[Air-L] CFP L2D at WSDM2021: 1st Intl. Workshop on Enabling Data-Driven Decisions from Learning on the Web
Elvira Popescu
popescu_elvira at software.ucv.ro
Mon Dec 7 06:18:13 PST 2020
Call for Papers
First International Workshop on Enabling Data-Driven Decisions from
Learning on the Web (L2D 2021) held as part of the ACM International
Conference on Web Search and Data Mining (WSDM)
March 12, 2021 - ONLINE EVENT
https://mirkomarras.github.io/l2d-wsdm2021/
-----------------------------------------------------
Important Dates
-----------------------------------------------------
Submissions: January 25, 2021
Notifications: February 22, 2021
Camera-Ready Contributions: March 1, 2021
Workshop: March 12, 2021 - ONLINE EVENT
All deadlines are 11:59pm, AoE time (Anywhere on Earth).
------------------------------------------------------
Workshop Aims and Scope
------------------------------------------------------
By offering courses and resources, learning platforms on the Web have been
attracting lots of participants, and the interactions with these systems
have generated a vast amount of learning-related data. Their collection,
processing and analysis have promoted a significant growth of learning
analytics and have opened up new opportunities for supporting and
assessing educational experiences. To provide all the stakeholders
involved in the educational process with timely support, being able to
understand learner's behavior and create models that provide data-driven
decisions pertaining to the learning domain is a primary feature of modern
online platforms. This workshop aims to present novel, high-quality,
high-impact, original research results reporting the current state of the
art of online education systems empowered with data mining (DM) and
machine learning (ML). Specifically, this workshop will pursue the
following objectives:
Raise attention on education in the DM and ML community.
Identify human aspects affected by DM and ML in education.
Solicit contributions targeting DM and ML in education.
Get insights on recent open issues and methods in this area.
Expose gaps between research and actual needs in this area.
Given the growing importance of these topics, the DM and ML community is
more and more eager to delve into this applicative domain and, as a
consequence, can strongly benefit from a dedicated event. For this reason,
this workshop would provide the WSDM community with rich, yet clear,
focused, and well-structured insights on this domain. L2D 2021 will be the
WSDM's workshop aimed at collecting new contributions in education-related
data mining and at providing a common ground for interested researchers
and practitioners. Given also the current situation faced by education
worldwide due to the pandemic, we expect that this workshop will foster a
strong outcome and a wide community dialog.
--------------------------------------------------------
Workshop Keywords
--------------------------------------------------------
Data Mining - Machine Learning - Education - Data-Driven Decisions
-------------------------------------------------------
Workshop Topics
-------------------------------------------------------
We are interested in novel contributions targeting DM and ML in education
on the Web, focused but not limited to the following areas:
Data Set Collection and Preparation:
New tools and systems for capturing educational data (e.g.,
eye-tracking, motion, physiological, etc.).
Proposals of procedures and tools to store, share and preserve
learning and teaching traces.
Knowledge graphs and annotation schemas for data that can be
leveraged for DM and ML in education.
Collecting and sharing data sets useful for applying DM and ML
in online education contexts.
Model, Tool, System Design and Implementation:
Semantic content-based retrieval of educational materials to
identify appropriate contents.
Tools for adaptive question-answering and dialogue or
automatically generating test questions.
Personalized support tools and systems for communities of
learners (e.g., recommendation).
Natural language processing applied on exam data in order to
assign a grade to them.
Behavioral and physiological analysis of learners while
interacting in online education platforms.
Student engagement assessment via machine-learning techniques
(e.g., sentiment analysis).
Systems that detect and/or adapt the platform to sentiment or
emotional states of learners.
Techniques to provide automated proctoring support during
online examinations, e.g., via biometric recognition.
Tools able to predict the learner's success or failure along
the educational path.
Evaluation Protocol Design and Implementation:
Evaluation techniques, metrics, and protocols relying on
computational analyses in online education contexts.
Interpretability and/or fairness of the models and the
resulting impact on real-world adoption.
Error analysis aiming at understanding, measuring, and
managing uncertainty in model design.
Strategies to evaluate effectiveness and impact of DM and ML
systems on educational environments.
Exploration of cognition, affect, motivation, and attitudes of
stakeholders, while deploying systems.
Learning-while-searching investigations conducted in the
current educational contexts.
Ethics and Privacy Investigation:
Analysis of issues and approaches to the lawful and ethical
use of intelligent DM and ML systems.
Tackling unintended bias and value judgements in DM and ML
intelligent systems.
Regulations and policies in data management ensuring privacy
while designing intelligent DM and ML systems.
Broad discussion on potential and pitfalls of intelligent
systems for educational contexts.
Studies on how teachers can be made part of the loop as
moderators instead of being replaced.
-------------------------------------------------------
Submission Details
-------------------------------------------------------
The submissions must be in English and adhere to the CEUR-WS one-column
template.
The papers should be submitted as PDF files to Easychair at
https://easychair.org/conferences/?conf=l2d2021. Please be aware that at
least one author per paper must be registered and attend the workshop to
present the work.
We will consider four different submission types:
Full Papers (10-12 pages) should be clearly placed with respect to
the state of the art and state the contribution of the proposal in the
domain of application, even if presenting preliminary results. In
particular, research papers should describe the methodology in detail,
experiments should be repeatable, and a comparison with the existing
approaches in the literature is encouraged.
Reproducibility Papers (10-12 pages) should repeat prior experiments
using the original source code and datasets to show (i) how, why, and
when the methods work or not, (ii) or should repeat prior experiments,
preferably using the original source code, in new contexts (e.g.,
different domains and datasets, different evaluation and metrics) to
further generalize and validate or not previous work.
Short Papers (5-9 pages) should describe significant novel work in
progress. Compared to full papers, their contribution may be narrower
in scope, be applied to a narrower set of application domains, or have
weaker empirical support than that expected for a full paper.
Submissions likely to generate discussions in new and emerging areas
of data mining and machine learning in education are encouraged.
Position Papers (4-5 pages) should introduce new point of views in
the workshop topics or summarize the experience of a group in the
field. Practice and experience reports should present in detail
real-world scenarios in which data mining and/or machine learning are
exploited in the educational context.
Submissions should not exceed the indicated number of pages, including any
diagrams and references.
Each submission will be reviewed by three independent reviewers on the
basis of relevance for the workshop, novelty/originality, significance,
technical quality and correctness, quality and clarity of presentation,
quality of references and reproducibility.
The accepted papers and the material generated during the meeting will be
available on the workshop website. The workshop proceedings will be sent
for inclusion in a CEUR-WS volume and consequently indexed on Google
Scholar, DBLP, and Scopus. Authors of selected papers may be invited to
submit an extended version in a journal special issue.
---------------------------------------------------------
Workshop Chairs
---------------------------------------------------------
Danilo Dessi
https://www.fiz-karlsruhe.de/en/forschung/lebenslauf-und-publikationen-dr-danilo-dessi
Information Service Engineering
FIZ- Karlsruhe Leibniz Institute for Information Infrastructure
Karlsruhe Institute of Technology (KIT) - Institute AIFB
Email: danilo.dessi at fiz-karlsruhe.de
Tanja Kaeser
https://people.epfl.ch/tanja.kaeser/?lang=en
Digital Vocation, Education and Training (D-VET) Laboratory & Machine
Learning for Education (ML4ED) Laboratory
Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
Email: tanja.kaeser at epfl.ch
Mirko Marras
http://www.mirkomarras.com/
Digital Vocation, Education and Training (D-VET) Laboratory & Machine
Learning for Education (ML4ED) Laboratory
Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
Email: mirko.marras at epfl.ch
Elvira Popescu
http://software.ucv.ro/~epopescu/
University of Craiova, Faculty of Automation, Computers and Electronics,
Computers and Information Technology Department
Email: elvira.popescu at edu.ucv.ro
Harald Sack
https://www.fiz-karlsruhe.de/en/forschung/lebenslauf-prof-dr-harald-sack
Information Service Engineering
FIZ- Karlsruhe Leibniz Institute for Information Infrastructure
Karlsruhe Institute of Technology (KIT) - Institute AIFB
Email: harald.sack at fiz-karlsruhe.de
-----------------------------------------------------------
Contacts
-----------------------------------------------------------
For general enquiries on the workshop, please send an email to
danilo.dessi at fiz-karlsruhe.de or mirko.marras at epfl.ch.
More information about the Air-L
mailing list