Learner, Know Thyself

As “Big Data” loom larger and larger, the value of owning your own data likewise increases. Learners need to have access to all of their prior educational data, just as much as patients need access to all of their prior medical records, especially as they move between multiple providers and change over time. Instead of locking up valuable information in the hands of individual organizations with their own proprietary or idiosyncratic institutional habits, this lets the learner share their data for new educational providers to analyze.

Putting data back in the learners’ hands also empowers them to act as their own student-advocates, not just recognizing patterns in when they are learning more effectively (or less), but having the evidence to support their position. With accurate self-assessment and self-regulated learning becoming increasingly important goals in education these days, having students take literal ownership of their own learning and assessment data can help them make progress toward those goals.

Beating cheating

Between cheating to learn and learning to cheat, current discourse on academic dishonesty upends the “if you can’t beat ’em, join ’em” approach.

From Peter Nonacs, UCLA professor teaching Behavioral Ecology:

Tests are really just measures of how the Education Game is proceeding. Professors test to measure their success at teaching, and students take tests in order to get a good grade.  Might these goals be maximized simultaneously? What if I let the students write their own rules for the test-taking game?  Allow them to do everything we would normally call cheating?

And in a new MOOC titled “Understanding Cheating in Online Courses,” taught by Bernard Bull at Concordia University Wisconsin:

The start of the course will cover the basic vocabulary and different types of cheating. The course will then move into discussing the differences between online and face-to-face learning, and the philosophy and psychology behind academic integrity. One unit will examine the best practices to minimize cheating.

Cheating crops up whenever there is a mismatch between effort and reward, something which happens often in our current educational system. Assigning unequal rewards to equal efforts biases attention toward the inflated reward, motivating cheating. Assigning equal rewards to unequal efforts favors the lesser effort, enabling cheating. The greater the disparities, the greater the likelihood of cheating.

Thus, one potential avenue for reducing cheating would be to better align the reward to the effort, to link the evaluation of outputs more closely to the actual inputs. High-stakes tests separate them by exaggerating the influence of a single, limited snapshot. In contrast, continuous, passive assessment brings them closer by examining a much broader range of work over time, collected in authentic learning contexts rather than artificial testing situations. Education then becomes a series of honest learning experiences, rather than an arbitrary system to game.

In an era where students learn what gets assessed, the answer may be to assess everything.