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Hi all,
Join us for the 13th International Learning Analytics and
Knowledge Conference, March 13-27, 2023! We are very excited to be
offering LAK23 as hybrid experience with in-person events in
Arlington, TX, USA and virtual or streamed events being shared
online with all LAK23 participants.
LAK23 Homepage:
https://www.solaresearch.org/events/lak/lak23/
GENERAL CALL
The 2023 edition of The International Conference on Learning
Analytics & Knowledge (LAK23) will take place in Arlington,
Texas, USA. LAK23 is organized by the Society for Learning
Analytics Research (SoLAR) with location hosts from University of
Texas at Arlington. LAK23 is a collaborative effort by learning
analytics researchers and practitioners to share the most rigorous
scientific work in learning analytics
The theme for the 13th annual LAK conference is Toward Trustworthy
Learning Analytics. The growth and development of the learning
analytics field has been fuelled through increased access to data
and the subsequent development of analytical models designed to
predict outcomes, establish recommendations or bring novel
insights into the learning process. Yet the implementation of
learning analytics impinges on social and educational concerns
such as privacy, fairness, and development of learner autonomy.
The application of learning analytics must consider how developed
models can lead to the reinforcement, identification or prevention
of bias. Ongoing work into data and algorithmic transparency can
help inform how end users interpret and enact LA information and
recommendations. There is further work to be undertaken by
researchers and practitioners to fully examine the impact of data
and algorithms including: potential misuse and mis-interpretation;
influence on society and education systems; ethics; privacy;
transparency; and accountability to move toward a responsible
education system that is established on a foundation of trust.
The LAK conference is intended for both researchers and
practitioners. We invite both researchers and practitioners of
learning analytics to join a proactive dialogue around the future
of learning analytics and its practical adoption, to develop and
transfer key knowledge to design, interpret and act on learning
analytics results. We further extend our invite to educators,
leaders, administrators, government and industry professionals
interested in the field of learning analytics and its related
disciplines.
Authors should note that:
· SoLAR recognizes the importance of open, accessible,
reproducible, repeatable, and replicable data and analyses
approaches. SoLAR also recognizes a diversity of epistemological,
ethical, and legal challenges and opportunities which such
approaches face.
· The LAK conference has received a CORE ranking of A (top 16% of
all 783 ranked venues).
· LAK is the only conference in the top 12 Google Scholar citation
ranks for educational technology publications.
CONFERENCE THEME AND TOPICS
We welcome submissions from both research and practice,
encompassing different theoretical, methodological, empirical and
technical contributions to the learning analytics field. Learning
analytics research draws on many distinct academic fields,
including psychology, the learning sciences, education,
neuroscience, computer science and design. We encourage the
submission of works conducted in any of these traditions. We also
welcome research that validates, replicates and examines the
generalizability of previously published findings, as well as
examines aspects of adoption of existing learning analytics
methods and approaches.
This year, we encourage contributors to consider how collective
action can tackle concerns and issues associated with the
implementation of learning analytics. Learning analytics impacts
on both technical and social systems. We invite papers that
address areas of bias, privacy, ethics, transparency and
accountability from multiple lenses including the design,
implementation and evaluation stages of learning analytics.
Accountable analysis refers to providing a certain degree of
transparency and explanation, and adjusting the transparency of
data and computation according to the differences of stakeholders.
Trust goes hand in hand with transparency in decision-making;
whether the decisions for predictions and interventions are fair
and explainable is an ethical issue. There is still much to be
done in human behavior and social values, such as respecting
privacy, providing equal opportunities, and accountability. Based
on diversity, equity, and belonging, inclusive learning analytics
identifies and breaks down systemic barriers to inclusion, fosters
a culture that every learner knows their belonging, feels
empowered to bring their whole self to learning, and is inspired
to learn.
For the 13th Annual conference, we encourage authors to address
the following questions related to LAK23's theme of "Towards
Trustworthy Learning Analytics:
· What are the essential components of building a trustworthy LA
system?
· How do we give diverse stakeholders a voice in defining what
will make LA trustworthy?
· How can we develop and evaluate instruments or frameworks for
measuring the trustworthiness of a LA system?
· Is there anything distinctive about trustworthiness in teaching
and learning or can we borrow unproblematically from notions of
trustworthiness from other fields?
· How can we develop models or frameworks that can measure the
fairness, bias, transparency or explainability level of a LA
system?
· How do we develop human-in-the-loop predictive or prescriptive
analytics that benefit from instructor judgement?
· How can we enable students or instructors to share their
perceptions of the level of trustworthiness of a LA system?
· How can we reliably and transparently model student
competencies?
Other topics of interest include, but are not limited to, the
following:
Implementing Change in Learning & Teaching:
· Ethical issues around learning analytics: Analysis of issues and
approaches to the lawful and ethical capture and use of
educational data traces; tackling unintended bias and value
judgements in the selection of data and algorithms; perspectives
and methods that empower stakeholders.
· Learning analytics adoption: Discussions and evaluations of
strategies to promote and embed learning analytics initiatives in
educational institutions and learning organizations. Studies that
examine processes of organizational change and practices of
professional development that support impactful learning analytics
use.
· Learning analytics strategies for scalability: Discussions and
evaluations of strategies to scale capture and analysis of
information in useful and ethical ways at the program, institution
or national level; critical reflections on organizational
structures that promote analytics innovation and impact in an
institution.
· Equity, fairness and transparency in learning analytics:
Consideration of how certain practices of data collection,
analysis and subsequent action impact particular populations and
affect human well-being, specifically groups that experience long
term disadvantage. Discussions of how learning analytics may
impact (positively or negatively) social change and transformative
social justice.
Understanding Learning & Teaching:
· Data-informed learning theories: Proposals of new
learning/teaching theories or revisions/reinterpretations of
existing theories based on large-scale data analysis.
· Insights into specific learning processes: Studies to understand
particular aspects of a learning/teaching process through the use
of data science techniques, including negative results.
· Learning and teaching modeling: Creating mathematical,
statistical or computational models of a learning/teaching
process, including its actors and context.
· Systematic reviews: Studies that provide a systematic and
methodological synthesis of the available evidence in an area of
learning analytics.
Evidencing Learning & Teaching:
· Finding evidence of learning: Studies that identify and explain
useful data for analysing, understanding and optimising learning
and teaching.
· Assessing student learning: Studies that assess learning
progress through the computational analysis of learner actions or
artefacts.
· Analytical and methodological approaches: Studies that introduce
novel analytical techniques, methods, and tools for modelling
student learning.
· Technological infrastructures for data storage and sharing:
Proposals of technical and methodological procedures to store,
share and preserve learning and teaching traces, taking
appropriate ethical considerations into account.
Impacting Learning & Teaching:
· Human-centered design processes: Research that documents
practices of giving an active voice to learners, teachers, and
other educational stakeholders in the design process of learning
analytics initiatives and enabling technologies.
· Providing decision support and feedback: Studies that evaluate
the use and impact of feedback or decision-support systems based
on learning analytics (dashboards, early-alert systems, automated
messages, etc.).
· Data-informed decision-making: Studies that examine how
teachers, students or other educational stakeholders come to, work
with and make changes using learning analytics information.
· Personalised and adaptive learning: Studies that evaluate the
effectiveness and impact of adaptive technologies based on
learning analytics.
· Practical evaluations of learning analytics efforts: Empirical
evidence about the effectiveness of learning analytics
implementations or educational initiatives guided by learning
analytics.
CONFERENCE TRACKS
The conference has three different tracks with distinct types of
submissions that are described below. Please see the submission
guidelines page for information on paper format and other
technical details of submission for each track.
1. RESEARCH TRACK
The focus of the research track is on advancing scholarly
knowledge in the field of learning analytics through rigorous
reports of learning analytics research studies. The primary
audience includes academics, research scientists, doctoral
students, postdoctoral researchers and other types of educational
research staff working in different capacities on learning
analytics research projects.
Submission types for the research track are similar to other
years, starting for LAK21, LAK follows ACM’s one column format for
submissions. Templates and formatting details are included in the
submission guidelines. Please note that published Proceedings will
appear in ACM two column format.
· Full research papers (up to 16 pages in ACM 1 column format,
including references) include a clearly explained substantial
conceptual, technical or empirical contribution to learning
analytics. The scope of the paper must be placed appropriately
with respect to the current state of the field, and the
contribution should be clearly described. This includes the
conceptual or theoretical aspects at the foundation of the
contribution, an explanation of the technical setting (tools used,
how are they integrated into the contribution), analysis, and
results. See bulleted list of questions above for more detailed
ideas on useful elements to include.
· Short research papers (up to 10 pages in ACM 1 column format,
including references) can address on-going work, which may include
a briefly described theoretical underpinning, an initial proposal
or rationale for a technical solution, and preliminary results,
with consideration of stakeholder engagement issues. See bulleted
list of questions above for more detailed ideas on useful elements
to include.
NOTE: If you are a newcomer to the LAK conference, it might be
helpful to review the LAK22 ACM proceedings, openly available from
the SoLAR website via ACM’s OpenTOC service. For Tips on writing
LAK papers see here.
Should you have further questions regarding paper length or
format, please contact us at
lakconference@gmail.com
2. PRACTITIONER AND CORPORATE LEARNING ANALYTICS TRACK
The Practitioner and Corporate Learning Analytics (PaC-LA) track
is complementary to the research track as part of the main
conference program and provides a way in which real-world learning
analytics implementations and/or related tools, products, product
development and researched-based product evaluations in use by
practitioners can be shared with the entire community. The intent
of the stream is to contribute to our collective understanding of
learning analytics in practice, including product development and
improvement, researched-based product evaluations, learning
analytics deployment, intervention development and evaluation.
Specifically, some of the goals of PaC-LA presentations are to:
· contribute to the conversation between researchers and
practitioners around adoption, implementation, scaling and
evaluation of learning analytics,
· provide insights from practice around factors affording or
constraining learning analytics adoption and implementation,
· present effective learning analytics adoption strategies and
approaches, and
· share experiences on developing a business case for learning
analytics adoption.
To meet these goals, submissions are encouraged to reflect on the
context and purpose of the presented learning analytics
initiative, discuss implementation, outcomes, impacts, and
learning, and consider implications for others attempting similar
work. We also encourage submissions where an initiative did not
achieve what was expected, as we believe that such papers can also
provide valuable knowledge to the community.
We welcome submissions that fall in the scope described above from
anyone regardless of their professional roles. Some examples of
PaC-LA participants are:
· Developers, designers, analysts, and other representatives from
commercial and industry entities, non-profit organizations, and
government bodies.
· Policy makers, department leads, instructional technologists,
analysts, learning designers and other services staff from
education institutions
Successful submissions are expected to offer unique or distinct
insights into practical applications, intervention designs,
analyses, and/or the processes surrounding their implementation.
There is also special interest to explore the growing role of
learning analytics in corporate learning, including the skills
development of employees, alternative credentialing models,
reliance on non-traditional education providers, and the impact of
using data to guide corporate learning programs.
While submissions are not formal research papers, the more
complete the report of the work is, including usage of the
learning analytics and their impact, the higher the probability of
being selected for inclusion. Further, while the stream is
intended for non-researchers, papers are still expected to adhere
to high standards of scholarly writing, including:
· thorough description of the institutional context for the work
· detailed presentation of the innovation and the results found
about it
· discussion of issues that arose / lessons learned / implications
for future efforts by others attempting similar work
The following criteria will guide reviewers when selecting
submissions, although we recognise that this list may not be
applicable to all submissions. Authors are encouraged to consider
the following when preparing their submissions:
· Learning/education related: The submission should describe work
that addresses learning/academic analytics, either at an
educational institution or in an area (such as corporate training,
health care or informal learning) where the goal is to improve the
learning environment or professional learning outcomes.
· Implementation track record: The project should have been used
by an institution or have been deployed in a learning site. There
are no hard guidelines about user numbers or how long the project
has been running.
· Stakeholder involvement: All submissions should include
information collected from people who have used the tool or
initiative in a learning environment (such as faculty, students,
administrators and trainees).
· Overall quality, including potential interest and value for LAK
attendees: Project success (or failure) accounts are encouraged,
but a focus must be placed on what the community of other
practitioners and researchers can gain from learning about the
work. What was successful (and why)? What was unsuccessful (and
why)?
· No sales pitches: While submissions from commercial suppliers
are welcomed, reviewers will not accept overt (or covert) sales
pitches. Reviewers will look for evidence that the presentation
will take into account challenges faced, problems that have
arisen, and/or user feedback that needs to be addressed.
There is a single submission type for the PaC-LA track that has a
special format emphasizing practical aspects of project
implementations rather than a research paper format:
· PaC-LA Presentation Reports (2-4 page document, using the SoLAR
companion proceedings template) should include accounts and
findings that stem from practical experience in implementing
learning analytics projects. The report gives PaC-LA authors a
channel for sharing: the background of why the a) project was
implemented and/or b) product was developed; data and the design
process that drove the development of the project or product;
details about how the project or product has been implemented in a
real-world environment; findings from the project or product
implementation and its significance, including a reflection on the
importance of the reported initiatives in your paper to the
broader LAK community. See bulleted lists above for more detailed
ideas on useful elements to include and consider in crafting a
submission.
All accepted submissions to the PaC-LA track will be published in
the LAK23 Companion Proceedings and archived on the SoLAR website.
3. POSTERS AND DEMOS
· Posters (3 pages, SoLAR companion proceedings template)
represent i) a concise report of recent findings or other types of
innovative work not ready to be submitted as a full or short
research paper or ii) a description of a practical learning
analytics project implementation which may not be ready to be
presented as a practitioner report. Poster presentations are part
of the LAK Poster & Demo session, and authors are given a
physical board or virtual space to present and discuss their
projects with delegates.
· Interactive demos (200 words abstract in SoLAR companion
proceedings template + 5 min video) provide opportunities to
showcase interactive learning analytics tools. Interactive
demonstrations are part of the LAK Poster & Demo session, and
presenters are given a (virtual) space to demonstrate their latest
learning analytics projects, tools, and systems. Demos should be
used to communicate innovative user interface designs,
visualisations, or other novel functionality that tackles a real
user problem. Tools may be prototypes in an early stage of
development or relatively mature products. In whichever stage,
tools should have been field-tested with an authentic use case and
provide some results and feedback. Submissions for conceptual
products or for products that have not been used by instructors
and/or students are unlikely to be accepted.
4. PRE-CONFERENCE EVENT TRACK
The focus of pre-conference events is on providing space for new
and emerging ideas in learning analytics and their further
development. Events can have either research or practical focus
and can be structured in the way which best serves their
particular purpose.
The types of submissions for the pre-conference event track are:
· Workshops (4 pages, SoLAR companion proceedings template)
provide an efficient forum for community building, sharing of
perspectives, training, and idea generation for specific and
emerging research topics or viewpoints. Successful proposals
should be explicit regarding the kind of activities participants
should expect, for example from interactive/generative
participatory sessions to mini-conference or symposium sessions.
· Tutorials (4 pages, SoLAR companion proceedings template) aim to
educate stakeholders on a specific learning analytics topic and/or
stakeholder perspective. Proposals should be clear about what the
need is for particular knowledge, target audience and their prior
knowledge, and the intended learning outcomes.
REVIEW PROCESS
LAK23 will use a double-blind peer review process for all
submissions except demos and the doctoral consortium (which each
require elements that prevent blinding). To continue to strengthen
the review process for both authors and reviewers LAK23 will have
a rebuttal phase for full and short research papers in which
authors will be given five days to respond to remarks and comments
raised by reviewers in a maximum of 500 words. Rebuttals are
optional, and there is no requirement to respond. Authors should
keep in mind that papers are being evaluated as submitted and
thus, responses should not propose new results or restructuring of
the presentation. Therefore, rebuttals should focus on answering
specific questions raised by reviewers (if any) and providing
clarifications and justifications to reviewers. Meta-reviewers,
senior members of the research community, make final
recommendations for paper acceptance or rejection with
justification to the program committee chairs after the rebuttal
phase is concluded. Acceptance decisions are ultimately taken by
the program committee chairs based on all available information
from the review process in combination with the constraints of the
allowable space in the conference program.
Finally, please note that the conference timeline allows for
rejected submissions to be re-submitted in revised form as poster,
demo and workshop papers.
PROCEEDINGS PUBLICATION
Accepted full and short research papers will be included in the
LAK23 conference proceedings published and archived by ACM. Other
types of submissions (posters, demos, workshops, tutorials,
practitioner reports and doctoral consortium) will be included in
the open access LAK companion proceedings, published on SoLAR’s
website. Please note at least one of the authors of each accepted
submission must register for the conference by the Early Bird
deadline in order for the paper to be included in the ACM or LAK
Companion Proceedings.
IMPORTANT DATES FOR LAK23
Full / Short Research Papers
· 3 Oct 2022: Deadline for submission
· 7 Nov 2022: Rebuttal submissions open
· 14 Nov 2022: Deadline for rebuttal submissions
· 2 Dec 2022: Notification of acceptance
· 12 Dec 2022: Deadline for camera-ready versions of all accepted
full and short research papers
Practitioner Reports
· 3 Oct 2022: Deadline for submission
· 2 Dec 2022: Notification of acceptance
· 19 Dec 2022: Deadline for camera-ready versions of practitioner
reports
Posters / Demos
· 16 Dec 2022: Deadline for poster and interactive demo
submissions
· 13 Jan 2023: Notification of acceptance for posters/demos and
papers submitted to individual workshops
· 30 Jan 2023: Deadline for camera-ready versions of posters/demos
Doctoral Consortium
· 17 Oct 2022: Deadline for submission to doctoral consortium
· 2 Dec 2022: Notification of acceptance
· 19 Dec 2022: Deadline for camera-ready versions of all accepted
papers
Workshops / Tutorials
· 3 Oct 2022: Deadline for submission to organize
workshops/tutorials
· 20 Oct 2022: Notification of acceptance for workshop/tutorial
organization
· 16 Dec 2022: Deadline for submission of papers to individual
workshops that issue calls**
· 13 Jan 2023: Notification of acceptance for posters/demos and
papers submitted to individual workshops**
· 30 Jan 2023: Deadline for camera-ready versions of
workshop/tutorial organizer docs and any individual papers**
accepted by workshops
**Workshop Paper Submissions - this term refers to papers
submitted to be presented within an accepted LAK pre-conference
workshop. Many LAK workshops are mini-symposium style and issue
calls for papers. Please visit the pre-conference schedule when
available to view which workshops have CFP’s that you may submit
to.
Conference and registration dates:
· 14 Jan 2023: Early-bird registration closes at 11:59pm PST
· 13-17 March 2023: LAK23 conference, Arlington Texas
For continuous updates, please check the LAK23 website as more
information becomes available. We look forward to hosting you
in-person or online for another edition of the International
Learning Analytics and Knowledge Conference. If you have any
questions, please email
lakconference@gmail.com.
We are looking forward to seeing you at LAK23!!
Kind regards,
Organizing Committee of LAK23
https://www.solaresearch.org/events/lak/lak23/
Society for Learning Analytics Research (SoLAR)
https://solaresearch.org/
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