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A self training framework for exploratory discourse detection final

The document describes a self-training framework for detecting exploratory discourse in online conversations. It involves initially training a classifier on a small set of annotated data, then using the classifier to annotate additional unlabeled data and adding it to the training set. This allows the classifier to be retrained and improved without requiring manual annotation of large amounts of data. The framework is evaluated on chat data from an Open University conference, and a feature-based self-training approach is shown to improve performance over supervised classifiers and other baselines. Applications for visualizing discourse and participation are also discussed.

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A self-training framework for
exploratory discourse detection
                      Zhongyu Wei
 SoLAR symposiumOpen University,UK, 26 June 2012

 PhD student, SEEM, The Chinese University of Hong Kong, Hong Kong
              SocialLearn intern, Open University, UK
                      zywei@se.cuhk.edu.hk
Outline
 Exploratory dialogue analysis
 A self-training framework
 Datasets and experiments
 Applications
Online learning resources explosion


Learning                       Online
 Forum                        Seminar




                               Online
    Distant                   Conferen
   Educatio                     ce
       n
   Platform
the critical, knowledge-building
discourse?...
How many points in the webinar
 triggered learning/knowledge-building?


                        This person contributes a lot during the
                        chat.




                                                                   This part appears to have very good
                                                                   content that will provoke deeper learning




Data in this study taken from a 2 day OU conference in Elluminate & Cloudworks:
Exploratory dialogue analysis
             Exploratory dialogue
               ……represents a joint, coordinated from of co-reasoning
                in language, with speakers sharing knowledge,
                challenging ideas, evaluating evidence and considering
               Categor ... …
                options Description            Example
                    y
                    Challen           Identifies that something
                    ge                may be wrong and in need I disagree. Freemind is a superb
                                      of correction             piece of software to use...
                    Evaluati          Has a descriptive quality  That's a really interesting
                    on                                           approach
                    Extensio Builds on or provides                                   I've embedded helen's slide
                    n        resources that support                                  share over in cloudworks
                             discussion                                              http://link.com
                    Reasoni           The process of thinking an Why intranet only? What
Mercer, N. (2004). Sociocultural discourse analysis: analysing classroom talk as a social mode of thinking. Journal of Applied Linguistics, 1(2),
137-168.
                    ng                idea through.              meaning CLOSED in
Low exploratory dialogue
 Time      Contribution
3:12 PM   LOL
3:12 PM   It's not looking good.
3:13 PM   Sorry, had to do that.
3:13 PM   jaaa
3:13 PM   Ouch!
3:13 PM   It was a vuvuzela.
3:13 PM   I though that was you @Alistair
3:13 PM   I've taken away the vuvuzela from you now!
3:13 PM   LOL
Higher exploratory dialogue
 Time    Contribution
2:42 PM I hate talking. :-P My question was whether "gadgets" were just
        basically widgets and we could embed them in various web sites,
        like Netvibes, Google Desktop, etc.
2:42 PM Thanks, that's great! I am sure I understood everything, but looks
        inspiring!
2:43 PM Yes why OU tools not generic tools?
2:43 PM Issues of interoperability
2:43 PM The "new" SocialLearn site looks a lot like a corkboard where you
        can add various widgets, similar to those existing web start pages.
2:43 PM What if we end up with as many apps/gadgets as we have social
        networks and then we need a recommender for the apps!
2:43 PM My question was on the definition of the crowd in the wisdom of
        crowds we acsess in the service model?
Exploratory dialogue detection
 Problem Statement
   Given an online chatting session S = {d0, d1 … …dn}, dk
    stands for the kth dialogue, classify dk as exploratory or
    non-exploratory.
 Solution from learning analytics
   Sociocultural discourse analysis method
     Manual
     High precision and low recall
   Category          Cue phrases
   Challenge         But if, have to respond, my view
   Evaluation        Good example, good point
   Extension         More links, for example
   Reasoning         That is why, next step
Exploratory dialogue classification

                                                  Explorator
                       Explorator                     y
                           y
    Dialog             Discourse
      ue                                            Non-
                       Classifier
                                                  Explorator
                                                      y
 Dialogue is represented by a feature vector.
 {I think she is right}{I, think, she, is, right, I-
  think, think-she, she-is, is-right, I-think-she, think-
  she-is, she-is-right}
Exploratory dialogue classification
       Instance-based supervised classifier training
              Explor
               Explor
                Explorato
               atory
                atory                                   Explorator
                   ry          Classifier                   y
                               Training                 Discourse
                Non-
                 Non-
                  Non-                                  Classifier
              Explorat
               Explorat
               Explorator
                 ory
                  ory
                    y

       Feature-based supervised classifier training
Explor
 Explor
  Explorato                                                  Explorato
 atory
  atory            Feature                  Classifie
     ry                       Featu                              ry
                   Generati     re              r
                               List                          Discourse
  Non-
   Non-              on                     Training
    Non-                                                     Classifier
Explorat
 Explorat
 Explorator
   ory
    ory
      y
An example of feature list

   Feature       Exploratory Non-
                             Exploratory
   what-is       0.9992      0.0008
   good-point    0.9995      0.0005
   your-audio-   0.001       0.999
   should
   thank-you     0.004       0.996
   my-name       0.07        0.93
A self-training framework


Annotat
            Classifier    Classifie
  ed
             training        r
 data




    Step 1: Training initial classifier on annotated
     data.
    Annotated data is time consuming to obtain
A self-training framework
                           Unlabele
                              d
                             data

Annotat
            Classifier     Classifie
  ed
             training         r
 data


  Annotat   Pseudo-        Instance
    ed      annotate       Selection
   data      d data

    Step 2: Classify unlabeled data, select high
     confidence instances and combine them with
     annotated data
    Step 3:Re-train classifier on the augmented training
A self-training framework
                           Unlabele
                              d
                             data

                                         Explorator
Annotat
            Classifier     Classifie         y            Resul
  ed
             training         r          Discourse         ts
 data
                                         Detection


  Annotat   Pseudo-        Instance           Test
    ed      annotate       Selection          data
   data      d data

    Step 4: Obtain final classifier: No improvement on
     validation dataset; After a certain iteration; No class
     label changes.
    Step 5: Detect exploratory dialogues on the test data.
A self-training framework
                         Unlabele
                            d
                           data

                                     Explorator
Annotat
            Classifier   Classifie       y          Resul
  ed
             training       r        Discourse       ts
 data
                                     Detection


  Annotat   Pseudo-      Instance         Test
    ed      annotate     Selection        data
   data      d data
    Self-training will introduce noisy instance.
KNN based Instance Selection approach
 K nearest neighbors classification
                         Blue stands for “exploratory”
                         Gray stands for “non-exploratory”
                         1 nearest neighbor is “exploratory”
                         2 nearest neighbors is
                         “exploratory”
                         5 nearest neighbors is “non-
                         exploratory”
KNN based Instance Selection approach
    Pseudo annotated instances P = {p1,p2,…
                         …pn }
      pk = (lk, ck) . Lk is pseudo label, ck is
                 confidence value

             Form a candidate list
          Choose instances with ck > r

     For pk in the candidate list, identify the K
    nearest neighbors and update the pseudo
                 label of pk by KNN

   Obtain new pseudo annotated instances P-
                  updated
Outline
 Exploratory dialogue analysis
 A self-training framework
 Datasets and experiments
 Applications
Data source: OU online
       conference
        4 sessions including 2634 posts.




Data in this study taken from a 2 day OU conference in Elluminate & Cloudworks:
Annotation
 2 Annotators with one morning training.
 Four categories are given.
 Kappa value (binary) is 0.5978 (moderate).
 Only posts with the consistent labels are
 collected. Total# Exploratory # Non-Exploratory
 Session
                                 #

  OU_22A     529        380             149
  M
  OU_22P     661        508             153
  M
  OU_23A     456        310             146
  M
Experiment Setup
 Baseline:
   CP: Cue phrase based method
   MEGE: Supervised Max Entropy GE (Generalized Expectation)
    approach (feature based)
   ME: Supervised Max Entropy approach (instance based)
   SMEGE: Self-training Max Entropy GE approach (feature based)
   SME: Self-training Max Entropy approach (instance based)
 Experiment Setup
   Use one session as training part, one session as testing part, one
      session as validation
     During the self-training process, examples include cue-phrase
      are added to training dataset at the first stage.
     Pseudo samples are added with the same ratio of exploratory
      and non-exploratory as training dataset.
     Confidence value 0.8
     Feature threshold 0.65
Evaluation Criterion

                       Exp     Exp
                       Exp     Exp


                               NonEx
                       Exp       p
                       NonEx   NonEx
                         p       p
Experiment Result
Approach     Accuracy     Precision      Recall          F1
Cue-           0.5389       0.9523       0.4241       0.5865
Phrase
MaxEnt         0.8099       0.8526       0.8675       0.8499
MaxEntGE       0.7932       0.8817       0.8078       0.8292
Self-          0.8088       0.8331       0.9011       0.8574
training
MaxEnt
Self-            0.8181      0.8818       0.8406        0.8554
 Cue-phrase method give high precision, but low accuracy.
training
 Feature-based self-training approach improve on all criteria
MaxEntGE
   (the last row).
 Instance-based self-training algorithm (4th row) perform even
   worse according to accuracy precision.
Experiment Result
Session     MaxEnt    MaxEnt-     MaxEntG   MaxEntG
                      Selftrain      E         E-
                                            Selftrain
OU_22AM     0.8190     0.8467      0.7887    0.8270
OU_22PM     0.8034     0.8311      0.7738     0.8116
OU_23AM     0.8268     0.8282      0.8114    0.8297
OU_23PM
 Instance-based self-training algorithm (2nd 0.8042
            0.7906     0.7294     0.7989
  column) is sensitive to the initial classifier’s
  performance.
 Feature-based self-training approach gives more
  stable results (the last column).
Outline
 Exploratory dialogue analysis
 A self-training framework
 Datasets and experiments
 Applications
Transcript level visualization
Time line Visualization

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            12:03
      -20

      -40
                                  1. anybody else with poor audio?
                                  2. is anyone else Exploratorydifficulty hearing this?
                                              Average having …
     -60                          3. background noise makes it difficult to hear
1. Sheffield, UK not as sunny as yesterday -                                              1. See you!
still warm                                                                                2. bye for now!
2. Greetings from Hong Kong                                                               3. bye, and thank
3. Morning from Wiltshire, sunny here!                                                    you
                                                                                          4. Bye all for now
Time line Visualization
Time       User Id    Content
                     added to which 2M often drops to 10% of that in peak
11:46 AM User_2
       80            times
                     I really disagree - ECDL was the starting point for many
11:47 AM User_3
       60            many first time users
11:47 AM User_1
       40            online basics won't load in final third first
11:47 AM User_1
       20            mobile won't work round her
11:47 AM User_1
       0             and satlellite costs 40 a month for 1 gig data transfer
              9:28
              9:32




            10:13




            11:48


            12:00

            12:04
            12:05
                     I think the issue about the skills needed to really embrace
             9:36
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           11:52
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           12:03
     -20
                     technologies is a huge one and with web 2.0 technologies
     -40             things are becoming more complicated, as I say often you
                     dont just get this stuff by attending a workshop, you have
                                               Average Exploratory …
     -60             to participate and appropriate them to your interests and
11:47 AM User_4      context and network of others.
                     We use myguide on mobile broadband for outreach.
                     Works OK, but not great and thats in city centre
11:47 AM User_5      boardering 3G/GPS.
User Visualization
                                       Contribution Distribution of Users
                              50
  Exploratory Message Count



                              45

                              40

                              35
                                         Time     User Id     Content
                              30                             because although some people can
                              25        11:42                get 'online' the feed is so poor that
                              20        AM        User_1     many pages won't load. eg myguide
                              15                             how much time and money was spent
                              10        11:42                getting everyone to use a mobile
                               5        AM        User_1     phone?
                               0                             nothing. because it was perceived to
                                   0     10      20       30        40        50        60
                                                             be useful, therefore there is no need
                                                 Total Message Count time and money on
                                                             to spend
                                        11:43                digitalinclusion, until the access to the
                                        AM        User_1     internet works
                                                             in order to get a 2meg connection to
                                        11:44                everyone we need fibre to the final
                                        AM        User_1     third
User Visualization
                                      Contribution Distribution of Users
                               Time
                              50       User Id             Content
  Exploratory Message Count



                              9:51
                              45                          Hello Im a tutor at Saudi arabia
                              AM
                              40
                                      User_6              branch
                              35
                              9:51
                              30
                              AM      Moderator            hello Saudi Arabia!
                              25
                              9:51
                              20
                              AM      User_6               hi
                              15
                              9:52                         Welcome Ashawa - did we meet in
                              10
                              AM      Moderator            Kuwait a couple of years ago?
                               5
                              9:52
                               0
                              AM 0    User_6
                                        10        20       no actually
                                                          30       40       50       60

                              9:52                Total Message Count
                              AM      Moderator            @ashawa - maybe next time
                              9:52
                              AM      User_6               yes I wish
A self training framework for exploratory discourse detection final
i
                                            i
This step appears to have very good
content that will provoke deeper learning




                                            i
                                            i
This step appears to have some content
that will provoke deeper learning




                                            i
                                            i
This step appears to have little content
that will provoke deeper learning
Conclusion
 We have extended our previously proposed self-
  training framework for exploratory discourse
  detection in synchronous textchat (Elluminate
  conference sessions).
 Propose a K Nearest Neighbors algorithm based
  instance selection method.
 Applied the proposed approach to SocialLearn
  platform.
Future Work
Text analytics:
 Integrate KNN instance selection method into the
  self-training framework
 Explore other features for exploratory dialogue
  classification: inter-dialogue features, global
  features.
 Build a more reliable dataset for sub-category
  classification, challenge, evaluation, reasoning, e
  xtension.
Future Work
Visual analytics:
 Investigate how these can be rendered most
  usefully for educators and learners
 Investigate user feedback when deployed
 Different users will appreciate different levels of
  detail
   Purdue Signals experience suggests that complex
    underlying analytics should be usefully distilled into
    very simple feedback
   But as analytics literacy grows, will users value
    more powerful insights?
Acknowledgments
 Thanks for the guidance and consideration of Dr.
  He Yulan, Dr. Simon and Dr. Rebecca.
 Thanks for the consideration from all the other
  colleagues in Knowledge Media Institute.
A self training framework for exploratory discourse detection final
Zhongyu Wei
The Chinese University of Hong Kong, Hong Kong
          http://www.se.cuhk.edu.hk/~zywei/

                   Yulan He
             The Open University, UK
          http://people.kmi.open.ac.uk/yulan/

            Simon Buckingham Shum
               The Open University, UK
    http://oro.open.ac.uk/view/person/sjb72.html

                  Rebecca Ferguson
                The Open University, UK
    http://oro.open.ac.uk/view/person/rf2656.html

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A self training framework for exploratory discourse detection final

  • 1. A self-training framework for exploratory discourse detection Zhongyu Wei SoLAR symposiumOpen University,UK, 26 June 2012 PhD student, SEEM, The Chinese University of Hong Kong, Hong Kong SocialLearn intern, Open University, UK zywei@se.cuhk.edu.hk
  • 2. Outline  Exploratory dialogue analysis  A self-training framework  Datasets and experiments  Applications
  • 3. Online learning resources explosion Learning Online Forum Seminar Online Distant Conferen Educatio ce n Platform
  • 5. How many points in the webinar triggered learning/knowledge-building? This person contributes a lot during the chat. This part appears to have very good content that will provoke deeper learning Data in this study taken from a 2 day OU conference in Elluminate & Cloudworks:
  • 6. Exploratory dialogue analysis  Exploratory dialogue  ……represents a joint, coordinated from of co-reasoning in language, with speakers sharing knowledge, challenging ideas, evaluating evidence and considering Categor ... … options Description Example y Challen Identifies that something ge may be wrong and in need I disagree. Freemind is a superb of correction piece of software to use... Evaluati Has a descriptive quality That's a really interesting on approach Extensio Builds on or provides I've embedded helen's slide n resources that support share over in cloudworks discussion http://link.com Reasoni The process of thinking an Why intranet only? What Mercer, N. (2004). Sociocultural discourse analysis: analysing classroom talk as a social mode of thinking. Journal of Applied Linguistics, 1(2), 137-168. ng idea through. meaning CLOSED in
  • 7. Low exploratory dialogue Time Contribution 3:12 PM LOL 3:12 PM It's not looking good. 3:13 PM Sorry, had to do that. 3:13 PM jaaa 3:13 PM Ouch! 3:13 PM It was a vuvuzela. 3:13 PM I though that was you @Alistair 3:13 PM I've taken away the vuvuzela from you now! 3:13 PM LOL
  • 8. Higher exploratory dialogue Time Contribution 2:42 PM I hate talking. :-P My question was whether "gadgets" were just basically widgets and we could embed them in various web sites, like Netvibes, Google Desktop, etc. 2:42 PM Thanks, that's great! I am sure I understood everything, but looks inspiring! 2:43 PM Yes why OU tools not generic tools? 2:43 PM Issues of interoperability 2:43 PM The "new" SocialLearn site looks a lot like a corkboard where you can add various widgets, similar to those existing web start pages. 2:43 PM What if we end up with as many apps/gadgets as we have social networks and then we need a recommender for the apps! 2:43 PM My question was on the definition of the crowd in the wisdom of crowds we acsess in the service model?
  • 9. Exploratory dialogue detection  Problem Statement  Given an online chatting session S = {d0, d1 … …dn}, dk stands for the kth dialogue, classify dk as exploratory or non-exploratory.  Solution from learning analytics  Sociocultural discourse analysis method  Manual  High precision and low recall Category Cue phrases Challenge But if, have to respond, my view Evaluation Good example, good point Extension More links, for example Reasoning That is why, next step
  • 10. Exploratory dialogue classification Explorator Explorator y y Dialog Discourse ue Non- Classifier Explorator y  Dialogue is represented by a feature vector.  {I think she is right}{I, think, she, is, right, I- think, think-she, she-is, is-right, I-think-she, think- she-is, she-is-right}
  • 11. Exploratory dialogue classification  Instance-based supervised classifier training Explor Explor Explorato atory atory Explorator ry Classifier y Training Discourse Non- Non- Non- Classifier Explorat Explorat Explorator ory ory y  Feature-based supervised classifier training Explor Explor Explorato Explorato atory atory Feature Classifie ry Featu ry Generati re r List Discourse Non- Non- on Training Non- Classifier Explorat Explorat Explorator ory ory y
  • 12. An example of feature list Feature Exploratory Non- Exploratory what-is 0.9992 0.0008 good-point 0.9995 0.0005 your-audio- 0.001 0.999 should thank-you 0.004 0.996 my-name 0.07 0.93
  • 13. A self-training framework Annotat Classifier Classifie ed training r data  Step 1: Training initial classifier on annotated data.  Annotated data is time consuming to obtain
  • 14. A self-training framework Unlabele d data Annotat Classifier Classifie ed training r data Annotat Pseudo- Instance ed annotate Selection data d data  Step 2: Classify unlabeled data, select high confidence instances and combine them with annotated data  Step 3:Re-train classifier on the augmented training
  • 15. A self-training framework Unlabele d data Explorator Annotat Classifier Classifie y Resul ed training r Discourse ts data Detection Annotat Pseudo- Instance Test ed annotate Selection data data d data  Step 4: Obtain final classifier: No improvement on validation dataset; After a certain iteration; No class label changes.  Step 5: Detect exploratory dialogues on the test data.
  • 16. A self-training framework Unlabele d data Explorator Annotat Classifier Classifie y Resul ed training r Discourse ts data Detection Annotat Pseudo- Instance Test ed annotate Selection data data d data  Self-training will introduce noisy instance.
  • 17. KNN based Instance Selection approach  K nearest neighbors classification Blue stands for “exploratory” Gray stands for “non-exploratory” 1 nearest neighbor is “exploratory” 2 nearest neighbors is “exploratory” 5 nearest neighbors is “non- exploratory”
  • 18. KNN based Instance Selection approach Pseudo annotated instances P = {p1,p2,… …pn } pk = (lk, ck) . Lk is pseudo label, ck is confidence value Form a candidate list Choose instances with ck > r For pk in the candidate list, identify the K nearest neighbors and update the pseudo label of pk by KNN Obtain new pseudo annotated instances P- updated
  • 19. Outline  Exploratory dialogue analysis  A self-training framework  Datasets and experiments  Applications
  • 20. Data source: OU online conference  4 sessions including 2634 posts. Data in this study taken from a 2 day OU conference in Elluminate & Cloudworks:
  • 21. Annotation  2 Annotators with one morning training.  Four categories are given.  Kappa value (binary) is 0.5978 (moderate).  Only posts with the consistent labels are collected. Total# Exploratory # Non-Exploratory Session # OU_22A 529 380 149 M OU_22P 661 508 153 M OU_23A 456 310 146 M
  • 22. Experiment Setup  Baseline:  CP: Cue phrase based method  MEGE: Supervised Max Entropy GE (Generalized Expectation) approach (feature based)  ME: Supervised Max Entropy approach (instance based)  SMEGE: Self-training Max Entropy GE approach (feature based)  SME: Self-training Max Entropy approach (instance based)  Experiment Setup  Use one session as training part, one session as testing part, one session as validation  During the self-training process, examples include cue-phrase are added to training dataset at the first stage.  Pseudo samples are added with the same ratio of exploratory and non-exploratory as training dataset.  Confidence value 0.8  Feature threshold 0.65
  • 23. Evaluation Criterion  Exp Exp Exp Exp NonEx Exp p NonEx NonEx p p
  • 24. Experiment Result Approach Accuracy Precision Recall F1 Cue- 0.5389 0.9523 0.4241 0.5865 Phrase MaxEnt 0.8099 0.8526 0.8675 0.8499 MaxEntGE 0.7932 0.8817 0.8078 0.8292 Self- 0.8088 0.8331 0.9011 0.8574 training MaxEnt Self- 0.8181 0.8818 0.8406 0.8554  Cue-phrase method give high precision, but low accuracy. training  Feature-based self-training approach improve on all criteria MaxEntGE (the last row).  Instance-based self-training algorithm (4th row) perform even worse according to accuracy precision.
  • 25. Experiment Result Session MaxEnt MaxEnt- MaxEntG MaxEntG Selftrain E E- Selftrain OU_22AM 0.8190 0.8467 0.7887 0.8270 OU_22PM 0.8034 0.8311 0.7738 0.8116 OU_23AM 0.8268 0.8282 0.8114 0.8297 OU_23PM  Instance-based self-training algorithm (2nd 0.8042 0.7906 0.7294 0.7989 column) is sensitive to the initial classifier’s performance.  Feature-based self-training approach gives more stable results (the last column).
  • 26. Outline  Exploratory dialogue analysis  A self-training framework  Datasets and experiments  Applications
  • 28. Time line Visualization 80 60 40 20 0 9:28 9:32 10:13 11:48 12:00 12:04 12:05 9:36 9:40 9:41 9:46 9:50 9:53 9:56 10:00 10:05 10:07 10:07 10:09 10:17 10:23 10:27 10:31 10:35 10:40 10:45 10:52 10:55 11:04 11:08 11:11 11:17 11:20 11:24 11:26 11:28 11:31 11:32 11:35 11:36 11:38 11:39 11:41 11:44 11:46 11:52 11:54 12:03 -20 -40 1. anybody else with poor audio? 2. is anyone else Exploratorydifficulty hearing this? Average having … -60 3. background noise makes it difficult to hear 1. Sheffield, UK not as sunny as yesterday - 1. See you! still warm 2. bye for now! 2. Greetings from Hong Kong 3. bye, and thank 3. Morning from Wiltshire, sunny here! you 4. Bye all for now
  • 29. Time line Visualization Time User Id Content added to which 2M often drops to 10% of that in peak 11:46 AM User_2 80 times I really disagree - ECDL was the starting point for many 11:47 AM User_3 60 many first time users 11:47 AM User_1 40 online basics won't load in final third first 11:47 AM User_1 20 mobile won't work round her 11:47 AM User_1 0 and satlellite costs 40 a month for 1 gig data transfer 9:28 9:32 10:13 11:48 12:00 12:04 12:05 I think the issue about the skills needed to really embrace 9:36 9:40 9:41 9:46 9:50 9:53 9:56 10:00 10:05 10:07 10:07 10:09 10:17 10:23 10:27 10:31 10:35 10:40 10:45 10:52 10:55 11:04 11:08 11:11 11:17 11:20 11:24 11:26 11:28 11:31 11:32 11:35 11:36 11:38 11:39 11:41 11:44 11:46 11:52 11:54 12:03 -20 technologies is a huge one and with web 2.0 technologies -40 things are becoming more complicated, as I say often you dont just get this stuff by attending a workshop, you have Average Exploratory … -60 to participate and appropriate them to your interests and 11:47 AM User_4 context and network of others. We use myguide on mobile broadband for outreach. Works OK, but not great and thats in city centre 11:47 AM User_5 boardering 3G/GPS.
  • 30. User Visualization Contribution Distribution of Users 50 Exploratory Message Count 45 40 35 Time User Id Content 30 because although some people can 25 11:42 get 'online' the feed is so poor that 20 AM User_1 many pages won't load. eg myguide 15 how much time and money was spent 10 11:42 getting everyone to use a mobile 5 AM User_1 phone? 0 nothing. because it was perceived to 0 10 20 30 40 50 60 be useful, therefore there is no need Total Message Count time and money on to spend 11:43 digitalinclusion, until the access to the AM User_1 internet works in order to get a 2meg connection to 11:44 everyone we need fibre to the final AM User_1 third
  • 31. User Visualization Contribution Distribution of Users Time 50 User Id Content Exploratory Message Count 9:51 45 Hello Im a tutor at Saudi arabia AM 40 User_6 branch 35 9:51 30 AM Moderator hello Saudi Arabia! 25 9:51 20 AM User_6 hi 15 9:52 Welcome Ashawa - did we meet in 10 AM Moderator Kuwait a couple of years ago? 5 9:52 0 AM 0 User_6 10 20 no actually 30 40 50 60 9:52 Total Message Count AM Moderator @ashawa - maybe next time 9:52 AM User_6 yes I wish
  • 33. i i This step appears to have very good content that will provoke deeper learning i i This step appears to have some content that will provoke deeper learning i i This step appears to have little content that will provoke deeper learning
  • 34. Conclusion  We have extended our previously proposed self- training framework for exploratory discourse detection in synchronous textchat (Elluminate conference sessions).  Propose a K Nearest Neighbors algorithm based instance selection method.  Applied the proposed approach to SocialLearn platform.
  • 35. Future Work Text analytics:  Integrate KNN instance selection method into the self-training framework  Explore other features for exploratory dialogue classification: inter-dialogue features, global features.  Build a more reliable dataset for sub-category classification, challenge, evaluation, reasoning, e xtension.
  • 36. Future Work Visual analytics:  Investigate how these can be rendered most usefully for educators and learners  Investigate user feedback when deployed  Different users will appreciate different levels of detail  Purdue Signals experience suggests that complex underlying analytics should be usefully distilled into very simple feedback  But as analytics literacy grows, will users value more powerful insights?
  • 37. Acknowledgments  Thanks for the guidance and consideration of Dr. He Yulan, Dr. Simon and Dr. Rebecca.  Thanks for the consideration from all the other colleagues in Knowledge Media Institute.
  • 39. Zhongyu Wei The Chinese University of Hong Kong, Hong Kong http://www.se.cuhk.edu.hk/~zywei/ Yulan He The Open University, UK http://people.kmi.open.ac.uk/yulan/ Simon Buckingham Shum The Open University, UK http://oro.open.ac.uk/view/person/sjb72.html Rebecca Ferguson The Open University, UK http://oro.open.ac.uk/view/person/rf2656.html

Editor's Notes

  1. Here is the example in Elluminate, which is a web conference tool that supports chat along sides video, slides and presentations. Everyday, there are hundreds of materials are recoded and uploaded.
  2. In the middle panel, there are chat texts for this record. And the left one shows us all the users in the chatting room. The material here can be hours. It is very time consuming for you to read all these content. Oh, god, would you please tell me which part is critical and worthy to read? Just like this! Isn’t it wonderful if someone help you figure out which part is most important? In addition, those users who are worthy to focus.OK, that is what we want show you.