Transcript Adaptive Learning in the Library

ADAPTIVE LEARNING IN THE
LIBRARY
DESIGNING A SUSTAINABLE AND EFFECTIVE
ONLINE INSTRUCTION PROGRAM
Joelle Pitts
Assistant Professor | Instructional Design Librarian
Kansas State University
OVERVIEW
• Distance Student Behaviors and
Expectations
• Adaptive Learning
• Library Applications
BACKGROUND
• SLIM
• Great Plains IDEA
•
•
•
•
•
•
Distance Education Consortium
Fully online degree programs/course sharing
Faculty and student interaction
Program building
Assessment
Research
GREAT PLAINS IDEA FACULTY
• Most teach on campus
• Most are under pressure
• Most completed their graduate work using
outdated technologies
• Focus is getting content online
• Most assume their audience is nontraditional
• And knows how to conduct research
• Information literacy gaps
• More training needed
ONLINE = NON-TRADITIONAL?
NON-TRADITIONAL?
NON-TRADITIONAL?
NON-TRADITIONAL GPIDEA STUDENTS
• Demographics
• Mostly female
• Avg. age 33
• Non traditional
• Work at least part time
• Family responsibilities
• Financial restrictions
NON-TRADITIONAL GPIDEA STUDENTS
Behavior
• 10+ years since
undergraduate work
• Technology learning curve
• Wikis
• Google applications
• Multimedia/collaborative
platforms
• Library use
• Aren’t aware they are able to
access it as a distance
student
• Perception of the library is
print based
Expectations
•
•
•
•
Consistency
Format
Efficiency
Cost
TRADITIONAL GPIDEA STUDENTS
• Fastest growing population in online
education
• Demographics
• Millennial (18-30)
• Work part time
• Some family obligations
TRADITIONAL GPIDEA STUDENTS
Behavior
• Technology
• Wired
• Social Media
• Multimedia/collaborative
platforms
• Mobile
• Gaming
Expectations
•
•
•
•
Consistency
Format
Efficiency
Cost
• Library use
• Aren’t aware they are able
to access it as a distance
student
• Perception of the library is
print based
Pew Research Center (2010)
GPIDEA COMMON STUDENT
BEHAVIORS AND EXPECTATIONS
Behavior
Expectations
• Library use
• Aren’t aware they are
able to access it as a
distance student
• Perception of the library is
print based
•
•
•
•
Consistency
Format
Efficiency
Cost
Information Literacy Level?
Online ≠ non-traditional
ADAPTIVE LEARNING
ADAPTIVE LEARNING BASICS
• A system which collects user information and
behavioral data to customize a learning
experience for an individual
• Encourages active participation rather than
passive receptacle
• Moves away from static hypermedia (same
page content and links for all users)
• Artificial Intelligence movement
Brusilovsky (2001)
THIS…
NOT THIS…
MACHINE LEARNING
• Machine collects data and recognizes
patterns in the data
• Algorithms – sequence of instructions to
transform the input into output
• Intelligent systems have the ability to learn in
a changing environment
Alpaydin (2010)
ADAPTATION PROCESS
• Data collection
• User interaction
• Direct input
•
•
•
•
•
Interpret data using models
Infer user requirements and preferences
Tailored aggregation
Presentation of tailored content (adaptive effect)
Synthesis with population data
Paramythis & LiodlReisinger, (2003)
ADAPTATION PROCESS
Brusilovsky & Maybury (2002)
MODELING
Jacko (2009)
CATEGORIES OF ADAPTATION
• Interaction with the system
• Course/object delivery
• Content adaptation
• Collaborative/social support
Paramythis & LiodlReisinger (2003)
CONTENT ADAPTATION
• Adaptive presentation
• content of a hypermedia page adapted to the user’s
goals, knowledge and other information
• Adaptive navigation
• link presentation and functionality adapted to the goals,
knowledge and characteristics of the user
•
•
•
•
Direct guidance
Link sorting
Link annotation
Link hiding
Brusilovsky (2000)
ASSESSMENT
• System feedback
• Embedded assessment
• Adaptive
• Timing/architecture
• Question level
EXAMPLES
•
•
•
•
•
•
•
•
•
Adaptive eLearning Research Group
AHA!
Andes Physics Tutor
ELM-ART
GRE
iKnow!
Learnthat
Khan Academy
Knewton
• More…
REFERENCES
• Alpaydin, E. (2010). Introduction to machine learning, ch. 1. MIT Press
• De Bra, P., et al. (2003) AHA! The Adaptive Hypermedia Architecture. In Proceedings of
the fourteenth ACM conference on Hypertext and Hypermedia, Nottingham, August, pp.
81-84
• De Bra, P., Aroyo, L., & Chepegin, V. (2004). The next big thing: adaptive web-based
systems. Journal of Digital Information, 5(1).
• Brusilovsky, P. (2000). Adaptive hypermedia: from intelligent tutoring systems to webbased education. Intelligent Tutoring Systems: 5th International Conference.
• Brusilovsky, P. (2001). Adaptive hypermedia. User modeling and user-adapted interaction.
11: 87-110.
• Brusilovsky, P., & Maybury, M. T. (2002). From adaptive hypermedia to the adaptive web.
Communications of the ACM, vol. 45, No. 5.
• Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational
systems. International Journal of Artificial intelligence in Education 13, 159-172.
• Great Plains Interactive Distance Education Alliance. (2009). New student survey
• Jacko, J. A. (2009). Human-computer interaction: design issues, solutions, and
applications. Taylor & Francis.
• Paramythis, A., & Liodl-Reisinger, S. (2003). Adaptive learning environments and e-learning
standards. European conference on E-Learning.
• Pew Research Center. (2010). Millennials: a portrait of generation next.
http://pewsocialtrends.org/files/2010/10/millennials-confident-connected-open-tochange.pdf
IMAGE CREDITS
• http://web.mit.edu/newsoffice/2009/ai-overview1207.html
• http://s425.photobucket.com/albums/pp339/ridizle
4/?action=view&current=terminator.png&newest=1
• http://www.llift.com/pages/platform.htm
• http://www.gw.edu/academics/off/online/
• http://www.braintrack.com/college-and-worknews/articles/non-traditional-students-becomingthe-norm-10082502
• http://www.drexel.edu/univrel/digest/archive/1103
06/index.html