Tag Archives: why students don’t like school

Why Students Don’t Like School, and What Adaptive Learning Can Do About It (Part 4)

student ipad 006Miss Part 1, 2 or 3 of the series? Check it out here.

I recently read Daniel T. Willingham’s Why Don’t Students Like School?: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom.

As I was reading Willingham’s investigation, I noticed that most of the real reasons Willingham argues that students don’t like school can be eliminated or reduced through continuous adaptive learning technology. In my first three posts of the series, I discussed ten ways in which adaptive learning can improve the classroom experience.

Here is one more reason that students find school distasteful – along with an explanation of how adaptive learning can help.

Lack of connection with expert work and outside world.

In my previous post, I described how dissatisfying it is to students when they feel like the hoops and hurdles they face are essentially arbitrary and culminate in nothing. Another factor that contributes to the sense that schoolwork is meaningless is the degree to which it is removed from expert work (real history, mathematics, poetry, science). No matter what their skill or age level, students wants to feel like their work matters and requires skill and focus. As Willingham argues, this detachment from expert work concerns educators as well as students: “If we’re not giving students practice in doing the things that historians and scientists actually do, in what sense are we teaching them history and science?”

Adaptive learning can narrow the perceived gap between school work and expert work in a number of ways:

A) Sheer Practice.

First and foremost, the difference between student and expert thinking is that for experts, space in what Willingham terms “working memory” is increased because experts have automatized many of their “routine, frequently used procedures.” This affords them cognitive energy to solve more complex problems. Professional physicists, for instance, don’t need to look up basic formulas, and professional ballet dancers can observe a complicated bit of choreography and immediately replicate the series of movements. This kind of automatization happens naturally when students receive enough practice: after hundreds of problems, students don’t think twice about the product of 7 and 7 or the order of operations in which an expression should be evaluated.

An adaptive learning system can speed up the process through which these basic and routine procedures are automatized, by determining each student’s exact needs and serving up problems designed to target weaknesses on a precise level. In other words, an adaptive system can help students use their time more efficiently, allowing them to see gains in their ability at a more satisfying pace.

B) Organizing Information.

According to Willingham, expert thinking is characterized by an ability to transfer knowledge between domains, “access the right information” in a swift and accurate way, and formulate productive questions and hypotheses about new information. What exactly allows experts to do this? As Willingham points out, “it’s not just that students know less than experts; it’s also that what they know is organized differently in their memory.” Experts store knowledge in a way that emphasizes deep, functional, abstract relationships. So, instead of thinking of things in surface terms, experts think about pattern and structure, how each part relates to the whole. This allows them to do all the things experts do: use acquired skills in new contexts, locate and retrieve stored information, and process new material in a productive way.

A sophisticated adaptive learning system can identify students’ “blind spots” and get them to organize information in ways that were previously alien to them. For instance, a student who has trouble seeing the big picture can receive questions or activities that guide him to think in larger terms; another student who has difficulty memorizing what seem like isolated facts can be shown how those facts relate to overarching ideas.

C) Exposure.

Many students simply don’t know what real scientists, mathematicians, writers, and historians are working on. In schools, we place a mild emphasis on helping students consider future career options (such as “doctor,” “teacher,” “judge,” etc), but we do little to expose them to mature manifestations of the academic work they’re actually doing. We tend to think that expert territory is too complex or niche-oriented for students, so when students ask us the point of school work, we appeal to them on an economic, vocational, and practical level. We say they’ll end up on the lower rungs of society if they don’t master algebra or try to persuade them that studying trigonometry will help them pursue their dream of becoming a lawyer. Why not stimulate student interest by trusting the “interestingness” of the subject itself to engage students, by answering questions like “what’s the point?” with a real, robust answer? Why not show them that the fields they’re immersed in are so interesting that adults are working on them, too, and that their work infiltrates our lives on a daily basis? (The iPad they’re holding, for instance, employs some technology developed and patented by working physicists.)

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.) Adding even a slight degree of adaptivity to the sheer amount of digital content available has the power to significantly amplify the learning experiences we are currently familiar with.

A student who, say, demonstrates a facility with language can be introduced to the work of certain contemporary, practicing poets, and based on his preferences, be introduced to another set of poets and so forth. This experience is much more mature and individualized than working through a static, printed anthology that features a limited number of canonical poets. 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.

For a more detailed article on how we can teach subjects in a more mature and meaningful way, check out my post, Teaching Math Maturely.

D) Original Work.

The most salient difference between student and expert work is the fact that experts produce original work in their field. While we can’t expect students to match the quality of such work (though we may be surprised with what they produce given the right stimulation), we can guide them in a productive direction by exposing them to expert work and directing them to opportunities for them to produce and showcase their own work.

Because it processes thousands of data points on student performance, an adaptive system can help students find like-minded peers and organize communities of learning (just as it can organize cohorts in the classroom). These communities might form within individual classes or schools or across districts and beyond. By tapping into arenas where they can learn from peers, showcase their own work, and receive feedback (from those who have no interest in grading them), students get into the habit of professional academic exchange at an early age.

Realizing that their work has an audience beyond the teacher is enough to motivate some students to engage deeply with their studies. And, oddly enough, participating in this sort of community is a more realistic vision of real academic work than the model we currently provide. It happens to be more satisfying and more productive as well.

To learn more about Knewton adaptive learning, download the whitepaper here.


Why Students Don’t Like School, and What Adaptive Learning Can Do About It (Part 3)

computer classMiss Part I or Part II of the series? Check it out here.

I recently read Daniel T. Willingham’s Why Don’t Students Like School?: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom.

As I was reading Willingham’s investigation, I noticed that most of the real reasons Willingham argues that students don’t like school can be eliminated or reduced through continuous adaptive learning technology. In my first two posts of the series, I discussed seven ways in which adaptive learning can improve the classroom experience.

Here are three more reasons students find school distasteful – along with explanations of how adaptive learning can help.

1. Discomfort moving from the concrete to the abstract (and back again).

If the aim of school is to make students independent from it (so they can apply what they learn in school to real-life situations), the processes of learning, problem-solving, and synthesizing matter just as much as the factual knowledge used to transfer ability in these areas. It generally works like this: having encountered a range of material, students start to recognize patterns, in both the subject matter and in their own learning. Of course, this abstract pattern recognition isn’t the whole point of learning; the ability to be concrete (to recall facts, execute plans, and work with different materials) is just as fundamental to the educational experience. What matters ultimately, then, is the ability to move seamlessly between pattern and detail, between the abstract and the concrete.

Students gain these skills, according to Willingham, by mastering detailed tasks (ex. revising a sentence in an essay), and then figuring out how these details fit into the whole (ex. understanding the way in which that revised sentence changes the essay’s overall argument). Willingham argues that teachers facilitate this cognitive “muscle-building” in several ways: they provide examples and ask students to compare them; they ask questions that prompt students to identify patterns and remark on structural qualities in the information.

Helping students gain these skills, however, isn’t always easy. As with most productive (and unfamiliar) work, there is often a general level of discomfort involved. The process can be slow and ineffective, especially when students in a class are at varied levels of understanding. Learners at either end of the spectrum are likely to be either bored or confused, and as a result are more likely to “check out” of the lesson and begin to harbor resentment for school in general.

How can adaptive technology help? By tailoring questions and examples to each individual’s level of understanding and learning style, an adaptive system can improve engagement and facilitate success. Specifically, an adaptive system is able to: a) insert reinforcement moments that prompt students to think about meaning, structure, and process b) draw abstract moments back to the concrete by requiring students to apply principles, theories, and formulas to the contexts of new problems, and c) track through data the efficacy of these shifts to optimize the flow of cognitive work for each learner’s individual style. Ultimately, students walk away not only having learned more and in a deeper way – but also having become more confident and engaged in their learning.

2. Lack of connection between past and present learning.

The controversial writer, John Gatto, famously posited that public school as we know it, with its rigidly segmented class day and byzantine rules, teaches students that no subject really matters beyond the forty minutes during which it is taught and that the lack of continuity between subjects and grade levels teaches students to accept “confusion” as their destiny. Regardless of whether you agree with Gatto’s assessment of public schools, student engagement can be strengthened if academic work is imbued with a sense of continuity and meaning. After all, as Willingham suggests, the hardest part of many cognitive tasks is getting geared up to start over or start up again. Nothing is more dissatisfying to students than feeling like the hoops and hurdles they face are essentially arbitrary and culminate in nothing.

Adaptive learning can assist in knowledge recovery and transfer, reducing the extent to which students feel overwhelmed by the introduction of a new type of problem, skill, or knowledge area. A finely tuned adaptive system can accomplish this by quickly reminding students what they learned previously (in the form that sticks with them the best), highlighting certain patterns in the material (or nudging students to grasp them) or bringing certain structures into relief (so that students are guided to what they should be focusing on), or maybe even re-introducing a student’s past notes and commentary at a later point. (Imagine that you were given eternal access to all the notes you ever composed and all the material you ever underlined–how would this change your learning?) The message this sends students is that their learning extends in unfathomable ways beyond the assessment at hand–that what they’re learning today will form the foundation of what they learn tomorrow.

3. Lack of connection between different subjects and areas of learning.

As mentioned earlier, a lack of continuity between different learning episodes creates a sense of meaninglessness and implicitly teaches students that “nothing really matters.” What if, however, you could use student curiosity in one area to fuel interest in every other? What if the positive effects of every learning experience were capitalized upon exponentially?

In his book Disrupting Class, Clayton Christensen identifies the self-perpetuating cycle through which the curriculum and methods of instruction for various subjects are tailored for those who are gifted in them. Math classes, for instance, are taught by those who are gifted at math and through texts written by those who are gifted in the subject as well; and class itself is shaped by the questions and comments of gifted math students. (This leaves those who are not gifted at math feeling excluded and turns them off from the subject.) Imagine an alternative: what if you could use the confidence students develop in the areas in which they excel to help them learn in subjects for which they have less proclivity?

For the purposes of this discussion, I’ll introduce the “7 types of intelligence” that Willingham and other writers and researchers have identified:

  •  Linguistic: ability to use language and express thoughts
  • Logical/mathematical: ability to work with numbers and logic
  • Spatial: ability to think three-dimensionally
  • Bodily-kinesthetic: ability to use one’s hands and body in complex and fine-tuned ways
  • Musical: ability to work with pitch, melody, rhythm, and tone
  • Interpersonal: ability to interact with others;
  • Intrapersonal: ability to understand one’s self
  • Naturalist: ability to observe environment and work with patterns in nature

How might an adaptive learning system allow individuals of the above intelligence types to harness their strengths to approach the study of, say, math differently?

An adaptive system could process each individual’s performance, activity, and preferences to deliver the same material in different ways. Someone who is, say, a “naturalist” might develop his math ability by using math to conduct experiments and test hypotheses about the natural world. Someone who excels in the “interpersonal” might learn by teaching others what he knows. And someone who is “musical” might use math to grasp the science behind harmony.

This differentiation is a fairly blunt-edged example of how an adaptive system might use a student’s strengths to remediate weaknesses (an idea that Willingham introduces and which adaptive learning can make a reality). It could happen in a more subtle fashion as well. If a student excels in rapid-learning problems but fails at projects that require long-term planning and study, an adaptive system might encourage him to segment the longer project into less-intimidating chunks. And vice versa: a student who has difficultly absorbing and processing material quickly might have more luck conceiving of the activity as part of a long-term project. The possibilities are endless.

Why Students Don’t Like School, and What Adaptive Learning Can Do About It (Part 2)

Miss Part I of the series? Check it out here.

I recently read Daniel T. Willingham’s Why Don’t Students Like School: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom.

As I was reading Willingham’s investigation, I noticed that most of the real reasons Willingham argues that students don’t like school can be eliminated or reduced through continuous adaptive learning technology. In my first post of this series, I discussed how adaptive learning can improve school by fully engaging students, providing instantaneous or near-instantaneous feedback, establishing a knowledge and hinting scaffold that can guide students in the right direction, and ensuring that each student receives work pitched at just the right level.

Here are some more ways that adaptive learning can eliminate or reduce the reasons that students find school distasteful:

1. Lack of self-awareness about learning patterns.

Many students who dislike school feel overwhelmed by the work and do not know where to begin or how to approach the problems they are given. As a result they feel uncomfortable, internalize the idea that school is not for them, fail to seek help, and fall behind.

This feeling of being overwhelmed is caused by what Willingham calls an overload on “working memory” (the capacity to perform cognitive work using stored factual and procedural knowledge as well as information from the environment). This overload is often caused by the presence of one or more of the following: “Multistep instructions, lists of unconnected facts, chains of logic more than two or three steps long, and the application of a just-learned concept or new material.”

There are several ways that adaptive learning can increase the amount of space in “working memory” and ensure that students don’t feel overwhelmed by the complexity of problems they encounter. As I mentioned in the first post in this series, continuous adaptive learning can provide “factual and procedural knowledge” scaffolding and “chunking hints” that guide a student toward the solution while still allowing him to make the discovery for himself.

The benefits afforded by this approach are intertwined by nature; a “scaffold of factual and procedural knowledge” can improve a student’s “chunking” capacity, or ability to break problems down into multiple steps, which can increase both a student’s rate of learning and the overall exposure to knowledge a student receives. These increases can in turn reduce discomfort, aid in “chunking” ability, improve student performance (which generally improves student confidence), and thereby generate a never-ending positive cyclical effect on a student’s relationship with school. In other words, success breeds more success.

All these transformations and moments of insights will also yield greater self-awareness, the invisible ingredient in successful long-term learning. After all, the ultimate goal for school is that students learn how to teach themselves, how to encounter problems in life or on the job and break them down into steps, process the information, deliver solutions, measure results, and iterate. The processes taught in school should thus become so ingrained and automatic that students know exactly what to do when they encounter certain situations. And if they don’t know what to do, they should know what to do to make themselves know what to do–whether that involves defining the problem further, asking the proper questions, or conducting research and evaluating sources.

What adaptive learning can do about it. 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 the problem-solving process, 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 a continuously 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 idiosyncratic style.

2. Social Anxiety.

School is quite obviously more than the sum of its parts (homework, testing, grades, etc), in large part because of the opportunity it provides for students to develop an awareness of themselves in relation to others. Unfortunately this social component can sometimes detract from learning instead of enhancing it. Many students are quiet in class for reasons that are more complex than meets the eye; they may not know how to process information delivered to them or orient themselves within the material. They may have preconceived notions about the subject or not understand obscure vocabulary or jargon. They may not have picked up basic skills along the way and may be self-conscious as a result. Whatever the reason, the posturing or mere presence of other students can severely heighten the discomfort and result in total student shutdown.

Anyone who has ever been lost in a class environment knows that it takes a significant amount of grounding or traction in a subject area to even pinpoint a question to ask that might yield a productive response. Thus, those who ask questions are usually the ones who know the most, are the most confident and need help the least.

What can adaptive learning do in this respect? It can provide the appropriate factual and procedural knowledge, so that students feel grounded enough in the material that they can pinpoint the questions they need to ask. For example, imagine a student who is lost in a class session about metamorphic rock: everyone else mentions “sedimentary” and “igneous” rocks and the student can’t figure out what sedimentary and igneous rocks are and what they have to do with metamorphic rocks to begin with. The student can figure out from a scaffolding system (which can be anything from a sophisticated search engine to an online system tailored to individual thinking patterns) that metamorphic rocks are sedimentary or igneous rocks transformed by extreme heat and pressure, and get up to speed with the rest of the class–which is already on to discussing how the foliation on a particular metamorphic rock reflects the pressure and heat it was subjected to. Of course some productive discomfort is necessary in the classroom; but, if designed well, an adaptive learning system will reduce unproductive discomfort and proliferate the opportunity for productive discomfort.

Adaptive learning can identify social possibilities that build student confidence and make students more likely to participate in large discussions. By aggregating and analyzing data, an adaptive system can create situations in which students assist and mentor each other online. Depending on the aim of the class, teachers can use data regarding performance, learning style, and preferences, to create cohorts of students who complement each other academically. In an English class, for instance, you might be able to create mini workshops of 4 people each, with each workshop composed of an “organization” master, a “style” master, a “grammar” master and a “clarity of purpose” master. Teachers can also create opportunities for peer evaluation that allow students to grapple further with the material at hand (it’s an age-old principle that you don’t truly learn something until you teach it yourself). Using data culled from assessment and surveys following these activities, teachers can then determine the efficacy of these strategies.

3. Need for appropriate pacing.

When observing classrooms, it is always fascinating to chart the wax and wane of student energy and focus. One second, everyone is riveted; the next, everyone is distracted by a bee or a lawnmower, and momentum is lost entirely. The amount of debate surrounding something like block scheduling points to the underlying difference in student needs. Some students function better if they’re working on one project for an extended period and have ample opportunity for reflection, while others need a constant change-up or enjoy a rapid succession of drills.

Adaptive learning can help us out in several ways here. As described in the previous post, any continuous adaptive learning system is built on opportunities for students to “show what they know.” Such opportunities, in addition to engaging students more fully, also break up instruction and order activities in a natural way, ensuring that precious class or study time is not wasted.

An advanced system can also help educators discover the precise way that lectures, assessments, activities, and peer evaluation opportunities should be combined to produce maximum learning benefits for each individual student. One student, for example, might learn best in the sciences if she absorbs a lecture, is tested on it immediately, and then engages in group work. In English class, by contrast, that student might see the most gains if she engages in an activity, absorbs some instruction, then reinforces her understanding by evaluating someone else’s paper. Or for that student, the adaptive system might determine that it’s not the kind of classroom activity that matters but rather the kind of cognitive work she is doing. Maybe she needs rigorously analytical work (think logic games) before introspective creative work. Maybe her ideal “learning day” consists of math drills, nonfiction reading, then creative writing.

The data generated by an adaptive system can also help determine the ideal amount of time each student should spend doing each type of activity. The system might discover, for example, that one student functions best if he learns in 20 minute spurts for 3 hours at a time with approximately 5 very short breaks thrown in, while another student works best in 1 hour segments with two 10-minute breaks built in.

In summary, adaptive learning can help educators serve up academic material in a way that is tailored to each student’s unique learning style.

Stay tuned for more reasons why students don’t like school — and how adaptive learning can help!


Why Students Don’t Like School — and What Adaptive Learning Can Do About It (Part 1)

Ask students why they don’t like school, and you’ll get several answers: it’s “hard,” “boring,” “disconnected from reality” or “only for smart people.” The real answer is of course more complex than any of these responses would suggest. To get a deeper understanding of the matter, I recently read one man’s investigation: Daniel T. Willingham’s Why Don’t Students Like School: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom.

As I was reading, I noticed that most of the real reasons Willingham argues that students don’t like school can be eliminated or reduced through continuous adaptive learning technology. Here’s how:

1. Work pitched at the wrong level.

Willingham begins his book by debunking some conventional notions about what exactly the human mind is designed to do: “Contrary to popular belief, the brain is not designed for thinking. It’s designed to save you from having to think, because the brain is actually not very good at thinking. Thinking is slow and unreliable.” Willingham indicates, however, that “people enjoy mental work if it is successful.” Hence the popularity of crossword puzzles, sudoku games, and brain teasers. What makes mental work enjoyable? The snap of discovery, the sudden moment of insight. Mental work becomes fun and even entertaining if it consistently yields such moments.

When students complain that school is boring, what they’re probably saying is that it’s either too hard or too easy. The challenge is to get the balance just right: too easy and there’s no satisfaction; too hard, and students will invest effort only to feel frustrated and lose focus. Thus, the key to maintaining student engagement is to escalate the difficulty of the work incrementally, so that students receive a constant stream of questions targeted at the precise level at which thinking and real engagement are likely to occur. Continuous adaptive learning can provide this by determining a student’s ability and “serving up” questions at just the right level.

Of course real life doesn’t happen this way–you don’t get a series of challenges perfectly calibrated to your level, so that every exertion leads to maximum satisfaction; the hope is, however, that adaptive technology can be harnessed so that students engage productively with schoolwork and are therefore better equipped to tackle “imperfect” challenges in the real world. Think of it this way: an adaptive learning system is like a superior mental work-out machine that leaves you ready to scale intellectual cliffs and undertake marathons of critical thought.

2. Not enough opportunities for engagement.

The above paragraphs are premised on the fact that students have enough problems to solve in the first place. If students are only given lectures with minimal opportunity to exercise their cognitive muscles, they will obviously be less engaged.

These “cognitive-work” opportunities are inherent to adaptive learning systems. After all, a continuous adaptive system is based on the idea that what you see going forward depends on your previous activity and performance. In other words, it’s practically impossible to design a continuous adaptive learning system that doesn’t give students a chance to “show what they know” in a fairly constant way. Thus, keeping students mentally active throughout a classroom session is a fundamental challenge that adaptive learning solves.

3. Slow feedback.

The above point — that students need to be active to be engaged — seems an obvious one, but consider from a teacher’s perspective how difficult it is to build problem-solving into every single lesson. The trouble with student problem-solving is that it generally requires feedback of some sort (grading, evaluation, commentary) and good feedback takes time to generate. In this way, the administrative aspect of many productive class activities can make the work for teachers spiral out of control.

As far as evaluation is concerned, adaptive learning can efficiently provide high-quality student feedback, reducing administrative burden on teachers and enhancing student engagement. Whether it’s multiple choice, free response, or even an essay that’s submitted, a continuous adaptive learning system can process student work and deliver personalized assessment. (For more on how adaptive learning works with material as subjective as English composition, check out my post on adaptive learning for soft subjects.) Most importantly, the feedback provided by an adaptive learning engine (designed for continuous as opposed to single-point adaptivity) can be instantaneous or near-instantaneous. This enhances student engagement because students are less likely to lose focus if feedback is immediate and they can quickly self-correct. The result is pacing conducive to risk-taking, experimentation, iterative development, and rapid learning.

4. Lack of background knowledge.

Anyone who’s ever had trouble with the reading comp section on any standardized test (think GMAT or GRE) understands the soporific effect of subjects like the “electromagnetic spectrum” or “sessile organisms.” However, smart test-takers know that the subject itself is supposed to be irrelevant; critical reasoning ability is what’s being tested. For the most part, this isn’t a problem on standardized tests; the obscurity of the content is a neutralizing factor that makes the exam more fair. With schoolwork, however, the subject matter used to impart analytical and creative skills can put students on unequal ground and disadvantage students who have weak background knowledge or have simply not been exposed to certain vocabulary or jargon: “Research from cognitive science has shown that the sorts of skills that teachers want for students–such as the ability to analyze and to think critically–require extensive factual knowledge.” In this way, Willingham asserts, “factual knowledge must precede skill.”

Think of it this way. If you have no experience in economics, you can still read The Economist and get something out of it; but a trained economist will be able to read the magazine much faster, extract the important details, ask intelligent questions, and put the knowledge to work more quickly. Not because he’s a more gifted critical thinker but simply because he’s developed an intuition for the material due to deep functional exposure.

What does this have to do with adaptive learning?

A) A continuous adaptive learning system can provide a scaffolding of hints (definitions, encyclopedic knowledge, formulas) to help level the playing field for those students who have had less exposure to culture, world events, and certain types of vocabulary and jargon. This will allow students to absorb the background knowledge seamlessly and focus on the analytical and creative aspects of any exercise designed to improve their skills in those areas.

B) Adaptive learning can help students learn more efficiently and effectively and in the process, expose students to a range of material in a shorter amount of time (this is related to my point below). Depth and range of exposure can improve a student’s “chunking” ability. Even the simple act of locating a subject in relation to other subjects (an option afforded only by scope of exposure) can make something “click” for many students.

C) Willingham defines “chunking” as “the phenomenon of tying together separate pieces of information from the environment.” Students are thus able to absorb complex knowledge by breaking it down into smaller, manageable chunks. The same goes for problem solving: students tackle complex problems by perceiving them as a series of manageable steps. Adaptive learning can determine what a student needs to grasp before he can have this kind of insight–whether it’s background knowledge, a highlighting of structural qualities in the information, or a certain breadth of range, or a combination of all these elements. In this way, students can be guided toward making those “chunking insights” themselves.

To achieve benefit #3, it is especially important to develop “continuous” as opposed to “single-point” adaptivity.

Stay tuned for more ways that adaptive learning is changing the way students think about school!