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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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      <br>
      -------- Forwarded Message --------
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            <th align="RIGHT" nowrap="nowrap" valign="BASELINE">Subject:
            </th>
            <td>CORRECTION: Second Paper Presentation - Karen Chen -
              Thursday, May 4 at noon - Room 2003</td>
          </tr>
          <tr>
            <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>
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            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>
        <p class="MsoNormal"><span style="color:#1F497D"><o:p> </o:p></span></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: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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