Week 2 Reflection – LTEC 6010: Reflection on Humanity, Rationalism, and Learning Design in the Digital Age

"What makes us human? Balancing desire (psychological well-being), necessity (emotional well-being), meaning (social well-being)"

In my opinion, human existence is an ongoing negotiation between desire (psychological well-being) , necessity (emotional well-being) , and meaning (social well-being). Our human experience is characterized by the tension between autonomy and belonging, between what we want and what we need. This interplay takes on a behavioral nature as we try to align our mind, body and environment within a network of sociocultural influences (Vygotsky, 1978; Hoy, Davis, and Anderman, 2013).

To my mind, consciousness is not a solitary affair; it is relational: performative and reflective, conditioned through relation. In this regard, humanity is co-constructed. Our moral selves and epistemic selves arise through the confrontation of others whose perspectives differ from our own.  We urge you to help us build education systems and technologies, not designed for efficiency but for empathy and conversations that matter.

This philosophy, in learning design, corresponds to socio-cultural and constructivist traditions. The hypothesis that people construct knowledge through interaction and reflection is known as constructivism. The idea of sociocultural which focuses on collaborative and effective understanding of meanings. In environments that involve technology, this translates to designing for agency, exploration, and scaffolded participation, principles for cognitive engagement (Goldstein, 2014).

Today’s learning technologies need to not only support cognition but also reasoning and ethics. Here, rationalism provides a sharp perspective. According to classical rationalists (like Descartes and Spinoza), reason is independent of the senses (Wikipedia, 2026). Contemporary educational technology carries on this legacy in the form of algorithmic logic, but true rational thought cannot be fully automated. Human reasoning incorporates logic, ethics, emotion, and context. Simply put, rationalist principles tell designers that they must not just help people remember information but also structure reasoning.

Instructionally, teaching should be less about transmitting unchanging truths to passive students and more about creating reflective spaces where learners don’t receive knowledge but transform it, much like the content of the first law of thermodynamics: i.e. energy can neither be created nor destroyed, only transformed or transferred (Mayer, 1842; Joule, 1845; Clausius, 1850).

 In the learning sciences, it is the leap from individual cognition to shared understanding, mediated through reflective technologies that amplify learning as a process that is embodied, social, and rational. 

References

Clausius, R. (1850). On the moving force of heat and the laws regarding the nature of heat itself. Annalen der Physik.  https://archive.org/details/cu31924101120883.

Dual Coding Theory (Allan Paivio). (2013). Retrieved from http://www.instructionaldesign.org/theories/dual-coding.html

Goldstein, E. (2014). Cognitive psychology: Connecting mind, research, and everyday experience. Belmont, CA: Cengage Learning. https://books.google.com/books/about/Cognitive_Psychology_Connecting_Mind_Res.html?id=Hy8eCgAAQBAJ.

Hoy, A. W., Davis, H. A., & Anderman, E. M. (2013). Theories of learning and teaching in TIP. Theory Into Practice, 52(sup1), 9–21. https://doi.org/10.1080/00405841.2013.795437

Joule, J. P. (1845). On the mechanical equivalent of heat. Philosophical Transactions of the Royal Society of London, 140, 61–82. https://archive.org/details/philtrans00608634.

Mayer, J. R. (1842). Remarks on the forces of inorganic nature. Annalen der Chemie und Pharmacie, 42, 233–240.  https://web.lemoyne.edu/giunta/mayer.html.

Sadoski, M. (2009). Dual Coding Theory. Retrieved from http://www.education.com/reference/article/dual-coding-theory/

The Madeline Hunter Model of Mastery Learning. (n.d.). Retrieved from https://www.csun.edu/sites/default/files/Holle-Lesson-Planning.pdf

Wikipedia contributors. (2026, January). Rationalism. In Wikipedia, The Free Encyclopedia. Retrieved from https://en.wikipedia.org/wiki/Rationalism

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. https://www.hup.harvard.edu/books/9780674576292.

Week 1 Reflection – LTEC 6010: Peer Review Critique of Molenda et al.

Questioning the "Progress" Myth in Learning Design: My Take as a PhD Student Peer Reviewer

Acting as a peer reviewer for Molenda et al.’s chapter on the history of learning and instructional design technology (LIDT) was eye-opening, it forced me to stop skimming and start dissecting its claims. What I found wasn’t just a neutral timeline of “how we got here,” but a set of arguments about why certain ideas supposedly “advanced” the field. From where I sit in my PhD program, immersed in learning sciences and ed tech debates, two big ones strike me as shaky: the idea that design models can reliably control learning, and the neat story of the field marching linearly toward perfection. Here’s my breakdown, with some fallacies called out and my own rewrites to fix them.

Argument 1: Design Models Don’t "Control" Learning, They Just Set the Stage

Molenda et al. paint early systematic models, like those from military training or programmed instruction, as game-changers. The logic? Break instruction into objectives, sequence it smartly, pick the right media, and boom, effective learning follows. Designers are the heroes; learners are along for the ride.

Honestly, this feels outdated to me. We know from years of research that learning isn’t a vending machine output. It’s tangled up in what learners already know, their motivations, cultural backgrounds, and real-world contexts (think Gee on sociocultural stuff or Greeno’s activity theory). I see a false cause fallacy here: just because good design correlates with good outcomes in controlled settings doesn’t mean it causes them everywhere. In messy classrooms or online spaces, motivation tanks or contexts derail things.

Here’s how I’d rewrite it, in my own words for the blog:

Early design models gave us tools to align goals, steps, and checks, super useful for creating opportunities for learning. But let’s be real: they don’t guarantee results. Success comes from adapting on the fly with learners, not dictating from afar. Modern takes emphasize co-design, where students shape the process too (Molenda et al., 2013).

That dials back the overconfidence and nods to learner agency, which feels truer to how I see design working in practice.

Argument 2: Our Field’s History Isn’t a Straight Line - It’s a Messy Loop

The chapter frames LIDT’s story as steady progress: from basic AV aids to behaviorism, systems thinking, and now fancy learner-centered tech. Each step fixes the last, like we’re evolving toward some ideal.

I call BS on that tidy narrative, it’s too clean for a field full of hype cycles. Programmed instruction? Sounds a lot like today’s adaptive algorithms, both promising personalization but often stumbling on equity issues. This smells like an appeal to novelty (newer = better) mixed with post hoc thinking (it came later, so it solved the old problems). In my view, “progress” is selective, driven by funding, policy, and who holds the mic, while critical voices on culture and power get sidelined.

My fix? Ditch the hero’s journey for something honest:

LIDT hasn’t marched forward in a straight line; it’s looped through debates on efficiency vs. equity, control vs. freedom. Systems models added rigor, sure, but they echoed older tensions we’re still wrestling with today, like making tech work for all learners, not just the privileged few (Molenda et al., 2013; Selwyn, 2021).

This version owns the cycles and pushes us to question whose “advances” we celebrate.

Why This Exercise Kicked My Butt (In a Good Way)

Teasing out arguments from explanations was trickier than I thought. The chapter mostly explains historical shifts (“this is how systems design caught on”) but slips into arguing they were superior (“more scientific!”). As someone neck-deep in PhD reading, I realized how I unconsciously buy into these stories, they’re everywhere in our field. Spotting fallacies (thanks, TBS list!) was fun but humbling; it’s like putting on glasses for fuzzy assumptions.

For my own work on AI-driven learning tools, this is a wake-up call. I’ll be more upfront about limits, no more causal shortcuts, and push for designs that center underrepresented voices. Peer reviewing sharpened that instinct, turning a class assignment into real scholarly muscle.

References

Molenda, M., Pershing, J., & Reiser, R. A. (2013). Historical antecedents of instructional systems design. In Foundations of learning and instructional design technology. https://edtechbooks.org/foundations_of_learn/history_of_lidt​

Selwyn, N. (2021). Education and technology: Key issues and debates (3rd ed.). Bloomsbury Academic. https://www.bloomsbury.com/uk/education-and-technology-9781350145566/