Balancing human-human and human-computer interaction

A fundamental challenge in implementing personalized learning is in determining just how much it should be personal—or interpersonal, to be more specific. Carlo Rotella highlights the tension between the customization afforded by technology and the machine interface needed to collect the data supporting that customization. He narrows in on the crux of the problem thus:

For data to work its magic, a student has to generate the necessary information by doing everything on the tablet.

That invites worries about overuse of technology interfering with attention management, sleep cycles, creativity, and social relationships.

One simple solution is to treat the technology as a tool that is secondary to the humans interacting around it, with expert human facilitators knowing when and how to turn the screens off and refocus attention on the people in the room. As with any tool, recognizing when it is hindering rather than helping will always remain a critical skill in using it effectively.

Yet navigating the human-to-data translation remains a tricky concern. In some cases, student data or expert observations can be coded and entered into the database manually, if worthwhile. Wearable technologies (e.g., Google Glass, Mio, e-textiles) seek to shorten the translation distance by integrating sensory input and feedback more seamlessly in the environment. Electronic paper, whiteboards, and digital pens provide alternate data capture methods through familiar writing tools. While these tools bring the technology closer to the human experience, they require more analysis to convert the raw data into manipulable form and further beg the question of whether the answer to too much technology is still more technology. Instructional designers will always need to evaluate the cost-benefit equation of when intuitive human observation and reflection is superior, and when technology-enhanced aggregation and analysis is superior.

What should we assess?

Some thoughts on what tests should measure, from Justin Minkel:

Harvard education scholar Tony Wagner was quoted in a recent op-ed piece by Thomas Friedman on what we should be measuring instead: “Because knowledge is available on every Internet-connected device, what you know matters far less than what you can do with what you know. The capacity to innovate—the ability to solve problems creatively or bring new possibilities to life—and skills like critical thinking, communication and collaboration are far more important than academic knowledge.”

Can we measure these things that matter? I think we can. It’s harder to measure critical thinking and innovation than it is to measure basic skills. Harder but not impossible.

His suggestions:

For starters, we need to make sure that tests students take meets [sic] three basic criteria:

1. They must measure individual student growth.

2. Questions must be differentiated, so the test captures what students below and above grade-level know and still need to learn.

3. The tests must measures [sic] what matters: critical thinking, ingenuity, collaboration, and real-world problem-solving.

Measuring individual growth and providing differentiated questions are obvious design goals for personalized assessment. The third remains a challenge for assessment design all around.

Misplaced critical thinking

In Physics Today‘s Science controversies past and present, Steven Sherwood compares the current public response to anthropogenic climate change to the historical responses to heliocentrism and relativity. Even though theories of climate change pale in comparison to the others on the scale of scientific revolutions, he notes many fundamental similarities in their effects on people’s conception of the world. Here are some choice quotes that capture important scientific principles which tend to escape lay understanding and which may make acceptance of scientific theories more difficult. On scientific elegance and parsimony in model comparison:

Surely, the need for a new tweak to the model each time more accurate observations came along should have been a tip-off that something fundamental was wrong.

On deduction vs. observation:

the worked-out consequences of evident physical principles rather than direct observation

A common refrain is the disparagement of new paradigms as mere theories with too little observational basis.

On the backfire effect:

Instead of quelling the debate, the confirmation of the theory and acclaim for its author had sparked an organized opposition dedicated to discrediting both theory and author.

As [confirmatory] evidence continues to accumulate… skepticism seem[s] to be growing rather than shrinking…

provocative ideas… have shattered notions that make us feel safe. That kind of change can turn people away from reason and toward emotion, especially when the ideas are pressed on them with great force.

why the backlash happens: the frailty of human reason and supremacy of emotional concerns that we humans all share but do not always acknowledge

On communicating scientific uncertainty:

“All our science, measured against reality, is primitive and childlike—and yet it is the most precious thing we have.” (Einstein)

One of the most difficult yet fundamental principles of science is that we don’t and can’t know if we’re right. We can only get closer to what is probably right. Yet science is seldom conveyed or perceived that way. And what makes science so precious is its ability to show us, through inference and deduction, that which seems to contradict our casual observation and which most surprises us. This suggests caution both when employing discovery learning, as we cannot always trust ourselves to discover accurately, and when employing lecture-based instruction, as we are also unlikely to trust authoritarian telling that threatens our preferences for and sense of security about our world. Understanding the relationship between our flawed tools of reason—through cognitive science—and our imperfect tools of science—probabilistic inference, mathematical proof, model comparison—can help us learn better from both. — Sherwood, S. (2011). Science controversies past and present. Physics Today, 64(10), 39-44.

Direct instruction of discovery learning

From Lisa Guernsey’s A False Debate about Preschool (and K-12) Learning:

When a child sees an intriguing model of how to ask questions, explore and test hypotheses, that child will want to do the same.

What children need are more learning environments – not just in preschool, but throughout their early, middle and later years of school – that give them day-to-day experience with adults who offer them effective and engaging models of what it looks like to learn.

Maybe we could think of this as direct instruction of discovery learning, especially if we note that modeling and imitation learning can be much more powerful and direct than declarative description / prescription.