Adaptive Learning
AI technique (Almohammad et al.,2017)
Bayesian networks (Dziuban et al., 2016)
Genetic Algorithms
Decision tree
Hidden Markov model
Fuzzy logic (FL)
Downside
Core elements (Dziuban et al., 2016)
Definition(ELI, 2017; Komar & Troka, 2015)
System
Purpose
time (Dziuban et al., 2016)
detailed mapping (ELI, 2017)
cost
content development
lack of familiarity (Daines et al., 2016)
vague algorithm effectiveness (ELI, 2017)
cultural differences between online and F2F
multiple intervention
rapport: instructor, student (importance of 1st and 2nd classes)(Daines et al., 2016)
interaction
media
technologies
classroom training
feedback (O'Connell, 2017; Straumsheim, 2017)
syllabus change
assessment
incentives
Algorithm- "same aspect of learning will never be measured with any algorithm..."
instructor's assessment/intervention
faculty active preparation
training
analysis & interpretation
intervention
strategies for the data use (real-time info in dashboard)
Data
(Komar & Troka, 2015)
Report
Variables
Analysis
Assessment quality
Delivery method
Effectiveness of content
monitoring student's progress
instructor's action
Weekly analysis to modify content and assessment (Komar & Troka, 2015)
avg final score, ave weekly learning growth rate..
total amount of time spent in the platofrm
percentage of assigned learning activities completed
num of interactions in the platform course retention rate
course persistence rate
decrease dropout rate (Milano, 2017)
increase retention rate (Milano, 2017)
improving performance/outcome & learning process (Dziuban et al., 2016)
improve pedagogy
reduce gaps in understanding (ELI, 2017)
interactive support when problem solving (dreambox, na)
great flexibility & multiple paths to achievement with reduced time (Dziuban et al., 2016)
customized presentation
build strong foundation skills (e.g. "unit zero") (Komar & Troka, 2015
evidence of knowledge
customized learning path
competency-based
data-driven
self-paced
real-time information
personalized learning
knowledge progress/process
non-linear
Learning path
User profile(Almohammad et al.,2017; Howlin & Lynch, 2014; Komar & Troka, 2015)
desired learning objectives
prior knowledge/experience
psychometric cognitive information/ability (perceptual speed, processing speed, working memory capacity, reasoning ability, verbal ability, spatial ability and other cognitive abilities)(Forsyth et al., 2016)
characteristics
"intellipath"(Daines et al., 2016)
startpoint, "zero unit"
"learning map"
Learning model (Almohammad et al.,2017)
contents & assessment development
effective instruction based on learner behavior
learning nodes & map
Platform (Howlin & Lynch, 2014)
content
curriculum
Faculty dashboard
Individual performance
data-driven decision
lecture driven
content layout
weakness & strength
classroom activities
assignment
1:1 interaction
personnel (Dziuban et al., 2016; ELI, 2017)
Project manager
Content experts
Data Analysis, personalized learning team
Technologiest
Faculty training team
Business manager
ID
Faculty
Vendor (Dziuban et al., 2016)
CogBooks
SmartSparrow
Knewton
Realizeit
...
Acrobatiq
Type (Dziuban et al., 2016)
Open
Hybrid
Closed
Structure
Interactive content
Curriculum - hierarchical structure of knowledge
Assessment
Challenges
ZPD (dreambox, na)
limitation in course, entry-level (ELI, 2017)
impatience with technology (Daines et al., 2016)
learning style (Surjono, 2014)
personality, attitude, behaviour
Visual, auditory, kinesthetic, global, sequential
big data, detailed design, quick adaptation, Feedback mechanisms, AI (Holz, 2017)
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'granular' level (Kerr, 2017)
preferences for specific learning materials (Forsyth et al., 2016)
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Benefit (ref)
Students
Reseacher and designer
Instructor
Revising/adding lessons for student's mastery
Save time and enables more meaningful interaction with students
Empowering teachers with granular level information
More productive interactions with instructor
Reducing the risk of falling behind and giving up
Produce scientific data on learner's behavior
Provides focused remediation based on each student's performance
Personalized learning experience
Maximizing student's learning efficiency
Prepares students more effectively for lectures and course exams
Various methods and media
Enhance engagement with the material