The following experience is common to most teachers: a meticulously planned class activity succeeds in capturing student interest for a few minutes, but attention evaporates quickly and afterward no one can remember the point of the lesson. Despite the flashy visualizations, the expensive 3-D models, the age-appropriate allusions (references to Justin Bieber and Lindsay Lohan), the clever asides, and sensational content, the material failed to stick. What went wrong?
According to Daniel Willingham, cognitive scientist and author of Why Don’t Students Like School?, the mystery of student engagement comes down largely to one thing: meaning. He asserts that it is the extent to which we get students to think about what everything means that determines whether or not we truly earn their attention and successfully transmit knowledge. He concludes that structuring lessons so that they emphasize (or bring into relief) the “meaning” of the material is the most effective way to ensure student engagement and retention of knowledge.
But what exactly does it mean in practice to get students to think about “meaning”? Though it may seem like an elusive concept, thinking about meaning generally involves thinking about structure, synthesizing information, and applying knowledge to new circumstances. In all these instances, students are engaged with both the idiosyncratic texture of the material they’re working with (whether that’s language, numbers, code, or clay) as well as deep structure and overarching process (abstractions and ideas).
With the advent of new technology, there are more ways than ever before to engage students in a deep, serious fashion. Adaptive learning, a teaching method premised on the idea that the curriculum should adapt to each user, can harness the power of data mining to provide a wealth of opportunities for students to think about meaning. Here’s a brief look at how:
A) Students think about meaning when they think about pattern and the bigger picture.
Whether the subject matter is poetry or earth science, students think about meaning when they start to recognize how details fit into the bigger picture–when they notice how a twist in phrase contributes to the rhythm of an entire stanza, or how the presence of certain rock in a region indicates that volcanic activity occurred thousands of years ago. The ability to grasp this kind of relationship generally signifies a level of cognitive maturity. It requires students to move back and forth between pattern and detail, the abstract and the concrete. An adaptive system can help students develop big picture and pattern-recognition ability by a) drawing concrete moments back to the abstract (asking students to compare and contrast details and comment on structure and process) b) drawing abstract moments back to the concrete by asking students to apply principles, theories, and formulas to the idiosyncrasies of new problems and situations and c) tracking the efficacy of these shifts to optimize the flow of cognitive work for each learner’s individual style.
B) Students think about meaning when they synthesize learning across domains and subject areas.
Students think about meaning when knowledge in one area shows up unexpectedly somewhere else–when they’re studying biology, and everything they learned in chemistry comes into play, or when they’re using writing skills they acquired in composition class to organize a history essay. In other words, students think about meaning when they encounter familiar material from an unfamiliar angle or through the lens of a new context. An adaptive system can facilitate these experiences by using an individual’s subject area strengths to remediate his weaknesses. How might this work with a subject like math? Someone who is, say, a naturally scientific thinker might develop his math ability by using math to conduct experiments and test hypotheses about the natural world, while someone who is “musical” might use math to grasp the science behind harmony. The benefit here is that you can use student curiosity in one area to fuel interest in every other.
C) Students think about meaning when they’re forced to reflect on their learning processes.
Studies show that the very act of reflecting on your process (whether or not the reflection is even read by an instructor) improves learning outcomes because it helps students become more self-aware. While developing greater self-awareness is a natural byproduct of learning, adaptive learning can stimulate and speed up the process by inserting “reinforcement” moments into cognitive work–moments that prompt a student to reflect on his particular solution, underscore the concept behind the solution, or describe the structure of some body of information. Even if a student happens to correctly guess the answer to a question, he will not be able to complete the lesson without proving his grasp of the underlying concept. This of course increases the chance he will experience repeat success with a similar problem. Any online learning program can achieve these aims in a basic way, but an adaptive system can bring reinforcement to a new level by evaluating how well such moments are working and by providing reflective moments (and even longer exercises) tailored for each learner’s style.
D) Students think about meaning when they’re judging/reacting to the work of others.
Many students engage deeply with their studies when they begin to develop a set of personal standards and aesthetics that pertain to academic work–when they know what they like and dislike and find impressive, effective, or compelling. How does this sense of standard and self evolve? When a student evaluates the work of others, he makes decisions that force him to define his own value system. He asks himself questions like, “What does it mean for an argument to be sound and logical? How can this essay be more persuasive? What would make this story more suspenseful?” When giving and accepting feedback in this respect, students also develop valuable interpersonal skills and the ability to accept criticism graciously.
The right social context is necessary to facilitate this kind of interaction. Because it processes thousands of data points on student activity and performance, an adaptive system can help students find like-minded peers and organize communities of learning. These communities might form within individual classes or schools or across districts and beyond. And depending on the aim of the class, teachers can use data regarding performance, learning style, and preferences, to create cohorts of students within classes who complement each other academically. By tapping into arenas where they can learn from peers, showcase their own work, and receive feedback, students get into the habit of professional exchange at an early age.
E) Students think about meaning when they’re exposed to expert work.
Students think about meaning when they know that the subject they’re studying is alive, evolving and not just an arbitrary segment of knowledge that unknown authorities dictate they must master. Students recognize this when exposed to work done by experts at the forefront of their field. The material stops being dead and static, and students begin to fathom the true ramifications of the knowledge they’re immersed in. Exposure to expert work also illuminates a path to expert-level work, should students be interested in becoming experts themselves.
A sophisticated adaptive learning system can not only use student performance and activity to identify weaknesses and serve up problems that eliminate them; it can motivate and enrich student learning in a way that is equally precise. Because an adaptive system is computerized and involves tagged content, it can be hooked up to enormous repositories of expert material that normally lie beyond the realm of school (if those repositories are tagged). When appropriate, such a system can direct students to specific articles, studies, reports and books created by experts, for experts. (In this way, students can function as “apprentices” to experts, just as they did centuries earlier.) Companies already employ similar recommendation engines to figure out consumer preferences and recommend purchases; an adaptive learning system harnesses the same kind of technology for intellectual endeavor.