About BayLAN

Our mission is to build bridges between Bay Area folks from different backgrounds working on analytics for education and educational technology. We target a broad audience of researchers and developers from academia and industry, education and computer science, as well as practitioners bringing everyday experience developing and using educational technology.

 

Learning analytics concerns the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the learning environments. Broadly defined, the learning environments may include offline classrooms, online platforms (e.g., learning and assessment systems, intelligent tutoring systems, chatbots, and recommender systems), and the integration of the two. 


This year, we are coordinating five complimentary sessions that approach learning analytics from different angles: 1) Equity in Learning Analytics, 2) Application of Learning Theory in Learning Analytics  3) Models for Online Learning  4) Machine Learning in Education and 5) Efficacy of Educational Products. A large poster session will ensure that everyone (including students) can share their work on one of these five themes as well.

Submission deadline: Midnight, March 15, 2020

Submission Guidelines

We solicit abstract submissions reporting on research in the broadly defined topic of learning analytics. This includes technical work that applies data science or other quantitative methods to improve education, as well as interventions, methodologies, tools or technology that are intended to improve learning outcomes.

Abstracts will be assessed by the program committee on the relevance to the field of Learning Analytics and will be accepted on an ongoing basis. We aim to provide a platform for discussion on how to gain insight into best educational practices, and this includes interventions that are well designed but showed no positive effect on learning.

 

Accepted work will be presented as one of:

  • Short research talk.  15 -20 minutes (including Q&A) 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 in an experience.

  • Demo. A live demonstration is a great opportunity to communicate ideas and concepts in a powerful way that a regular presentation cannot. Show aspects of learning analytics in an interactive hands-on form. Feel free to include a link to a video on your abstract.

  • Poster. Research in progress, ideas for projects, etc.

List of Themes 

Session 1: Equity in Learning Analytics

This session will aim to discuss equity issues in learning analytics. In the hope of improving education for all, we invite presentations on research and practices that take into account all learners and aim to promote equitable learning across diverse groups. Since we seek to reduce inequity in measurable ways, in addition, we welcome contributions that explore the operational definitions and metrics of “equity in education,” and what they mean to educationally underserved groups, educators, researchers, and policymakers in all aspects (e.g. representativeness, practices, and outcomes).

Session chair: Ranjeet Tate

Co-organizers:  Sanghamitra Deb and Diego Sierra.

Session 2:  Application of Learning Theory in Learning Analytics

This session will focus on the integration of learning theories in learning analytics. In 2016, Professor Paul A. Kirschner at LAK16 posed the question “What do the learning sciences have to do with learning analytics (LA)?” His utopian vision was accompanied by a dystopian future where learning science and learning analytics had not yet come together. Since then, how have researchers and practitioners across the fields of learning science and learning analytics worked together to advance learning outcomes for students using big data? We invite submissions that answer this question and showcase learning analytics and educational data mining research that is rooted in learning science principles. We invite papers that examine learning processes in real-life learning and ed-tech products to influence learning outcomes.

Session chair: Ruchi Bhanot

Session 3:  Models for Online Learning

This session will target statistical and computational approaches to studying online learning, with emphasis on interpretable models, ideally, those grounded in psychometrics and cognitive theory. This includes modeling dynamic aspects of the learning process such as knowledge acquisition, reinforcement, interference, and forgetting, as well as student-dependant factors such as attentional or motivational state, and item-level attributes including difficulty or salience.

Session chairs: Shane Mooney and Anna Khazenzon

Session 4: Machine Learning in Education

This session will mainly focus on the application of artificial intelligence, machine learning, and data mining to understanding or optimizing learning and its environment. Recent advances in computer and data sciences have expanded the type and scale of measurements we take, as well as the interventions we exert, in various learning environments. In this session, we foster multidisciplinary communication and collaboration in educational innovation by presenting state-of-the-art research and applications that highlight:

  • Novel use of machine learning in various learning environments;

  • Affective computing; and

  • Automated generation of educational content.

Session chair: TBA

Session 5:  Efficacy of Educational Products

This session will explore the barriers and benefits of conducting efficacy research on educational technology products. We will discuss major obstacles to efficacy research, practical strategies for overcoming these obstacles, and the benefits of conducting this type of research for different stakeholders involved. We invite submissions from all methodological, empirical, and ethical aspects of efficacy research as well as contributors from industry and philanthropy who can offer practical insights, guidelines, and examples that are highly relevant to the evaluation of educational technology products.

Session chair: Molly Zielezinski

 

While submissions from for-profit companies are welcomed, reviewers will not accept sales pitches.

 

Submission link: 

https://easychair.org/conferences/?conf=baylan2020

Need Help or Have Questions?

 

Please email us at baylearnnetwork@gmail.com.

 

 

 
 
 
 
 
 

 

 

© The Bay Area Learning Analytics Conference.