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  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>Automated CSCL Group Assessment: Activity Theory based Computational Method</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Wanli Xing</string-name>
          <email>wxdg5@mail.missouri.edu</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Sean Goggins</string-name>
          <email>Gogginss@missouri.edu</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>University of Missouri</institution>
          ,
          <country country="US">USA</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>This workshop paper describes automated assessment of CSCL groups based on activity theory. In contrast to our prior work, the algorithms and analysis presented here focus entirely at the small group unit of analysis. The work presented is exploratory and preliminary in nature. It is our hope to refine it through participation in this workshop.</p>
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>1. INTRODUCTION</title>
      <p>
        It is known that assessment can strongly affect learning [
        <xref ref-type="bibr" rid="ref1">1</xref>
        ].
Interestingly, though several studies [
        <xref ref-type="bibr" rid="ref2 ref3 ref4">2, 3, 4</xref>
        ] have extensively
studied the theoretical and methodological positions in CSCL,
assessment of collaborative learning is still an implicit issue [
        <xref ref-type="bibr" rid="ref5">5</xref>
        ].
Group learning is a foundational concept in CSCL, but a good
deal of assessment continues to focus on the individual. Even
group assessment remains largely summative in nature [
        <xref ref-type="bibr" rid="ref3 ref6">3, 6</xref>
        ]. For
example, after a review of 186 articles [
        <xref ref-type="bibr" rid="ref6">6</xref>
        ], it was determined that
the most common practice for group assessment was to evaluate
collaboration products including many types of assignments and
feedback mixed with self-report questionnaires and interviews.
These assessments are usually administered after the
collaboration, which fundamentally undermines the theoretical
constructs of CSCL. After all, ‘ongoing’ and ‘real-time’ are key
characteristics of the core concepts of CSCL [
        <xref ref-type="bibr" rid="ref7 ref8">7,8</xref>
        ]. Assessment
methods using collaboration products or ex post facto
questionnaires/interviews were unable to study change on longer
time scales [15] and also disconnected from the instructional
setting [
        <xref ref-type="bibr" rid="ref3">3</xref>
        ].
      </p>
      <p>
        Essentially, without an established way to assess group
performance and learning during collaboration, research
communities are diverging in their identification of indicators of
group performance. According to Gress, et al [
        <xref ref-type="bibr" rid="ref6">6</xref>
        ], there are
roughly four possible directions for group assessment and more
than 70 measurements used as to assess successful collaboration,
tools usage, social interaction and communication, and group
knowledge and meaning construction. The diversity of views on
measuring group learning indicates that experimentation and
validation of different approaches is required. Such research must
be systematic, as the problem space is not clearly bounded, and
the approaches are varied. From this, a fundamental question is
raised: how could we systematically assess group performance in
CSCL?
In response to this basic question, we approach group assessment
in CSCL through the lens of activity theory, from which we
holistically frame group process, interaction dynamics in the
activity system. In addition, a quantification model of activity
theory is built based on the electronic trace data generated by the
students for the group level assessment. As a result, the whole
process could be easily automated to release the teachers’
assessment burdens. This paper is organized as follows: We first
describe the theoretical framework that guided this study. Then
the VMT background and dataset is described. Last, we propose
the research methodology and results.
      </p>
    </sec>
    <sec id="sec-2">
      <title>2. THEORETICAL FRAMEWORK</title>
      <p>
        The Activity System model developed by Engestrom [
        <xref ref-type="bibr" rid="ref9">9</xref>
        ] offers a
way to comprehensively frame collaborative knowledge
development process while linking together social behavior and
its interdependencies [
        <xref ref-type="bibr" rid="ref10 ref11">10, 11</xref>
        ]. To illustrate, an activity system
provides three characteristics for analyzing learning in group work
[
        <xref ref-type="bibr" rid="ref12">12</xref>
        ]: activity theory focuses on contextuality and is oriented
towards comprehending systematically group dynamics, objects,
mediating artifacts and social organization; it also relies on
dialogical theory of knowledge and thinking (language and
communication) with a concentration in human cognition; further
it is a developmental theory that aims to explain changes in
human practices over time.
      </p>
      <p>
        Figure 1 - Activity Theory analysis in Group
A model of the structure of an activity system was formulated by
[
        <xref ref-type="bibr" rid="ref9">9</xref>
        ], and includes the interacting components namely: subjects,
tools, rules, community and division of labor (see Fig. 1). The
activity of group learning is “the joint activity of a student,
physical/symbolic tool(s), and another person(s) performing
together as a working social system to achieve some outcome
under constraints such as rules.” In our CSCL group assessment
context, the outcome and process of this transformation may both
be seen as learning and knowledge. It is the sum of the system
components and the tensions among them that make up the
learning and knowledge construction and influence the learning
outcomes. Current assessment practices of group performance
from various learning theories often address part of the activity of
the learning system, focusing only on process, context, cognition
or artifacts or mix of two or three. Activity theory helps us to
address the complex interactions and see into group performance
in the socio-technical CSCL environment (see Table 1).
Complete learning tasks together such as solving
a problem or producing an artifact (e.g. essays)
Students involve and participate in the learning
activity. When assessing group performance,
effort, motivation, relationships, group
composition etc. should be taken into account
Computers, online tools, systems, and
environments that mediate the learning and
collaboration activity
Direct and indirect communication enables the
group of students to maintain a sense of
community and belongings.
      </p>
      <p>Implicit and explicit rules and guidelines that
constrain the activity. For example, teachers can
set specific rules for a learning task (explicit)
and an students can only use the functions
residing in the supporting tools or bounded by
social established norms (implicit)</p>
      <sec id="sec-2-1">
        <title>Division of</title>
      </sec>
      <sec id="sec-2-2">
        <title>Labor</title>
        <p>Coordination between group members in the
overall object.</p>
      </sec>
    </sec>
    <sec id="sec-3">
      <title>3. METHODOLOGY</title>
    </sec>
    <sec id="sec-4">
      <title>3.1 Research Context</title>
      <p>In this study, we operationalize activity theory as a lens for
making sense of electronic trace data from a synchronous math
discussion board, focusing on several modules of a course
designed to be taught with Virtual Math Teams with Geogebra
(VMTwG) software (Figure 2). The class is called “Dynamic
Geometry.” Each includes a team of three to four. This course
also contained different modules or sub-set problems for a group
of students to solve collaboratively. The full curriculum currently
includes a total of 18 topics, and is available at the project website
(http://vmt.mathforum.org).
in those windows, in concert with GeoGebra gestures. Sections C
and D are related to Geogebra actions. C is the “Take Control”
button mentioned previously. Section D is the GeoGebra window
itself. Students are working to create an equilateral triangle within
an equilateral triangle, and many approaches are being tried. This
is an ordinary part of how VMTwG facilitates interactive problem
solving discourse among teams.</p>
    </sec>
    <sec id="sec-5">
      <title>3.2 Dataset Description</title>
      <p>We collected all the log data for this study in .txt format, which
centers on specific event types from the CSCL environment
(VMT): Awareness, Geogebra, System, Chat, and WhiteBoard
(Wb). The Chat event type logs all the messages that students
communicate with each other. Awareness records the actions of
erasing the chat messages when the student realizes they are full
on the chat bar. Geogebra logs information on how students
visually construct a geometry artifact (e.g. add a point, or update a
segment etc.). The System event type records information on how
the VMT environment is accessed. For example, a student joins a
virtual room, leaves a virtual room or views different tabs created
by the students or teachers. Wb logs more specific actions on how
tools are being used in the white board areas such as resizing of
objects, creating a textbox, etc. For every event type, we have logs
of what action (adding a point, sending a chat, erasing a message,
or creating a text box, etc.) the student makes under what
subjects/tasks (modules and tasks) as well as the starter (source)
and receiver (Target) of those messages. In addition, the
environment logs the information about when this action takes
place (time) and in which virtual room (group) the event occurs.</p>
    </sec>
    <sec id="sec-6">
      <title>4. MEASURE CONSTRUCTION</title>
    </sec>
    <sec id="sec-7">
      <title>4.1 Subject</title>
      <p>
        Even though the individual learns as result of the group learning
and that group in CSCL, according to [
        <xref ref-type="bibr" rid="ref7">7</xref>
        ], group could only learn
by assuring the individuals learn. Subject in Activity Theory
represents the individual student efforts to the problem solving.
When mapped to our log data, it represents all endeavors that all
the individuals in that group make during the whole training under
the all modules.
      </p>
    </sec>
    <sec id="sec-8">
      <title>4.2 Rules</title>
      <p>According to Figure 5, Rules includes implicit and explicit rules.
Under the social-technological construct, the rules are the implicit
rules that constrain students’ actions. In this VMT context,
students have to perform actions that the VMT environment offers.
Therefore, the rules are reflected by the actions the student uses
across all the modules.</p>
    </sec>
    <sec id="sec-9">
      <title>4.3 Tools</title>
      <p>
        Group knowledge and meaning is speeded across people and
artefacts [
        <xref ref-type="bibr" rid="ref7">7</xref>
        ]. Tools dimension in activity theory help focus on the
process where tool facilitate the group knowledge development.
VMT tools that facilitate the learning activity. Under the VMT
context, the tools are the System and Wb where the groups action
for tool usage is registered.
      </p>
    </sec>
    <sec id="sec-10">
      <title>4.4 Community</title>
      <p>All the communications that help maintain the community
structure. In terms of the VMT context, students use chat to
directly communicate with other group members, and use the
awareness function to erase the chat messages which can be
categorized as an indirect contribution to the community.
Therefore, accumulation of these two dimensions of group
members was used to indicate the community dimension.</p>
    </sec>
    <sec id="sec-11">
      <title>4.5 Division of Labor</title>
      <p>It is a measure of how balanced the workload is shared among
team members. This dimension would have the highest value if all
the members in a group shares equally for the workload and
would have the lowest value if just one of the member takes care
of the whole problem. Therefore, our method to indicate the
balance of the work among team members, also division of labor
is based on the standard deviation of the group effort with the
perfect division.</p>
    </sec>
    <sec id="sec-12">
      <title>4.6 Object</title>
      <p>The CSCL activity is to achieve the object of a group student
active involvement in the whole class. Hence, the first factor to
consider is the number of modules the group of students
participate in. In order to quantify whether the group is active in
those learning modules, we incorporate the totally frequency of
participation and the number of event types. By doing this, we can
avoid too high of ratings for the group who participates in all the
modules but makes very few actions or contributions.
In sum, based on activity theory, we have built a quantified model
for a group performance in CSCL activities specific to the VMT
environment: [Subject, Rules, Tools, Community, Division of
Labor, Object] in Figure 5.</p>
    </sec>
    <sec id="sec-13">
      <title>5. RESULTS</title>
      <p>Group performance is represented as 6 dimension sets (after
standardization) (see the table below). By investigation into those
numbers alone, the teacher can provide specific advice to a
particular group. For example, if the value of a group in the
Community dimension is very low, the teacher could suggest for
the group to communicate more between team members. Future
work requires test this methodology in other settings.</p>
      <p>Dimension
Group</p>
      <p>Group 1</p>
    </sec>
  </body>
  <back>
    <ref-list>
      <ref id="ref1">
        <mixed-citation>
          [1]
          <string-name>
            <surname>Frederiksen</surname>
            ,
            <given-names>Norman. "</given-names>
          </string-name>
          <article-title>The real test bias: Influences of testing on teaching and learning</article-title>
          .
          <source>" American Psychologist</source>
          <volume>39</volume>
          , no.
          <issue>3</issue>
          , (
          <year>1984</year>
          ):
          <fpage>193</fpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref2">
        <mixed-citation>
          [2]
          <string-name>
            <surname>Dennen</surname>
            ,
            <given-names>Vanessa</given-names>
          </string-name>
          <string-name>
            <surname>Paz</surname>
          </string-name>
          .
          <article-title>Looking for evidence of learning: Assessment and analysis methods for online discourse</article-title>
          .
          <source>Computers in Human Behavior</source>
          <volume>24</volume>
          , no.
          <issue>2</issue>
          (
          <year>2008</year>
          ):
          <fpage>205</fpage>
          -
          <lpage>219</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref3">
        <mixed-citation>
          [3]
          <string-name>
            <surname>Strijbos</surname>
            ,
            <given-names>J-W.</given-names>
          </string-name>
          "
          <article-title>Assessment of (computer-supported) collaborative learning</article-title>
          .
          <source>" Learning Technologies, IEEE Transactions on 4, no. 1</source>
          (
          <year>2011</year>
          ):
          <fpage>59</fpage>
          -
          <lpage>73</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref4">
        <mixed-citation>
          [4]
          <string-name>
            <surname>Stahl</surname>
            , Gerry,
            <given-names>Timothy</given-names>
          </string-name>
          <string-name>
            <surname>Koschmann</surname>
            , and
            <given-names>Dan</given-names>
          </string-name>
          <string-name>
            <surname>Suthers</surname>
          </string-name>
          .
          <article-title>Computer-supported collaborative learning: An historical perspective</article-title>
          .
          <source>Cambridge handbook of the learning sciences 2006</source>
          (
          <year>2006</year>
          ).
        </mixed-citation>
      </ref>
      <ref id="ref5">
        <mixed-citation>
          [5]
          <string-name>
            <surname>Strijbos</surname>
            ,
            <given-names>J-W.</given-names>
          </string-name>
          "
          <article-title>Assessment of (computer-supported) collaborative learning</article-title>
          .
          <source>" Learning Technologies, IEEE Transactions on 4, no. 1</source>
          (
          <year>2011</year>
          ):
          <fpage>59</fpage>
          -
          <lpage>73</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref6">
        <mixed-citation>
          [6]
          <string-name>
            <surname>Gress</surname>
          </string-name>
          ,
          <string-name>
            <surname>Carmen</surname>
            <given-names>LZ</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Meghann</surname>
            <given-names>Fior</given-names>
          </string-name>
          ,
          <string-name>
            <given-names>Allyson F.</given-names>
            <surname>Hadwin</surname>
          </string-name>
          , and
          <string-name>
            <surname>Philip</surname>
            <given-names>H.</given-names>
          </string-name>
          <string-name>
            <surname>Winne</surname>
          </string-name>
          .
          <article-title>"Measurement and assessment in computersupported collaborative learning</article-title>
          .
          <source>" Computers in Human Behavior</source>
          <volume>26</volume>
          , no.
          <issue>5</issue>
          (
          <year>2010</year>
          ):
          <fpage>806</fpage>
          -
          <lpage>814</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref7">
        <mixed-citation>
          [7]
          <string-name>
            <surname>Sfard</surname>
            ,
            <given-names>Anna.</given-names>
          </string-name>
          <article-title>"On two metaphors for learning and the dangers of choosing just one." Educational researcher 27</article-title>
          , no.
          <issue>2</issue>
          (
          <year>1998</year>
          ):
          <fpage>4</fpage>
          -
          <lpage>13</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref8">
        <mixed-citation>
          [8]
          <string-name>
            <surname>Reimann</surname>
            ,
            <given-names>Peter.</given-names>
          </string-name>
          <article-title>"Time is precious: Variable-and eventcentred approaches to process analysis in CSCL research." International Journal of Computer-Supported Collaborative Learning 4</article-title>
          , no.
          <issue>3</issue>
          (
          <year>2009</year>
          ):
          <fpage>239</fpage>
          -
          <lpage>257</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref9">
        <mixed-citation>
          [9]
          <string-name>
            <surname>Leont</surname>
          </string-name>
          <article-title>'ev, Aleksei N. The problem of activity in psychology</article-title>
          .
          <source>Journal of Russian and East European Psychology</source>
          <volume>13</volume>
          , no.
          <issue>2</issue>
          (
          <year>1974</year>
          ):
          <fpage>4</fpage>
          -
          <lpage>33</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref10">
        <mixed-citation>
          [10]
          <string-name>
            <surname>Nardi</surname>
          </string-name>
          , Bonnie A., ed. Context and
          <article-title>consciousness: Activity theory and human computer interaction</article-title>
          . The MIT Press,
          <year>1996</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref11">
        <mixed-citation>
          [11]
          <string-name>
            <surname>M.F. de Laat</surname>
          </string-name>
          , “Networked Learning”,
          <source>PhD dissertation</source>
          , Utrecht Univ., the Netherlands,
          <year>2006</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref12">
        <mixed-citation>
          [12]
          <string-name>
            <surname>Duda</surname>
          </string-name>
          , Richard O.,
          <string-name>
            <surname>Peter</surname>
            <given-names>E.</given-names>
          </string-name>
          <string-name>
            <surname>Hart</surname>
            , and
            <given-names>David G.</given-names>
          </string-name>
          <string-name>
            <surname>Stork</surname>
          </string-name>
          .
          <article-title>Pattern classification</article-title>
          . John Wiley &amp; Sons,
          <year>2012</year>
          .
        </mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>