dimitris.pierrakos
/
dnet-hadoop
forked from D-Net/dnet-hadoop
1
0
Fork 0
Code Pull Requests Activity

tests for EOSC Future

This commit is contained in:
Alessia Bardi 2022-11-23 12:18:44 +01:00
parent 0e3edc5018
commit 90c8f9cb61
3 changed files with 748 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
dhp-workflows/dhp-graph-provision/src/test/java/eu/dnetlib/dhp/oa/provision/IndexRecordTransformerTest.java
View File

@ -128,6 +128,20 @@ public class IndexRecordTransformerTest {
testRecordTransformation(record); testRecordTransformation(record);
} }
@Test
public void testForEOSCFutureSoftwareNotebook() throws IOException, TransformerException {
final String record = IOUtils
.toString(getClass().getResourceAsStream("eosc-future/software-justthink.xml"));
testRecordTransformation(record);
}
@Test
public void testForEOSCFutureSoftwareNotebookClaim() throws IOException, TransformerException {
final String record = IOUtils
.toString(getClass().getResourceAsStream("eosc-future/software-justthink-claim.xml"));
testRecordTransformation(record);
}
@Test @Test
void testDoiUrlNormalization() throws MalformedURLException { void testDoiUrlNormalization() throws MalformedURLException {

305
dhp-workflows/dhp-graph-provision/src/test/resources/eu/dnetlib/dhp/oa/provision/eosc-future/software-justthink-claim.xml Normal file
View File

@ -0,0 +1,305 @@
<record>
<result xmlns:dri="http://www.driver-repository.eu/namespace/dri">
<header xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<dri:objIdentifier>od______2659::3801993ea8f970cfc991277160edf277</dri:objIdentifier>
<dri:dateOfCollection>2022-08-08T03:06:13Z</dri:dateOfCollection>
<status>under curation</status>
<counters/>
</header>
<metadata>
<oaf:entity xmlns:oaf="http://namespace.openaire.eu/oaf"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://namespace.openaire.eu/oaf https://www.openaire.eu/schema/1.0/oaf-1.0.xsd">
<oaf:result>
<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>
<dateofacceptance/>
<resulttype classid="software" classname="software"
schemeid="dnet:result_typologies" schemename="dnet:result_typologies"/>
<language classid="eng" classname="English" schemeid="dnet:languages"
schemename="dnet:languages"/>
<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>
</description>
<country classid="" classname="" schemeid="" schemename=""/>
<subject classid="" classname="" schemeid="" schemename=""/>
<relevantdate classid="" classname="" schemeid="" schemename=""/>
<publisher>Zenodo</publisher>
<embargoenddate/>
<journal issn="" eissn="" lissn="" ep="" iss="" sp="" vol=""/>
<source/>
<fulltext/>
<format/>
<storagedate/>
<resourcetype classid="" classname="" schemeid="" schemename=""/>
<device/>
<size/>
<version/>
<lastmetadataupdate/>
<metadataversionnumber/>
<documentationUrl/>
<codeRepositoryUrl/>
<programmingLanguage classid="" classname="" schemeid="" schemename=""/>
<contactperson/>
<contactgroup/>
<tool/>
<originalId>oai:zenodo.org:4675070</originalId>
<collectedfrom name="ZENODO" id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<pid classid="oai" classname="Open Archives Initiative"
schemeid="dnet:pid_types" schemename="dnet:pid_types"
>oai:zenodo.org:4675070</pid>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types"
>10.5281/zenodo.4675070</pid>
<bestaccessright classid="OPEN" classname="Open Access"
schemeid="dnet:access_modes" schemename="dnet:access_modes"/>
<eoscifguidelines code="EOSC::Jupyter Notebook" label="EOSC::Jupyter Notebook"
url="" semanticrelation="compliesWith"/>
<datainfo>
<inferred>false</inferred>
<deletedbyinference>false</deletedbyinference>
<trust>0.9</trust>
<inferenceprovenance/>
<provenanceaction classid="user:insert" classname="user:insert"
schemeid="dnet:provenanceActions" schemename="dnet:provenanceActions"/>
</datainfo>
<rels>
<rel inferred="false" trust="0.9" inferenceprovenance=""
provenanceaction="user:claim">
<to class="isProducedBy" scheme="dnet:result_project_relations"
type="project">corda__h2020::c4515ebef538a734cf11f795347f5dac</to>
<code>765955</code>
<acronym>ANIMATAS</acronym>
<title>Advancing intuitive human-machine interaction with human-like
social capabilities for education in schools</title>
<contracttype classid="" classname="" schemeid="" schemename=""/>
<funding>
<funder id="ec__________::EC" shortname="EC"
name="European Commission" jurisdiction=""/>
<funding_level_0 name="H2020"
>ec__________::EC::H2020</funding_level_0>
</funding>
<websiteurl/>
</rel>
</rels>
<children>
<instance id="od______2659::3801993ea8f970cfc991277160edf277">
<instancetype classid="0029" classname="Software"
schemeid="dnet:publication_resource"
schemename="dnet:publication_resource"/>
<collectedfrom name="ZENODO"
id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<hostedby name="ZENODO"
id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<accessright classid="OPEN" classname="Open Access"
schemeid="dnet:access_modes" schemename="dnet:access_modes"/>
<dateofacceptance/>
<webresource>
<url>https://zenodo.org/record/4675070</url>
</webresource>
</instance>
</children>
</oaf:result>
</oaf:entity>
</metadata>
</result>
</record>

429
dhp-workflows/dhp-graph-provision/src/test/resources/eu/dnetlib/dhp/oa/provision/eosc-future/software-justthink.xml Normal file
View File

@ -0,0 +1,429 @@
<record>
<result xmlns:dri="http://www.driver-repository.eu/namespace/dri">
<header xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<dri:objIdentifier>doi_dedup___::c054151b6a8c4f41c7acf160651a6503</dri:objIdentifier>
<dri:dateOfCollection>2022-10-13T00:15:44+0000</dri:dateOfCollection>
<dri:dateOfTransformation>2022-10-13T07:44:29.152Z</dri:dateOfTransformation>
</header>
<metadata>
<oaf:entity xmlns:oaf="http://namespace.openaire.eu/oaf"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://namespace.openaire.eu/oaf https://www.openaire.eu/schema/1.0/oaf-1.0.xsd">
<oaf:result>
<collectedfrom name="ZENODO" id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<collectedfrom name="Datacite" id="openaire____::9e3be59865b2c1c335d32dae2fe7b254"/>
<originalId>oai:zenodo.org:4675070</originalId>
<originalId>50|od______2659::3801993ea8f970cfc991277160edf277</originalId>
<originalId>oai:zenodo.org:6974562</originalId>
<originalId>50|od______2659::9c87ff4a5e7710052b873088e7265072</originalId>
<originalId>10.5281/zenodo.4675069</originalId>
<pid classid="doi" classname="Digital Object Identifier" schemeid="dnet:pid_types"
schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>10.5281/zenodo.4675070</pid>
<pid classid="doi" classname="Digital Object Identifier" schemeid="dnet:pid_types"
schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>10.5281/zenodo.6974562</pid>
<pid classid="doi" classname="Digital Object Identifier" schemeid="dnet:pid_types"
schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:actionset" trust="0.9">10.5281/zenodo.4675069</pid>
<measure id="influence" score="4.916186E-9" class="C5"/>
<measure id="popularity" score="6.885733E-9" class="C5"/>
<measure id="influence_alt" score="0" class="C5"/>
<measure id="popularity_alt" score="0.0" class="C5"/>
<measure id="impulse" score="0" class="C5"/>
<title classid="main title" classname="main title" schemeid="dnet:dataCite_title"
schemename="dnet:dataCite_title" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9">JUSThink Alignment
Analysis</title>
<bestaccessright classid="OPEN" classname="Open Access" schemeid="dnet:access_modes"
schemename="dnet:access_modes"/>
<creator rank="1" name="Utku" surname="Norman" orcid_pending="0000-0002-6802-1444"
>Norman, Utku</creator>
<creator rank="2" name="Tanvi" surname="Dinkar">Dinkar, Tanvi</creator>
<creator rank="3" name="Barbara" surname="Bruno" orcid_pending="0000-0003-0953-7173"
>Bruno, Barbara</creator>
<creator rank="4" name="Chloé" surname="Clavel" orcid_pending="0000-0003-4850-3398"
>Clavel, Chloé</creator>
<dateofacceptance>2022-08-08</dateofacceptance>
<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>
<subject classid="keyword" classname="keyword"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>alignment</subject>
<subject classid="keyword" classname="keyword"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9">situated
dialogue</subject>
<subject classid="keyword" classname="keyword"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9">collaborative
learning</subject>
<subject classid="keyword" classname="keyword"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9">spontaneous
speech</subject>
<subject classid="keyword" classname="keyword"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>disfluency</subject>
<subject classid="keyword" classname="keyword"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9">mutual
understanding</subject>
<language classid="eng" classname="English" schemeid="dnet:languages"
schemename="dnet:languages"/>
<relevantdate classid="issued" classname="issued" schemeid="dnet:dataCite_date"
schemename="dnet:dataCite_date" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>2021-04-09</relevantdate>
<relevantdate classid="issued" classname="issued" schemeid="dnet:dataCite_date"
schemename="dnet:dataCite_date" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>2022-08-08</relevantdate>
<publisher>Zenodo</publisher>
<resulttype classid="software" classname="software"
schemeid="dnet:result_typologies" schemename="dnet:result_typologies"/>
<resourcetype classid="UNKNOWN" classname="UNKNOWN"
schemeid="dnet:dataCite_resource" schemename="dnet:dataCite_resource"/>
<programmingLanguage/>
<context id="EC" label="European Commission" type="funding">
<category id="EC::H2020" label="Horizon 2020 Framework Programme">
<concept id="EC::H2020::MSCA-ITN-ETN" label="European Training Networks"/>
</category>
</context>
<eoscifguidelines code="EOSC::Jupyter Notebook"
label="EOSC::Jupyter Notebook"
url=""
semanticrelation="compliesWith"/>
<datainfo>
<inferred>true</inferred>
<deletedbyinference>false</deletedbyinference>
<trust>0.8</trust>
<inferenceprovenance>dedup-result-decisiontree-v3</inferenceprovenance>
<provenanceaction classid="sysimport:dedup" classname="Inferred by OpenAIRE"
schemeid="dnet:provenanceActions" schemename="dnet:provenanceActions"/>
</datainfo>
<rels>
<rel inferred="false" trust="0.9" inferenceprovenance=""
provenanceaction="sysimport:actionset">
<to class="IsSupplementTo" scheme="dnet:result_result_relations"
type="publication">doi_dedup___::ae235765bbc422195a6c9f632b2d77eb</to>
<collectedfrom name="arXiv.org e-Print Archive"
id="opendoar____::6f4922f45568161a8cdf4ad2299f6d23"/>
<pid classid="arXiv" classname="arXiv" schemeid="dnet:pid_types"
schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>2104.04429</pid>
<collectedfrom name="Infoscience - EPFL scientific publications"
id="opendoar____::eecca5b6365d9607ee5a9d336962c534"/>
<publisher>arXiv</publisher>
<collectedfrom name="Crossref"
id="openaire____::081b82f96300b6a6e3d282bad31cb6e2"/>
<dateofacceptance>2022-08-05</dateofacceptance>
<title classid="main title" classname="main title"
schemeid="dnet:dataCite_title" schemename="dnet:dataCite_title">Studying
Alignment in a Collaborative Learning Activity via Automatic Methods:
The Link Between What We Say and Do</title>
<collectedfrom name="ORCID"
id="openaire____::806360c771262b4d6770e7cdf04b5c5a"/>
<collectedfrom name="Datacite"
id="openaire____::9e3be59865b2c1c335d32dae2fe7b254"/>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:actionset" trust="0.9"
>10.48550/arxiv.2104.04429</pid>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types"
>10.5210/dad.2022.201</pid>
</rel>
<rel inferred="false" trust="0.9" inferenceprovenance=""
provenanceaction="sysimport:actionset">
<to class="isProducedBy" scheme="dnet:result_project_relations"
type="project">corda__h2020::c4515ebef538a734cf11f795347f5dac</to>
<title>Advancing intuitive human-machine interaction with human-like social
capabilities for education in schools</title>
<code>765955</code>
<funding>
<funder id="ec__________::EC" shortname="EC" name="European Commission"
jurisdiction="EU"/>
<funding_level_0 name="H2020">ec__________::EC::H2020</funding_level_0>
<funding_level_1 name="MSCA-ITN-ETN"
>ec__________::EC::H2020::MSCA-ITN-ETN</funding_level_1>
</funding>
<acronym>ANIMATAS</acronym>
</rel>
<rel inferred="false" trust="0.9" inferenceprovenance=""
provenanceaction="sysimport:actionset">
<to class="IsSupplementedBy" scheme="dnet:result_result_relations"
type="dataset">doi_dedup___::0a6314b0ed275d915f5b57a259375691</to>
<dateofacceptance>2021-03-22</dateofacceptance>
<publisher>Zenodo</publisher>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>10.5281/zenodo.4627104</pid>
<title classid="main title" classname="main title"
schemeid="dnet:dataCite_title" schemename="dnet:dataCite_title"
inferred="false" provenanceaction="sysimport:crosswalk:repository"
trust="0.9">JUSThink Dialogue and Actions Corpus</title>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:actionset" trust="0.9"
>10.5281/zenodo.4627103</pid>
<collectedfrom name="ZENODO"
id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<collectedfrom name="Datacite"
id="openaire____::9e3be59865b2c1c335d32dae2fe7b254"/>
</rel>
</rels>
<children>
<result objidentifier="doi_________::c054151b6a8c4f41c7acf160651a6503">
<publisher>Zenodo</publisher>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>10.5281/zenodo.4675070</pid>
<title classid="main title" classname="main title"
schemeid="dnet:dataCite_title" schemename="dnet:dataCite_title"
inferred="false" provenanceaction="sysimport:crosswalk:repository"
trust="0.9">JUSThink Alignment Analysis</title>
<dateofacceptance>2021-04-09</dateofacceptance>
<collectedfrom name="ZENODO"
id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
</result>
<result objidentifier="doi_________::04aaa160a921cafdc90e03483de0a26f">
<dateofacceptance>2022-08-08</dateofacceptance>
<publisher>Zenodo</publisher>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>10.5281/zenodo.6974562</pid>
<collectedfrom name="ZENODO"
id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<title classid="main title" classname="main title"
schemeid="dnet:dataCite_title" schemename="dnet:dataCite_title"
inferred="false" provenanceaction="sysimport:crosswalk:repository"
trust="0.9">JUSThink Alignment Analysis (v1.1.0)</title>
</result>
<result objidentifier="doi_________::684a8fbe0ff09f288e9d29db897233bb">
<title classid="main title" classname="main title"
schemeid="dnet:dataCite_title" schemename="dnet:dataCite_title">JUSThink
Alignment Analysis (v1.1.0)</title>
<dateofacceptance>2022-08-08</dateofacceptance>
<publisher>Zenodo</publisher>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:actionset" trust="0.9"
>10.5281/zenodo.4675069</pid>
<collectedfrom name="Datacite"
id="openaire____::9e3be59865b2c1c335d32dae2fe7b254"/>
</result>
<instance>
<accessright classid="OPEN" classname="Open Access"
schemeid="dnet:access_modes" schemename="dnet:access_modes"/>
<collectedfrom name="Datacite"
id="openaire____::9e3be59865b2c1c335d32dae2fe7b254"/>
<hostedby name="ZENODO" id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<dateofacceptance>2022-08-08</dateofacceptance>
<instancetype classid="0029" classname="Software"
schemeid="dnet:publication_resource"
schemename="dnet:publication_resource"/>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:actionset" trust="0.9"
>10.5281/zenodo.4675069</pid>
<refereed classid="0000" classname="UNKNOWN" schemeid="dnet:review_levels"
schemename="dnet:review_levels"/>
<license>https://opensource.org/licenses/MIT</license>
<webresource>
<url>https://doi.org/10.5281/zenodo.4675069</url>
</webresource>
</instance>
<instance>
<accessright classid="OPEN" classname="Open Access"
schemeid="dnet:access_modes" schemename="dnet:access_modes"/>
<collectedfrom name="ZENODO"
id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<hostedby name="ZENODO" id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<dateofacceptance>2022-08-08</dateofacceptance>
<instancetype classid="0029" classname="Software"
schemeid="dnet:publication_resource"
schemename="dnet:publication_resource"/>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>10.5281/zenodo.6974562</pid>
<refereed classid="0000" classname="UNKNOWN" schemeid="dnet:review_levels"
schemename="dnet:review_levels"/>
<license>https://opensource.org/licenses/MIT</license>
<webresource>
<url>https://doi.org/10.5281/zenodo.6974562</url>
</webresource>
</instance>
<instance>
<accessright classid="OPEN" classname="Open Access"
schemeid="dnet:access_modes" schemename="dnet:access_modes"/>
<collectedfrom name="ZENODO"
id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<hostedby name="ZENODO" id="opendoar____::358aee4cc897452c00244351e4d91f69"/>
<dateofacceptance>2021-04-09</dateofacceptance>
<instancetype classid="0029" classname="Software"
schemeid="dnet:publication_resource"
schemename="dnet:publication_resource"/>
<pid classid="doi" classname="Digital Object Identifier"
schemeid="dnet:pid_types" schemename="dnet:pid_types" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
>10.5281/zenodo.4675070</pid>
<refereed classid="0000" classname="UNKNOWN" schemeid="dnet:review_levels"
schemename="dnet:review_levels"/>
<license>https://opensource.org/licenses/MIT</license>
<webresource>
<url>https://doi.org/10.5281/zenodo.4675070</url>
</webresource>
</instance>
</children>
</oaf:result>
</oaf:entity>
</metadata>
</result>
</record>