This document discusses data-centric education and learning. It begins by outlining past and present technologies used in education. It then discusses how data-centric learning is enabled by devices that connect to the cloud and collect real-time student data. This data can provide adaptive instruction, feedback, and insights into learning processes. Examples are given of social network analysis and predictive analytics projects using large educational datasets. Finally, frameworks for designing data-driven learning environments and strategies to improve performance are presented. The conclusion emphasizes using data and analytics responsibly and strategically to improve education.
1. DATA CENTRIC EDUCATION & LEARNING
Seung Won Yoon, Ph.D. hrdswon@gmail.com
Instructional Design & Technology, Western Illinois University
2. Project & Case Examples
Outline
Technology in Education: Past
Now and Future
Design Frameworks
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2
3
4
3. Assumptions
Strategies first, then technologies.
If great people/tool vs. bad culture/system, latter wins.
Most technology integration has failed. Innovation adoption
has been tough. Workshop or training to (big) data/analytics
will always have minimal impacts.
Learning and performance must be integrated.
4. Technologies in the Past
Technologies in education
Process: Instructional design, learning strategy, 4C/ID (for complex
contents), learning environments, design based research, etc.
Media: radio, TV, CBT, video, PC, multimedia, Web, virtual worlds, …
now, analytics & network analysis (paradigm change? Anything new?)
Education: Industry lowest in adopting technologies
People will always focus more on new media
Media vs. methods debate
New technology has never replaced ‘old’
5. Present & Future
NMC Horizon report
Near: MOOC and Tablet computing
Middle: Learning analytics & gaming
Far: 3D printing & wearable technology
A day made of glass
by Amber Case, from Flick’r
6. What’s New about Data-centric Learning?
Tablet, phone, devices connected to the cloud
Real-time feedback (pacing)
Adaptive contents (individualization)
Instructional precision/effectiveness (previous knowledge)
Analyzed and used to augment teacher/student capabilities
Watters. A. (2011, July). How data and analytics will change education.
7. Data-centric Education & Learning
LMS: grade, log, forum postings
Student/institutional records
Web 2.0/3.0 – digital trails
Top 100 learning tools – more data
Data sources (in addition to social media):
PSLC’s learning datasets
Carnegie Melon University
Stanford’s multimodal learning analytics
Stanford’s large network data collection
Society for Learning Analytics Research datasets
9. Building Blocks
Nodes, edges, density, centrality, community, motif
Data: Affiliation matrix, edge list
Sources:
Social media
Email/listserv/forum
RDBM
Open communities
Tools
NodeXL, Gephi…
10. Project & Case Examples
Use of SNAPP (Moodle, BlackBoard, D2L, Angel)
At risk students/non-participants
Identify communities
Identify brokers
Before and after new strategy (e.g., each post vs. responding to
instructor prompt)
LATF at U of Michigan: Cross-course analysis - Time on tasks,
frequency of contacts, network position, resource use, instructor
reuse of contents, learning-reflecting assessments, contextual
resources (privacy, security, governance)
Bakharia, A., & Dawson, S. (2011). SNAPP: A bird’s eye view of temporal participant interaction
17. Learning & Performance Architecture
Rosenberg, M. (2006). Beyond E-Learning: Approaches and technologies to enhance
organizational knowledge, learning, and performance. New York: Pfeiffer.
18. Strategic Blending
Yoon, S. W., & Lim, D. H. (2007). Strategic blending: A conceptual framework to improve learning and performance. International Journal on E-Le
Yoon, S. W., & Lim, D. H. (2007). Strategic blending: A conceptual framework to improve learning and
performance. International Journal on E-Learning, 6(3), 475-489.
Yoon, S. W., & Lim, D. H. (2010). Virtual learning and technologies for managing organizational
competency and talents. Advances in Developing Human Resources, 12(6), 715-728.
19. HPT Model
Van Tiem, D. M., Moseley, J. L. & Dessinger, J. C. (2012). Fundamentals of Performance Improvement: A
guide to optimizing results through people, process, and organizations
20. Learning & Knowledge at Macro Level
Figure 1. Esterby-Smith’s (2003) Key Topics of Learning in Organizations
Song J.H., Uhm, D., Yoon, S. W. (2011). Organizational knowledge creation practice., LODJ.
21. Interactions & Others
PBL, PJL, AL – Activity Theory
4C/ID, Learning environment
Online interactions
Figure. Activity Theory (Engeström, 2000)
Figure. Online interactions (Hirumi, 2002)
23. Conclusions
It’s not about if (big) data/analytics is different;
it’s about doing the right thing & doing things right
Must be treated as the same as business intelligence
Tools & frameworks are here, are you ready?