon Nov 19 22 04 02 CST 2012 INFO Imported C Users Francis Desktop EDM 3 v3 5 latest Distribution Copy Nov 5 Sample data files DataShop ds student problem 2011 0610 000919 txt Mon Nov 19 22 06 25 CST 2012 INFO Imported C Users Francis Desktop EDM 1 v3 5 latest Distribution Copy Nov 5 Sample data files csv 2010 2011 CS2la lt A EDM Workbench3 53 20121106 s Figure 16 EDM sample Data Set Daiana Dan Xd Logs of Students in Section A
15. 8 True Range Column v Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each columns Row v Add Column Name Output Column Names use to seperate each columns N Numbers Only 0 Figure 34 Default CountLastN function Window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not True Value assigned to the result in the Output Column Name if operation returns a true False Value assigned to the result in the Output Column Name if operation returns a false Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Range Column Range of values used for computation Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected columns N Numbers Only if more elements in a group are found only the last N items are kept for processing start count every N rows Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default Copy O False Input Column Names choose column name s to be used in this feature lt Add lt ow lt lt Add All lt lt Anon Student Id z BA oblem Hierarchy gt gt Remove All gt gt oblem Name Swap Contents lt Output Column Names use to seperate
16. 8c66e60 C Usersc79a9d40971830909becc3748c66e60 Windows 7 6 1 x86 A labi S 20100708 227 12 CompileData csv 1 1278571088 20060907 2 6 c7929d40971230909becc3748c66e60 C Usersc7929d40971a30909becc3748c66e60 Windows 7 6 1 x86 la labi S 20100708 F227_12_CompileData csv 2 1278571088 20060907 2 6 c79a9d4097 1230909becc3748c66e60 C Usersc79a9d40971a30909becc3748c66260 Windows 7 6 1 x86 la lLab1 5_20100708 F227 12 CompileData csv 3 0 1278571088 20060907 26 c79a9d4097 1230909becc3748c66260 C Usersc79a9d40971a30909becc3748c66e60 Windows 7 6 1 x86 A labi S 20100708 F227 12 CompieData csv 4 p 1278571088 20060907 2 6 7929d40971830909becc3748c66e60 C Usersc79a9d40971830909becc3748c66e60 Windows 7 6 1 x86 A labi 20100708 F227 12 CompieData csv S lo 1278571088 20060907 26 e78a9d40971330909becc3748c66 60 CiUsersc7939d40971a30909becc3748c66e60 Windows 7 6 1 x86 A abi 20100708 F227 12 CompieData csv 6 g 1278571089 20060907 2 6 c7929d40971230909becc3748c66e60 C Usersc7989d40971a30909becc3748c66e60 Windows 7 6 1 x86 la ab1 5_20100708 F227_12_CompileData csv 7 0 1278571089 20060907 2 6 79a9d4097 1a30909becc3748c66e60 C Usersc79a9d40971a30909becc3748c66e60 Windows7 6 1 x86 A labi 5 20100708 F227 12 CompileData csv 8 o 1278571089 20060907 26 c7929d40971230909becc3748c66e60 C Usersc79a9d40971a30909becc3748c66260 Windows 7 6 1 x86 A labis 20100708 F227 12 CompileData csv 9 0 1278571089 20060907 26 e73a9d40971230909becc
17. Column Hour Minute Second v Date Time Column Year Month Date Hour Minute Second v Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected columns True Value assigned to the result in the Output Column Name if operation returns a true False Value assigned to the result in the Output Column Name if operation returns a false Date Column s value should be in the Date Year Month Date format Time Column s value should be in the Time Hour Minute Second format Date Time Column s value should be in the Date and Time Year Month Date Hour Minute Second format Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default Inverse Function Name Default Inverse Enabled 8 True O False Input Column Names choose column name s to be used in this feature lt Add lt lt lt Add All lt lt gt Remove gt gt gt Remove All gt gt Swap Contents Output Column Names use to seperate each columns Figure 37 Default Inverse function window Parameters Needed Enabled indicates whe
18. DELTA_NAME DELTA_SEQ_NUMBER DELTA_START_TIME LO TrA CUND THU Figure 58 Sample System Process List File Functions Help x fent gi z NE Save Load Import Export AddProccess Clip Sampling Labeling Add Feature F227 18 Compile dsi student pro CS21a CS21a dipped 4 dip dipSize a a nia a a a nan na in a na na nn nin Figure 59 Sample Clipping display Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 IgE Row Count 3202 E Tue Feb ZI 08 44 28 GMT 08 00 2012 INFO Process Default Pr I My Row done Tue Feb 21 08 44 28 GMT 08 00 2012 INFO Process Default Pr 2 New Time started Tue Feb 21 08 44 28 GMI 08 00 2012 INFO Process Default Pr 2 New Time done Tue Feb 21 08 44 28 GMT 08 00 2012 INFO Process Default Pr 3 Size Clip Process started Tue Feb 21 08 44 29 GMI 08 00 2012 INFO Process Default Pr 3 Size Clip Process done Tue Feb 21 08 44 29 GMT 08 00 2012 INFO Process Default Pr done Figure 60 Clipping feedback KC Unique KC Catego Class New Time Kcese 1 2005 10 15 KC814 2 2005 10 15 KC1592 3 2005 10 15 KC238 4 2005 10 15 KC1422 5 2005 10 15 KC1415 6 2005 10 15 KC1356 7 2005 10 15 KC13
19. DM Workbench A beta version of this Workbench now available online at http penoy admu edu ph alls downloads is described in this user manual The Workbench currently allows learning scientists to 1 Label previously collected educational log data with behaviour categories of interest e g gaming the system help avoidance considerably faster than is possible through previous live observation or existing data labelling methods 2 Collaborate with others in labelling data 3 Automatically distil additional information from log files for use in machine learning such as estimates of student knowledge and context about student response time i e how much faster or slower was the student s action than the average for that problem step Through the use of this tool we hope that the process of developing a detector of relevant metacognitive motivational engagement or collaborative behaviours can eventually be sped up Just the use of text replays on previously collected log data has been shown to speed a key phase of detector development by about 40 times with no reduction in detector goodness 3 This user manual is intended as a guide to the functions and features of the EDM Workbench Please send comments and suggestions to mrodrigo ateneo edu Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 B Definition of Terms Batch A group of log files Th
20. Error Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MEN e Default pKnow Function Name Default pKnow Enabled True O False Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each columns Row v Add Column Name Output Column Names use to seperate each columns Check Values use to seperate each columns LO Numbers Only 1 0 Submit Save Load Cancel Figure 45 Default pKnow function window Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected column Check Value is the value to be compared against the Selected Input Column Names This value can either be a string or integer depending on the feature used LO Number Only probability that the skill is already known before the first instance in using the skill in problem solving S Number Only probability that the student will commit a fault if the skill was already known beforehand G Number Only probability that the student will deduce the correct answer given that skill is not kno
21. NUMBER COMPILES_PER_FILE TOTAL_COMPILES Labels Labeler TimeStamp Time Elapsed 1 38 47 onfused Francis 2012 11 19 0 1 39 48 ot Confused Francis 2012 11 19 5 la static context 7 1 49 onfused Francis 2012 11 19 61 a static context 7 2 50 ot Confused Francis 2012 11 19 61 29 40 51 onfused Francis 2012 11 19 62 1 14 36 onfused Francis 2012 11 19 62 1 9 37 onfused rancis 2012 11 19 62 1 10 38 onfused Francis 2012 11 19 63 29 2 2 ot Confused Francis 2012 11 19 63 26 1 1 ot Confused Francis 2012 11 19 63 27 6 6 ot Confused Francis 2012 11 19 64 27 5 5 No 26 3 3 ot Confused Francis 2012 11 19 64 1 10 10 ot Confused Francis 2012 11 19 64 nt int int 26 9 9 ot Confused Francis 2012 11 19 64 ht int int 26 8 8 onfused Francis 2012 11 19 65 29 11 11 onfused ancis 2012 11 19 65 28 13 13 ot Confused Francis 2012 11 19 65 28 14 14 onfused rancis 2012 11 19 65 27 4 4 ot Confused Francis 2012 11 19 66 01 1c 15 anfi icad nec 0012 11 10 IRA b gt Figure 67 Sample labelling output Save Saves the dataset in the current tab by clicking the Save button located either in File menu Figure 6 or Toolbar Figure 9 The system will ask for the directory and then save it in zip format Note Saving files will take time depending on the size of the dataset and speed of the computer Load Loads EDM files by clicking the load button locat
22. Task Behavior in Intelligent Tutoring Systems Proceedings of ACM CHI 2007 Computer Human Interaction 1059 1068 3 84 3 Baker R S J d amp de Carvalho 2008 Labeling Student Behavior Faster and More Precisely with Text Replays 1 International Conference on Educational Data Mining 38 47 5 84 8 Walonoski J amp Heffernan N T 2006 Detection and Analysis of Off Task Gaming Behavior in Intelligent Tutoring Systems In Ikeda Ashley amp Chan Eds Proceedings of the 8th International Conference on Intelligent Tutoring Systems Springer Verlag Berlin pp 382 391 14 84 9 Witten I H amp Frank E 2005 Data Mining Practical Machine Learning Tools and Techniques Second Edition Morgan Kaufmann 15 ccccccccssscccssssececseseeecseaeeeceesaeeeesesaeeecsesaeeeceesaeeeesesaeeeceesaeeeeseaaes 84 Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 m Revision History Reason For Changes Version John Paul Contillo 20111121 First draft V1 00 Alipio Gabriel 20111122 Edit the context of the draft V1 00 Alipio Gabriel 20111123 Add and edit the content V1 00 J Contillo 20120221 User manual for version 2 V2 00 Gamaliel dela Cruz 20120526 Edit content V3 00 Francis Bautista 20120607 Formatting and editing V3 00 John Paul Contillo 20111121 Content Addition V3 10 Franc
23. W cz ron A A o ete diia cida das 51 Default RUMMING COUNT caciones cea adi adidas 53 Default RunningPrevCount sessssssssssseseeeeee eene enhn nennen enhn esee nnns seen nns nnnn nnn 54 Default StD Oy EUER 56 Default SUM umi 58 Default SUmLastN coi ai 60 DELAETA rios etae eta ne te Pha de oa eee Tha Le eeu ba eee de Dua e Ue a deese ades 62 Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MZ Default TiMesSD osito eii 63 Add Feature Buttons ccciosrds lila idad 65 e SUBMIT BUTTON italia 65 Save BUON eR 65 e Load BUON 65 e Cancel Button cities 65 Add Feature Parameters cedido e t hide eee hee t oe eo RT Eo de e aac dee seo tica 65 Pre defined functions ree tenete dica 70 o Add Features in the Clip Level 2 rone etna dee tnn vacent acabadas 72 EP ga E ERE ERUIT 73 SEE CREME aeeke 73 O Cancel BUTTON oe etie ua leid bee eu pode cte en ioter aede eee et vaa 73 OQ Save BU lttol uie Ie cca itae ice tc uie pee o ECRIRE cese 73 Load EIDEM 73 o JRunProcess BUTTON cies ssccsccccececesssicensedscenses stsngie seevsesssaseaccdtbansesssteedees ianiai dadea aaa 73 Labelling orria AO 75 A Set Up Labelling pararneters a adas aiii 77 o Use Tem plates ici adidas 78 Setup Labelli
24. alues for selected columns Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default Mean v Sort Columns use to seperate each columns Row Group Columns use to seperate each columns Output Column Names use to seperate each columns v Add Column Name Row v Add Column Name Es Figure 40 Default Mean function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected columns Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default MeanCountlf Function Name Default MeanCountIf Enabled 8 True Range Column v Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each columns Row v Add Column Name Output Column Names use to seperate each columns Check Values use to seperate each columns Figure 41 Default MeanCountlf function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for select
25. ample EDM Workbench3 53 20121106 Title Bar Figure 3 EDM workbench upon system launch Figure 4 System Title Bar The name of the system may change in later versions e g EDM Workbench version 3 53 is displayed here Menu Bar File Functions Help Figure 5 EDM Menu Bar Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Composed of 3 Menu options File Functions and Help consisting of actions buttons o File Menu File Functions Help Es Load TU Save Import Export JH bi Ctri Q Figure 6 File Menu Dropdown o Function Menu File Help g Clipping Samplin lo Y pling Labeling Y Add Process Figure 7 EDM Function menu Dropdown o Help Menu File Functions Help Tz Y About eB a Load Save Import Export Add The File Menu is composed of 5 actions Load Save Import Export and Exit that handle the files and logs to be displayed and or saved in the DataGrid The Function Menu consists of 4 log processing actions that will either be enabled or disabled depending on the state of the system The Help Menu contains the About action that displays the system description and the current product version e g 20120227 Figure 8 EDM Help Menu showing the About button Ateneo Laboratory for the Learning Sciences F206 AAMU 3 Educational Data Mining Workbench User Manual V
26. ass A Section Lab Time Column Sec Class v Custom Sort lid lrevision Interval 5 TIMESTAMP IDELTA VERSION poa Submit Save Load Cancel SYSUSER HOME OSNAME OSVER OSARCH IIPADDR IHOSTNAME ILOCATION ID IPROJECT ID SESSION ID IPROJECT PATH IPACKAGE PATH DELTA NAME IDELTA SEQ NUMBER IDELTA START TIME IDELTA END TIME FILE PATH NAME we Figure 23 Load Window Note From the list of clipping xml files the selected template is Clipping Sample Time clipping xml o Submit Button This closes the Clipping Form clips the dataset from the current tab and displays it with its properties set in a new tab Double click a row to view the logs within it Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 IZ font F227_31_Compile 3 id revision TIMESTAMP X DELTA VERSION BJ_EXT_VERSION SYSUSER 1278571103 20060907 2 6 c79a9d40971a30909becc3748c66e60 C Usersc79a9d4097 1a30909becc3748c66 1278571103 C Usersc79a9d40971a30909becc3748c66 1278571103 1278571103 1278571103 1278571103 1278571103 y 1278571103 A 793904097 1a30909becc3748c66e60 1278571103 A 79a9d40971a30909becc3748c66e60 1278571103 F c7929d40971a30909becc3748c66e60 6 1278571103 J c7929d40971230909becc3748c66e60 C Usersc79a9d40971a30909becc3748c66 BOOTS A oH alae o o jo o O O O O O O OfB2 m
27. composed of a parent container and a dataset representing each clip Non clip level operations will append output columns to each of the enclosed clips however a clip level operation will append output columns only to the parent container Add Clipping Allows user to set the desired clipping properties The form applies the selected properties in the clipping form Add Sampling Allows user to set desired sampling properties The form applies the sampling properties set in the sampling form Cancel Button Cancels and closes the Add Process form Save Button The system shall save all the properties set in the Processes List which are then checked into a process xml file Load Button The system will load the all the configured processed list process xml files available in the process directory upon clicking the load button Run Process Button The system runs all checked processes in the process list The system will display information feedback in the Status Bar on what process it is currently taking and throws an error dialogue when the system encounters an error Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MZ Required Columns Class Section Lab id revision TIMESTAMP DELTA_VERSION BJ_EXT_VERSION SYSUSER OSNAME OSVER OSARCH IPADDR HOSTNAME LOCATION_ID PROJECT_ID Delete Process SESSION_ID PROJECT_PATH PACKAGE_PATH Edit Process
28. d laborious task made even more difficult by the lack of tools available to support it Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 A second challenge is the engineering and distillation of relevant and appropriate data features for use in detector development 9 The data that is directly available from log files typically lacks key information needed for optimal machine learned models For instance the gaming detectors of both 3 and 8 rely upon assessments of how much faster or slower a specific action is than the average across all students on a problem step as well as assessments of the probability that the student knew the cognitive skills used in the current problem step This information can be distilled and or calculated by processing data across an entire log file corpus but there are currently no standard tools to accomplish this Feature distillation is time consuming and many times a research group re uses the same feature set and feature distillation software across several projects the second author for instance has been using variants of the same feature set within Cognitive Tutors for nine years Developing appropriate features can be a major challenge to new entrants in this research area To address this data labeling bottleneck and the difficulty in distilling relevant features for machine learning we are developing an Educational Data Mining E
29. e and so on Clips can be divided by Size Time or Per Value Changed o Size as Clip Type By choosing Size as the Clip Type the user will need to specify the desired number of transactions in a clip Complete Clips Only when checked the system will only select clips where the number of logs is equal to the inputted clip size Allow Overlap when checked the system will produce clips with overlapping logs Given logs 1 2 3 4 5 and a clip size of 3 three clips will be produced 1 2 3 2 3 4 and 3 4 5 Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MY Select Column Name s to compare Select Clip Type Size lid revision TIMESTAMP IDELTA_SEQ_NUMBER IDELTA_START_TIME ICOMPILE_ SUCCESSFUL A Clip Size Complete dips only Figure 19 EDM Clipping Window Custom Sort Button This allows the user to set how the transactions within a clip are ordered by sorting them according to criteria Add Level Button adds another sorting criterion while Delete Level deletes the selected Row Clicking the Submit button will implement the selected formatting properties Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Delete Level revision Ascending Figure 20 EDM Custom Sort Time as Clip Type By choosing T
30. e criteria for grouping are determined by the user Examples of the criteria for grouping include source and timing Clip A subset of logs from a given batch Column A single attribute within the dataset Dataset The data from the imported files DataGrid The central area where all the datasets are displayed EDM Educational Data Mining Log A record of a single action Log File A file that contains a collection of logs Model A detector of meta cognitive and motivational behaviour Row A set of attributes in the dataset that usually refers to 1 log Interface Refers to the system graphical user interface Overall Description The EDM Workbench is a tool that helps researchers with processing data from various sources for developing meta cognitive and behavioural models The concept diagram in figure 1 illustrates the system functionalities and entities interacting with it The EDM Workbench s functions allow users to Define and modify behaviour categories of interest Label previously collected educational log data with the categories of interest considerably faster than current methods Collaborate with others in Labelling data by providing ways to communicate and document Labelling guidelines and standards Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 ME Validate inter rater reliability between multiple labellers of the same educational log
31. eck Values SIR Name with Check Values and its output is All Strings based on the Operation type used Operation Type Copy the values from a column Values Copy Input Column Names from Selected Input Column Name CountlfLastN Counts how many in the last n entries including the current cell are equal to a given value or values Sort Columns Group Columns Range Columns N Numbers Only Check Values Counts how many in the last n entries Sort Columns Group Columns CountLastN including the current cell are equal to the current cell Range Columns N Numbers Only Sort Columns Group Columns Computes how many seconds the action Duration Date Column took Time Column Date Time Column Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 True Value False Value Group Columns 7 First Attempt Determines if it is the first attempt Date Column Time Column Date Time Column Returns the inverse of a Boolean If the Input Column Names column values equal the true value 8 Inverse True Value return the false value instead and vice versa False Value Creates a new column with all the unique 9 ListUnique Input Column Names data from the selection Sort Columns Determines the maximum value in the 10 Maximum E Group Columns selection provided Range Column i Sort Columns Compute
32. ed columns Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Check Value is the value to be compared against the Selected Input Column Names This value can either be a string or integer depending on the feature used Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default Minimum Y Sort Columns use to seperate each columns Group Columns use to seperate each columns Output Column Names use to seperate each columns Row v Add Column Name Row v Add Column Name Figure 42 Default Minimum function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected column Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default Or Function Name Default Or Enabled 8 True O False Input Column Names choose column name s to be used in this feature lt Add lt ow lt lt Add All lt lt Sample Anon Student Id gt Remove gt blem Hierarchy gt gt Remove All gt gt oblem Name Swap Contents lt Output Column Names use to seperate each columns
33. ed either in the File menu Figure 6 or Toolbar Figure 9 Error dialogues will be displayed if any error is found with the specified directory or file Note The action button will be enabled depending on the file loaded Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Export By clicking the export button located either in the File menu Figure 6 or Toolbar Figure 9 the system will save the current active tab into a CSV file or into another specified format Users must specify the directory in which the file will be saved Note Exporting a file will take time depending on the dataset s size Note In this version we replaced the term the erroneous feature with the more correct operation We apologize for the confusion this has caused and are undertaking measures to correct these in the next version Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 References u Alcala Fdez J Sanchez L Garcia S de Jesus M J Ventura S Garrell J M Otero J Romero C Bacardit J amp Rivas V M 2009 KEEL A software tool to assess evolutionary algorithms for data mining problems Soft Computing A Fusion of Foundations Methodologies and Applications 13 3 307 318 1 Baker R S J d 2007 Modeling and Understanding Students Off Task Behavior in Intelligent Tutoring Syste
34. ences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default Sum v Sort Columns use to seperate each columns Group Columns use to seperate each columns Output Column Names use to seperate each columns Row v Add Column Name Row v Add Column Name t Figure 49 Default Sum function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected column Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Range Column Range of values used for computation Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 LE e Default SumLastN Function Name Default SumLastN Enabled 8 True Range Column v Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each columns Row v Add Column Name Output Column Names use to seperate each columns N Numbers Only 0 Figure 50 Default SumLastN function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Grou
35. his feature Add ow lt lt Add All lt lt Sample Student Id E x oblem Hierarchy gt gt Remove All gt gt oblem Name Swap Contents lt Output Column Names use to seperate each columns Check Values use to seperate each columns All Strings Check Values use to seperate each columns All Strings 8 True O False Operation Type Numbers Only f 0 Submit Save Load Cancel Figure 32 Default Compare window Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not True Value assigned to the result in the Output Column Name if operation returns a true False Value assigned to the result in the Output Column Name if operation returns a false Check Value is the value to be compared against the Selected Input Column Names This value can either be a string or integer depending on the feature used All String checks if all the column values are strings not numbers or any other type Operation Type contains values from 1 6 that correspond to different operations Strings or integers can be compared in this feature Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default CountlfLastN Function Name Default C
36. his feature on lt Add lt oblem Name lt lt Add All EET Anon Student Id oblem Hierarchy gt Remove gt Lim View gt gt Remove All gt gt Problem Start Time v Swap Contents lt gt Output Column Names use to seperate each columns Value 1 Figure 30 Modified function window with the feature And selected Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Add Feature Operations e Default And Function Name Default And Enabled True O False Input Column Names choose column name s to be used in this feature lt Add lt ow lt lt Add All lt lt gt x Student Id ve oblem Hierarchy Swap Contents lt Output Column Names use to seperate each columns Figure 31 Default And function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not True Value assigned to the result in the Output Column Name if operation returns a true False Value assigned to the result in the Output Column Name if operation returns a false Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MEM e Default Compare Function Name Default Compare Enabled True O False Input Column Names choose column name s to be used in t
37. ime as the Clip Type the user will specify a time period per clip e g 1 clip 5 minutes interval The column name with a time element measured in seconds must be specified When done click the submit button and double click the clips to view the inclusive logs Per Value Change as Clip Type Per Value Change creates a new clip every time the value within the specified column changes Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Meg Select Column Name s to compare Class Section E jrevision TIMESTAMP IDELTA VERSION BJ EXT VERSION SYSUSER HOME OSNAME OSVER OSARCH IPADDR STNAME OCATION_ID IPROJECT_ID SESSION_ID JPROJECT_PATH ACKAGE_PATH TA_NAME TA_SEQ_NUMBER IDELTA START TIME IDELTA END TIME FILE PATH FILE NAME Figure 21 Window showing the Time as Clip Type o Cancel Button This cancels clipping o Save Button The Save button saves the set properties applied in the Clipping Form The user supplies a file name and clicks OK File name Clipping Sample Time Figure 22 Save Dialogue Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 o Load Button Allows the user to select and load a previously saved file from a drop down list see Figure 23 Clipping Box Select Column Name s to compare Select Clip Type Time v Cl
38. is Bautista 20120728 Formatting and editing V3 20 Nadia Leetian 20120814 Edit content V3 50 Dominique Isidro 20120821 Edit content V3 51 Francis Bautista 20121013 Addition of content V3 52 Francis Bautista 20121103 Addition of content V3 53 Introduction In recent years educational data mining methods have afforded the development of detectors of a range of constructs of educational importance from gaming the system 3 to off task behaviour 2 to motivation 5 to collaboration and argumentation moves 6 The development of these detectors has been supported by the availability of machine learning packages such as RapidMiner 7 WEKA 9 and KEEL 1 These packages provide large numbers of algorithms of general use reducing the need for implementing algorithms locally however they do not provide algorithms specialized for educational data mining such as the widely used Bayesian Knowledge Tracing 4 Furthermore effective use of these packages by the educational research and practice communities presumes that key steps in the educational data mining process have already been completed For example many of these detectors have been developed using supervised learning methods which require that labelled instances indicative of the categories of interest be provided Typically many labelled instances on the order of hundreds if not thousands are required to create a reliable behaviour detector Labelling data is a time consuming an
39. lt in the Output Column Name if operation returns a false see figure 53 Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 ME Required Columns Processes List OSMANE OSVER Y Default Compare OSARCH IPADDR HOSTNAME LOCATION_ID PROJECT_ID SESSION_ID PROJECT_PATH PACKAGE_PATH DELTA_NAME DELTA_SEQ_NUMBER DELTA_START_TIME DELTA_END_TIME FILE_PATH FILE_NAME FILE_CONTENTS FILE_ENCODING Delete Process COMPILE_SUCCESSFUL Edit Process MSG MESSAGE MSG LINE NUMBER COMPILES PER FILE TOTAL COMPILES Figure 55 Add Feature Window with updated column Check Value is the value to be compared against the Selected Input Column Names This value can either be a string or integer depending on the feature used Operation Type contains values from 1 6 that correspond to different operations Strings or integers can be compared in this feature Example Compare feature was the selected feature The Check Value will be compared to the Selected Column Name and the output will depend on what operation selected below Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 LE 1 Greater than operation 2 Greater than or Equal to operation 3 Less than operation 4 Less than or Equal to operation 5 Equal to operation 6 Starts with operation Date Column s va
40. lue should be in the Date Year Month Date format Time Column s value should be in the Time Hour Minute Second format Date Time Column s value should be in the Date and Time Year Month Date Hour Minute Second format Time 2005 10 15 02 08 56 0 Figure 56 Time in YYYY MM DD HH MM SS Date Format is the format of the Date Column where M month H hour d day m minutes y year s seconds e g 31 12 12 11 59 dd MM yy HH mm 12 31 2012 11 59 59 MM dd yyyy HH mm ss All String checks if all the column values are strings not numbers or any other type pKnowColumn s value should be the pKnow column Calculate first the pKnow value using pKnow operation Afterwards use pKnowDirect with the pKnow value N Numbers Only if more elements in a group are found only Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 LE the last N items are kept for processing start count every N rows Range Column Range of values used for computation Group Column Used for grouping rows with the same values for selected columns Sort Column used for sorting the rows within the same group Problem Column name of the column corresponding to the problem Skill Column name of the column specifying the skill Outcome Column name of the column used by certain features Error Values used to specify which values constitute an error for use by percentErr
41. may save his or her work and can continue the labelling process in a later session Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 y A 3 49 TOTAL COMPILES Confused Not Confused 1 2 Figure 42 Dataset labelling window Note In the above example the user can press the number keys 1 and 2 as shortcut keys for the buttons Confused and Not Confused respectively Press Enter to choose Next to go to the next row abelling Time Elapsed The GUI now displays how much time each labelling action took Labels Labeler TimeStamp Time Elapsed Good ancis 2012 Novf0 0 tral ancis 2012 Nov 0 1 tral ancis 2012 Nov 0 5 ancis 2012 Nov 0 6 Figure 66 Time Elapsed Column for Labels Labelling Output As we can see in the figure 67 below the labels are shown with their corresponding timestamps and labeller These column names are present for data organization Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 EZ File Functions Help E s Bee B Load Save Import Export AddProccess Clip Sampling Label Add Feature hd F227 18 Compile dsi student pro CS21a CS21a dipped Default Dataset Default Dataset 4 MSG_LINE_
42. mes use to seperate each columns Figure 47 Default RunningPrevCount window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 the same group Group Column Used for grouping rows with the same values for selected column Range Column Range of values used for computation Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default StDev Function Name Default StDev Enabled e True Range Column v Sort Columns use to seperate each columns Group Columns use to seperate each columns Output Column Names use to seperate each columns Row v Add Column Name Row v Add Column Name ri Figure 48 Default StDev function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected column Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Range Column Range of values used for computation Ateneo Laboratory for the Learning Sci
43. ms Proceedings of ACM CHI 2007 Computer Human Interaction 1059 1068 3 Baker R S J d amp de Carvalho 2008 Labeling Student Behavior Faster and More Precisely with Text Replays 1 International Conference on Educational Data Mining 38 47 5 Corbett A T amp Anderson J R 1995 Knowledge Tracing Modeling the Acquisition of Procedural Knowledge User Modeling and User Adapted Interaction 4 253 278 7 de Vicente A Pain H 2002 Informing the detection of the students motivational state an empirical study Proceedings of the 6th International Conference on Intelligent Tutoring Systems 933 943 8 McLaren B M Scheuer O Miksatko J 2010 Supporting collaborative learning and e Discussions using artificial intelligence techniques International Journal of Artificial Intelligence in Education UJAIED 20 1 1 46 11 Mierswa Wurst M Klinkenberg R Scholz M Euler T 2006 YALE Rapid Prototyping for Complex Data Mining Tasks In Proc of the 12th ACM SIGKDD Int Conference on Knowledge Discovery and Data Mining KDD 2006 pp 935 940 ACM Press 12 Walonoski J amp Heffernan N T 2006 Detection and Analysis of Off Task Gaming Behavior in Intelligent Tutoring Systems In Ikeda Ashley amp Chan Eds Proceedings of the 8th International Conference on Intelligent Tutoring Systems Springer Verlag Berlin pp 382 391 14 Witten I H amp Frank E 2005 Data Mi
44. nde Figure 2 EDM System Process Map Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Mg Chapter 1 System Overview This section discusses the interface of the system from Top to Bottom including its features buttons and functions 1278571103 DELTA VERSION BJ_EXT_VERSION SYSUSER 20060907 c79a9d40971a30909becc3748c66e60 HOME Usersc79a9d40971a30909becc3748c66 1278571103 20060907 c79a9d4097 1a30909becc3748c66e60 1278571103 20060907 c7929d40971a30909becc3748c66e60 1278571103 20060907 c7929d40971a30909becc3748c66e60 1278571103 20060907 c79a9d4097 1a30909becc3748c66e60 1278571103 20060907 c7989d40971a30909becc3748c66e60 1278571103 20060907 c7929d40971a30909becc3748c66e60 1278571103 20060907 c79a9d4097 1a30909becc3748c66e60 Usersc79a9d40971a30909becc3748c66 1278571103 20060907 c79a9d4097 1a30909becc3748c66e60 Usersc79a9d40971a30909becc3748c66 OO On aerem o 1278571103 20060907 c79a9d4097 1a30909becc3748c66e60 Usersc79a9d40971a30909becc3748c66 ojoloiloilojloloilojojojo 1278571103 20060907 c79a9d40971a30909becc3748c66e60 Usersc79a9d4097 1a30909becc3748c66 v Wed Nov 07 09 12 56 CST 2012 INFO Imported C Users Francis Desktop EDM v3 5 latest Distribution Copy Nov 51S
45. ng Parameters cs iiic cccsscecceesseecsvssecccasasccccuasscccsusssccecuesscccchausececesvscccduessccecdavbicecensbiees 78 Label Text BOX coo 78 e Labeller s Name User Name cccccccssscccssscecssecesseecssseecsaeceeseecesseccsaeeecsaeceeseecsseeeesaeeeeaeeeees 79 e Parameter and sentence textbox ooooooccccncccnnoninoncncconccononcnnnnncnnnoccnnnnnn nn nn enne nemen nnne nnne 79 Labelling BUTTON m 79 Add Parameter BUtton eer aa a a eee ehh rufo a aTa 79 Save Template coco ida 79 e Load Template tucan dais 79 Labelling the dataset nire reise te tec dete seti cen sea eee ie ceasavitedasiteicessviaicdbarblies 80 Labelling Time Elapsed rerit di e nene reno eer n ae rad 81 Labelling Out ut EET 81 Hr e 82 Load M M ibi 82 epp E RE 83 Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 ME 1 Alcala Fdez J Sanchez L Garcia S de Jesus M J Ventura S Garrell J M Otero J Romero C Bacardit J amp Rivas V M 2009 KEEL A software tool to assess evolutionary algorithms for data mining problems Soft Computing A Fusion of Foundations Methodologies and Applications 13 3 307 SEA mE 84 2 Baker R S J d 2007 Modeling and Understanding Students Off
46. ning Practical Machine Learning Tools and Techniques Second Edition Morgan Kaufmann 15 2 iua 3 pets 4 paar 5 eat 6 Md 7 pa 8 pre 9 ot Ateneo Laboratory for the Learning Sciences F206 AAMU
47. olumns Row v Add Column Name Output Column Names use to seperate each columns Figure 52 Default TimeSD function window Parameters Needed Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected column Range Column Range of values used for computation Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Add Feature Buttons Submit Button The submit button will execute the feature set by the user Save Button The save button will save the user selected properties to a file to allow the same values to be used again later Load Button The load button allows the user to reload a template Cancel Button This cancels the selected feature and removes it from the process list Add Feature Parameters To add a new feature the user will have to set several parameters Depending on the operation that the user needs to perform the user will have to supply a subset of the parameters listed below Input Column Names lists the selected values The user can remove and or add values to the columns Click one or multiple items and click lt Add lt to add the value
48. or LO Number Only probability that the skill is already known before the first instance in using the skill in problem solving S Number Only probability that the student will commit a fault if the skill was already known beforehand G Number Only probability that the student will deduce the correct answer given that skill is not known T Number Only probability that the skill will be learned at each opportunity to use the skill regardless whether the answer is correct or incorrect Attempt Column Either of the two depends on how it was used Is this the first attempt of the student to answer or get Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MY help on the problem step or How many attempts did they answer or ask for help on the problem step Pre defined functions The system has 23 default operations available Four parameters are common to all operations Output Column Names Feature Name Enabled Listed below are the current operations their descriptions and parameters needed aside from the previously mentioned parameters Function Description s Other Parameters Needed Executes a logical AND operation on the Input Column Names And selection and returns the corresponding True Value Boolean results False Value Compares if two values are identical Input Column Names E Compare 1 selected Input Column Ch
49. ountIfl astN Enabled 8 True Range Column v Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each columns Row v Add Column Name Output Column Names use to seperate each columns N Numbers Only 0 Check Values use to seperate each columns Figure 33 Default CountlfLastN function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not True Value assigned to the result in the Output Column Name if operation returns a true False Value assigned to the result in the Output Column Name if operation returns a false Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Range Column Range of values used for computation Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected columns N Numbers Only if more elements in a group are found only the last N items are kept for processing start count every N rows Check Value is the value to be compared against the Selected Input Column Names This value can either be a string or integer depending on the feature used Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default CountLastN Function Name Default Counti astN Enabled
50. p 19 Custom Sort BUEEOR ine ains 20 Ateneo Laboratory for the Learning Sciences F206 AAMU O O Educational Data Mining Workbench User Manual V3 53 ME Time as Clip TYpe oiiiiocii da dadas 21 Per Value Change as Clip Type cccccccccsscceceessececeeseeeceeaeeeeseaeeeeseaueeeeseaaeeesseaueeeseeaaeeeenenaes 21 A RN 24 Random Sampling cirio cia ie di ida 25 Stratified Sampling iii ai 25 Save BURNOM it ai 26 Load Button cc 26 Add Proceso cia dade isse iter Bese a hia ede 28 Add Feature escote 28 Add Feature Operations oir orae ease YER ERR Ee ean RE HERE EC Y Ra RR ERE RR Pe nane iN e Ve gu ERCY 30 Default And nne eee ica 30 Default Compare oia 31 Default CountlfLastN AA 0U0UO 0 O OOAO nennen nennen sternit nene en nnne r entrent nene 33 Default COUNtLASEN 000 cece ce enceceeeeeceaeeeeaaeeeeaaeceeeeecsaeeeeaaecseaeecaeeeesaeeeeaaeseeaeeseaeeeeaaeeeeaaeeeeeees 35 Default Co poy eases casas ccc cates ca RN 37 Default DC ata cscs oro nia 38 Default FirstAttem pt scisstcdiccacteceoctessechssacelecsiceat beasties bendesstsns sesentextdastsreasderbosPeastsneaedarkevacastirsastaltends 39 Default INVerse cid 41 Default listUnig es assisia TRU 42 Default Ern e di 43 Default Mean omnia a 44 Default MeanCountlt c voca ias 45 Default Minimum coco Dead dicci n 47 Detalla te enti pate ated 48 Default PercenteE rrr pe 49 Default PKNO
51. p Column Used for grouping rows with the same values for selected column Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Range Column Range of values used for computation N Numbers Only if more elements in a group are found only the last N items are kept for processing start count every N rows Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default TimeElapsed Function Name Default TimeElapsed Enabled True O False Output Column Names use to seperate each columns Date Column Year Month Date v Date Format e g 12 31 2012 11 59 59 MM dd yyyy HH mm ss Figure 51 Default TimeElapsed function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Date Column s value is the date when the actions were taken time stamp Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Date Format is the format of the Date Column where M month H hour d day m minutes y year s seconds e g 31 12 12 11 59 dd MM yy HH mm 12 31 2012 11 59 59 MM dd yyyy HH mm ss e Default TimeSD Function Name Default TimeSD v Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each c
52. r parameters Labelling Button Allows the user to supply ground truth labels for clip 9 Add Feature Allows the user to tailor functions to their specification M Ld DataGrid E distilltest txt 3 row lesson name outcome prod type skill 1 ZGeneticsZGeneZInteractioy2PS student WRONG CROSSIPARENT1 STRING PICK PARENTS 2 ZGeneticsZGeneZInteractioy2PS studentO WRONG CROSSIPARENT1 STRING PICK PARENTS 3 ZGeneticsZGeneZInteractioy2PS student WRONG CROSSIPARENT1 STRING PICK PARENTS Ia ZGeneticsZGeneZInteractioy2PS studentO HELP CROSSIPARENT1 BLANK PICK PARENTS l5 ZGeneticsZGeneZInteractioy2PS student WRONG CROSSIPARENT1 STRING PICK PARENTS le ZGeneticsZGeneZInteractioy2PS student HELP CROSSIPARENT1 BLANK PICK PARENTS 7 ZGeneticsZGeneZInteractioy2PS student WRONG CROSSIPARENT 1 STRING PICK PARENTS 118 ZGeneticsZGeneZInteractioy2PS studentO HELP CROSSIPARENT 1 BLANK PICK PARENTS IE ZGeneticsZGeneZInteractioy2PS studentO HELP CROSS1PARENT2 INA PICK PARENTS 10 ZGeneticsZGeneZInteractioy2PS student WRONG CROSSIPARENT1 STRING PICK PARENTS 1 ZGeneticsZGeneZInteractioy2PS studentO HELP CROSSIPARENT 1 BLANK PICK PARENTS 12 ZGeneticsZGeneZInteractioy2PS studentO WRONG CROSSIPARENT1 STRING PICK PARENTS 13 ZGeneticsZGeneZInteractioy2PS student HELP ICROSSIPARENT1 IBLANK PICK PARENTS NS i lt 4 n
53. r to choose a previously saved sampling template from a list and apply it to the current dataset Ateneo Laboratory for the Learning Sciences F206 AAMU 26 Educational Data Mining Workbench User Manual V3 53 Figure 27 Load Prompt o Submit Button The submit button closes the Sampling Form implements the sampling process and then displays the result in a new tab Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MEE Add Process This allows the user to create a script composed of multiple processes and run them in a single thread Required Columns Class A id revision TIMESTAMP DELTA_VERSION BJ_EXT_VERSION SYSUSER HOME OSNAME OSVER Uncheck All Processes OSARCH IPADDR Invert Checked Processes HOSTNAME LOCATION_ID PROJECT_ID Delete Process SESSION_ID PROJECT_PATH PACKAGE_PATH Edit Process DELTA_NAME DELTA_SEQ_NUMBER DELTA_START_TIME UCA TAN TA Check All Processes Run Processes Figure 28 Feature selection window o Add Feature This function allows users to add features to the dataset through the application of predefined operations Figure 29 Load Function Dialogue Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MEE Function Name myFunction Enabled 8 True O False Input Column Names choose column name s to be used in t
54. s or click lt lt Add All lt lt to add all column name Click gt Remove gt to delete one or multiple input column name or gt gt Remove All gt gt to remove all input column names Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 LE Add Process Default Compare Feature Name Default Compare Enabled True C False Input Column Names choose column name s to be used in this feature Irevision lt Add lt id A lt lt Add All lt lt STAMP R TA_VERSION A J EXT VERSION gt gt Remove All gt gt sysusER y Swap Contents lt gt Figure 53 Sample add feature window Output Column Names are columns added later in the Datagrid after the user selected values have been processed These columns will also be included in the Required Columns in the Add Process Window Figure 54 Output Column Names use to seperate each columns TimeOnTask Figure 54 Selection of column names Feature Name is the name to be displayed in the Process List see Figure 53 Enabled indicates whether the selected feature will be used in the process or not In Figure 31 the Enabled option was set to true After submission we now see that the feature is checked in the process list see Figure 53 True Value assigned to the result in the Output Column Name if operation returns a true see Figure 53 False Value assigned to the resu
55. s the arithmetic mean of all the 11 Mean Group Columns values in the selection Range Column Sort Columns Computes the average number of entries Group Columns 12 MeanCountlf that are equal to a given value or values over all entries Range Column Check Value 23 Sort Columns Determines the minimum value in the 13 Minimum f Group Columns selection provided Range Column Executes a logical OR operation and Input Column Names 14 Or returns the corresponding Boolean True Value results False value Sort Column h t Group Colum omputes the percentage of past P P B P Problem Column 15 PercentError problems where errors were made on a Skill Column skill Outcome Column Error Values Sort Columns Group Columns P es ee Out Column omputes for the probability that the P P a H Check Values 16 pKnow student knows the skill involved in an action LO Numbers Only S Numbers Only G Numbers Only T Numbers Only Ateneo Laboratory for the Learning Sciences F206 AAMU 71 Educational Data Mining Workbench User Manual V3 53 17 pKnowDirect Checks if the current action is the student s first attempt on this problem step If true pknow direct is equal to pknow otherwise pknow direct is equal to 1 Attempt Column pKnow Column Check Value False Value 18 RunningCountif Computes the number of entries that are equal to a given value or values up to the current cell incl
56. t possibly between two or more students as in the case of collaborative learning scenarios The successfully imported logs may be saved in the Workbench s format for work files a compressed file containing the data in CSV format plus metadata specific to the EDM Workbench Import log file by clicking Import Button located either in File menu Figure 6 or Toolbar Figure 9 The system will then pop up a dialog box asking what type of logs you want to import CSV or Datashop Text file Figure 13 Click the Select Button after selecting the type of Log Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 MS Figure 13 Log Selection Another dialog box will ask for the location of the log file Look in Lab1_20100702 l F227_15_CompileData El F227 16 CompileData 4 F227 18 CompileData El F227 19 CompileData l F227 21 CompileData 4 F227_29 CompileData Figure 14 Selection of Data File to be imported Case 1 Importing a single log file If a user imports a single log file after locating and choosing the log file the Workbench displays the file in the DataGrid Figure 10 Case 2 Importing batches of log files Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 The Workbench can also import nested folders of data where each folder level represents a meaningful s
57. ters enclosed with from the drop down menu right above the textbox that will depend on the values of the row currently being labelled Labelling Button e Add Parameter Button In constructing sentences users can manually input the parameters by enclosing it in a bracket and with the correct spelling or by selecting a parameter from the dropdown list and then clicking on the Add Parameter button to insert the selected parameter e Save Template The system allows the user to save the selected Labelling properties A dialogue will be popped up and will ask for a template name The file will be saved as a Labelling xml file Template name My Labeling Template Figure 640 File Name input window e Load Template The user may select a template from the list of labelling templates displayed by the system The Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 system will then load the properties of the selected template to the labelling form Figure 651 Labelling template loading window B Labelling the dataset The Workbench then displays text replays of the clips together with the labelling options Figure 3 A coder reads through the text replay and selects the label that best describes the clip The labels are saved under a new column in the data set NOTE Because a coder may have to label tens of thousands of clips 5 the coder
58. ther to the selected feature will be used in the process or not True Value assigned to the result in the Output Column Name if operation returns a true False Value assigned to the result in the Output Column Name if operation returns a false Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default ListUniques Function Name Default ListUniques Enabled True O False Input Column Names choose column name s to be used in this feature gt gt Remove All gt gt Swap Contents Output Column Names use to seperate each columns Figure 38 Default ListUniques function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default Maximum v Sort Columns use to seperate each columns Group Columns use to seperate each columns Output Column Names use to seperate each columns Row v Add Column Name Row v Add Column Name Figure 39 Default Maximum function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same v
59. ubset of the data For example if data from a section of students is collected several times over a school year the researcher may have one folder for the school year one subfolder for each section within the school year one subfolder for a session within each section and finally one file or folder for each student within a session The Workbench allows users to label each level of subfolder creating new columns for these labels appending them to the data tables during importation process After locating and choosing the batch of log files another dialog box will appear asking for a label describing the log files imported e g Class Figure 14 Clicking Submit aggregates all the logs and displays them in the DataGrid Column Header 1 Name sample values D E Class Column Header 2 Name sample values Lab1_20100702 Lab1 5_201 Section Column Header 3 Name sample values F227 12 Co csv F227_10_Co csv Lab Figure 15 Label Column with sample parameters Once the logs are loaded the DataGrid should be populated Figure 16 All actions buttons save for the Labelling button should be enabled at this point Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 File Functions Help 18 Load Save Import Export x Pan e e lcu Add Proccess Clip Sampling Labeling Add Feature
60. uding the current cell Sort Columns Group Columns Range Column Check Value 19 RunningPrevCo unt Computes the number of entries that are equal to the current cell up to the cell before the current cell Sort Columns Group Columns Range Column Computes the standard deviation of a Sort Columns 20 StDev es Group Columns specified column Range Column Sort Columns Computes the sum of the last n numbers Group Columns 21 SumLastN a m in the selection specified Range Column N Numbers Only Sort Columns Computes time taken in terms of number 22 TimeSD a 2 Group Columns of standard deviations from mean time Range Column Computes for the time interval per action Output Column 23 TimeElapsed in seconds date of current row minus the Date Column date of the first row Date Format Figure 57 Function List Submit Button will include the user selected feature to the Process List Load Button will load available features Save Button will save the user selected feature and add it to the directory of features for later use o AddFeatures in the Clip Level In th clips e clip level there are 5 features which can be imposed on the mean max min stdev and listUnique These features Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 functionalities are similar to the ones above Clipped dataset are
61. wn T Number Only probability that the skill will be learned at each opportunity to use the skill regardless whether the answer is correct or incorrect Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 e Default RunningCountlf Function Name Default RunningCountIf v Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each columns Row v Add Column Name Output Column Names use to seperate each columns Check Values use to seperate each columns Figure 46 Default RunningCountlf function window Parameters Needed Enabled indicates whether to the selected feature will be used in the process or not Sort Column used for sorting the rows within the same group Group Column Used for grouping rows with the same values for selected column Ateneo Laboratory for the Learning Sciences F206 AAMU Educational Data Mining Workbench User Manual V3 53 Check Value is the value to be compared against the Selected Input Column Names This value can either be a string or integer depending on the feature used e Default RunningPrevCount Function Name Default RunningPrevCount Enabled e True Range Column Sort Columns use to seperate each columns Row v Add Column Name Group Columns use to seperate each columns Row v Add Column Name Output Column Na

Educational Data Mining Workbench User Manual V3.53

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