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                <creator rank="2" name="Tanvi" surname="Dinkar">Dinkar, Tanvi</creator>
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                <description>&amp;lt;strong>1. Description&amp;lt;/strong> This repository
                    contains&amp;lt;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&amp;lt;/strong> (i.e. their learning outcomes and task performance). As
                    a use case, it processes data from a collaborative problem solving activity
                    named JUSThink [1, 2], i.e. JUSThink Dialogue and Actions Corpus data set that
                    is available from the Zenodo Repository, DOI: 10.5281/zenodo.4627104, and
                    reproduces the results and figures in [3]. In brief: &amp;lt;strong>JUSThink
                    Dialogue and Actions Corpus&amp;lt;/strong> contains transcripts, event logs,
                    and test responses of children aged 9 through 12, as they participate in the
                    JUSThink activity [1, 2] in pairs of two, to solve a problem on graphs together.
                    &amp;lt;strong>The JUSThink activity and its study&amp;lt;/strong> is first
                    described in [1], and elaborated with findings concerning the link between
                    children's learning, performance in the activity, and perception of self, the
                    other and the robot in [2]. &amp;lt;strong>Alignment analysis in our work
                    [3]&amp;lt;/strong> studies the participants' 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. &amp;lt;strong>Changes in Release
                    v1.1.0:&amp;lt;/strong> updated with the publication information, finalized
                    paper structure, research questions and hypotheses as in the published article:
                    U. Norman*&amp;lt;em>, &amp;lt;/em>T. Dinkar*, B. Bruno, and C. Clavel,
                    "Studying Alignment in a Collaborative Learning Activity via Automatic Methods:
                    The Link Between What We Say and Do," Dialogue &amp;amp;amp; Discourse, 13(2),
                    1–48. *Contributed equally to this work. 10.5210/dad.2022.201.
                    &amp;lt;strong>Full Changelog:&amp;lt;/strong>
                    https://github.com/chili-epfl/justhink-alignment-analysis/compare/v1.0.0...v1.1.0
                    &amp;lt;strong>2. Publications&amp;lt;/strong> If you use this work in an
                    academic context, please cite the following publications: Norman*, U., Dinkar*,
                    T., Bruno, B., &amp;amp;amp; Clavel, C. (2022). Studying Alignment in a
                    Collaborative Learning Activity via Automatic Methods: The Link Between What We
                    Say and Do. Dialogue &amp;amp;amp; Discourse, 13(2), 1–48. *Contributed equally
                    to this work. https://doi.org/10.5210/dad.2022.201 Norman, U., Dinkar, T.,
                    Bruno, B., &amp;amp;amp; Clavel, C. (2021). JUSThink Alignment Analysis. In
                    Dialogue &amp;amp;amp; Discourse (v1.1.0, Vol. 13, Number 2, pp. 1–48). Zenodo.
                    https://doi.org/10.5281/zenodo.6974562 &amp;lt;strong>3. Content&amp;lt;/strong>
                    The tools provided in this repository consists of 7 Jupyter Notebooks written in
                    Python 3, and two additional external tools utilised by the notebooks.
                    &amp;lt;strong>3.1. Jupyter Notebooks&amp;lt;/strong> 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. &amp;lt;strong>Extract task
                    performance (and other features) from the logs
                    &amp;lt;/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. &amp;lt;strong>Extract learning
                    outcomes from the test responses&amp;lt;/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 [4] &amp;lt;strong>Select and visualise a subset of
                    teams for transcription&amp;lt;/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. &amp;lt;strong>Extract routines from transcripts&amp;lt;/strong>
                    (tools/4_extract_routines_from_transcripts.ipynb) (uses dialign to extract
                    routines): Extracts routines of referring expressions that are "fixed", i.e.
                    become shared or established amongst interlocutors. &amp;lt;strong>Combine
                    transcripts with logs&amp;lt;/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. &amp;lt;strong>Recognise
                    instructions and detect follow-up actions&amp;lt;/strong>
                    (tools/6_recognise_instructions_detect_follow-up_actions.ipynb): Extracts
                    verbalised instruction such as "connect Mount Basel to Montreux", and pairs them
                    with the follow-up action that may &amp;lt;em>match&amp;lt;/em> (e.g. if the
                    other connects Basel to Montreux) or &amp;lt;em>mismatch&amp;lt;/em> (e.g. if
                    the other connects Basel to Neuchatel) with the instruction. &amp;lt;strong>Test
                    the hypotheses &amp;lt;/strong>in [3] (tools/7_test_the_hypotheses.ipynb) (uses
                    &amp;lt;strong>effsize&amp;lt;/strong> to estimate effect size, specifically
                    Cliff's Delta): Considers each research questions and hypotheses studied in [3]
                    and generates the results in [3]. &amp;lt;strong>3.2. External
                    Tools&amp;lt;/strong> &amp;lt;strong>dialign tool&amp;lt;/strong> to extract
                    routines, specifically Release 1.0 from dialign-1.0.zip:&amp;lt;br> It extracts
                    routine expressions that are "shared" among the participants from transcripts.
                    &amp;lt;br> It is used as an external module (in accordance with its CeCILL-B
                    License, see &amp;lt;strong>License&amp;lt;/strong>). &amp;lt;strong>effsize
                    tool&amp;lt;/strong> to compute estimators of effect size.&amp;lt;br> We
                    specifically use it to compute Cliff's Delta, which quantifies the amount
                    difference between two groups of observations, by computing the Cliff's Delta
                    statistic.&amp;lt;br> It is taken from project DABEST (see
                    &amp;lt;strong>License&amp;lt;/strong>). &amp;lt;strong>4. Research Questions
                    and Hypotheses in [3]&amp;lt;/strong> &amp;lt;strong>RQ1 Lexical
                    alignment&amp;lt;/strong>: How do the interlocutors &amp;lt;em>use&amp;lt;/em>
                    expressions related to the task? Is this associated with task success?
                    &amp;lt;strong>H1.1&amp;lt;/strong>: Task-specific referents become routine
                    early for more successful teams. &amp;lt;strong>H1.2&amp;lt;/strong>: Hesitation
                    phenomena are more likely to occur in the vicinity of priming and establishment
                    of task-specific referents for more successful teams. &amp;lt;strong>RQ2
                    Behavioural alignment&amp;lt;/strong>: How do the interlocutors
                    &amp;lt;em>follow up&amp;lt;/em> these expressions with actions? Is this
                    associated with task success? &amp;lt;strong>H2.1&amp;lt;/strong>: Instructions
                    are more likely to be followed by a corresponding action early in the dialogue
                    for more successful teams. &amp;lt;strong>H2.2&amp;lt;/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. The RQs and Hs are addressed in the notebook for testing
                    the hypotheses (i.e. tools/7_test_the_hypotheses.ipynb).
                    &amp;lt;strong>Acknowledgements&amp;lt;/strong> This project has received
                    funding from the European Union's Horizon 2020 research and innovation programme
                    under grant agreement No 765955. Namely, the ANIMATAS Project.
                    &amp;lt;strong>License&amp;lt;/strong> The whole package is under MIT License,
                    see the &amp;lt;strong>LICENSE&amp;lt;/strong> file. Classes under the
                    &amp;lt;strong>tools/effsize&amp;lt;/strong> package were taken from project
                    &amp;lt;strong>DABEST&amp;lt;/strong>, Copyright 2016-2020 Joses W. Ho. These
                    classes are licensed under the BSD 3-Clause Clear License. See
                    &amp;lt;strong>tools/effsize/LICENSE&amp;lt;/strong> file for additional
                    details. Classes under the &amp;lt;strong>tools/dialign-1.0&amp;lt;/strong>
                    package were taken from project &amp;lt;strong>dialign&amp;lt;/strong>. These
                    classes are licensed under the CeCILL-B License. This package is used as an
                    "external module", see&amp;lt;strong>
                    tools/dialign-1.0/LICENSE.txt&amp;lt;/strong> for additional
                    details.</description>
                <description>{"references": ["[1] J. Nasir, U. Norman, B. Bruno, and P. Dillenbourg,
                    \"You Tell, I Do, and We Swap until we Connect All the Gold Mines!,\" ERCIM
                    News, vol. 2020, no. 120, 2020, [Online]. Available:
                    https://ercim-news.ercim.eu/en120/special/you-tell-i-do-and-we-swap-until-we-connect-all-the-gold-mines",
                    "[2] J. Nasir*, U. Norman*, B. Bruno, and P. Dillenbourg, \"When Positive
                    Perception of the Robot Has No Effect on Learning,\" in 2020 29th IEEE
                    International Conference on Robot and Human Interactive Communication (RO-MAN),
                    Aug. 2020, pp. 313\u2013320, doi: 10.1109/RO-MAN47096.2020.9223343", "[3] U.
                    Norman*, T. Dinkar*, B. Bruno, and C. Clavel, \"Studying Alignment in a
                    Collaborative Learning Activity via Automatic Methods: The Link Between What We
                    Say and Do,\" Dialogue &amp;amp;amp; Discourse, vol. 13, no. 2, pp. 1\u201348,
                    Aug. 2022, doi: 10.5210/dad.2022.201.", "[4] M. Sangin, G. Molinari, M.-A.
                    N\u00fcssli, and P. Dillenbourg, \"Facilitating peer knowledge modeling: Effects
                    of a knowledge awareness tool on collaborative learning outcomes and
                    processes,\"\" Computers in Human Behavior, vol. 27, no. 3, pp. 1059\u20131067,
                    May 2011, doi: 10.1016/j.chb.2010.05.032."]}</description>
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