home.social

#activelearning — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #activelearning, aggregated by home.social.

fetched live
  1. The Impact of Active Learning on Student Course Performance in STEM Varies by Type and Intensity: A Meta-Analysis mdpi.com/2227-7102/16/7/1044
    "active learning’s impact on low-income and underrepresented minority students showed that achievement gaps in both exam scores and failure rates only closed in course designs where more than 66% of class time was spent in active learning"
    #ActiveLearning #Teaching #STEMeducation #EdDev

  2. The Impact of Active Learning on Student Course Performance in STEM Varies by Type and Intensity: A Meta-Analysis mdpi.com/2227-7102/16/7/1044
    "active learning’s impact on low-income and underrepresented minority students showed that achievement gaps in both exam scores and failure rates only closed in course designs where more than 66% of class time was spent in active learning"
    #ActiveLearning #Teaching #STEMeducation #EdDev

  3. Learning physics can derail some students: New research shows the best way to keep them on track phys.org/news/2026-05-physics-
    Summary of: Relative benefits of different active learning methods to conceptual physics learning nature.com/articles/s41567-026
    #PhysicsEd #STEMeducation #ActiveLearning #Teaching

  4. Learning physics can derail some students: New research shows the best way to keep them on track phys.org/news/2026-05-physics-
    Summary of: Relative benefits of different active learning methods to conceptual physics learning nature.com/articles/s41567-026
    #PhysicsEd #STEMeducation #ActiveLearning #Teaching

  5. What are the conditions which make it possible to learn with AI?

    This thoughtful pre-print by Favero et al offered a pleasingly straight forward answer to this question. Ultimately we know what makes for effective learning:

    • It has to involve active engagement in which the learner is reflecting, connecting and synthesising material rather than simply passively receiving it.
    • It has to integrate the new understanding which emerges into their existing understanding through direct experience and dialogue.
    • It has to involve the phased withdrawal of support as learners gain competence and confidence.

    Rather than ask ‘what are the conditions which make it possible to learn with AI’ we can instead ask ‘what are the conditions which make it possible to use AI in these ways‘? The problem with commercial chatbots is not the technology itself but rather the design decisions involved in making a technology for a mass audience (as Nick Srnicek has plausibly argued AGI is ultimately an ambition to create a product which work universally without sector-specific fine tuning) which mean that, not only is it not adapted to these specific educational requirements, it actively works against them in a number of ways:

    • The role of prompting has become decreasingly important with successive models
    • The models are increasingly agentive in the sense of able and willing to actively do stuff for the user
    • The role of model memory makes it easier for habits to form over time so that users get locked into certain ways of using the chatbot

    This means there’s a fundamental tension in using these chatbots for educational purposes. It doesn’t mean it’s impossible. We also shouldn’t look the perfect be the enemy of the good: as the WonkHE report plausibly argued their widespread use reflects weaknesses of existing provision in that students are drawing on them to meet needs we are failing to meet. But we need to be realistic about the underlying tension, even as we try and mitigate it through AI literacy. In part this means helping students relate to chatbots in a way which doesn’t avoid difficulty. I really like how the authors describe the problem here:

    However, learners often try to avoid such an effort. Studies show that high perceived difficulty and low short-term performance can discourage engagement, despite clear long- term benefits. Interestingly, Deslauriers et al. [29] found that students in active learning environments learned more but felt they learned less. Mental effort was misinterpreted as failure, while smooth lectures mistakenly felt more effective, though they were not. This cognitive bias leads students to favor fluent, low-effort activities that give the illusion of learning, such as re-reading polished explanations, without fostering deep processing [27, 30, 16].

    AI tools, and particularly chatbots, may exacerbate this issue. By offering quick, fluent, and simplified answers, they reduce the cognitive struggle which is essential to learning [14]. Their convenience may lead to passive consumption, decreased research and reasoning skills, and a growing dependence on pre-digested knowledge [31]. Ease of use is appealing, but true learning comes from effort, complexity, and time.

    This isn’t just significant for their studies. There’s AI slop proliferating in academic workplaces which appear to embody the same tendency, in which people in a rush can produce something superficially plausible which lets them tick it off a list and leaves them feeling more accomplished. Knowing how to sit with difficulty, to avoid the temptation of superficially fluent outputs, will I’m fairly confident be a skill that employers will value ever more in the coming years. This means recognising the difference between ‘desirable difficulties’ and contingent barriers which can be automated away. It’s impossible to do this unless use remains active throughout what Milan Sturmer and I call the user-model interaction cycle:

    • Positioning: establishing a role for the model in interaction
    • Articulation: putting ideas into words which are provided by the model
    • Attunement: feeling the model has ‘understood’ what the user has brought to the interaction

    Each of these dimensions can be (relatively) passive. Each of these dimensions can be active. The thread uniting active use is metacognition, in the sense the authors talk about here:

    Routine use of AI tools can hinder the development of metacognitive skills, independent thinking, and intellectual agency [5, 12]. As students begin to outsource decision-making to the ma- chine, they risk becoming passive recipients of information rather than critical participants in the learning process [4]. This passivity is linked to broader deficits, including reduced creativity, increased mental laziness, and diminished capacity for critical thought [16]. Moreover, dependency on AI tools can lead to the uncritical acceptance of their outputs. When students perceive these systems as convenient, accurate, and reliable, they may stop questioning the information provided, which fosters cognitive dependency, i.e., the erosion of the ability to assess, verify, and challenge content independently

    We need to help students name what it feels like to be actively thinking when interacting with a model. There’s a distinct phenomenology to this. It’s also something which cannot be sustained indefinitely. As you get tired, it’s easy to slide into increasingly passive uses of the model, with ever more capable models almost seamlessly picking up the load from you.

    #activeLearning #AI #constructivism #effectiveLearning #metacognition
  6. What are the conditions which make it possible to learn with AI?

    This thoughtful pre-print by Favero et al offered a pleasingly straight forward answer to this question. Ultimately we know what makes for effective learning:

    • It has to involve active engagement in which the learner is reflecting, connecting and synthesising material rather than simply passively receiving it.
    • It has to integrate the new understanding which emerges into their existing understanding through direct experience and dialogue.
    • It has to involve the phased withdrawal of support as learners gain competence and confidence.

    Rather than ask ‘what are the conditions which make it possible to learn with AI’ we can instead ask ‘what are the conditions which make it possible to use AI in these ways‘? The problem with commercial chatbots is not the technology itself but rather the design decisions involved in making a technology for a mass audience (as Nick Srnicek has plausibly argued AGI is ultimately an ambition to create a product which work universally without sector-specific fine tuning) which mean that, not only is it not adapted to these specific educational requirements, it actively works against them in a number of ways:

    • The role of prompting has become decreasingly important with successive models
    • The models are increasingly agentive in the sense of able and willing to actively do stuff for the user
    • The role of model memory makes it easier for habits to form over time so that users get locked into certain ways of using the chatbot

    This means there’s a fundamental tension in using these chatbots for educational purposes. It doesn’t mean it’s impossible. We also shouldn’t look the perfect be the enemy of the good: as the WonkHE report plausibly argued their widespread use reflects weaknesses of existing provision in that students are drawing on them to meet needs we are failing to meet. But we need to be realistic about the underlying tension, even as we try and mitigate it through AI literacy. In part this means helping students relate to chatbots in a way which doesn’t avoid difficulty. I really like how the authors describe the problem here:

    However, learners often try to avoid such an effort. Studies show that high perceived difficulty and low short-term performance can discourage engagement, despite clear long- term benefits. Interestingly, Deslauriers et al. [29] found that students in active learning environments learned more but felt they learned less. Mental effort was misinterpreted as failure, while smooth lectures mistakenly felt more effective, though they were not. This cognitive bias leads students to favor fluent, low-effort activities that give the illusion of learning, such as re-reading polished explanations, without fostering deep processing [27, 30, 16].

    AI tools, and particularly chatbots, may exacerbate this issue. By offering quick, fluent, and simplified answers, they reduce the cognitive struggle which is essential to learning [14]. Their convenience may lead to passive consumption, decreased research and reasoning skills, and a growing dependence on pre-digested knowledge [31]. Ease of use is appealing, but true learning comes from effort, complexity, and time.

    This isn’t just significant for their studies. There’s AI slop proliferating in academic workplaces which appear to embody the same tendency, in which people in a rush can produce something superficially plausible which lets them tick it off a list and leaves them feeling more accomplished. Knowing how to sit with difficulty, to avoid the temptation of superficially fluent outputs, will I’m fairly confident be a skill that employers will value ever more in the coming years. This means recognising the difference between ‘desirable difficulties’ and contingent barriers which can be automated away. It’s impossible to do this unless use remains active throughout what Milan Sturmer and I call the user-model interaction cycle:

    • Positioning: establishing a role for the model in interaction
    • Articulation: putting ideas into words which are provided by the model
    • Attunement: feeling the model has ‘understood’ what the user has brought to the interaction

    Each of these dimensions can be (relatively) passive. Each of these dimensions can be active. The thread uniting active use is metacognition, in the sense the authors talk about here:

    Routine use of AI tools can hinder the development of metacognitive skills, independent thinking, and intellectual agency [5, 12]. As students begin to outsource decision-making to the ma- chine, they risk becoming passive recipients of information rather than critical participants in the learning process [4]. This passivity is linked to broader deficits, including reduced creativity, increased mental laziness, and diminished capacity for critical thought [16]. Moreover, dependency on AI tools can lead to the uncritical acceptance of their outputs. When students perceive these systems as convenient, accurate, and reliable, they may stop questioning the information provided, which fosters cognitive dependency, i.e., the erosion of the ability to assess, verify, and challenge content independently

    We need to help students name what it feels like to be actively thinking when interacting with a model. There’s a distinct phenomenology to this. It’s also something which cannot be sustained indefinitely. As you get tired, it’s easy to slide into increasingly passive uses of the model, with ever more capable models almost seamlessly picking up the load from you.

    #activeLearning #AI #constructivism #effectiveLearning #metacognition
  7. "Stop Reading Passively — How Annotation Transforms the Way You Learn." — When you annotate, you stop receiving information and start responding to it. That's where real learning begins. ✍️ paulopinto.xyz/notes/2026/0... #ActiveLearning #Annotation #LearningTools

  8. Mrs Murphy’s Spring Veggie Frittata Muffins are a simple, nourishing way to celebrate the shift towards lighter days and fresh flavours. Packed with seasonal vegetables and baked into perfectly portioned muffins, they are ideal for picnics or a quick family supper.

    Cooking together builds far more than practical skills. It strengthens communication, independence and a sense of contribution within the family.

    #recipe #activelearning #reggioemilia #reggioinspired

  9. Mrs Murphy’s Spring Veggie Frittata Muffins are a simple, nourishing way to celebrate the shift towards lighter days and fresh flavours. Packed with seasonal vegetables and baked into perfectly portioned muffins, they are ideal for picnics or a quick family supper.

    Cooking together builds far more than practical skills. It strengthens communication, independence and a sense of contribution within the family.

    #recipe #activelearning #reggioemilia #reggioinspired

  10. Results from our #RCT of Jigsaw #ActiveLearning vs. traditional lectures for teaching #anatomy and #physiology:

    Both methods improved student knowledge, but no evidence of *differences* in learning between the two methods (immediately or after three months). However, many more students in the ‘traditional teaching’ group than in the Jigsaw group *preferred* their assigned teaching method over others (78% vs. 38%).

    doi.org/10.1186/s12909-026-086 (#OpenAccess)

    #education #teaching #HigherEd #MedEd

  11. Results from our #RCT of Jigsaw #ActiveLearning vs. traditional lectures for teaching #anatomy and #physiology:

    Both methods improved student knowledge, but no evidence of *differences* in learning between the two methods (immediately or after three months). However, many more students in the ‘traditional teaching’ group than in the Jigsaw group *preferred* their assigned teaching method over others (78% vs. 38%).

    doi.org/10.1186/s12909-026-086 (#OpenAccess)

    #education #teaching #HigherEd #MedEd

  12. National Storytelling Week reminds us that stories don’t only live in books 📚✨

    With a light, a screen, and a little imagination, shadow puppets bring stories to life in a whole new way- through movement, voices, problem-solving and creativity. Children become the storytellers, the characters, and the directors all at once.
    This is a great activity to try at home!

    #nationalstorytellingweek #eyfs #earlyyears #activelearning #enablingenvironment #reggio #reggioemilia