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                    <title classid="main title" classname="main title"
                           schemeid="dnet:dataCite_title" schemename="dnet:dataCite_title">JUSThink
                        Alignment Analysis</title>
                    <creator rank="1" name="" surname="">Norman, Utku</creator>
                    <creator rank="2" name="" surname="">Dinkar, Tanvi</creator>
                    <creator rank="3" name="" surname="">Bruno, Barbara</creator>
                    <creator rank="4" name="" surname="">Clavel, Chloé</creator>
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                    <description>
                        <p>
                            <strong>1. Description</strong>
                        </p>
                        <p>This repository contains<strong> tools to automatically analyse how
                            participants align their use of task-specific referents in their
                            dialogue and actions for a collaborative learning activity, and how
                            it relates to the task success</strong> (i.e. their learning
                            outcomes and task performance).</p>
                        <p>As a use case, it processes data from a collaborative problem solving
                            activity named JUSThink <a
                                    href="https://zenodo.org/record/4675070#references">[1, 2]</a>, i.e.
                            JUSThink Dialogue and Actions Corpus data set that is available from the
                            Zenodo Repository, DOI: <a href="http://doi.org/10.5281/zenodo.4627104"
                            >10.5281/zenodo.4627104</a>, and reproduces the results and figures
                            in <a href="https://zenodo.org/record/4675070#references">[3]</a>.</p>
                        <p>In brief: </p>
                        <ol>
                            <li><strong>JUSThink Dialogue and Actions Corpus</strong> contains
                                transcripts, event logs, and test responses of children aged 9
                                through 12, as they participate in the JUSThink activity <a
                                        href="https://zenodo.org/record/4675070#references">[1, 2]</a>
                                in pairs of two, to solve a problem on graphs together. </li>
                            <li><strong>The JUSThink activity and its study</strong> is first
                                described in <a href="https://zenodo.org/record/4675070#references"
                                >[1]</a>, and elaborated with findings concerning the link
                                between children&#39;s learning, performance in the activity, and
                                perception of self, the other and the robot in <a
                                        href="https://zenodo.org/record/4675070#references">[2]</a>. </li>
                            <li><strong>Alignment analysis in our work <a
                                    href="https://zenodo.org/record/4675070#references"
                            >[3]</a></strong> studies the participants&#39; use of
                                expressions that are related to the task at hand, their follow up
                                actions of these expressions, and how it links to task success.</li>
                        </ol>
                        <p>
                            <strong>2. Publications</strong>
                        </p>
                        <p>If you use this work in an academic context, please cite the following
                            publications:</p>
                        <ul>
                            <li>
                                <p>Norman*, U., Dinkar*, T., Bruno, B., &amp; Clavel, C. (2022).
                                    Studying Alignment in a Collaborative Learning Activity via
                                    Automatic Methods: The Link Between What We Say and Do. Dialogue
                                    &amp; Discourse, 13(2), 1 - ;48. *Contributed equally to this
                                    work. <a href="https://doi.org/10.5210/dad.2022.201"
                                    >https://doi.org/10.5210/dad.2022.201</a></p>
                            </li>
                            <li>
                                <p>Norman, U., Dinkar, T., Bruno, B., &amp; Clavel, C. (2021).
                                    JUSThink Alignment Analysis. In Dialogue &amp; Discourse
                                    (v1.0.0, Vol. 13, Number 2, pp. 1 - ;48). Zenodo. <a
                                            href="https://doi.org/10.5281/zenodo.4675070"
                                    >https://doi.org/10.5281/zenodo.4675070</a></p>
                            </li>
                        </ul>
                        <p>
                            <strong>3. Content</strong>
                        </p>
                        <p>The tools provided in this repository consists of 7 Jupyter Notebooks
                            written in Python 3, and two additional external tools utilised by the
                            notebooks.</p>
                        <p>
                            <strong>3.1. Jupyter Notebooks</strong>
                        </p>
                        <p>We highlight that the notebooks up until the last (i.e. to test the
                            hypotheses (tools/7_test_the_hypotheses.ipynb)) present a general
                            pipeline to process event logs, test responses and transcripts to
                            extract measures of task performance, learning outcomes, and measures of
                            alignment.</p>
                        <ol>
                            <li><strong>Extract task performance (and other features) from the logs
                            </strong>(tools/1_extract_performance_and_other_features_from_logs.ipynb):
                                Extracts various measures of task behaviour from the logs, at
                                varying granularities of the activity (i.e. the whole corpus, task,
                                attempt, and turn levels). In later notebooks, we focus on one of
                                the features to estimate the task performance of a team: (minimum)
                                error.</li>
                            <li><strong>Extract learning outcomes from the test responses</strong>
                                (tools/2_extract_learning_gain_from_test_responses.ipynb): Extracts
                                measures of learning outcomes from the responses to the pre-test and
                                the post-test. In later notebooks, we focus on one of the features
                                to estimate the learning outcome of a team: relative learning gain
                                <a href="https://sandbox.zenodo.org/record/742549#references"
                                >[4]</a></li>
                            <li><strong>Select and visualise a subset of teams for
                                transcription</strong>
                                (tools/3_visualise_transcribed_teams.ipynb): Visualises the
                                transcribed teams among the other teams in the feature space spanned
                                by task performance and learning outcome, as well as the
                                distribution of their number of attempts and turns.</li>
                            <li><strong>Extract routines from transcripts</strong>
                                (tools/4_extract_routines_from_transcripts.ipynb) (uses <a
                                        href="https://github.com/GuillaumeDD/dialign">dialign</a> to
                                extract routines): Extracts routines of referring expressions that
                                are &quot;fixed&quot;, i.e. become shared or established amongst
                                interlocutors.</li>
                            <li><strong>Combine transcripts with logs</strong>
                                (tools/5_construct_the_corpus_by_combining_transcripts_with_logs.ipynb):
                                Merges transcripts with event logs to have a combined dialogue and
                                actions corpus, to be processed e.g. to detect follow-up
                                actions.</li>
                            <li><strong>Recognise instructions and detect follow-up actions</strong>
                                (tools/6_recognise_instructions_detect_follow-up_actions.ipynb):
                                Extracts verbalised instruction such as &quot;connect Mount Basel to
                                Montreux&quot;, and pairs them with the follow-up action that may
                                <em>match</em> (e.g. if the other connects Basel to Montreux) or
                                <em>mismatch</em> (e.g. if the other connects Basel to
                                Neuchatel) with the instruction.</li>
                            <li><strong>Test the hypotheses </strong>in <a
                                    href="https://sandbox.zenodo.org/record/742549#references"
                            >[3]</a> (tools/7_test_the_hypotheses.ipynb) (uses
                                <strong>effsize</strong> to estimate effect size, specifically
                                Cliff&#39;s Delta): Considers each research questions and hypotheses
                                studied in <a
                                        href="https://sandbox.zenodo.org/record/742549#references"
                                >[3]</a> and generates the results in <a
                                        href="https://sandbox.zenodo.org/record/742549#references"
                                >[3]</a>.</li>
                        </ol>
                        <p>
                            <strong>3.2. External Tools</strong>
                        </p>
                        <ol>
                            <li><strong><a href="https://github.com/GuillaumeDD/dialign">dialign</a>
                                tool</strong> to extract routines, specifically <a
                                    href="https://github.com/GuillaumeDD/dialign/releases/tag/v1.0"
                            >Release 1.0</a> from <a
                                    href="https://github.com/GuillaumeDD/dialign/releases/download/v1.0/dialign-1.0.zip"
                            >dialign-1.0.zip</a>:\n It extracts routine expressions that are
                                &quot;shared&quot; among the participants from transcripts. \n It is
                                used as an external module (in accordance with its CeCILL-B License,
                                see <strong>License</strong>).</li>
                            <li><strong>effsize tool</strong> to compute estimators of effect
                                size.\n We specifically use it to compute Cliff&#39;s Delta, which
                                quantifies the amount difference between two groups of observations,
                                by computing the Cliff&#39;s Delta statistic.\n It is taken from
                                project <a
                                        href="https://acclab.github.io/DABEST-python-docs/index.html"
                                >DABEST</a> (see <strong>License</strong>).</li>
                        </ol>
                        <p>
                            <strong>4. Research Questions and Hypotheses in <a
                                    href="https://sandbox.zenodo.org/record/742549#references"
                            >[3]</a></strong>
                        </p>
                        <ul>
                            <li><strong>RQ1 Lexical alignment</strong>: How do the interlocutors
                                <em>use</em> expressions related to the task? Is this associated
                                with task success? <ul>
                                    <li><strong>H1.1</strong>: Task-specific referents become
                                        routine early for more successful teams.</li>
                                    <li><strong>H1.2</strong>: Hesitation phenomena are more likely
                                        to occur in the vicinity of priming and establishment of
                                        task-specific referents for more successful teams.</li>
                                </ul>
                            </li>
                            <li><strong>RQ2 Behavioural alignment</strong>: How do the interlocutors
                                <em>follow up</em> these expressions with actions? Is this
                                associated with task success? <ul>
                                    <li><strong>H2.1</strong>: Instructions are more likely to be
                                        followed by a corresponding action early in the dialogue for
                                        more successful teams.</li>
                                    <li><strong>H2.2</strong>: When instructions are followed by a
                                        corresponding or a different action, the action is more
                                        likely to be in the vicinity of information management
                                        phenomena for more successful teams.</li>
                                </ul>
                            </li>
                        </ul>
                        <p>The RQs and Hs are addressed in the notebook for testing the hypotheses
                            (i.e. tools/7_test_the_hypotheses.ipynb).</p>
                        <p>
                            <strong>Acknowledgements</strong>
                        </p>
                        <p>This project has received funding from the European Union&#39;s Horizon
                            2020 research and innovation programme under grant agreement No 765955.
                            Namely, the <a href="https://www.animatas.eu/">ANIMATAS Project</a>.</p>
                        <p>
                            <strong>License</strong>
                        </p>
                        <p>The whole package is under MIT License, see the <strong>LICENSE</strong>
                            file.</p>
                        <p>Classes under the <strong>tools/effsize</strong> package were taken from
                            project <a href="https://acclab.github.io/DABEST-python-docs/index.html"
                            ><strong>DABEST</strong></a>, Copyright 2016-2020 Joses W. Ho.
                            These classes are licensed under the BSD 3-Clause Clear License. See
                            <strong>tools/effsize/LICENSE</strong> file for additional
                            details.</p>
                        <p>Classes under the <strong>tools/dialign-1.0</strong> package were taken
                            from project <strong><a href="https://github.com/GuillaumeDD/dialign"
                            >dialign</a></strong>. These classes are licensed under the
                            CeCILL-B License. This package is used as an &quot;external
                            module&quot;, see<strong> tools/dialign-1.0/LICENSE.txt</strong> for
                            additional details.</p>
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