Category: Learning Design

  • Flexible Learning and Policy Challenges

    Flexible Learning and Policy Challenges

    What impact is flexible learning having on learners from K-12 through to professional development?

    New Zealand has remarkably high levels of digital access across the population. Why aren’t we out performing other countries in educational measurements?

    This piece serves to introduce a series of six challenges faced by policy makers around flexible learning.

    These six challenges are:

    1. Unequal Access to Technology and Connectivity
    2. Socioeconomic Disparities
    3. Digital Literacy and Skills Gaps
    4. Quality Assurance and Consistent Experience
    5. Teacher Preparedness and Support
    6. Policy and Funding Models

    In this first piece I want to establish what I mean by ‘flexible learning’.

    Like many I struggle to have a single, concise, and consistent “definition” of flexible learning. I would say that flexible learning is a model of delivery that offers learners agency and control over various aspects of their learning experience. Flexible learning is a spectrum. Formal learning courses exist on a continuum between “rigid” and “flexible” delivery. The more control and choice given to the learner, the more flexible the learning experience.

    Flexible learning aims to “empower the student to choose what learning should be studied face-to-face and that which should be studied online, and how to go about engaging with that learning” (2022). This Means empowering the learner to make choices regarding:

    • When: synchronous or asynchronous learning, pace-mandated or self-paced progression.
    • Where: Learning in different locations (home, campus, workplace, etc.).
    • How: Different modes of engagement (online, in-person, blended, hybrid, hyflex).
    • What: Some degree of choice over content or learning pathways, though this is often more associated with “open learning.” Indeed in a world where students are overwhelmed with choices, there are strong arguments that having a prescriptive programme serves students well.

    In my article “Definitions of the Terms Open, Distance, and Flexible in the Context of Formal and Non-Formal Learning,” (2023) I argued that flexible learning is a model of delivery, rather than a fundamental mode of learning. I posit that there are only two core modes of learning: in-person (or face-to-face) and distance learning. Flexible learning then emerges from various combinations and approaches to curriculum design that empower learners to choose amongst these two modes

    As education has a habit of inventing new terms for marginally different practices it might be worth just pointing out the relationship I think exists between flexible learning and forms of Blended, Hybrid, and HyFlex learning. I perceive blended, hybrid, and HyFlex learning as specific models of delivery that fall under the umbrella of flexible learning. They all aim to give agency to the learner regarding how they engage with the material, combining elements of in-person and distance learning.

    I believe that designing for flexible learning means considering the learner’s context and perspective, and creating learning experiences that are relevant, meaningful, motivating, realistic, and feasible within an agreed timeframe. This also involves careful consideration of learning outcomes and assessment in diverse delivery contexts. This means course creators need clarity about learning design principles in relation to flexible approaches, such as working with Notional Study Hours (2020a) and the importance of Learning Outcomes (2020b).

    Based on my broad definition thatFlexible Learning refers to educational approaches and models of delivery that provide learners with a significant degree of choice and control over the time, place, pace, and mode of their learning, leveraging combinations of in-person and distance learning to enhance accessibility and cater to diverse learner needs, how do we face those six policy challenges?

    Watch this space…

    Atkinson, S. P. (2020a, April 14). Working with Notional Study Hours (NSH) or “How much is enough?” Simon Paul Atkinson. https://sijen.com/2020/04/14/working-with-notional-study-hours-nsh-or-how-much-is-enough/

    Atkinson, S. P. (2020b, April 4). Designing Courses: Importance of Learning Outcomes. Simon Paul Atkinson. https://sijen.com/2020/04/04/designing-courses-importance-of-learning-outcomes/

    Atkinson, S. P. (2022a, July 15). How do you define hybrid, or hyflex, learning?. Simon Paul Atkinson. Retrieved from https://sijen.com/2022/07/15/how-do-you-define-hybrid-or-hyflex-learning/

    Atkinson, S. P. (2023). Definitions of the Terms Open, Distance, and Flexible in the Context of Formal and Non-Formal Learning. Journal of Open, Flexible, and Distance Learning, 26(2).3 Retrieved from https://jofdl.nz/index.php/JOFDL/article/view/521

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  • 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.

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