Phil Nichols describes his youthful adventures reappropriating the humble graphing calculator to program games:
For me, it began with “Mario” — a TI-BASIC game based loosely on its Nintendo-trademarked namesake. In the program, users guided an “M” around obstacles to collect asterisks (coins, presumably) across three levels. Though engaging, the game could be completed in a matter of minutes. I decided to remedy this by programming an extended version. I studied the game’s code, copying every line into a notebook then writing an explanation beside each command. I sought counsel from online tutorials, message boards, and chat rooms. I sketched new levels on graph paper, strategically placing asterisks in a way that would present a challenge to experienced players. Finally, after a grueling process of trial and error, I transformed my designs into code for three additional stages.
As he summarizes, his non-school-sanctioned explorations of an otherwise school-based tool led to sophisticated discoveries and creations:
[W]ith the aid of my calculator, I’d crafted narratives, drawn storyboards, visualized foreign and familiar environments and coded them into existence. I’d learned two programming languages and developed an online network of support from experienced programmers. I’d honed heuristics for research and discovered workarounds when I ran into obstacles. I’d found outlets to share my creations and used feedback from others to revise and refine my work. The TI-83 Plus had helped me cultivate many of the overt and discrete habits of mind necessary for autonomous, self-directed learning. And even more, it did this without resorting to grades, rewards, or other extrinsic motivators that schools often use to coerce student engagement.
While he positions calculator programming as a balance between the complementary educational goals of “convention” and “subversion,” this also echoes tradeoffs between routine expertise and adaptive expertise, between efficiency and creativity, or between convergent and divergent thinking. It remains an ongoing risk in overly restrictive learning environments. Standards that dictate the time and sequence of each stage of students’ progression fail to allow for the different paths which personalization accommodates. Yet even adaptive learning systems that seek to anticipate every next step a student might take must be careful not to add so many constraints that crowd out productive paths the student might otherwise have pursued. Personalized learning needs to leave room for error and open-ended discovery, because some things just aren’t known yet.
In The Coming Big Data Education Revolution, Doug Guthrie argues that “big data”, rather than MOOCs, represent the true revolution in education:
MOOCs are not a transformative innovation that will forever remake academia. That honor belongs to a more disruptive and far-reaching innovation – “big data.” A catchall phrase that refers to the vast numbers of data sets that are collected daily, big data promises to revolutionize online learning and, in doing so, higher education.
I agree that there are exciting new discoveries and innovations still yet to be made through the advent of big data in education, and I also agree that MOOCs’ current reliance on scaling up delivery of existing content isn’t particularly revolutionary. Yet I see the two movements as overlapping and complementary, rather than as competing forces.
While MOOCs may not (yet) have revolutionized instruction, they have revolutionized access for many learners. Part of their appeal for those interested in their growth is their potential for enabling large-scale analysis due to the high enrollments as well as the availability of online data. The opportunity to study such large numbers of students across such disparate contexts is rare in traditional academic settings, and it permits discoveries of learning trajectories and error patterns that might otherwise get missed as noise amidst smaller samples.
Another potential innovation which traditional MOOCs (xMOOCs) have not yet explored is new models of building cohorts and communities from amidst a large pool of learners, a goal at the heart of “connectivist MOOCs” (cMOOCs) that highlights peer-learning pedagogy. Combine xMOOCs and cMOOCs, and you can improve educational access even further by enabling courses to spring up whenever and wherever enough people, interest, and resources converge. Add in the analytical power of big data, and then you have the capacity to truly personalize learning, by providing both the experiences that best support students’ learning and the human interactions that will enrich those experiences.
As described in “Giving Preschoolers Choice Increases Sharing Behavior“:
[S]haring when given a difficult choice leads children to see themselves in a new, more beneficent light. Perceiving themselves as people who like to share makes them more likely to act in a prosocial manner in the future.
Previous research has shown that this idea — as described by the over-justification effect — explains why rewarding children for sharing can backfire. Children come to perceive themselves as people who don’t like to share since they had to be rewarded for doing so. Because they don’t view themselves as “sharers” they are less likely to share in the future.
Developmental psychologists Nadia Chernyak and Tamar Kushnir found that compared to children who were given a non-costly choice or who were required to share, preschoolers given a costly choice were more likely to share again at a subsequent opportunity.
- I would be interested in an analysis comparing the effect of the conditions on the children who did not share– that is, collecting baseline data on children’s initial propensity to share, and then comparing how the interventions affected them across the range of initial tendencies.
- I wonder how well this would apply to long-term planning and diligence (e.g., completing homework, practicing a difficult skill, doing chores).
The results do suggest that choice can be a powerful mechanism for promoting positive habits and attitudes, something which I think parents and schools could harness more productively. That choice can potentially foster empathy and perspective-taking is very encouraging.
Full reference: N. Chernyak, T. Kushnir. Giving Preschoolers Choice Increases Sharing Behavior. Psychological Science, 2013; DOI: 10.1177/0956797613482335
Posted in Empathy & perspective-taking, Motivation, Parenting, Self-directed learning, discovery, & play, Social learning & development
- Tagged Choice, Conflicting beliefs, Empathy, Metacognition, Motivation, Perspective-taking, Rewards and punishment, Self-regulation, Social relations