OLDaily

Welcome to Online Learning Daily, your best source for news and commentary about learning technology, new media, and related topics.

Methodological Pluralism in Practice: A systemic design approach for place-based sustainability transformations
Haley Fitzpatrick, Tobias Luthe, Birger Sevaldson, Contexts, 2024/04/08

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So much goodness in this paper (42 page PDF)! The authors offer "an illustrated example of applying a diverse and reflexive systems oriented design approach" but what they deliver feels like so much more than that. The practice of 'reflexive research' is based on 'methodological plurality,' or "the act of incorporating methods from different disciplines and knowledge domains to expand ways of engaging in complex systems" and based on situated place-based examples. We can see an example of these methods and practices in the current example from this diagram. I'm thinking this isn't just a good way to think about research but also of learning and decision-making in general.

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Is Artificial Intelligence in Education an Object or a Subject? Evidence from a Story Completion Exercise on Learner-AI Interactions
George Veletsianos, Shandell Houlden, Nicole Johnson, Tech Trends, 2024/04/08

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On the surface this preprint article (35 page PDF) is "a thematic analysis of 92 participant responses to a story completion exercise which asked them to describe a classroom agreement between an AI
instructor and a learner twenty years into the future." It's made more interesting through the adoption of a theoretical framework based on "the mutual interrelatedness of all living beings and nonliving elements, impacting each other synergistically, within a dynamic network of nested living systems" as found in Indigenous knowledge. It reflects a desire on the part of the researchers to "adopt a non-Western, relational worldview to understand relationships between human learners and AI, one which emphasizes the need for centring complexity and avoiding simplicity in the context of teaching and learning." Four sets of themes emerge from the research: elements of education, AI affordances, boundaries, and social convention.

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A framework for inclusive AI learning design for diverse learners
Yukyeong Song, et al., Computers and Education: Artificial Intelligence, 2024/04/08

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The point of this article (13 page PDF) is "to identify AI learning design-related frameworks and synthesized them into our proposed framework, which includes the core component of AI learning content (i.e., five big ideas), anchored by the three universal design for learning (UDL) principles (the 'why,' 'what,' and 'how' of learning), and six praxes with pedagogical examples of AI instruction." As such, even if we disagree with some points in detail, it provides a useful framework from which to consider AI instruction. If you're wondering, "the Five Big Ideas of AI are: (1) "computers perceive the world using sensors", (2) "agents maintain models/representations of the world and use them for reasoning", (3) "computers can learn from data" , (4) "making agents interact comfortably with humans is a substantial challenge for AI developers," and (5) "AI applications can impact society in both positive and negative ways." These wouldn't be my five main ideas, but it's still a good starting point.

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Speculative Practicescapes of Learning Design and Dreaming
Eamon Costello, et al., Postdigital Science and Education, 2024/04/08

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So I would definitely have approached the subject matter differently, but the core thrust of the paper is sound, in my view. The main argument is found at the mid point of the paper in the section on reclaiming, recentring, and rehabilitating voices attributed to Felicitas Macgilchrist, who cites the "the 'shambles' of learning theory". She writes, "the EdTech industry makes all-encompassing promises, yet imagines students as versions of Pavlov's dogs... The equivalent would be something like '[App Name]: Behaviourist drills, but kind of fun for a few minutes'."  Reviewer number two (the reviews are helpfully appended to the paper) sums it best: " By embracing emotions, personal narratives, and the concept of practicescapes, the article advocates for a more holistic, heart-centre approach to learning design."

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Theory Is All You Need: AI, Human Cognition, and Decision Making
Teppo Felin, Matthias Holweg, SSRN, 2024/04/08

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I think this is a popular but ineffective argument. The authors argue (46 page PDF) that "Human cognition—in important instances—is better conceptualized as a form of theorizing rather than data processing, prediction, or even Bayesian updating." I don't exactly agree with this, but it is certainly the most widely-held view. They explain, "Theories provide a mechanism for identifying new data and evidence, a way of "intervening" in the world, experimenting, and problem solving." Theories are, according to the authors, the difference between recognizing critical new evidence, such as the verification of Kepler's elliptical orbits, rather than simply the majority vie. "The LLM can only represent and mirror the predominant and existing conceptions—in this case, support for the geocentric view of the universe—it finds in the statistical association of words in its training data." All this sounds great - until your LLMs start producing theories. Which, in my view, they can. Via Luiza Jarovsky.

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We publish six to eight or so short posts every weekday linking to the best, most interesting and most important pieces of content in the field. Read more about what we cover. We also list papers and articles by Stephen Downes and his presentations from around the world.

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