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.

Open

• 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)

Open

• 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       • Negatives: "Snapshots" of learner's competency gets outdated; Over-reliance on tests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.