Philipp Behr’s Post

I have always enjoyed learning new things mostly because I like exploring topics that interest me for various reasons. But learning is not easy, it takes commitment and utter focus. However, it involves strategy. Now, I am at a point where I can easily interpolate missing pieces in a given topic when provided with a few information. Another way of learning is to assert an opinion and fact-check to see if it agrees with the original topic. In other words, you guess to see if your opinions match the answers or align with the subject matter. This is the basic idea behind weak-to-strong learning in machine learning. What is Weak-to-Strong Learning? Weak-to-strong learning is a way to build smart ML algorithms or models. It uses many simple models to create one powerful model. Here's how it works: o Start with weak learners: It is a base model that is simple and not complex. It's only a little better than random guessing. Think of it as asking a friend who knows a bit about a topic. o Combine many weak learners: To make it stronger we use many of these simple models together. Each one helps a little bit. Together, they can solve hard problems. o Focus on mistakes: This method pays extra attention to tough examples. It keeps trying to improve where it's weak. This helps it get better and better over time. o Create a strong learner: The final result is a powerful program. It can solve complex problems accurately. One of the core concepts of this method is marginal importance. Imagine drawing a line, a decision boundary, between two groups of dots. The margin is the space around this line where no points fall. A bigger space (margin) means more confidence in decisions. Additionally, it also means better performance on new, unseen data. With a bigger margin, small variations in input don't easily change the outcome. How does this relate to weak-to-strong learning? In weak-to-strong learning, especially in methods like AdaBoost, the process naturally tends to maximize the margin. As the algorithm combines weak learners, it's essentially creating a larger and larger margin. o Weak-to-Strong Learning in LLM Interestingly, today many LLM systems use this method for their advancement. For instance, in:  o Content Moderation: Weak-to-strong learning can help LLMs identify and filter harmful or inappropriate content. By training smaller models on safe content, the larger model can better understand and enforce community guidelines. o Personalized Recommendations: This technique can enhance LLMs in providing personalized suggestions, such as books, movies, or products. Smaller models can be tuned to reflect user preferences, guiding the larger model to generate more relevant recommendations. o Customer Support Automation: Weak-to-strong learning can improve LLMs used in customer service chatbots. By training smaller models on effective responses, the larger model can generate more accurate and helpful replies to customer inquiries.

How can the concepts of Zone of Proximal Development, Experienced Other and Authoring be used to redesign learning to incorporate GPTs – discuss in relation to a specific module that you have studied? DRAFT/STUDY TIPS: Redesigning Learning with GPTs: Integrating the Zone of Proximal Development, Experienced Other, and Authoring Introduction The rapid advancement of artificial intelligence (AI) and its integration into various educational contexts have necessitated a rethinking of traditional learning paradigms. Among the most promising AI tools are Generative Pre-trained Transformers (GPTs), which offer vast potential to enhance and transform educational experiences. To effectively harness the capabilities of GPTs, it is essential to integrate foundational educational theories that support scaffolded and personalized learning. This paper explores how the concepts of the Zone of Proximal Development (ZPD), Experienced Other, and Authoring can be employed to redesign learning modules to incorporate GPTs, thereby creating more dynamic and supportive educational environments. The analysis will focus on the application of these concepts to a specific module: Advanced Writing and Composition. Theoretical Framework Zone of Proximal Development The Zone of Proximal Development, introduced by Lev Vygotsky, is a foundational concept in educational psychology. It refers to the range of tasks that a learner can perform with the guidance and support of a more knowledgeable other but cannot yet accomplish independently. This concept emphasizes the importance of scaffolded learning, where instructional support is gradually removed as the learner gains proficiency. By identifying the ZPD, educators can tailor their instructional strategies to provide appropriate challenges and support, optimizing the learning experience.

AI + L&D = perfectly aligned partnership in fast paced, overdosed and flooded with options learning enviroments! The PARADOX of CHOICE perfectly mirrors today’s learning environment. In a world flooded with endless options—be it courses, platforms, or formats—learners often find themselves paralyzed by too many choices. As the renowned psychologist Barry Schwartz puts it, “The more choices we have, the harder it is to choose.” This “decision fatigue” not only stifles motivation but also impedes meaningful engagement. It’s like deciding on a movie to watch: with so many streaming services and endless options, the process of choosing often takes longer than the actual viewing experience, and in the end, you may end up not watching anything at all. The same happens with learning—when faced with an overwhelming array of choices, the excitement of discovery gives way to frustration and indecision. In this landscape, Learning & Development (L&D) professionals have the power to cut through the clutter and light the way. Rather than adding to the chaos, L&D can focus on CURATING what truly matters, offering learners a clear path forward. As Steve Jobs wisely said, “Deciding what not to do is as important as deciding what to do.” L&D teams can play a pivotal role in simplifying the decision-making process, delivering content that is not only relevant but also PURPOSEful. One of the most exciting ways to reduce choice overload and enhance the learning experience is through the power of Artificial Intelligence (AI). AI has the ability to PERSONALIZE learning at a level that was once unimaginable. By analyzing a learner’s preferences, performance, and goals, AI can recommend the most relevant content, offering a tailored experience that feels intuitive and focused. Instead of scrolling endlessly through a sea of options, learners can receive curated suggestions that truly ALIGN with their needs. As AI expert Andrew Ng said, “Artificial intelligence is the new electricity.” It has the potential to transform learning by cutting through the noise and providing the personalized guidance that learners need to succeed. As AI continues to evolve, its role in Learning & Development becomes even more crucial. As Satya Nadella, CEO of Microsoft, emphasizes, “AI will transform every software category, starting with the most important—learning.” With AI, L&D can be more efficient, ENGAGING, and IMPACTFUL. Adaptive learning platforms powered by AI can adjust the difficulty of material in real-time, ensuring that content challenges learners just enough without overwhelming them. Immediate feedback and targeted suggestions can keep learners motivated and on track, allowing to focus on what truly matters. By harnessing AI, L&D can not only SIMPLIFY the learning journey but also make it far more relevant and effective, EMPOWERING learners to achieve full potential. #extraordinaryYOU #development #powerofAI #leadership #learning #ShapeYourFutureWithConfidence #EYMENA

Brilliant way to begin exploring methods of mondernizing traditional assignment structures. The key is in testing and iterating.

99% of people misunderstand personalized learning. In the Edtech ecosystem, personalized learning is a hot topic. While it's not quite the holy grail, it's definitely something every service and platform aims to implement. In recent years, this concept has received a significant boost from two magic letters: AI. However, there is a widespread misunderstanding about personalized learning. The common rationale behind personalized learning is often the notion that everyone learns differently. It's based on the idea that each individual has a unique brain and learning style, requiring a tailored approach to education. Let's unpack some hidden problems here. Firstly, personalization has become a prevalent trend across various domains, from fashion to nutrition to hobbies. Demonstrating uniqueness has become a value in itself. However, in many cases, the process of personalization offers few real advantages to the individual. Moreover, personalization is a strategy to distinguish oneself in a world inundated with information and services. Now, let's turn our focus back to pedagogy. As someone deeply invested in learning, I've explored various seemingly unrelated skills and disciplines, such as language learning, trail running, dance, meditation, physics, and more. Across all these domains, I've observed that each has established protocols, systems, and learning methods designed to efficiently progress from point A to B. Contrary to common belief, general learning protocols do exist. Regardless of one's background or preferences, there are systematic steps built to facilitate skill acquisition. This holds true for mastering a handstand, perfecting an arm wave, or solving differential equations. The issue arises when personalization serves as compensation over bad pedagogy or an excuse for students to avoid challenging, yet meaningful, work because it doesn't align with their preferred learning style. Personalization can and should be a valuable tool in modern pedagogy. Here are two approaches to ensure its effectiveness: 1. Personalized Feedback - Great coaches and teachers excel at identifying students' precise barriers/gaps and crafting tailored challenges to facilitate breakthroughs. Unfortunately, this type of personalization is not that easy to achieve automatically but it is certainly where we need to aim towards.     2. Personalized Protocols - Different goals necessitate varied tools and methods. We must offer students improved protocols aligned with their specific interests or objectives. This subtle distinction addresses the core issue highlighted earlier. What are your thoughts on the recent changes in personalized learning? Let me know in the comments :)

Want to turn a traditional assignment into Project-Based Learning? This prompt will give you some ideas. ⬇️⬇️⬇️ Simply: 1. Run the prompt in any AI program. 2. Give it your normal assignment. 3. Look through the AI's ideas. 4. Revise, revise, revise. -------- Prompt for Turning a Traditional Assessment to a PBL [Role] You are an educator with a decade of in-the-classroom experience as well as a firm grounding in strong pedagogical principles. You believe in student-centered learning experiences that provide students with control. You are a follower of Daniel Pink's idea that people are motivated by autonomy, a quest for mastery, and a sense of purpose. You work those ideas into your assignments. [Instructions] I will provide you with a traditional assessment (such as a paper). You will go through the following steps, marked as [Step 1] to [Step 3]. Do not move on from one step until it is completed. [Step 1] You will ask me for the traditional assignment. I will provide it. [Step 2] You will provide 3 ideas for a Project-Based Learning assignment, based on the traditional assignment I provided you in [Step 1]. You will write these exact words, "Which one would you like me to work out in more detail? Or would you like me to generate 3 new options?" [Step 3] If I asked you to generate 3 new options, do that and move on to [Step 4]. If I asked you to give more details about one of the 3 options you've already given me, then provide me with a full outline of the assignment. This will include a full write-up of the assignment for students and a grading rubric (use concrete, specific criteria. format it as a table). Then, you are done. Ask me if there is anything else I want. [Step 4] Keep going until I say I am satisfied with one of your options. Then, provide me with a full outline of the assignment. This will include a full write-up of the assignment for students and a grading rubric (use concrete, specific criteria. format it as a table). Then, you are done. Ask me if there is anything else I want. [Details] When generating the alternative assignments, you will stick as close as possible to the principles of Project-Based Learning (PBL). This means creating an assignment that is constructive, collaborative, contextual, self-directed, and flexible. Essentially, it should invite students to own their own learning and apply course principles to a personal project or passion.

L&D Professionals - how often do you slow down to consider the readability of the content you're creating? This article was a great reminder to me that "As learning and development professionals, we have the privilege and responsibility to shape the way information is presented to learners." This isn't new information for me - but I am guilty of rushing through and not taking the time to check the things I know to lower readability. The way that #AI can aid in this process has made this easier than ever. Time for a new post-it note on my computer with these main influencing factors. Then I can easily ask #AI to check my content for them before I send it off to learners. 1. Sentence Structure: Short and straightforward sentences are easier to comprehend than long and convoluted ones. It’s essential to break down complex ideas into digestible chunks, aiding learners in processing information efficiently. 2. Word Choice: The choice of words impacts readability significantly. Avoiding technical jargon or explaining it when necessary can make a substantial difference in learners’ understanding. 3. Active Voice vs. Passive Voice: The active voice is generally more straightforward and engaging, as it conveys actions directly, making the subject and action clear. 4. Paragraph Length and Formatting : Long paragraphs can be overwhelming and challenging to follow. Breaking content into smaller paragraphs and using formatting elements such as bullet points and headings can improve readability. 5. Visual Elements: Incorporating visual elements like images, graphs, and diagrams can enhance comprehension by providing visual cues that complement the text. 6. Consistent Tone and Style: Maintaining a consistent tone and style throughout the content ensures a cohesive and coherent learning experience. *Thanks to Enqurious (formerly Mentorskool) for this great artcile. https://lnkd.in/gWURBSsa

Is there a relationship between organizational theory and decision making?  As a teacher and a program designer, I tried to find a relationship between the learning theory and what was actually happening in the classroom and the shop. There were definitely metaphorical relationships that seemed to explain learning and decision making. For example there does seem to be some relationship between IQ tests and academic learning. Also, we can metaphorically justify learning as a style. We may be able to metaphorically justify the idea of multiple intelligences or the idea of learning taxonomies. But no matter how useful we may find those metaphors, deep down we know they don’t justify the decisions we make about education and learning. It’s the same thing in the workplace. There are no real connections between the metaphorical systems we use to explain experience and the actual experience of working. We can talk around it forever, but the talk just produces more talk. There is no definitive agreement, even on the iconic metaphors that have been around forever. Metaphorical management only works when people are indoctrinated to the metaphor and can only be justified with data within the particular metaphorical context. The further back we go in time, the more we see the similarities between learning metaphors and work metaphors. In my experience the ones that are most universal are the experiential learning metaphors. While existential learning and work metaphors seem to have some utility, they rarely hold up in the context of experiential metaphors. This is the case I am making for optologics and systemism. Optologics is not a metaphor. It is based on the reality of how we use visual information. It is not a judgmental framework. It puts information in a context to be analyzed. Systemism is integration of optological constructs that are existential and experiential at the same time, as is systemism itself. Optologics and systemism do not have the aura of philosophy. They are just common sense. They are not a philosophical entity in themselves; they are just tools used to get the job done. All anyone has to do is grab the concept and go with it. It’s like learning to drive a car - all practical and minimal theory. In fact it can be used to cut through nonsense and invoke accountability on a continuous basis. So if you want an alternative to metaphors and want to manage your learning and decision making, just go with the learning and decision making graphic. If you go to my website you will see how I adapted it to a large number of situations. If you get my book there are even more. It always helps to have a few examples to guide you as you learn. Optologics and systemism are do it yourself tools. With a little bit of experience, you will be able to get started, and the more you use it, the better you will get.

I have often wondered why training courses generally feel so dull, boring, and unengaging. Traditional methods seem to miss the mark, failing to capture our interest or cater to our unique learning styles. What if we could change that? Imagine a world where training is personalized, adapting to each individual's needs and strengths. This approach could transform the learning experience, making it not only more effective but also more enjoyable. Personalized learning has the potential to ignite our curiosity and drive, turning mundane training into an exciting journey of discovery.#PersonalizedLearning #Engagement #FutureOfEducation #LearningRevolution