JLA 2015 - Special section on dataset descriptions for learning analytics

Journal for Learning Analytics:
Special section on dataset descriptions for learning analytics
http://epress.lib.uts.edu.au/journals/index.php/JL...
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IMPORTANT DATES
31 MAY 2015: Submission deadline
31 JUN 2015: Review feedback
01 SEP 2015: Camera-ready version
Fall 2015: Special section publication
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RATIONAL
Although learning analytics is increasingly being applied in education, it
is still an application area that lacks publicly available and
interoperable datasets. Though a lot of research is being conducted on
learning analytics, the community lacks a sufficient amount of open,
reusable and publicly available datasets that would allow the reproduction
and experimental evaluation of algorithms, methods and tools in the
Learning Analytics area. Given the data-centric nature of the LA domain,
availability of data is seen as a key enabler for maturing the field. While
the LAK Dataset (http://lak.linkededucation.org) has provided a an
unprecedented and publicly available resource for structured data about
Learning Analytics research - i.e. the actual research works as captured in
scholarly papers from the community - actual learning analytics data used
by such research works is either not publicly available at all, or, spread
across distributed and disparate endpoints.
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RELEVANT DATA
Here we seek to collect such datasets, i.e. data that arises from actual
learning processes in any domain (kindergarten, K12, HE, or workplace) that
is used within LA research and practice. Such datasets could originate from
formal or informal online learning environments (for instance MOOCs, LMSs,
digital games for learning, online inquiry tools or professional learning
communities); they could also be gathered from face-to-face learning
environment (for instance eye-tracking or motion capture traces). Datasets
of relevance could include data about cognitive development, social
learning, discourse progression, network interactions, learning paths
through courses, competency completion, help seeking behaviour, and
distributed multi-spaced interactions. While such primary data about
learning processes are of central importance, we are also interested in
complementary data gathered through surveys, for instance, about learner
demographics, background knowledge, goals, perceptions, experiences and
attitudes.
What kind of shared datasets are useful to advance learning analytics
research and practice? This might involve datasets that help to further
methodological development, or that are of direct utility for learning
analytics. We welcome submissions that are relevant to this broad objective
and datasets that are useful in learning analytics contexts. These might
include, but should not necessarily be limited to datasets that:
- Enrich learning analytics or educational data mining scenarios
- Help evaluation of learning analytics, educational data mining or related
tools and methods
- Provide a challenging test case for algorithmic or model development;
- Provide a scenario for visualisation techniques;
- Express data in an existing or emerging data standard, either as a
collection of reference examples or as a test case for interoperability
(i.e. to test whether sufficient meaning can be reconstructed without
knowledge of the source system);
- Specific combinations of datasets designed to test a particular theory or
hypothesis, accompanied by explanatory rationale and any research findings
already derived, to aid replication studies and theoretical development
For any dataset it is important to explain how privacy has been protected
(simulated data may be admissible, but this should be justified).
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ELIGIBILITY
Datasets need to comply with the following criteria:
- All data needs to be made available under open license terms (eg CC-BY,
Open Data License) available for reuse by third parties
- The dataset provider needs to hold all rights to share the data publicly
on the Web
- Data needs to be accessible online and preferable as a dump or via a
public HTTP-accessible API or SPARQL endpoint
- Data needs to be accessible in standardised serialisations and formats,
such as, XML, CSV, JSON or RDF. Date should be complemented with a
description of the fields, a schema file and/or vocabulary description
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SUBMISSION
Submission system & author instructions are available at
http://epress.lib.uts.edu.au/journals/index.php/JL...=
eSubmissions
In the journal submission system, the manuscripts need to be submitted to
=E2=80=9CSpecial section: Dataset Descriptions for Learning Analytics=E2=80=
=9D.
The normal journal submission template is available at
http://www.solaresearch.org/wp-content/uploads/201...=
.
All the submissions need to follow the formatting guidelines strictly and
each submission needs to be formatted (including font styles and sizes,
spacing, margins and other formatting issues) exactly as the papers
published in the journal to date.
A typical learning analytics data paper will be around 1000-1500 words in
length and should have the following sections:
- Abstract
- Introduction
- The dataset
-- Creator / Owner
-- Access details
-- Date / Version
-- Format, schema, vocabularies, codebook
-- Restrictions to use (if any)
- Provenance, extraction, maintenance
- Ethical and privacy considerations*
- Limitations
- Acknowledgements
- References (including references to the research papers with the data)
*The data paper itself gives the information about where and under what
conditions the data can be accessed. It needs to include details of the
ethical guidelines that were followed in collecting the data and that other
researchers should follow.
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PUBLICATION
Accepted dataset papers will be published in a special section of the
Journal of Learning Analytics (http://learning-analytics.info). In
addition, in order to further disseminate and encourage reuse of datasets,
we intend to set up a separate repository as part of the LAK Dataset (
http://lak.linkededucation.org), where all datasets will be cataloged and
made available according to Linked Data principles.