Providing truly differentiated, individualized instruction has been a goal of educators for decades, but new technologies available today are empowering schools to implement this form of education in a way never before possible. Intelligent adaptive learning software is able to tailor instruction according to each student’s unique needs, understandings and interests while remaining grounded in sound pedagogy.
Attend this web seminar to hear the latest findings from Cheryl Lemke, of the research firm Metiri Group, about how intelligent adaptive learning works, the role the technology can play in raising student achievement, and the research base required for districts to invest wisely in these new tools.
8. Intelligent Adaptive Learning
Intelligent adaptive learning (IAL) is defined as
digital learning that:
• immerses students in modular learning
environments
• where every decision a student makes is
captured, considered in the context of sound
learning theory, and
• is used to guide the student’s learning
experiences, to adjust the student’s path
and pace within and between lessons, and
to provide formative and summative data to
the student’s teacher.
13. Intelligent Adaptive Learning
Student
Database
of
Student
Data
Continuous
capture and
storing of data
Cognitive Model/
Data Analysis
Continuous
data feed
*Designed pedagogically to engage students.
Classroom
Intelligent
feedback
to student
Data on
student progress
Adapt sequencing,
navigation, pace, pedagogy,
and presentation*
Intelligent feedback
to system
Modular Curriculum*
Learning Activities*
Embedded, Adaptive,
Continuous Assessment*
Real-time
data capture
of student
actions,
solutions,
and
explorations
online
Teacher
15. IAL Design Elements
1) Tutoring
2) Sequencing
3) Pacing
4) Regulating cognitive load
5) Engaging through gaming
… In the context of the cognitive model
16. Design Element: Tutoring
• Tutoring, whether on the computer or in-person,
when done well, is twice as effective as classroom
learning
• The secret sauce in tutoring is targeted feedback
• Targeted feedback has been found to increase the
average student’s learning by 27 percentile points
17. Researchers have found that students
receive little if any feedback in the
classroom.
Intelligent adaptive learning systems
can change that.
23. Design Element: Sequencing
• Sequenced curriculum and learning activities based on
student’s prior knowledge and skill levels
• Choice, challenge, engagement, and motivation
33. Design Element: Pacing
• Mastery learning works. Average student gains 22
percentile points in academic achievement over
results from conventional classroom
• The use of computers is more effective when the
student is in control of the pacing, time allocations
for mastery, sequencing and pacing of instructional
materials, choice of practice items, and review
process
36. Design Element: Regulating
Cognitive Load
• Working memory is limited to:
~ 7 things in verbal working memory
~ 4 things in visual working memory
• Manage cognitive load
37. Working Memory
Thinking:
• Integrating
• Diagnosing
• Analyzing
• Sense making
• Schema developing
Long-term
Memory
Visual
Working
Memory
Visuals/Video
Verbal
Working
Memory
Talk/Sound
38. Manage Cognitive Load
• Organization of information
on screen
• Alignment of visual and
verbal information
• Screen design
• Schema building
41. Design Element: Engaging
through Gaming
• Student engagement can be increased through:
• Logical sequencing of curriculum
• Novelty and variety
• Student choice
• Intellectual safety
• Clarity of goals
42. NON-INTERACTIVE
Multimodal Learning
INTERACTIVE
Multimodal Learning
BASIC SKILLS HIGHER ORDER SKILLS
Average
Student
**Percentile Ranking on Higher Order or
Transfer Skills
*Percentile Ranking on Retention of
Basic Skills
MULTIMODAL VS. TRADITIONAL, UNIMODAL LEARNING
+9
Percentile*
increase for
average student
II.
+32
Percentile**
increase for
average student
III.
+21
Percentile*
increase for
average student
I.
+20
Percentile**
increase for
average student
IV.
43. Gaming
Five principles of effective gaming also serve as important
elements of intelligent adaptive learning systems:
• Sequenced challenges
• “Just in time” and “on demand” information
• Performance before competence
• Motivation and attention
• Timely and specific feedback
– Gee 2003
47. In summary
• Think research-based
• Think blended learning
• Think “next generation” cognitive modeling
• Think big data – learning analytics
• Think prototyping
48. Prototyping
- U.S. Department of Education
Purchasers of digital learning resources and
those who mandate their use should seek out
and use evidence with respect to the claims
made about each resource’s capabilities,
implementation, and effectiveness.
49. Bottom Line
• Definitive research studies are underway
• In the meantime, the design of intelligent adaptive
learning systems is research based and warrants a
serious look, use, and prototyping by educators
• In doing so, it is critical that the cognitive model upon
which the IAL is based aligns with the school’s
pedagogical approach