[on behalf of Yoav Bergner]
Interested in presenting at our LAK workshop but missed the deadline?
You’re in luck!
We are extending the deadline for submissions to our upcoming Workshop on
Methodology in Learning Analytics (MLA) at LAK 2017 (Mar 13-17, 2017,
Vancouver, Canada). Please note that the workshop is completely open to
non-presenters as well. Presented papers will be collected in a proceedings
volume in the http://ceur-ws.org online archive.
Extended Deadline: February 6, 2017
More information about the workshop can be found here:
https://sites.google.com/a/nyu.edu/lakmla2017/
Call for Papers
As the learning analytics community matures, it is particularly important
to establish standards for good practice. Clear methodological guidelines
increase the quality of work and facilitate communication not only within
the community but also with practitioners in other research communities,
where norms may be clearer.
Developing guidelines that encompass the extensive range of methods used in
learning analytics is a challenging but important task. Such guidelines
must also address the fact that quantitative analytics techniques can be
sensitive to both data collection and variable operationalization choices,
impacting on reproducibility of result. Adding to that complexity is that
fact that measurable factors are often proxies for latent constructs, with
a consequential ambiguity in model interpretation.
The present workshop seeks to build a community of researchers with an
interest in methodology and its rigorous application and development to the
field of learning analytics. Recognizing the cross-disciplinarity of
learning analytics, the workshop welcomes contributions from education and
data science researchers. Specifically, we welcome papers on the following:
Position papers that take a critical look at methodological practice in the
learning analytics community.
Exploration of new metrics and methods that are relevant, or indigenous, to
learning analytics.
Constructive analysis of metrics for both model evaluation, and comparison
of model results where multiple models are reported on.
Critique of sensitivities arising from data collection and variable
operationalization choices in learning analytics.
Considerations on inferring latent constructs of learning from measurable
factors.
Papers of any length up to 10 pages will be considered.
The extended deadline for submission is February 6, 2017.
More information about the workshop can be found here:
https://sites.google.com/a/nyu.edu/lakmla2017/
Please submit proposals to EasyChair at the following link:
https://easychair.org/conferences/?conf=methlab17