Amos Glenn - Portfolio

Contribution Highlights

Bridging Research and Practice

Explored how cognitive science, instructional design, and AI-driven learning technologies can work together to create effective, personalized learning experiences.

Design Loop Thinking

Discussed how instructional designers can think about inner and outer response loops to create multiple levels of adaptivity.

What Not to Adapt To

Debunked the use of learning styles and learning preferences as a basis for adaptation, and suggested more effective learner characteristics to target.

Co-author Contribution

Recent advances in adaptive learning sciences (such as machine learning, natural language processing, and Bayesian knowledge tracing) are transforming a designer's ability to personalize instruction.

For this reason a new chapter was added to the second edition of The Handbook on Learning and Instruction discussing the current state of the science and how practitioners can apply it effectively.

As a co-author of this chapter, I explored two questions from a designer's perspective:

Adapting to Learners

Learners differ in a great many ways (knowledge state, interest, goals, affective state, strategic behaviors, learning styles, etc.), so which learner characteristics should we adapt to?

Adapting Effectively

Adaptations can happen in several time-frames (per action, per problem, per course, etc.), so which loops are effective at adapting to available data?

Co-author Contribution

Recent advances in adaptive learning sciences (such as machine learning, natural language processing, and Bayesian knowledge tracing) are transforming a designer's ability to personalize instruction.

For this reason a new chapter was added to the second edition of The Handbook on Learning and Instruction discussing the current state of the science and how practitioners can apply it effectively.

As a co-author of this chapter, I explored two questions from a designer's perspective:

Adapting to Learners

Learners differ in a great many ways (knowledge state, interest, goals, affective state, strategic behaviors, learning styles, etc.), so which learner characteristics should we adapt to?

Adapting Effectively

Adaptations can happen in several time-frames (per action, per problem, per course, etc.), so which loops are effective at adapting to available data?

Why It Matters

This chapter contributes to the scientific foundation of adaptive learning, offering guidance on how to design, implement, and refine adaptive learning technologies based on evidence-based research.

It complements my hands-on work in adaptive learning by demonstrating thought leadership in the field and a deep understanding of the theoretical foundations of instructional adaptation.

Where to Read It

Cover image The Handbook of Research on Learning and Instruction, 2nd Edition

The Handbook of Research on Learning and Instruction, 2nd Edition (published by Routledge)

Personalizing
Learning Experiences

Chapter Co-author

Translating research on
adaptive learning technologies
into principles of instructional design for personalized learning.

Contribution Highlights

Bridging Research and Practice

Design Loop Thinking

What Not to Adapt To

Co-author Contribution

Co-author Contribution

Why It Matters

Where to Read It

PersonalizingLearning Experiences

Chapter Co-author

Translating research on adaptive learning technologies into principles of instructional design for personalized learning.

Contribution Highlights

Bridging Research and Practice

Design Loop Thinking

What Not to Adapt To

Co-author Contribution

Co-author Contribution

Why It Matters

Where to Read It

Personalizing
Learning Experiences

Translating research on
adaptive learning technologies
into principles of instructional design for personalized learning.