430 lines
33 KiB
XML
430 lines
33 KiB
XML
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provenanceaction="sysimport:crosswalk:repository" trust="0.9">JUSThink Alignment
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Analysis</title>
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schemename="dnet:access_modes"/>
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<creator rank="1" name="Utku" surname="Norman" orcid_pending="0000-0002-6802-1444"
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>Norman, Utku</creator>
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<creator rank="2" name="Tanvi" surname="Dinkar">Dinkar, Tanvi</creator>
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<creator rank="3" name="Barbara" surname="Bruno" orcid_pending="0000-0003-0953-7173"
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>Bruno, Barbara</creator>
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<creator rank="4" name="Chloé" surname="Clavel" orcid_pending="0000-0003-4850-3398"
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>Clavel, Chloé</creator>
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<dateofacceptance>2022-08-08</dateofacceptance>
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<description>&lt;strong>1. Description&lt;/strong> This repository
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contains&lt;strong> tools to automatically analyse how participants align
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their use of task-specific referents in their dialogue and actions for a
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collaborative learning activity, and how it relates to the task
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success&lt;/strong> (i.e. their learning outcomes and task performance). As
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a use case, it processes data from a collaborative problem solving activity
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named JUSThink [1, 2], i.e. JUSThink Dialogue and Actions Corpus data set that
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is available from the Zenodo Repository, DOI: 10.5281/zenodo.4627104, and
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reproduces the results and figures in [3]. In brief: &lt;strong>JUSThink
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Dialogue and Actions Corpus&lt;/strong> contains transcripts, event logs,
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and test responses of children aged 9 through 12, as they participate in the
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JUSThink activity [1, 2] in pairs of two, to solve a problem on graphs together.
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&lt;strong>The JUSThink activity and its study&lt;/strong> is first
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described in [1], and elaborated with findings concerning the link between
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children's learning, performance in the activity, and perception of self, the
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other and the robot in [2]. &lt;strong>Alignment analysis in our work
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[3]&lt;/strong> studies the participants' use of expressions that are
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related to the task at hand, their follow up actions of these expressions, and
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how it links to task success. &lt;strong>Changes in Release
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v1.1.0:&lt;/strong> updated with the publication information, finalized
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paper structure, research questions and hypotheses as in the published article:
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U. Norman*&lt;em>, &lt;/em>T. Dinkar*, B. Bruno, and C. Clavel,
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"Studying Alignment in a Collaborative Learning Activity via Automatic Methods:
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The Link Between What We Say and Do," Dialogue &amp;amp; Discourse, 13(2),
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1–48. *Contributed equally to this work. 10.5210/dad.2022.201.
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&lt;strong>Full Changelog:&lt;/strong>
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https://github.com/chili-epfl/justhink-alignment-analysis/compare/v1.0.0...v1.1.0
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&lt;strong>2. Publications&lt;/strong> If you use this work in an
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academic context, please cite the following publications: Norman*, U., Dinkar*,
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T., Bruno, B., &amp;amp; Clavel, C. (2022). Studying Alignment in a
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Collaborative Learning Activity via Automatic Methods: The Link Between What We
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Say and Do. Dialogue &amp;amp; Discourse, 13(2), 1–48. *Contributed equally
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to this work. https://doi.org/10.5210/dad.2022.201 Norman, U., Dinkar, T.,
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Bruno, B., &amp;amp; Clavel, C. (2021). JUSThink Alignment Analysis. In
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Dialogue &amp;amp; Discourse (v1.1.0, Vol. 13, Number 2, pp. 1–48). Zenodo.
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https://doi.org/10.5281/zenodo.6974562 &lt;strong>3. Content&lt;/strong>
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The tools provided in this repository consists of 7 Jupyter Notebooks written in
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Python 3, and two additional external tools utilised by the notebooks.
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&lt;strong>3.1. Jupyter Notebooks&lt;/strong> We highlight that the
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notebooks up until the last (i.e. to test the hypotheses
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(tools/7_test_the_hypotheses.ipynb)) present a general pipeline to process event
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logs, test responses and transcripts to extract measures of task performance,
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learning outcomes, and measures of alignment. &lt;strong>Extract task
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performance (and other features) from the logs
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&lt;/strong>(tools/1_extract_performance_and_other_features_from_logs.ipynb):
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Extracts various measures of task behaviour from the logs, at varying
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granularities of the activity (i.e. the whole corpus, task, attempt, and turn
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levels). In later notebooks, we focus on one of the features to estimate the
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task performance of a team: (minimum) error. &lt;strong>Extract learning
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outcomes from the test responses&lt;/strong>
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(tools/2_extract_learning_gain_from_test_responses.ipynb): Extracts measures of
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learning outcomes from the responses to the pre-test and the post-test. In later
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notebooks, we focus on one of the features to estimate the learning outcome of a
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team: relative learning gain [4] &lt;strong>Select and visualise a subset of
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teams for transcription&lt;/strong>
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(tools/3_visualise_transcribed_teams.ipynb): Visualises the transcribed teams
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among the other teams in the feature space spanned by task performance and
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learning outcome, as well as the distribution of their number of attempts and
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turns. &lt;strong>Extract routines from transcripts&lt;/strong>
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(tools/4_extract_routines_from_transcripts.ipynb) (uses dialign to extract
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routines): Extracts routines of referring expressions that are "fixed", i.e.
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become shared or established amongst interlocutors. &lt;strong>Combine
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transcripts with logs&lt;/strong>
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(tools/5_construct_the_corpus_by_combining_transcripts_with_logs.ipynb): Merges
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transcripts with event logs to have a combined dialogue and actions corpus, to
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be processed e.g. to detect follow-up actions. &lt;strong>Recognise
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instructions and detect follow-up actions&lt;/strong>
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(tools/6_recognise_instructions_detect_follow-up_actions.ipynb): Extracts
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verbalised instruction such as "connect Mount Basel to Montreux", and pairs them
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with the follow-up action that may &lt;em>match&lt;/em> (e.g. if the
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other connects Basel to Montreux) or &lt;em>mismatch&lt;/em> (e.g. if
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the other connects Basel to Neuchatel) with the instruction. &lt;strong>Test
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the hypotheses &lt;/strong>in [3] (tools/7_test_the_hypotheses.ipynb) (uses
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&lt;strong>effsize&lt;/strong> to estimate effect size, specifically
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Cliff's Delta): Considers each research questions and hypotheses studied in [3]
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and generates the results in [3]. &lt;strong>3.2. External
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Tools&lt;/strong> &lt;strong>dialign tool&lt;/strong> to extract
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routines, specifically Release 1.0 from dialign-1.0.zip:&lt;br> It extracts
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routine expressions that are "shared" among the participants from transcripts.
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&lt;br> It is used as an external module (in accordance with its CeCILL-B
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License, see &lt;strong>License&lt;/strong>). &lt;strong>effsize
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tool&lt;/strong> to compute estimators of effect size.&lt;br> We
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specifically use it to compute Cliff's Delta, which quantifies the amount
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difference between two groups of observations, by computing the Cliff's Delta
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statistic.&lt;br> It is taken from project DABEST (see
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&lt;strong>License&lt;/strong>). &lt;strong>4. Research Questions
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and Hypotheses in [3]&lt;/strong> &lt;strong>RQ1 Lexical
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alignment&lt;/strong>: How do the interlocutors &lt;em>use&lt;/em>
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expressions related to the task? Is this associated with task success?
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&lt;strong>H1.1&lt;/strong>: Task-specific referents become routine
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early for more successful teams. &lt;strong>H1.2&lt;/strong>: Hesitation
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phenomena are more likely to occur in the vicinity of priming and establishment
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of task-specific referents for more successful teams. &lt;strong>RQ2
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Behavioural alignment&lt;/strong>: How do the interlocutors
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&lt;em>follow up&lt;/em> these expressions with actions? Is this
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associated with task success? &lt;strong>H2.1&lt;/strong>: Instructions
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are more likely to be followed by a corresponding action early in the dialogue
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for more successful teams. &lt;strong>H2.2&lt;/strong>: When
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instructions are followed by a corresponding or a different action, the action
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is more likely to be in the vicinity of information management phenomena for
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more successful teams. The RQs and Hs are addressed in the notebook for testing
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the hypotheses (i.e. tools/7_test_the_hypotheses.ipynb).
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&lt;strong>Acknowledgements&lt;/strong> This project has received
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funding from the European Union's Horizon 2020 research and innovation programme
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under grant agreement No 765955. Namely, the ANIMATAS Project.
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&lt;strong>License&lt;/strong> The whole package is under MIT License,
|
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see the &lt;strong>LICENSE&lt;/strong> file. Classes under the
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&lt;strong>tools/effsize&lt;/strong> package were taken from project
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&lt;strong>DABEST&lt;/strong>, Copyright 2016-2020 Joses W. Ho. These
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classes are licensed under the BSD 3-Clause Clear License. See
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&lt;strong>tools/effsize/LICENSE&lt;/strong> file for additional
|
||
details. Classes under the &lt;strong>tools/dialign-1.0&lt;/strong>
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||
package were taken from project &lt;strong>dialign&lt;/strong>. These
|
||
classes are licensed under the CeCILL-B License. This package is used as an
|
||
"external module", see&lt;strong>
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tools/dialign-1.0/LICENSE.txt&lt;/strong> for additional
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details.</description>
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<description>{"references": ["[1] J. Nasir, U. Norman, B. Bruno, and P. Dillenbourg,
|
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\"You Tell, I Do, and We Swap until we Connect All the Gold Mines!,\" ERCIM
|
||
News, vol. 2020, no. 120, 2020, [Online]. Available:
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https://ercim-news.ercim.eu/en120/special/you-tell-i-do-and-we-swap-until-we-connect-all-the-gold-mines",
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"[2] J. Nasir*, U. Norman*, B. Bruno, and P. Dillenbourg, \"When Positive
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Perception of the Robot Has No Effect on Learning,\" in 2020 29th IEEE
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International Conference on Robot and Human Interactive Communication (RO-MAN),
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Aug. 2020, pp. 313\u2013320, doi: 10.1109/RO-MAN47096.2020.9223343", "[3] U.
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Norman*, T. Dinkar*, B. Bruno, and C. Clavel, \"Studying Alignment in a
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Collaborative Learning Activity via Automatic Methods: The Link Between What We
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Say and Do,\" Dialogue &amp;amp; Discourse, vol. 13, no. 2, pp. 1\u201348,
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Aug. 2022, doi: 10.5210/dad.2022.201.", "[4] M. Sangin, G. Molinari, M.-A.
|
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N\u00fcssli, and P. Dillenbourg, \"Facilitating peer knowledge modeling: Effects
|
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of a knowledge awareness tool on collaborative learning outcomes and
|
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processes,\"\" Computers in Human Behavior, vol. 27, no. 3, pp. 1059\u20131067,
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May 2011, doi: 10.1016/j.chb.2010.05.032."]}</description>
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Alignment in a Collaborative Learning Activity via Automatic Methods:
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The Link Between What We Say and Do</title>
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