McGraw-Hill (article)

• Meta-cognitive: know thyself → confidence
• Efficiency: deliberate practice
• Engaging: challenged appropriately, per game design
• Retention: long-term knowledge via spaced repetition

Area-9 Learning (infographic)

• Corporate training that
  • meets them where they are
  • meets the specific needs of their job
• One-size-fits-none, "Biological approach"
• Designing learning programs → creating customized learning paths.

• Retention: Recharges your brain
• Efficiency: Cuts down on learning time by 50%
• Meta-cognitive: Uncovers what you don't know you don't know
• Track Progress: Enables reporting

BitSchool (whitepaper)

CogBooks (whitepaper)

• Categories of self-paced learning platforms
  • Linear - fixed linear sequences.
    • Negatives: Inefficient; Disengaging
  • Macro-adaptive - pre-determined sequence based on either
    1. Learner's preference
       • Negatives: Not supported by research; Extensive authoring effort
    2. Learner's performance on pretests 
       • Negatives: "Snapshots" of learner's competency gets outdated; Over-reliance on tests
  • Micro-adaptive - continuously tailor learning delivery based on the student’s ongoing actions at every step.
    • Positives:
      • Dynamically adjust based on most recent actions.
      • Skip unnecessary activities.
      • Automated, personalized support.
    • 3 types:
      1. Preference-based - adapt based on learner's style and behavior
         • Negatives: Not supported by research; Extensive authoring effort
      2. Rule-based - pre-programmed sequence based on user's actions.
         
         • Negatives: Limited extendibility; Extensive authoring effort
         • Most applicable to subjects such as basic math and basic science.
      3. Algorithm-based - data-driven algorithms determine sequence as student progresses.
         • Positives:
           • Less authoring effort.
           • Data-driven: amenable to self-improvement and data analysis.
         • 3 types:
           • Memory retention - spaced repetition (per pedagogical research)
             • Most applicable to memory intensive learning activities such as learning language vocabulary or medical exam study.
           • Assessment-driven - optimizes individual assessments and dynamically adjust sequence of assessments.
             • Assessments ranked by:
               • differentiation (how well they assess what the learner knows)
               • difficulty (the relative likelihood that a member of the population will get the assessment correct)
             • Frequently uses Item Response Theory.
             • Negatives: Heavy focus on testing detracts from learning effectiveness.
             • Most applicable to test preparation, exercise drills, and teach-to-test scenarios.
           • Integrated network - Algorithm determines next best learning item, relying on relationships identified between learning activities.
             • Inputs include: learner’s knowledge profile, learner’s behavior, retention, recommendation, and profile data.
             • Positives: highly personalized at each step; flexible; scalable for authors; content agnostic; based on recent learning theory.