Serendipity35

I have referenced microlearning in earlier posts, but I want to say more about how microlearning works effectively with andragogy (adult learning theory), which differs from the more commonly heard pedagogy (children).

Microlearning provides the flexible format and focused content that perfectly complements the goal-oriented, self-directed nature of the adult learner. (Not that children don't want their learning to be self-directed, but they are less capable of doing that on their own.) Andragogy principles are strengthened by microlearning's ability to combat the forgetting curve. Microlearning often incorporates spaced repetition through short, periodic knowledge checks or quizzes. By revisiting core concepts in brief intervals, the information is reinforced, helping to move the content from short-term to long-term memory, which is vital for busy adult learners who may not have dedicated study time.

Adult learners, by definition, value autonomy and prefer to be self-directed in their education. So, microlearning modules are typically accessed on demand via mobile devices or learning platforms. Much of that learning occurs outside of traditional learning spaces. This allows adults to choose what they need to learn and when it fits into their busy personal and professional schedules, fully supporting their desire to take control of their learning path.

Adults are motivated to learn when the content is immediately relevant and can be applied to solve a real-life problem or job-related task. Each microlearning module is intentionally designed to focus on one specific learning objective. That might be "how to change the blade on a lawn mower," but also  'how to execute X function in the software." This problem-centered focus provides just-in-time training, ensuring the information is practical, immediately useful, and valuable for their current role.

Adults are most ready to learn when they encounter a specific need or challenge in their work or life.

Younger learners are more likely to accept the "authority" of the teacher that something needs to be learned at this time, even if they don't see a need for it themselves. It's not that younger learners don't sometimes do the same kind of "just in time," self-motivated learning. They might search for a video on how to do something when starting a task. But this is more likely to occur with older learners.

Adult learners have accumulated a wealth of experience and are often battling time shortages. They need efficient learning that builds on what they already know. Microlearning usually respects the adult's time by eliminating filler and focusing only on the "need-to-know" core information. 

AI chatbots are certainly the latest form of just-in-time microlearning that is being used outside classrooms. Its use is not unlike someone earlier looking for a help video on YouTube, but it is incredibly fast and personalized.  

There was an unexpected shift to virtual learning triggered by the global pandemic. It's not that virtual hadn't already existed for decades in various formats.
The global shift to virtual education has highlighted the crucial need for effective instructional design, particularly in enhancing student engagement. Traditional long lectures struggle to maintain attention in the digital environment, making the strategic adoption of microlearning important for success.

Microlearning delivers content in small, focused segments, which are far more effective for learners to absorb and retain information. This approach consists of “bite-sized” educational chunks, typically lasting only a few minutes. By delivering short, structured, and fine-grained activities, microlearning aligns with how working memory functions, fitting within the constraints of human cognitive capacity. This technique significantly enhances engagement and reduces cognitive overload, helping to move information from short-term to long-term memory more effectively than traditional, lengthy content.

A major advantage of microlearning is its ability to address the forgetting curve . The forgetting curve demonstrates how humans naturally lose a substantial amount of newly learned information over time unless it's reinforced. Microlearning counteracts this decline through spaced repetition techniques. This involves recalling the same material multiple times over a period, which successfully solidifies the information in long-term memory with each recall.

Furthermore, microlearning enhances online student engagement by allowing students to complete lessons according to their own schedule, rather than a fixed external one. This flexibility enables students to be entirely focused and more engaged in the learning process. Since online learning often happens outside the classroom, microlearning allows for a greater potential for application by integrating learning with real-life experience. Instructors can seamlessly integrate microlearning into online education using various digital tools to incorporate interactive quizzes, short videos, or specific micro lessons that run parallel to the main course, ensuring a more dynamic and interactive experience.

 

A bit of a promo, but still a useful overview of good structures for short-form learning and information about using YouTube, Descript, Kajabi, TalentCards, LearnWorlds, TalentLMS, et al as microlearning platforms.

AI agents are something of concern for OpenAI, Google, and any other players. "AI agents" are software programs designed to perform specific tasks or solve problems by using artificial intelligence techniques. These agents can work autonomously or with minimal human intervention, and they're capable of learning from data, making decisions, and adapting to new situations.

Gartner suggests that agentic AI is the most important strategic technology for 2025 and beyond. The tech analyst predicts that, by 2028, at least 15% of day-to-day work decisions will be taken autonomously through agentic AI, up from 0% in 2024. Does that excite or frighten you?

They can automate processes, analyze data, and interact with users or other systems to achieve specific goals. You probably already interact with them in applications (Siri or Alexa), customer service chatbots, and recommendation systems (Netflix or Amazon). They may be less obvious to you when using an autonomous vehicle or a financial trading system.

There are many categories into which we might place these agents because there are different types of AI agents, each with unique capabilities and purposes:

Here are some possible categorizations:

Reactive agents respond to specific stimuli and do not have a memory of past events. They work well in environments with clear, predictable rules.

Model-based agents have a memory and can learn from past experiences. They use this knowledge to predict future events and make decisions.

Goal-based agents are designed to achieve specific goals. They use planning and reasoning techniques to determine the best actions to take to reach their objectives.

Utility-based agents consider multiple factors and choose actions that maximize their overall utility or benefit. They can balance competing goals and make trade-offs.

Learning agents can improve their performance over time by learning from their experiences. They use techniques like machine learning to adapt to new situations and improve their decision-making abilities.You could also categorize agents in other ways, for example, in an educational contex.

For personalized learning, agents can adapt educational content to meet individual students' needs, learning styles, and pace. By analyzing data on students' performance and preferences, AI can recommend personalized learning paths and resources. In a related way, intelligent tutoring systems can provide one-on-one tutoring by offering explanations, feedback, and hints the way that a human tutor might. They might even be able to create more inclusive learning environments by providing tools like speech-to-text, text-to-speech, and translation services, ensuring that all students have access to educational content. By analyzing students' performance data, they could identify at-risk students and provide early interventions to help them succeed.

AI agents can automate administrative tasks for faculty, such as grading, attendance tracking, and scheduling, freeing up educators' time to focus more on teaching and interacting with students.

Agents can "assist" in creating educational materials. I would hope faculty would be closely monitoring AI creation of tests, quizzes, lesson plans, and interactive simulations.

Though I see predictions of fully AI-powered virtual classrooms that can facilitate remote learning, I believe this is the most distant application - and probably the one that most makes faculty apprehensive.