Reflection on tech in teaching

The presence of digital technology in teaching and learning cannot and does not in itself make instruction more meaningful, equitable, or intellectually productive. Setting aside for the moment the mass surveillance and data mining of our youth, other consequences of educational technologies in action include fragmented learning,

obscuring the processes that produce answers,

shifting instructional responsibility away from teachers,

and arbitrary ‘digitization’ of conventional practices in no need of a new medium. Practitioner use of educational technology therefore requires judgment about when a technology contributes to learning, what intellectual work it asks students to perform, what biases and assumptions are embedded within it, and its implications for the forms of teaching and learning, and most importantly the nature of relationship between teacher and learner.

One big change in my thinking is to draw a clearer distinction between using technology in instruction and designing instruction in which technology has a defensible intellectual purpose. I have used digital tools throughout my career teaching mathematics and science, but my learning has me looking more closely at what each technology actually contributes to education. Just because a tool is engaging, visually attractive, or convenient does not mean it gives students an opportunity to think. The more important question is what students are being asked to do intellectually in the presence of the technology.

That question is especially important with computational tools such as Jupyter Notebook, Voilà, Desmos, and video-analysis software. These technologies can offer students complex data, models, and representations, but they can also be black boxes. If student activity is reduced to entering values and accepting outputs, the tech conceals rather than deepens the reasoning. I increasingly value transparency. Which means students must be able to ask what was measured, what was calculated, what relationships were programmed, and what biases and assumptions were built into a model.

My thinking about “flipped” learning went the same way. In the rotational-inertia lesson, I moved toward the principle of flipping preparation rather than instruction. The pre-class videos do not explain the concept; they present a physical contradiction for students to observe and predict. The classroom is still the place where students and teacher together investigate the phenomenon, develop the physics, compare physical and computational evidence, and critique the model, IRL.

I have changed how I think about the relationship between physical and digital investigation. A digital model, video analysis, or computational simulation is most useful when students understand it as a representation of a material system and not a replacement for one. The productive sequence becomes phenomenon, prediction, measurement, model, comparison, and revision. Technology extends inquiry and keeps its assumptions and limitations open to critical examination.

I now divide technology usages into those that open intellectual work to students and those that obscure or displace it. Even an ancient technology is powerful when it helps a learner observe a pattern, test a conjecture, compare evidence, or communicate reasoning.