Tag: design

  • What’s love got to do with neurodiversity and HE art and design?

    What’s love got to do with neurodiversity and HE art and design?

    by Kai Syng Tan

    A loveless storm and a love-filled symposium

    On 18 November I was ill. I recovered in time to travel to Helsinki for a symposium two days later, but winter storms shut down the airport, delayed flights and lost luggage, including mine. The symposium director Dr Timothy Smith (image 2 below, to the left) had to step in to act as my wardrobe assistant. Like many neurodivergent academics, Tim works across an astonishing range of knowledges, including political science, fine art, public policy and pedagogy. But I’m quite certain that sourcing for clothes to fit 155cm grumpy people isn’t part of their typical repertoire.  

    Image 2
    Image 3: Fidget toys placed on top of a paper file that reads ‘UNIARTS HELSINKI’, with a name tag with a lime green strap and name ‘KAI’. 
    Image 3

    Image 2: A symposium with person standing to the left holding a microphone; another in the middle, seated, in front of a projection with book cover and QR codes and next to a screen showing live captioning; more people in the foreground on different forms of seating and being

    Image 3: Fidget toys placed on top of a paper file that reads ‘UNIARTS HELSINKI’, with a name tag with a lime green strap and name ‘KAI’. 

    Tim’s Neurodiversity in the Arts Symposium, which took two years of advocacy and planning, and draws on several more of research across neurodiversity and art education, took place at the University of the Arts Helsinki, modelling best practices for inclusivity, not just for neurodivergent folx. Universities, watch and learn. Yes it can be done. So, what does a symposium led by love look like in action? Let’s spell out a few ways how:   

    • Programming not to neo-liberalist but ‘crip time’ (Kafer 2013), enabling us to process our thoughts, with 30 minute breaks between sessions, and a 2-hour lunch break;
    • Employment of live professional CART (Communication Access Real-Time Translation) captioning – not the still racist AI captioning that does not grasp ‘non-standard’ accents (image 2, to the right);
    • Where divergent modes of being – including horizontally, in motion etc, not just seated or erect – are affirmed (image 2, foreground);
    • Inclusion of fidget toys in the goody bag (image 3);
    • Provision of quiet spaces – no, we’re not talking about a broom cupboard or first aid room doubling, but a (care-)fully decked out sensory rooms for group or solo use, with low lighting, different soft furnishings as well as more sensory objects for people to shut off, calm down and/or regroup (image 4);
    • Detailed maps, diagrams and instructions for ‘walking or wheeling’ to venues; including for a dinner, at a five-star hotel, which was a delicious vegan spread – and entirely free of charge;    
    • Priced at less than one-third the fee of a usual conference at €100 – and that’s for ‘participants receiving full institutional financial support’; otherwise, ‘please select the €0 fee option’;
    • Elevating and celebrating diverse body-minds-worlds whose research, creative and professional practice gather, collide and transcend disciplines, fields of knowledge, cultures, geopolitical borders, and specialisms and in the lineup. This includes shy*play, a pedagogical platform, collective, and art practice comprising teacher-researchers from Netherlands-Spain Antje Nestel and Aion Arribas, who invite us to ‘do neurodiversity’ (images 5a-5b); Estonian-UK PhD candidate Iris Sirendi discussing their Curating for Change curatorial fellowship at the Museum of Liverpool and urging – no, daring – the arts and cultural sector to step up and ‘crip the museum’ (image 6); US-Canadian-Polish feminist researcher and author of several books including Asexual Erotics Professor Ela Przybylo disclosing their new identity/positionality of being autistic, and inviting responses Towards a Neuroqueer Conference Manifesto/a/x.
    Image 4: Sensory room, with low blue-green lighting, soft furnishings and soft toys
    Image 4
    Image 5a: shy*play’s Antje Nestel and Aion Arribas, both holding microphones and reading from papers strewn on a long table
    Image 5a
    Image 5b: people ‘doing neurodiversity’ in different ways, including by displaying their creations on a wall that acts as a shared canvas
    Image 5b
    Image 6: Estonian-UK PhD candidate Iris Sirendi at a long desk speaking to a projection with a slide with the heading ‘What’s Next?’ and a logo that reads ‘The Neurodiverse Museum’.
    Image 6

    Image 4: Sensory room, with low blue-green lighting, soft furnishings and soft toys

    Image 5a: shy*play’s Antje Nestel and Aion Arribas, both holding microphones and reading from papers strewn on a long table

    Image 5b: people ‘doing neurodiversity’ in different ways, including by displaying their creations on a wall that acts as a shared canvas

    Image 6: Estonian-UK PhD candidate Iris Sirendi at a long desk speaking to a projection with a slide with the heading ‘What’s Next?’ and a logo that reads ‘The Neurodiverse Museum’

    The above are just a few of the highlights from the in-person session on 22 November 2024, which complements an online symposium with a different programme a week prior on 15 November 2024 for those who prefer the digital interface, both of which are recorded with transcripts which all participants can freely access. 

    I’m not singing the praises of the Neurodiversity in the Arts Symposium because I was the keynote speaker.

    I’m saying the above as I’ve been a keynote as well as participant in more than 100 conferences – and I’m still allergic to them, not least as someone who is hyperactive and literally cannot sit still. I’m also saying this as someone who’s curated several, including one on running as an arts and humanities discourse that a 2014 Guardian article said ‘other conferences could take a leaf out of’, for its 8-minute sprint formats and multi-modal approaches including film screening, meditation sessions and run-chats.

    But Tim’s conference was way better. The symposium is prioritising not just neurodivergent and queer – neuroqueer (Walker 2021) – perspectives. Following the positionality of multiply-minoritised researchers in higher education Angel L. Miles, Akemi Nishida and Anjali J. Forber-Pratt at the University of Illinois at Chicago and Vanderbilt University as expressed in their powerful open letter to White disability studies and ableist institutions of higher education (2017), the symposium focuses on research that counter ‘white supremacy and racism; colonialism and xenophobia; ageism; sexism and misogyny; cisnormativity and transphobia; and heteronormativity and heterosexism’.

    And I’m sure that Tim, like me, wants other conferences to come, to even better ours. 

    So, take our baton. Run with it.

    Why neurodiversity? Why now?

    ‘Neurodiversity’ – broadly the coexistence of different ways of processing information, learning and being – has exploded as a buzzword in the past few years. If you didn’t know that 15-20% (Doyle 2020) of humans are autistic, with dyslexia, Tourettes, ADHD and other forms of neurodevelopmental processes, you will have run into the extensive media coverage, or seen your Gen-Z students or kids declaring their ‘neuro-spiciness’ on Tik Tok.

    It is well-established that neurocognitive variants like dyslexia, ADHD and autism are over-represented in the arts and culture (above 30%, eg RCA 2001; Bacon and Bennett (2013); Universal Music (2020)). This is unsurprising, given how neurodiversity, innovation and change-making are powerfully entangled, being essential for human’s evolution, inventiveness, creativity and more. Networks, academic publications, research centres, educational research centres and conferences by/with/for neurodivergent creative researchers have been emerging in the last years too.

    This year alone, I was external examiner for two creative PhDs by/for/with neurodivergence, and helped deliver one PhD candidate to the finish line and whom, since 28 November, can now add ‘Dr’ to their name, likely to the chagrin of those who think that only clinicians are ‘real’ doctors and experts. Collectively, these efforts are countering medicalised and deficit approaches to cognitive difference. By 2050, 1.94 billion of the 9.4 billion population will be neurodivergent – making neurodivergence far from a ‘niche’ phenomena or area of research, but one with substantive critical mass.

    Those with social capital wear their difference as proud badges of honour. So far so ‘authentic’. 

    But surprise, surprise – for the multiply-minoritised, their difference continues to be demonised, pathologised, infantilised, and/or policed. This includes teachers and researchers who draw on their neurodivergence in their teaching and research. That’s also why many aren’t out – or have/want access to diagnosis (which themselves have long waiting lists, are costly and more), etc, and often aren’t reflected in the official figures and studies. It’s also only recently been understood in leadership studies that when a white heterosexual cis-man expresses his ‘true self’, it’s just not acceptable, or even laudable. For those who are not straight, not white, not of the right class, or the right skin tone etc – authenticity comes at a high cost – including literally so. Being dyslexic, I struggle with normative approaches to reading and writing – but reading and writing are literally bread and butter for an academic! Disclosing that you cannot read or write would be tantamount to career-suicide, especially if you are on a fixed-term contract – if you have been able to survive the ableist, racist and sexist HE system at all, that is.    

    Harvard, World Economic Forum, NESTA and other global bodies have been selling neurodivergence as the ‘next talent opportunity in the workplace’, ‘competitive advantage’ and a ‘neuroleadership’ antidote to in tackling wicked challenges for the Fourth Industrial Revolution — but without neurodivergent voices in this discussion, isn’t this objectifying and othering?

    Then, there’s a certain cartoon-tycoon who has been dominating the headlines. When not firing their critics from their factories and firms, or firing rockets to colonise the moon and Mars, this person is firing spats on social media — before buying up the site to make it their temple for ‘unmoderated toxicity’. After firing pot-shots at child-free cat ladies, they’re asking ‘high-IQ revolutionaries’ to work for no pay for an incumbent government. The latter call is interesting because this person had announced that they are ‘with Aspergers’, using the outdated terminology still instrumentalised by certain ‘high-functioning’ autistic people, to denote that they are a genius — ie a high-IQ revolutionary themselves!    

    Why neurodiversity, love and HE art and design?

    As an autistic child-free cat lady, it’s my duty to ask other neurodivergent artists, academics, activists and allies within Higher Education (HE) to do more and do better, to call out on dangerous neurodivergent figures and approaches, and to counter that with love. If Machiavellian misfits and messiahs weaponise their neurodivergence, so must neurodivergent movers and shakers dis-arm them.  

    Image 7

    Caption: Love-led guidelines for to make spaces more inclusive, in diagram form with 8 blocks of texts. From Tan, Kai Syng. Neurodiversity In/& Creative Research Network shared, LIVE, CO-CREATED Community Guidelines since 2022

    For several years, I’ve researched into and discussed the need to dismantle harmful narratives of neurodiversity. Through an art-psychiatry project, founding of a global 435-member network for neurodivergent innovators, I’ve urged for a decolonial  — ie shift of focus away from knowledge and practices in the West and global north — and intersectional — ie consideration of a how multiple, complex contexts interact and intersect  — approach. We’ve come up with love-led guidelines for activities (image 7). I’m editing a publication with a major academic publisher, which is possibly the first book with openly neurodivergent academics ranging from early career researchers to established, newly-‘out’ professors, to discuss our research through the prisms of neurodivergence and creativity (c2027). Along the way, we are introducing and foregrounding neurodivergent approaches to knowledge, creative research and writing with play, lived experience and more, thus challenging the dominant, normative habits demanded by the academic publishing industrial complex that emphasise the linear, causal, and ‘neutral’.   

    On this SRHE platform, I’ve previously discussed a neurodivergence-inspired pedagogical approach to transform HE culture, illustrating how this isn’t just an armchair exercise or a theoretical pontification from the ivory tower, with examples I have led, such as a four-day festival for Black History Month 2020 in Manchester. To mark Valentines’ day this year, I discussed the need to build love into HE curricula – standing on the shoulders of great artists, activists and teachers before us, like bell hooks, Paulo Friere and James Baldwin.

    My keynote at the Neurodiversity in the Arts Symposium was entitled ‘Neuro-Futurism and Reimagining Leadership’.  My performance-lecture was based on my book of the same title, subtitled ‘An A-Z Towards Collective Liberation’. Grasping how systemic oppressions are interconnected and how liberatory approaches to education must be joined up is vital in this discussion. I postulate a new intellectual agenda and action plan for ‘leadership’ as discourse and practice anchored in visual arts and arts education. Re-claiming the subject from business or arts management, and away from a trait/talent hinged on individualism, hierarchy, genes or luck, the book – and my performance – entangles critical leadership studies with socially-engaged art and relational aesthetics, embedding neuro-queering, futurity, and Chinese Daoist cosmology for the first time, to introduce ‘neuro-futurism’ as a beyond-colonial, (co-)creative change-making framework.

    The participants of the symposium grasped this, responding by describing the performance-lecture as ‘phenomenal’. Brazilian artist-researcher Fran Trento, a postdoctoral researcher at the Department of Geosciences and Geography at the University of Helsinki, even took live notes and pictures to add to their mobile participatory art installation, and wheeled it around, further spreading love in HE – literally (Image 8). If it hadn’t been snowing so heavily, Fran would have wheeled their installation outside, beyond the ivory tower, to make visible what the abstract yet very simple four-letter word – love – can look like.  

    Image 8: Dr Fran Trento standing next to their mobile installation that comprises a jacket onto which participants can make marks onto, scrolls of film, and a pail with cameras and other creative and critical tools to dismantle harmful narratives and approaches
    Image 8
    Image 9: A signboard ‘Neurodiversity in the Arts Symposium’ covered in snow, in a street raging with a snow-storm with cars passing by in front of a building across the road.
    Image 9

    Image 8: Dr Fran Trento standing next to their mobile installation that comprises a jacket onto which participants can make marks onto, scrolls of film, and a pail with cameras and other creative and critical tools to dismantle harmful narratives and approaches

    Image 9: A signboard ‘Neurodiversity in the Arts Symposium’ covered in snow, in a street raging with a snow-storm with cars passing by in front of a building across the road

    And love is critical if we want to dis-arm and dismantle violent master (sic) narratives and approaches of neurodivergence. If neurodivergence is a superpower — a trope I have also critiqued as, while useful, it can be reductive/fetishistic, and capitalised by the ‘high-functioning’ to self-select into an elitist club that excludes others — then there are also villains and Machiavellian messiahs who abuse their (super)power. The irony is — and yes, autistic people can grasp irony — is that these self-proclaimed ‘anti-establishment’ ‘outsiders’ are often the very personification and product of the system,as poster boys of capitalism and more. Remember the call for ‘weirdos and misfits’ outside the Oxbridge set to join Number 10 – by figures whose pedigrees were archetypal of the ruling class — private education, Oxford degree, political strategist to a prime minister similarly outfitted?

    Now that’s weird!

    Braving storms ahead

    My luggage got lost – again – on my way back to the UK, but academic and arts and cultural workers must lose neither our focus or hope. As hatred becomes even more mainstreamed and normalised, minoritised body-minds and approaches will remain hardest hit. There will be storms ahead (image 9). We – and that includes you – must step forward and step up. As US author Octavia E Butler (1947–2006) warns, unless we build ‘different leadership’ by ‘people with more courage and vision’, we’ll ‘all go down the toilet’. That’s why the Black science-fiction bestseller, who was also dyslexic, wrote story after story that reimagined different, better realities. 

    To not go down the toilet, we must disarm those who weaponise their neurodivergence. Here are some of the things that neurodivergent academics, artists, activists and allies can do:  

    • Shift your curricula to elevate and celebrate efforts that are truly leader-ful, joy-ful and equitable, and directed towards collective liberation. I’ve named several in this article. No excuses.  
    • Stop the hierarchy of normality – within neurodiversity groups in and beyond HE too – that props up antics that are white supremacist, patriarchal, misogynist, racist, transphobic, homophobic, xenophobic, colonialist, capitalist, ableist and extractive. Stop fuelling the misfits and messiahs with ill-intentions. 
    • Instead, invest in and donate your time, energy and skills to support love-led efforts. If you have a voice/ platform and can afford to, mobilise it to push back against the violence. People in senior management paygrades, make use of your position/proximity to the top of the food-chain to action positive change beyond lip service or generic policy statements about the civic duty of HE, and bring to life its promises about equity, social justice and inclusion.

    On that latter note, I’m seeking to curate a 3-day international summit in 2026 that re-imagines HE art and design as a change-making and future-making force through neuroqueer, social justice and leadership prisms. This welcomes anyone with a stake in the arts and culture, higher education, social change and inclusive futures, to get together to explore the coexistence of different ways to (un-)learning and being in the world, to share best practices about inclusion, and to collectivise and co-create action plans for more inclusive futures within and beyond the art school and HE. Through quickfire provocations, transdisciplinary speed-dating, reverse-mentoring, co-creation of toolkits, skateboarding tours, running-discourses and other embodied forms of engagement, we will not just learn about ways to make ‘reasonable’ adjustments for neuro-divergent students and staff, but to learn about their innovative approaches, and thus reimagine ways to understand and do ‘leadership’, so as to make positive changes, within and beyond art and design and HE. This shift in paradigm to position art and design higher education is aligned with – and can amplify – other ongoing efforts in the sector, such as the Creative Education Manifesto. Get in touch if you’re keen to help do the work.

    All that said, clearly, neither Tim’s symposium or my proposed summit are the only or last word in this matter. You, too, can lead with love, if you don’t already. Prioritise an intersectional approach to neuroqueer the curricula, towards dis-arming stories and approaches that are white supremacist, racist, colonialist, xenophobic, ageist, sexist, misogynistic, classcist, transphobic and heteronormative.

    CREDITS: Photographs by Kai. Photograph of Kai by neurodivergent artist-curator-activist-PhD-candidate Aidan Moseby

    Kai Syng Tan is an artist, academic, author, and agitator who adores cats and alliteration. Their book Neuro-Futurism and Re-Imagining Leadership: An A-Z Towards Collective Liberation re-imagines leadership as a co-creative, neuro-queered practice centring anti-oppression and futurity: it was published in Summer 2024. See here to join the book tour. Sign up here to participate in the CHEAD Leadership Programme taster entitled What’s love got to do with leadership? led by Kai as a new CHEAD Trustee, which will feature a response by Pascal Matthias, Associate Vice President EDI and Social Justice, University of Southampton and Co-Founder at FACE (Fashion Academics Creating Equality). Kai is Associate Professor in Arts and Cultural Leadership, University of Southampton, UK. All views here are their own.

    Author: SRHE News Blog

    An international learned society, concerned with supporting research and researchers into Higher Education

    Source link

  • the future of learning design. – Sijen

    the future of learning design. – Sijen

    There is a looming skills deficit across all disciplines currently being taught in Universities today. The vast majority of degree programmes are, at best, gradual evolutions of what has gone before. At their worst they are static bodies of knowledge transmission awaiting a young vibrant new member of faculty to reignite them. Internal reviews are too often perfunctory exercises, seldom challenging the future direction of graduates as long as pass rates are sustained. That is until is to late and failure rates point to a ‘problem’ at a fundamental level around a degree design.

    We, collectively, are at the dawn of a new knowledge-skills-cognition revolution. The future of the professionals has been discussed for some years now. It will be a creeping, quiet, revolution (Susskind and Susskind, 2017). Although we occasionally hear about some fast food business firing all of its front-of-house staff in favour of robotic manufacturing processes and A.I. Ordering services, the reality is that in the majority of contexts the intelligent deployment of A.I. to enhance business operations requires humans to describe how these systems operate with other humans. This is because at present none of these systems score highly on any markers or Emotional Intelligence or EQ.

    Image generaed by Windows Copilot

    Arguably it has become increasingly important to ensure that graduates from any and all disciplines have been educated as to how to describe what they do and why they do it. They need to develop a higher degree of comfort with articulating each thought process and action taken. To do this we desperately need course and programme designers to desist from just describing (and therefore assessing) purely cognitive (intellectual) skills as described by Bloom et.al, and limit themselves to one or two learning outcomes using those formulations. Instead they need to elevate the psychomotor skills in particular, alongside an increasing emphasis on interpersonal ones.

    Anyone who has experimented with prompting any large language model (LLM) will tell you the language used falls squarely under the psychomotor domain. At the lowest levels one might ask to match, copy, imitate, then at mid-levels of skill deployment one might prompt a system to organise, calibrate, compete or show, rating to the highest psychomotor order of skills to ask A.I. systems to define, specify, even imagine. This progressive a type of any taxonomy allows for appropriate calibration of input and output. The ability to use language, to articulate, is an essential skill. There are some instructive (ad entertaining) YouTube videos of parents supporting their children to write instructions (here’s a great example), a skill that is seldom further developed as young people progress into tertiary studies.

    Being able to assess this skill is also challenging. When one was assessing text-based comprehension, even textual analysis, then one could get away with setting an essay question and having a semi-automated process for marking against a rudimentary rubric. Writing instructions, or explanations, of the task carried out, is not the same as verbally describing the same task. Do we imagine that speech recognition technology won’t become an increasingly part of many productive job roles. Not only do courses and programmes need to be designed around a broader range of outcomes, we also need to be continuously revising our assessment opportunities for those outcomes.

    References

    Susskind, R., & Susskind, D. (2017). The Future of the Professions: How Technology Will Transform the Work of Human Experts (Reprint edition). OUP Oxford.

    Source link

  • Only 12% of Faculty Use AI Regularly Despite Seeing Value for Course Design & Delivery

    Only 12% of Faculty Use AI Regularly Despite Seeing Value for Course Design & Delivery

    TORONTO – September 19, 2024 – Top Hat, the leader in student engagement solutions for higher education, today announced the release of its latest faculty survey report, From Promise to Practice: Harnessing Gen AI for Evidence-Based Teaching. The report details the current use of AI among 300+ college and university educators in improving the quality and impact of instruction.

    Key findings:

    • Faculty who receive formal training are more likely to agree generative AI is helpful in enhancing course design and delivery and to use AI for guidance on incorporating evidence-based teaching practices.
    • While ChatGPT debuted almost two years ago, only 12% of instructors use generative AI on a daily basis to support their teaching practice. 
    • Institutions are not meeting the demand for faculty development. Of the 49% percent who’ve received training on generative AI, 48% have relied on organizations outside of their institution. 

    Access the report.

    The conversation around AI has been dominated by concerns over academic integrity and growing urgency to promote student AI literacy. What has received less attention is the role of AI in supporting evidence-based teaching practices proven to positively impact student persistence and success. 

    “Using AI to improve course design and delivery remains a promising yet largely unrealized opportunity,” said Dr. Bradley Cohen, Chief Academic Officer at Top Hat. “By putting evidence-based teaching practices at the heart of efforts to advance faculty adoption of AI, institutions stand to realize the combined benefits of ensuring more faculty appreciate the potential value of AI—while advancing teaching methods shown to improve student persistence and success.”

    The report found that most instructors express optimism about the potential of AI to enhance instruction. Yet lack of exposure and inconsistent training remain key obstacles in realizing the potential of AI in accelerating evidence-based teaching practices like active learning, frequent low-stakes assessments, and helping students to ‘learn how to learn.’ 

    Read the report, along with insights and guidance on accelerating faculty AI adoption.

    About Top Hat

    As the leader in student engagement solutions for higher education, Top Hat enables educators to employ proven student-centered teaching practices through interactive content and tools enhanced by AI, and activities in in-person, online and hybrid classroom environments. To accelerate student impact and return on investment, the company provides a range of change management services, including faculty training and instructional design support, integration and data management services, and digital content customization. Thousands of faculty at 750 leading North American colleges and universities use Top Hat to create meaningful, engaging and accessible learning experiences for students before, during, and after class.

    Contact [email protected] for media inquiries.

    Source link

  • 20 Montessori Toddler Bedrooms (Design Inspiration) (2024)

    20 Montessori Toddler Bedrooms (Design Inspiration) (2024)

    Montessori spaces are designed for independent, hands-on learning in a child-friendly environment. They encourage exploration and development across multiple areas of learning. Common features you might find in a Montessori learning environment include:

    1. Child-sized furniture: Easy for kids to use.
    2. Open shelves: Accessible learning materials on display.
    3. Practical life area: Activities for daily skills.
    4. Sensory area: Tools for sensory exploration.
    5. Orderly layout: Organized, clutter-free learning environment.
    6. Freedom of movement: Children choose activities freely.
    7. Mixed-age groups: Older and younger children together.
    8. Cozy reading corner: Comfortable, quiet reading space.
    9. Individual workstations: Personal spaces for focused work.
    10. Natural light: Bright, inviting learning environment.

    Montessori Bedroom Ideas (20 Inspiring Pictures)

    #1.

    Montessori Toddler Bedroom

    Montessori Fact: Montessori spaces are designed to foster independence and self-directed learning.

    #2.

    Montessori Toddler Bedroom

    Montessori Fact: Montessori bedrooms are designed to be aesthetically pleasing, natural, and educational.

    #3

    Montessori Toddler Bedroom

    Montessori Fact: Montessori spaces use hands-on learning materials to teach concepts.

    #4

    Montessori Toddler Bedroom

    Montessori Fact: The environment in a Montessori bedroom is carefully prepared to be orderly and inviting.

    #5

    Montessori Toddler Bedroom

    Montessori Fact: The Montessori philosophy emphasizes practical life skills alongside academic learning.

    #6

    Montessori Toddler Bedroom

    Montessori Fact: Montessori-inspired parents act as guides to support curiosity and holistic development.

    #7

    Montessori Toddler Bedroom

    Montessori Fact: Montessori spaces allow children to choose their activities from a range of options.

    #8

    Montessori Toddler Bedroom

    Montessori Fact: Montessori materials are designed to be self-correcting to encourage independent problem-solving.

    #9

    Montessori Toddler Bedroom

    Montessori Fact: Montessori spaces in your home can include areas for reading, math, science, and art.

    #10

    Montessori Toddler Bedroom

    Montessori Fact: The Montessori philosophy aims to develop a child’s natural curiosity and love of learning.

    #11

    Montessori Toddler Bedroom

    Montessori Fact: Montessori spaces encourage collaborative learning between siblings and friends, and with parents.

    #12

    Montessori Toddler Bedroom

    Montessori Fact: Maria Montessori suggests we should give children uninterrupted blocks of play time.

    #13

    Montessori Toddler Bedroom

    Montessori Fact: The Montessori philosophy places a strong emphasis on respect for others and the environment.

    #14

    Montessori Toddler Bedroom

    Montessori Fact: The Montessori philosophy often includes ample outdoor learning.

    #15

    Montessori Toddler Bedroom

    Montessori Fact: The Montessori philosophy is based on the ideas of Italian doctor and educator Dr. Maria Montessori.

    #16

    Montessori Toddler Bedroom

    Montessori Fact: The Montessori philosophy uses real-life activities to teach practical skills.

    #17

    Montessori Toddler Bedroom

    Montessori Fact: The Montessori method promotes the development of fine motor skills through activities like pouring and threading.

    #18

    Montessori Toddler Bedroom

    Montessori Fact: Montessori spaces are designed to be aesthetically pleasing and comfortable.

    #19

    Montessori Toddler Bedroom

    Montessori Fact: Montessori education focuses on developing the whole child, including emotional and social development.

    #20

    Montessori Toddler Bedroom

    Montessori Fact: Montessori spaces use low shelves and child-sized furniture to make materials accessible to children.


    Chris

    Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

    Source link

  • The Ultimate Guide to Universal Design for Learning

    The Ultimate Guide to Universal Design for Learning

    Universal Design for Learning (UDL) is a framework that involves using a variety of teaching methods to respond to the needs of all your higher ed students. It is a teaching technique that provides flexibility in how instructional materials are delivered—and is ideal for supporting students of all backgrounds, cultures and abilities.

    Table of contents

    1. What is Universal Design for Learning?
    2. What are the three core tenets of Universal Design for Learning?
    3. Why should you use universal design in your online course?
    4. How can you apply universal design in your online course?
    5. Universal Design for Learning examples
    6. Questions to ask yourself when designing an accessible course
    7. The history of Universal Design for Learning
    8. Bring UDL to life with Top Hat
    9. References

    1. What is Universal Design for Learning?

    Universal Design for Learning (UDL) helps college and university educators create flexible programs that are easy to adapt to the unique needs of all students. This teaching framework involves using many techniques—in everything from course delivery to media use—to reduce barriers and reach every college student.1

    UDL guidelines are put into practice in a variety of facets, including outside of academia. For instance, you may use closed captioning when watching television. Your smartphone can read the information on a website aloud. Meanwhile, automatic doors make grocery stores more accessible. In these three scenarios, UDL helps level the playing field for everyone, regardless of ability.

    The UDL guidelines are presented as a graphic organizer, or a matrix table. Vertically, the principles are grouped under engagement, representation, and action and expression. Horizontally, the UDL principles are presented as access, build and internalize. Each guideline has corresponding checkpoints that include best practices. The graphic organizer helps viewers understand how criteria differ across the three principles. View the table below for a breakdown of the UDL guidelines.

    Provide multiple means of engagement Provide multiple means of representation Provide multiple means of action and expression
    Recruiting interest Perception Physical action
    Sustaining effort and persistence Language and symbols Expression and communication
    Self-regulation Comprehension Executive function

    2. What are the three core tenets of Universal Design for Learning?2

    • Action and expression: This learning approach involves giving students multiple ways to access information. Universal Design for Learning examples include giving students multiple ways to demonstrate that they’ve learned the material.
    • Representation: A key tenet of this approach is providing information in multiple formats. Universal Design for Learning examples include offering students videos, books or interactive demos that cover the material covered in a lecture. Students can then choose the format that best suits their needs.
    • Engagement: This approach is designed to motivate college students in a variety of ways. Universal Design for Learning examples include assigning projects that relate to a student’s background and interests. This pillar may be put into practice through gamification or discussions.

    2.1. Multiple means of action and expression

    This topic covers the ‘how’ of learning. It is also referred to as the strategic network. Each learner navigates the classroom in a different way, and they also express themselves uniquely. If someone has an executive function disorder, meaning they have trouble planning, organizing and problem solving, they will express themselves differently than someone who has a language barrier. For example, some postsecondary students are able to use written communication with ease, while others can only communicate through speech.

    2.1.1. Executive functions

    Executive functions encapsulate a human being’s highest level of skills. This ability allows learners to overcome their impulses to make long-term goals and strategies. Executive functions are limited by the individual’s working memory when executive functioning capacity is devoted to managing “lower level” skills that don’t come as naturally to a person. The individual’s capacity for executive functions can also be lowered by disabilities.

    With universal design for learning, instructors can expand a learner’s executive function by scaffolding—that is breaking up lessons into bite-sized pieces—to ensure they do not use up the learner’s working memory. Educators can help students scaffold executive skills so that they can use them more effectively. Setting goals and creating plans to reach these goals can help. Professors can also support students by helping them self-monitor their progress over time, which helps students guide their practice through the semester. It also helps learners better understand what advice to ask their instructors for.3

    2.1.2. Expression and communication

    Every learner has a different capacity for expression and communication. In addition, various types of media may support different learning styles. For example, a learner who has dyslexia may perform better at expressing their thoughts through verbal conversations versus written essays.

    Educators must provide different modalities for communication. This may involve using various types of media or tools for communication. Professors can help students become more fluent in a variety of communication modalities, which will then lend to independent learning.4

    2.1.3. Physical action

    Interactive software, textbooks and other curricular materials are recognized as Universal Design for Learning examples that can help students understand information. For instance, a postsecondary student may need a different type of workbook if they require assistive technology. These assistive technologies can help students who have disabilities. Some students could benefit from having expanded keyboards or voice-activated switches, for example.5

    With this tenet of UDL, it’s important to vary the methods for response and navigation. This involves creating an accessible learning environment that all students can thrive in. The second part of this UDL principle involves opening doors to the tools that will create equal opportunity.

    2.2. Multiple means of representation

    This topic can be considered the ‘what’ of learning. It’s also referred to as the recognition network. Every learner absorbs the information that is presented to them in a different way. While a sensory or learning disability may cause this difference, cultural differences and different personalities may also affect how the student learns.

    2.2.1. Comprehension

    The goal of education is to help learners turn information into knowledge that they can apply in their everyday lives. With Universal Design for Learning, educators should get higher ed students involved in transforming and gathering usable knowledge. Gaining usable knowledge is an active process, so students need to be involved in order for this process to work. This means students need to use skills like consumption, categorization and active memorization. They must also learn how to integrate new knowledge with their prior understanding of the subject. The best Universal Design for Learning example here involves relying on organizers such as concept maps to help students visually draw connections between learning units. Metaphors, stories and analogies are also effective in helping students see the importance and relevance of topics.

    To achieve these goals, instructors must present information in an accessible way. They should connect the information to previous experiences and offer background information as well. They can highlight relationships, patterns and information so that students can see how the information relates to their goals and other knowledge. Through breaking up lessons into digestible amounts, professors can support students as learners find meaning in their new knowledge and process the information. Finally, educators can help students apply information to new contexts.6

    2.2.2. Language, mathematical expressions and symbols

    Students interpret linguistic and non-linguistic symbols in different ways. While symbols like division signs provide clarity for some students, others do not understand specific symbols. Likewise, college students attribute different meanings to pictures or words based on their familial backgrounds and culture.

    Because of this, educators must provide alternative representations for students. They should clarify different symbols, vocabulary, mathematical notations and syntax. If there is a pattern in different equations, grammar or musical notations, they should be explicit about it. When necessary, instructors should use translations, graphics, movement, activities, simulations, images and videos to make learning active.7 Try pairing a chemistry formula with an illustration or simulation to put this principle into practice.

    2.2.3. Perception

    Provide the same information through formats that stimulate different senses—ranging from sight, hearing or touch—to make lessons perceptible to learners. This practice will help students absorb information through audio, tactile or visual means. Instructors should also give students formats that can be adjusted such as text that can be increased in size. Other examples of perception include offering alternatives for video and auditory information.8 Such examples are providing videos featuring American Sign Language (ASL) and complementing audio clips with transcripts.

    2.3. Multiple means of engagement

    Providing multiple means of engagement can be thought of as the ‘why’ of learning. It’s also referred to as the affective network. Every college student is motivated in a different way academically. Likewise, students have different learning styles. While some love spontaneity, others will only feel comfortable when there is a daily routine followed in the classroom. Additionally, some prefer to work alone or in groups.

    2.3.1. Self-regulation

    Ideally, the learning environment should support and encourage the student’s motivation. When students are in higher education, they should be encouraged to learn how to regulate their own emotions and become self-motivated. By regulating their emotions, students can better cope with changes in their environment. A Universal Design for Learning example includes asking students how they’re feeling today on a scale of 1-5 to understand their needs and any accommodations.

    Postsecondary students frequently learn how to self-regulate by observing their professors. Virtual classrooms can encourage this skill by modeling motivation and self-regulation. Then, educators can prompt students to use these abilities in real life. Instructors should discuss their expectations and help students set personal goals. They can support students as they self-assess their abilities and become more aware of their mistakes. Over time, students can learn how to develop healthy emotional responses to a variety of events.9

    2.3.2. Sustaining effort and persistence

    In order for a student to learn, they must make an effort to pay attention in class. If a student is motivated correctly, they can focus and make a sustained effort in the classroom. Each learner self-regulates in a different way, and these disparities are often due to their motivation levels. Other factors like contextual interference—a disruption in the motor learning phenomenon that affects how people learn and practice new skills—and self-regulation skills can also impact the student’s concentration. This is caused in part by the learner not seeing the value of certain goals and objectives.

    Educators can support students by providing different resources to help them learn. Students are more likely to rise to the expectations set to them, so teachers should provide the tools that students need to succeed and help them achieve a certain level of understanding. When introducing goals, instructors should also discuss why each goal matters—put into practice through detailed criteria—so that students feel motivated to achieve each objective.10

    2.3.3. Recruiting interest

    If the information does not engage the student’s interest, it is essentially inaccessible. Students must be able to absorb and process information in their minds. Each student is interested in different topics of discussion, so teachers must figure out how to gauge their interest in different ways.

    Instructors can attract the student’s interest by providing multiple choices whenever it is possible to do so. By allowing for individual choices and autonomy, educators can empower students to take control of their learning. Teachers should also connect the subject matter to experiences outside of the classroom. If university students believe the subject matter has value, they are more likely to be interested in learning it. Finally, teachers should attempt to eliminate distractions so that students have a safe space to learn new information.11

    3. Why should you use universal design in your online course?

    When using the UDL framework in an online course, you create equal opportunity for students of different backgrounds and abilities to learn the course material. If you are only teaching students in one specific way, many of your students may not be learning the information properly. Universal Design for Learning can help you make your course accessible to all types of learners.

    When using the UDL framework in an online course, you create equal opportunity for students of different backgrounds and abilities to learn the course material.

    4. How can you apply universal design in your online course?

    When using the principles of UDL in your online course, consider the course’s engagement, representation, action and expression goals. Here are four ways to put Universal Design for Learning examples to use in your class.

    4.1. Syllabus

    Your course syllabus shows college students what they will be learning in the upcoming quarter. It should give students multiple ways to engage with the content, such as through class readings, podcasts, webinars and guest lectures. The syllabus can communicate regular routines, assessment formats and expectations. It should also include the ways that students can access the course content. Consider adding headers and subheadings in your syllabus to make it accessible for screen readers. Download Top Hat’s syllabus template now, available in either Google Docs or Word format.

    4.2. Course materials

    To follow the principles of universal design, you should select an array of course materials. Instead of only using a textbook, your course can incorporate podcasts, discussion boards, essays, videos and physical activities. By changing the way students can absorb information, you can make the course information accessible to every student.

    4.3. Assessment

    While some students can take a written test without any issues, others have trouble reading written tests or turning them in. In the realm of assessment, Universal Design for Learning examples might include using video conferencing software to measure student understanding. Depending on the coursework, you could also use assessment techniques like recorded videos.

    4.4. Teaching

    In order to make your course more accessible, vary the way you teach your course. Images, graphs and textbooks are effective in helping students understand information. Some students learn better through audio tracks, so you may want to use a recorded lecture, video or podcast as well. In addition, you can teach students through discussion groups and other techniques as well.

    Top Hat’s Student Engagement toolkit is packed with templates and strategies to create accessible assessments, lesson plans and classroom discussions. Access now.

    5. Universal Design for Learning examples

    The following list includes Universal Design for Learning examples that you can use to create an equitable and accessible learning environment. You can also create other feedback, assignment and learning options that help diverse learners master the course material in your class.

    5.1. Assignment options

    Students can achieve the course’s learning objectives through a variety of assignment styles. Beyond traditional homework, students can also submit video recordings of a presentation or speech. They can alternatively create comic strips or podcasts. Another Universal Design for Learning example involves flexible assignment due dates. This policy accommodates students who may have obligations outside of class, such as work or family responsibilities, or who may need additional time due to disabilities or health issues.

    5.2. Regular feedback

    In order for students to improve, they must regularly receive detailed feedback. Formative assessments can help with this goal, where the student’s ongoing learning is monitored. These assessments also provide feedback that educators can use to improve their course delivery.

    5.3. Digital and audio text

    Textbooks are just one way that university students can learn. Audiobooks and text-to-speech programs can also help. In addition, you can use audio transcripts and videos with closed captions for students who require assistive technologies. Professors can use multiple modes of communication to convey information, including verbal instructions, written handouts, visual aids, and online resources. These specific Universal Design for Learning examples help students with diverse language abilities, learning styles, and sensory preferences to comprehend the material effectively.

    6. Questions to ask when designing an accessible course

    6.1. Multiple means of engagement

    1. Does the course encourage independent student responsibilities? In order for college students to learn, they have to be motivated and engaged. When students work independently on coursework, they feel responsible for the outcome. They also become more engaged in studying the material. Consider facilitating surveys or a student interest inventory at the start of the semester to gauge interests, hobbies and strengths. You can then use this information to tailor units of study accordingly.
    2. Can students complete at least some course content at their own pace and in any order they wish? Everyone learns at a different pace. When the course moves too quickly, students can fall behind. Over time, this can cause students to lose motivation. Letting students learn at their own pace in a blended or online course acknowledges their unique circumstances and provides them with the time they need to engage with the course material. Consider implementing an assignment ‘grace period’ to allow students to plan their schedules accordingly based on priorities.
    3. Are course learning goals and outcomes clear? When students are confused or lost, it is impossible for them to become engaged in the course material. You can remove this obstacle by clarifying your learning goals. When students know what to expect and what they need to do, they are more likely to achieve the course’s goals.

    6.2. Multiple means of representation

    1. Is the course content provided in multiple ways? With Universal Design for Learning, the goal is to help postsecondary students from all backgrounds and abilities. Students may have visual or aural impairments or have a disability. They may also come from a culture where subject matter is taught differently. Ideally, your course content should be provided through multiple techniques so that everyone has an equal opportunity to learn. Universal Design for Learning examples include complementing course readings with interviews or videos.
    2. Do learning opportunities and assignments use students’ prior knowledge? You can help students retain information by connecting it to their prior knowledge. You can show how new information relates to a previous class or personal experience. Sometimes, you can even assign students the task of reflecting on everything they have learned and how the new topics related to their personal experiences.
    3. Does the course have interactive learning activities online? If you are teaching online, ensure you have multiple ways to get students involved. Interactive activities help students feel like active participants in the learning process. Because students are more likely to learn when they are actively involved, this technique is extremely important for online learners. It ensures that students are just as motivated in their studies, even from a distance. Complement lectures or readings with simulations or other opportunities for students to apply their knowledge.

    6.3. Multiple means of action and expression

    1. Have you provided students with note taking support? There are many different disabilities that can make it difficult for students to take notes. To help your students, offer them multiple ways to take notes in class. They can use videos, audio recordings or written techniques to recall information. Some students may also benefit from making graphs or drawings of the new course material.
    2. Does the course include a variety of assessment methods? While some students can take written tests, this is not the best way to assess every student. Universal Design for Learning examples that support a flexible assessment strategy include video interviews, recordings, posters and other techniques. If you use written tests, you may need to offer support such as audio recordings of the questions and answers. Balance summative assessments with low stake formative assessments that enable students to receive more regular and timely feedback.
    3. Are college students encouraged to communicate with faculty and classmates in the course? Open communication between faculty and students—and especially on the student-student level—helps many learn and absorb new information. Ideally, instructors should offer multiple ways for students to communicate with their classmates and teachers. Universal Design for Learning examples could include using online forums, video conferences, interviews or essay feedback to host conversations with students.

    Our Teaching with Top Hat Toolkit offers videos and helpful resources to design an accessible learning community in our platform. Browse the toolkit today.

    7. The history of Universal Design for Learning

    Originally, this technique started out as an architectural concept. In architecture, universal design refers to creating designs that appeal to everyone. These designs must also comply with the Americans with Disabilities Act (ADA). Because of the ADA, many schools began using inclusive facilities and providing equal access to their courses. Originally, the idea of universal design in architecture was created by architect Ron Mace at North Carolina State University.

    In 1984, the Center for Applied Special Technology (CAST) was formed. CAST applied the original universal design guidelines to the educational space as a means of facilitating reform. Today, the CAST website houses plenty of instructional design tips and professional development strategies to make the postsecondary education experience more accessible.12

    8. Bring UDL to life with Top Hat

    Top Hat’s feature suite ensures your students have access to an equitable learning environment. Postsecondary students are able to learn from anywhere—the platform’s offline mode still lets students complete their homework assignments, even without having an Internet connection. Similarly, students can use multiple devices to engage with content stored in Top Hat. Top Hat is compatible with assistive technologies such as screen readers, plus keyboard navigation makes it simple to navigate through content.

    Outside of class time, alternative text on images in textbook readings helps students using screen readers understand the visuals alongside the text. Instructors can personalize the learning experience for individual students by customizing assignment due dates and grade weights. This ensures students have multiple ways to express their knowledge—and at a time that suits them.

    Top Hat’s accessibility features create a level playing field in your course. Learn more about what Top Hat can do for your students here.

    9. References

    • Morin, A. (n.d.). What is Universal Design for Learning (UDL)? Understood. https://www.understood.org/en/learning-thinking-differences/treatments-approaches/educational-strategies/universal-design-for-learning-what-it-is-and-how-it-works
    • CAST. (n.d.). The UDL Guidelines. http://udlguidelines.cast.org/
    • CAST. (n.d.). Executive Functions. http://udlguidelines.cast.org/action-expression/executive-functions/executive-functions
    • CAST. (n.d.). Expression & Communication. http://udlguidelines.cast.org/action-expression/expression-communication
    • CAST. (n.d.). Physical Action. http://udlguidelines.cast.org/action-expression/physical-action
    • CAST. (n.d.). Comprehension. http://udlguidelines.cast.org/representation/comprehension
    • CAST. (n.d.). Language & Symbols. http://udlguidelines.cast.org/representation/language-symbols
    • CAST. (n.d.). Perception. http://udlguidelines.cast.org/representation/perception
    • CAST. (n.d.). Self regulation. http://udlguidelines.cast.org/engagement/self-regulation
    • CAST. (n.d.). Sustaining Effort & Persistence. http://udlguidelines.cast.org/engagement/effort-persistence
    • CAST. (n.d.). Recruiting Interest. http://udlguidelines.cast.org/engagement/recruiting-interest
    • OCALI. (n.d.). History of UDL. https://www.ocali.org/project/learn_about_udl/page/udl_history

    Tagged as:

    Source link

  • AI Learning Design Workshop: Solving for CBE –

    AI Learning Design Workshop: Solving for CBE –

    I recently announced a design/build workshop series for an AI Learning Design Assistant (ALDA). The idea is simple:

    • If we can reduce the time it takes to design a course by about 20%, the productivity and quality impacts for organizations that need to build enough courses to strain their budget and resources will gain “huge” benefits.
    • We should be able to use generative AI to achieve that goal fairly easily without taking ethical risks and without needing to spend massive amounts of time or money.
    • Beyond the immediate value of ALDA itself, learning the AI techniques we will use—which are more sophisticated than learning to write better ChatGPT prompts but far less involved than trying to build our own ChatGPT—will help the participants learn to accomplish other goals with AI.

    In today’s post, I’m going to provide an example of how the AI principles we will learn in the workshop series can be applied to other projects. The example I’ll use is Competency-Based Education (CBE).

    Can I please speak to your Chief Competency Officer?

    The argument for more practical, career-focused education is clear. We shouldn’t just teach the same dusty old curriculum with knowledge that students can’t put to use. We should prepare them for today’s world. Teach them competencies.

    I’m all for it. I’m on board. Count me in. I’m raising my hand.

    I just have a few questions:

    • How many companies are looking at formally defined competencies when evaluating potential employees or conducting performance reviews?
    • Of those, how many have specifically evaluated catalogs of generic competencies to see how well they fit with the skills their specific job really requires?
    • Of those, how many regularly check the competencies to make sure they are up-to-date? (For example, how many marketing departments have adopted generative AI prompt engineering competencies in any formal way?)
    • Of those, how many are actively searching for, identifying, and defining new competency needs as they arise within their own organizations?

    The sources I turn to for such information haven’t shown me that these practices are being implemented widely yet. When I read the recent publications on SkillsTech from Northeastern University’s Center for the Future of Higher Education and Talent Strategy (led by Sean Gallagher, my go-to expert on these sorts of changes), I see growing interest in skills-oriented thinking in the workplace with still-immature means for acting on that interest. At the moment, the sector seems to be very focused on building a technological factory for packaging, measuring, and communicating formally defined skills.

    But how do we know that those little packages are the ones people actually need on the job, given how quickly skills change and how fluid the need to acquire them can be? I’m not skeptical about the worthiness of the goal. I’m asking whether we are solving the hard problems that are in the way of achieving it.

    Let’s make this more personal. I was a philosophy major. I often half-joke that my education prepared me well for a career in anything except philosophy. What were the competencies I learned? I can read, write, argue, think logically, and challenge my own assumptions. I can’t get any more specific or fine-grained than that. I know I learned more specific competencies that have helped me with my career(s). But I can’t tell you what they are. Even ones that I may use regularly.

    At the same time, very few of the jobs I have held in the last 30 years existed when I was an undergraduate. I have learned many competencies since then. What are they? Well, let’s see…I know I have a list around here somewhere….

    Honestly, I have no idea. I can make up phrases for my LinkedIn profile, but I can’t give you anything remotely close to a full and authentic list of competencies I have acquired in my career. Or even ones I have acquired in the last six months. For example, I know I have acquired competencies related to AI and prompt engineering. But I can’t articulate them in useful detail without more thought and maybe some help from somebody who is trained and experienced at pulling that sort of information out of people.

    The University of Virginia already has an AI in Marketing course up on Coursera. In the next six months, Google, OpenAI, and Facebook (among others) will come out with new base models that are substantially more powerful. New tools will spring up. Practices will evolve within marketing departments. Rules will be put in place about using such tools with different marketing outlets. And so, competencies will evolve. How will the university be able to refresh that course fast enough to keep up? Where will they get their information on the latest practices? How can they edit their courses quickly enough to stay relevant?

    How can we support true Competency-Based Education if we don’t know which competencies specific humans in specific jobs need today, including competencies that didn’t exist yesterday?

    One way for AI to help

    Let’s see if we can make our absurdly challenging task of keeping an AI-in-marketing CBE course up-to-date by applying a little AI. We’ll only assume access to tools that are coming on the market now—some of which you may already be using—and ALDA.

    Every day I read about new AI capabilities for work. Many of them, interestingly, are designed to capture information and insights that would otherwise be lost. A tool to generate summaries and to-do lists from videoconferences. Another to annotate software code and explain what it does, line-by-line. One that summarizes documents, including long and technical documents, for different audiences. Every day, we generate so much information and witness so many valuable demonstrations of important skills that are just…lost. They happen and then they’re gone. If you’re not there when they happen and you don’t have the context, prior knowledge, and help to learn them, you probably won’t learn from them.

    With the AI enhancements that are being added to our productivity tools now, we can increasingly capture that information as it flies by. Zoom, Teams, Slack, and many other tools will transcribe, summarize, and analyze the knowledge in action as real people apply it in their real work.

    This is where ALDA comes in. Don’t think of ALDA as a finished, polished, carved-in-stone software application. Think of it as a working example of an application design pattern. It’s a template.

    Remember, the first step in the ALDA workflow is a series of questions that the chatbot asks the expert. In other words, it’s a learning design interview. A learning designer would normally conduct an interview with a subject-matter expert to elicit competencies. But in this case, we make use of the transcripts generated by those other AI as a direct capture of the knowledge-in-action that those interviews are designed to tease out.

    ALDA will incorporate a technique called “Retrieval-Augmented Generation,” or “RAG.” Rather than relying on—or hallucinating—the generative AI’s own internal knowledge, it can access your document store. It can help the learning designer sift through the work artifacts and identify the AI skills the marketing team had to apply when that group planned and executed their most recent social media campaign, for example.

    Using RAG and the documents we’ve captured, we develop a new interview pattern that creates a dialog between the human expert, the distilled expert practices in the document store, and the generative AI (which may be connected to the internet and have its own current knowledge). That dialogue will look a little different from the one we will script in the workshop series. But that’s the point. The script is the scaffolding for the learning design process. The generative AI in ALDA helps us execute that process, drawing on up-to-the-minute information about applied knowledge we’ve captured from subject-matter experts while they were doing their jobs.

    Behind the scenes, ALDA has been given examples of what its output should look like. Maybe those examples include well-written competencies, knowledge required to apply those competencies, and examples of those competencies being properly applied. Maybe we even wrap your ALDA examples in a technical format like Rich Skill Descriptors. Now ALDA knows what good output looks like.

    That’s the recipe. If you can use AI to get up-to-date information about the competencies you’re teaching and to convert that information into a teachable format, you’ve just created a huge shortcut. You can capture real-time workplace applied knowledge, distill it, and generate the first draft of a teachable skill.

    The workplace-university CBE pipeline

    Remember my questions early in this post? Read them again and ask yourself whether the workflow I just described could change the answers in the future:

    • How many companies are looking at formally defined competencies when evaluating potential employees or conducting performance reviews?
    • Of those, how many have specifically evaluated catalogs of generic competencies to see how well they fit with the skills their specific job really requires?
    • Of those, how many regularly check the competencies to make sure they are up-to-date? (For example, how many marketing departments have adopted relevant AI prompt engineering competencies in any formal way?)
    • Of those, how many are actively searching for, identifying, and defining new competency needs as they arise?

    With the AI-enabled workflow I described in the previous section, organizations can plausibly identify critical, up-to-date competencies as they are being used by their employees. They can share those competencies with universities, which can create and maintain up-to-date courses and certification programs. The partner organizations can work together to ensure that students and employees have opportunities to learn the latest skills as they are being practiced in the field.

    Will this new learning design process be automagic? Nope. Will it give us a robot tutor in the sky that can semi-read our minds? Nuh-uh. The human educators will still have plenty of work to do. But they’ll be performing higher-value work better and faster. The software won’t cost a bazillion dollars, you’ll understand how it works, and you can evolve it as the technology gets better and more reliable.

    Machines shouldn’t be the only ones learning

    I think I’ve discovered a competency that I’ve learned in the last six months. I’ve learned how to apply simple AI application design concepts such as RAG to develop novel and impactful solutions to business problems. (I’m sure my CBE friends could express this more precisely and usefully than I have.)

    In the months between now, when my team finishes building the first iteration of ALDA, and when the ALDA workshop participants finish the series, technology will have progressed. The big AI vendors will have released at least one generation of new, more powerful AI foundation models. New players will come on the scene. New tools will emerge. But RAG, prompt engineering, and the other skills the participants develop will still apply. ALDA itself, which will almost certainly use tools and models that haven’t been released yet, will show how the competencies we learn still apply and how they evolve in a rapidly changing world.

    I hope you’ll consider enrolling your team in the ALDA workshop series. The cost, including all source code and artifacts, is $25,000 for the team. You can find an application form and prospectus here. Applications will be open until the workshop is filled. I already have a few participating teams lined up and a handful more that I am talking to.

    You also find a downloadable two-page prospectus and an online participation application form here. To contact me for more information, please fill out this form:

    You can also write me directly at [email protected].

    Please join us.

    Source link

  • Announcing a Design/Build Workshop Series for an AI Learning Design Assistant (ALDA) –

    Announcing a Design/Build Workshop Series for an AI Learning Design Assistant (ALDA) –

    Want to build an AI tool that will seriously impact your digital learning program? Right now? For a price that you may well have in your professional development budget?

    I’m launching a project to prove we can build a tool that will change the economics of learning design and curricular materials in months rather than years. Its total cost will be low enough to be paid for by workshop participation fees.

    Join me.

    The learning design bottleneck

    Many of my friends running digital course design teams tell me they cannot keep up with demand. Whether their teams are large or small, centralized or instructor-led, higher education or corporate learning and development (L&D), the problem is the same; several friends at large shops have told me that their development of new courses and redesigns of old ones have all but ground to a halt. They don’t have time or money to fix the problem.

    I’ve been asking, “Suppose we could accelerate your time to develop a course by, say, 20%?” Twenty percent is my rough, low-end guess about the gains. We should be able to get at least that much benefit without venturing into the more complex and riskier aspects of AI development. “Would a 20% efficiency gain be significant?” I ask.

    Answer: “It would be huge.”

    My friends tend to cite a few benefits:

    • Unblocked bottlenecks: A 20% efficiency gain would be enough for them to start building (or rebuilding) courses at a reasonable speed again.
    • Lower curricular materials costs: Organizations could replace more licensed courses with ones that they own. No more content license costs. And you can edit it any way you need to.
    • Better quality: The tool would free up learning designers to build better courses rather than running just to get more courses finished.
    • More flexibility with vendors: Many departments hire custom course design shops. A 20% gain in efficiency would give them more flexibility in deciding when and how to invest their budgets in this kind of consulting.

    The learning design bottleneck is a major business problem for many organizations. Relatively modest productivity gains would make a substantial difference for them. Generative AI seems like a good tool for addressing this problem. How hard and expensive would it be to build a tool that, on average, delivers a 20% gain in productivity?

    Not very hard, not very expensive

    Every LMS vendor, courseware platform provider, curricular materials vendor, and OPM provider is currently working on tools like this. I have talked to a handful of them. They all tell me it’s not hard—depending on your goals. Vendors have two critical constraints. First, the market is highly suspicious of black-box vendor AI and very sensitive to AI products that make mistakes. EdTech companies can’t approach the work as an experiment. Second, they must design their AI features to fit their existing business goals. Every feature competes with other priorities that their clients are asking for.

    The project I am launching—AI Learning Design Assistant (ALDA)—is different. First, it’s design/build. The participants will drive the requirements for the software. Second, as I will spell out below, our software development techniques will be relatively simple and easy to understand. In fact, the value of ALDA is as much in learning patterns to build reliable, practical, AI-driven tools as it is in the product itself. And third, the project is safe.

    ALDA is intended to produce a first draft for learning designers. No students need to see content that has not been reviewed by a human expert or interact directly with the AI at all. The process by which ALDA produces its draft will be transparent and easy to understand. The output will be editable and importable into the organization’s learning platform of choice.

    Here’s how we’ll do it:

    • Guided prompt engineering: Your learning designers probably already have interview questions for the basic information they need to design a lesson, module, or course. What are the learning goals? How will you know if students have achieved those goals? What are some common sticking points or misconceptions? Who are your students? You may ask more or less specific and more or less elaborate versions of these questions, but you are getting at the same ideas. ALDA will start by interviewing the user, who is the learning designer or subject-matter expert. The structure of the questions will be roughly the same. While we will build out one set of interview questions for the workshop series, changing the design interview protocol should be relatively straightforward for programmers who are not AI specialists.
    • Long-term memory: One of the challenges with using a tool like ChatGPT on its own is that it can’t remember what you talked about from one conversation to the next and it might or might not remember specific facts that it was trained on (or remember them correctly). We will be adding a long-term memory function. It can remember earlier answers in earlier design sessions. It can look up specific documents you give it to make sure it gets facts right. This is an increasingly common infrastructure component in AI projects. We will explore different uses of it when we build ALDA. You’ll leave the workshop with the knowledge and example code of how to use the technique yourself.
    • Prompt enrichment: Generative AI often works much better when it has a few really good, rich examples to work from. We will provide ALDA with some high-quality lessons that have been rigorously tested for learning effectiveness over many years. This should increase the quality of ALDA’s first drafts. Again, you may want your learning designs to be different. Since you will have the ALDA source code, you’ll be able to put in whatever examples you want.
    • Generative AI export: We may or may not get to building this feature depending on the group’s priorities in the time we have, but the same prompt enrichment technique we’ll use to get better learning output can also be used to translate the content into a format that your learning platform of choice can import directly. Our enrichment examples will be marked up in software code. A programmer without any specific AI knowledge can write a handful of examples translating that code format into the one that your platform needs. You can change it, adjust it, and enrich it if you change platforms or if your platform adds new features.

    The consistent response from everyone in EdTech I’ve talked to who is doing this kind of work is that we can achieve ALDA’s performance goals with these techniques. If we were trying to get 80% or 90% accuracy, that would be different. But a 20% efficiency gain with an expert human reviewing the output? That should be very much within reach. The main constraints on the ALDA project are time and money. Those are deliberate. Constraints drive focus.

    Let’s build something useful. Now.

    The collaboration

    Teams that want to participate in the workshop will have to apply. I’m recruiting teams that have immediate needs to build content and are willing to contribute their expertise to making ALDA better. There will be no messing around. Participants will be there to build something. For that reason, I’m quite flexible about who is on your team or how many participate. One person is too few, and eight is probably too many. My main criterion is that the people you bring are important to the ALDA-related project you will be working on.

    This is critical because we will be designing ALDA together based on the experience and feedback from you and the other participants. In advance of the first workshop, my colleagues and I will review any learning design protocol documentation you care to share and conduct light interviews. Based on that information, you will have access to the first working iteration of ALDA at the first workshop. For this reason, the workshop series will start in the spring. While ALDA isn’t going to require a flux capacitor to work, it will take some know-how and effort to set up.

    The workshop cohort will meet virtually once a month after that. Teams will be expected to have used ALDA and come up with feedback and suggestions. I will maintain a rubric for teams to use based on the goals and priorities for the tool as we develop them together. I will take your input to decide which features will be developed in the next iteration. I want each team to finish the workshop series with the conviction that ALDA can achieve those performance gains for some important subset of their course design needs.

    Anyone who has been to one of my Empirical Educator Project (EEP) or Blursday Social events knows that I believe that networking and collaboration are undervalued at most events. At each ALDA workshop, you will have time and opportunities to meet with and work with each other. I’d love to have large universities, small colleges, corporate L&D departments, non-profits, and even groups of students participating. I may accept EdTech vendors if and only if they have more to contribute to the group effort than just money. Ideally, the ALDA project will lead to new collaborations, partnerships, and even friendships.

    Teaching AI about teaching and learning

    The workshop also helps us learn together about how to teach AI about teaching and learning. AI research is showing us how much better the technology can be when it’s trained on good data. There is so much bad pedagogy on the internet. And the content that is good is not marked up in a way that is friendly to teach AI patterns. What does a good learning objective or competency look like? How do you write hints or assessment feedback that helps students learn but doesn’t give away the answers? How do you create alignment among the components of a learning design?

    The examples we will be using to teach the AI have not only been fine-tuned for effectiveness using machine learning over many years; they are also semantically coded to capture some of these nuances. These are details that even many course designers haven’t mastered.

    I see a lot of folks rushing to build “robot tutors in the sky 2.0” without a lot of care to make sure the machines see what we see as educators. They put a lot of faith in data science but aren’t capturing the right data because they’re ignoring decades of learning science. The ALDA project will teach us how to teach the machines about pedagogy. We will learn to identify the data structures that will empower the next generation of AI-powered learning apps. And we will do that by becoming better teachers of ALDA using the tools of good teaching: clear goals, good instructions, good examples, and good assessments. Much of it will be in plain English, and the rest will be in a simple software markup language that any computer science undergraduate will know.

    Wanna play?

    The cost for the workshop series, including all source code and artifacts, is $25,000 for your team. You can find an application form and prospectus here. Applications will be open until the workshop is filled. I already have a few participating teams lined up and a handful more that I am talking to.

    You also find a downloadable two-page prospectus and an online participation application form here. To contact me for more information, please fill out this form:

    [Update: I’m hearing from a couple of you that your messages to me through the form above are getting caught in the spam filter. Feel free to email me at [email protected] if the form isn’t getting through.]

    I hope you’ll join us.

    Source link