BayLAN spring 2017

BayLANA4L*, SRI International and The Carnegie Foundation for the Advancement of Teaching invite you to:

BayLAN Spring 2017, March 10

The use of analytics to improve educational products, processes, and outcomes

Across industry and academic sectors, rapid design approaches are critical for the development and improvement of educational innovations. Design and improvement sciences are providing intellectual rigor to the approaches used. This meeting will focus on the role of analytics in design and improvement of educational innovations.

Time and Location: 

March 10, Carnegie Foundation  (51 Vista Lane, Stanford)  

WARNING: There is NO PARKING opportunity at the Carnegie foundation (51 Vista Lane, Stanford), they advise to consider taking alternative transportation (e.g. a ride-sharing service or public transport).  We will however provide a shuttle at 12.45 from the Dutch Goose (where our networking session will be afterwards) at 3567 Alameda de las Pulgas, Menlo Park, and one back to the Dutch Goose at 5. There should be plenty of street parking around that area. There will be a backup shuttle at 1.15, but you will miss the first part of the meeting. 

1.00 - 1.30  Introduction of BayLAN and meeting participants 

We aim to grow a local network of researchers and professionals in the field of learning analytics, which can be used to exchange and validate innovative ideas and to provide local access to research and expertise.  As a first networking activity, participants will introduce themselves. If you are attending the meeting, please complete a Google slide with your information (instructions and slide template can be found here).

1.30 - 3.00  First round of speakers

SRI International 
The Intersection of Improvement Science and Data Science: Examples, Methods, and Lessons Learned

Carnegie Foundation
Developing the Faculty Mentor Dashboard to Support New Faculty in the Carnegie Math Pathways

Summit
Understanding our System: Incompletes and English Learners

3.00 - 3.30 Break, networking  

3.30 - 5.00 Second round of speakers

Carnegie Foundation 
Using Multi-Level Propensity Score Matching to Understand Student Success in the Carnegie Math Pathways

SRI International
Rapid Cycle Evaluations and Learning System Data

Chegg and Kidaptive
Evaluating Outcomes of Educational Products in Industry: Case studies from Chegg and evaluating educational games at Kidaptive.  

5.00 - 7.00  Drinks and networking at the Dutch Goose 


3567 Alameda de las Pulgas, Menlo Park

Meeting Format and Presentations:

Key ideas behind using analytics to improve can include the ways in which data products helped individuals better understand a learning environment, how data products supported identifying opportunities for improvement, or how data products were used by individuals in taking action within a learning environment. 

SIGN UP 
Please register here, there is a limit on the number of registrations!




















Suggested topics for presentations:
  • Development and interpretation of data products - What are the ways use data to identify bright spots and opportunities for improvement of educational innovations.
  • Identification of change/redesign/improvement strategies – (Co)development of concrete strategies that are explicit and whose impact is testable
  • Evaluation of identified strategies - Analysis and interpretation of impacts of improvement strategies
  • Description of context of implementation – Identification of high leverage structures, processes, and norms within each educational organizations and settings. 
  • Structure/organization of cycles of analysis and improvement – Instances and rationale for various workflows of analysis and improvement cycles
  • Roles and forms of partnerships (multi-disciplinary, cross –sector, etc.) – Need to include a range of experts and stakeholders to support and enrich analysis and improvement cycles.
*Support for this event is provided by the Analytics for Learning (A4L) Network, which is supported by the National Science Foundation (SMA-1338487), and is made up of researchers exploring the measurement of student learning behaviors and strategies in digital learning environments.