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Adaptive Learning (Core elements (Dziuban et al., 2016) (faculty active…
Adaptive Learning
Core elements (Dziuban et al., 2016)
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rapport: instructor, student (importance of 1st and 2nd classes)(Daines et al., 2016)
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feedback (O'Connell, 2017; Straumsheim, 2017)
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big data, detailed design, quick adaptation, Feedback mechanisms, AI (Holz, 2017)
Downside
time (Dziuban et al., 2016)
detailed mapping (ELI, 2017)
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lack of familiarity (Daines et al., 2016)
vague algorithm effectiveness (ELI, 2017)
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limitation in course, entry-level (ELI, 2017)
impatience with technology (Daines et al., 2016)
Definition(ELI, 2017; Komar & Troka, 2015)
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Purpose
decrease dropout rate (Milano, 2017)
increase retention rate (Milano, 2017)
improving performance/outcome & learning process (Dziuban et al., 2016)
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reduce gaps in understanding (ELI, 2017)
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great flexibility & multiple paths to achievement with reduced time (Dziuban et al., 2016)
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build strong foundation skills (e.g. "unit zero") (Komar & Troka, 2015
personnel (Dziuban et al., 2016; ELI, 2017)
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Data Analysis, personalized learning team
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Vendor (Dziuban et al., 2016)
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AI technique (Almohammad et al.,2017)
Bayesian networks (Dziuban et al., 2016)
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System
Learning path
"intellipath"(Daines et al., 2016)
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User profile(Almohammad et al.,2017; Howlin & Lynch, 2014; Komar & Troka, 2015)
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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)
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learning style (Surjono, 2014)
personality, attitude, behaviour
Visual, auditory, kinesthetic, global, sequential
preferences for specific learning materials (Forsyth et al., 2016)
Learning model (Almohammad et al.,2017)
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Platform (Howlin & Lynch, 2014)
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Structure
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'granular' level (Kerr, 2017)
Data
(Komar & Troka, 2015)
Report
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Weekly analysis to modify content and assessment (Komar & Troka, 2015)
Variables
avg final score, ave weekly learning growth rate..
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Type (Dziuban et al., 2016)
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