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Team,<br>
<br>
Karen will be presenting her qualifier work at Heinz college this
Thursday.<br>
Please join if you can.<br>
<br>
Thanks<br>
Artur<br>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">Subject:
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<td>CORRECTION: Second Paper Presentation - Karen Chen -
Thursday, May 4 at noon - Room 2003</td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">Date: </th>
<td>Fri, 28 Apr 2017 18:59:15 +0000</td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">From: </th>
<td>Michelle Wirtz <a class="moz-txt-link-rfc2396E" href="mailto:mwirtz@andrew.cmu.edu"><mwirtz@andrew.cmu.edu></a></td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">To: </th>
<td><a class="moz-txt-link-abbreviated" href="mailto:Heinz-phd@lists.andrew.cmu.edu">Heinz-phd@lists.andrew.cmu.edu</a>
<a class="moz-txt-link-rfc2396E" href="mailto:Heinz-phd@lists.andrew.cmu.edu"><Heinz-phd@lists.andrew.cmu.edu></a>,
<a class="moz-txt-link-abbreviated" href="mailto:heinz-faculty@lists.andrew.cmu.edu">heinz-faculty@lists.andrew.cmu.edu</a>
<a class="moz-txt-link-rfc2396E" href="mailto:heinz-faculty@lists.andrew.cmu.edu"><heinz-faculty@lists.andrew.cmu.edu></a>, Amy Ogan
<a class="moz-txt-link-rfc2396E" href="mailto:aeo@andrew.cmu.edu"><aeo@andrew.cmu.edu></a></td>
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<p class="MsoNormal"
style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span
style="font-size:12.0pt;font-family:"Times New
Roman","serif"">Hi all,<o:p></o:p></span></p>
<p class="MsoNormal"
style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span
style="font-size:12.0pt;font-family:"Times New
Roman","serif"">Please join us on Thursday,
May 4, 2017 in Hamburg Hall Room 2003 at noon when Karen
Chen will be presenting her second paper. <o:p></o:p></span></p>
<p class="MsoPlainText"><b><span
style="font-size:14.0pt;font-family:"Times New
Roman","serif"">Title:</span></b><span
style="font-size:14.0pt;font-family:"Times New
Roman","serif"">
</span><span style="font-size:12.0pt;font-family:"Times
New Roman","serif"">Peek into the Black Box:
A Multimodal Analysis Framework for Automatic
Characterization of the One-on-one Tutoring Processes<o:p></o:p></span></p>
<p class="MsoPlainText"><span
style="font-size:14.0pt;font-family:"Times New
Roman","serif""><br>
<b>Committee: </b></span><span
style="font-size:12.0pt;font-family:"Times New
Roman","serif"">Artur Dubrawski (chair),
Daniel Nagin and Amy Ogen (HCII,SCS)</span><o:p></o:p></p>
<p class="MsoNormal"><span
style="font-size:12.0pt;font-family:"Times New
Roman","serif""><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><b><span
style="font-size:14.0pt;font-family:"Times New
Roman","serif"">Abstract:<o:p></o:p></span></b></p>
<p class="MsoPlainText"><span
style="font-size:12.0pt;font-family:"Times New
Roman","serif"">Student-teacher interactions
during the one-on-one tutoring processes are rich forms of
inter-personal communications with significant educational
impact. An ideal teacher is able to pick up student's subtle
signals in real time and respond optimally to offer
cognitive and emotional support. However, until recently,
the characterization of this information rich process has
relied upon human observations which do not scale well. In
this study, I made an attempt to automate the
characterization process by leveraging the recent advances
in affective computing and multi-modal machine learning
techniques. I analyzed a series of video recordings of math
problem solving sessions by a young student under support of
his tutor, demonstrating a multimodal analysis framework to
characterize several aspects of the student-teacher
interaction patterns at a fine-grained temporal resolution.
I then build machine learning models to predict teacher's
response using extracted multi-modal features. In addition,
I validate the performance of automatic detector of affect,
intent-to-connect behavior, and voice activity, using
annotated data, which provides evidence of the potential
utility of the presented tools in scaling up analysis of
this type to large number of subjects and in implementing
decision support tools to guide teachers towards optimal
intervention in real time.<o:p></o:p></span></p>
<p class="MsoPlainText"><span style="font-family:"Times New
Roman","serif""><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span
style="font-size:12.0pt;font-family:"Times New
Roman","serif"">Paper:</span></b><span
style="font-size:12.0pt;font-family:"Times New
Roman","serif"">
</span><a moz-do-not-send="true"
href="https://drive.google.com/open?id=0B8SWduW_x8gYcnN6YkhZSDA3WE0">https://drive.google.com/open?id=0B8SWduW_x8gYcnN6YkhZSDA3WE0</a><o:p></o:p></p>
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<p class="MsoNormal"><span
style="font-size:12.0pt;font-family:"Times New
Roman","serif""><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><b><span
style="font-size:12.0pt;font-family:"Times New
Roman","serif""><o:p> </o:p></span></b></p>
<p class="MsoNormal"><span
style="font-size:14.0pt;font-family:"Times New
Roman","serif""><o:p> </o:p></span></p>
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