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Educational Design Research is an iterative process used by practitioners. It involves analysis, design, and evaluation to create and refine educational systems, often drawing on theory.

The idea that tailoring teaching to student learning styles (like visual, auditory, kinesthetic) improves learning lacks credible scientific evidence. There's no proof that matching instruction to a preference works.

Today's educators gather more information than ever. There's increasing recognition that diverse assessment data are necessary to completely describe student learning and improve schooling.

Actively scanning the external environment is key to identifying trigger signals for innovation. This involves looking for trends in technology, markets, and competitors.

While an educator may claim a theory-free approach to their practice, this isn't truly possible. Every instructional strategy fundamentally embodies a theory of human learning.

Your epistemology – your beliefs about how knowledge is created – does influence how you approach curriculum and instruction. It impacts what you believe the purpose of education is.

Frameworks are helpful tools for educators. They facilitate the work of replacing doxa (opinion-based) with logos (theory-informed), helping design coherent curriculum & instruction.

AI isn't about mimicking human intelligence; it's about computers as cognitive prostheses, doing things humans can't or wouldn't, like processing vast amounts of data.

Select response tests inherently have false positives and negatives. They can test game-playing skills rather than true understanding, lacking "construct validity".

Over time, the ZPD "goes up". Problems once "too complex" move into the ZPD, then into the "too easy" category.

For decisions with significant consequences, ensure a human is in the loop. AI should act as a copilot, assisting humans who make the final critical decisions.

Generative AI. The concerns are no different than the concerns raised about every other technology.

Deploying GenAI in enterprise comes with risks. Be cautious of hallucination, bias amplification, and misuse. Responsible AI practices are crucial for mitigating these pitfalls.

Problem-centered learning breaks down complex knowledge into teachable skills, provides diverse strategies for presenting and practicing these skills within authentic contexts, and emphasizes learner control, clear organization, effective use of multimedia, and consistent feedback

"Providing learners with control over their learning path and making the course structure and content organization transparent. " I wonder what it'd be like to organize school in this way.

Using multimedia can enhance demonstrations, for example, with successive disclosure of text/graphics synchronized with audio or animated graphic devices to focus attention. Keep information and portrayal concurrent.

The Problem-Centered Principle focuses instruction on real-world problems. This can be implemented via a problem progression, where learners solve increasingly complex versions of a problem.

Learning is promoted when learners observe a demonstration of the knowledge & skill to be learned. "Tell" presents information, while "Show" demonstrates with visuals.

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