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]]>Testing today can be anything from a mere bureaucratic hurdle to a nerve-wracking, future-determining experience for students. As computerized adaptive systems enable us to deliver truly continuous assessment, how will testing change? How will we use it to improve educational outcomes?
In Disrupting Class, Harvard Business School professor and expert on innovation Clayton Christensen provides an answer: “When students learn through student-centric online technology, testing doesn’t have to be postponed until the end of an instructional module and then administered in a batch mode. Rather, we can verify mastery continually to create tight, closed feedback loops. Misunderstandings do not have to persist for weeks until the exam has been administered and the instructor has had time to grade every student’s test.”
1. Clarified purpose of assessment for students
What exactly is the point of testing in the first place? To compare students? To facilitate their passing to another level? To assess their proficiency and award them a corresponding grade? Too often, the social context and negative emotions involved in assessment can get muddled with its purpose — generally to assess student proficiency and document it in some way. How often, for instance, are students allowed to pass, regardless of whether they truly demonstrate proficiency in a subject? And how often are they bored waiting for the next opportunity to level up and tackle new challenges? What percentage of school time is spent around academic anxieties and insecurities — and what percentage is spent actually doing the cognitive work of mastering new concepts, demonstrating mastery of that material, and developing a love of learning?
When assessment becomes continuous, students are given a constant stream of opportunities to prove their mastery. Assessment becomes embedded into the “flow” of learning, and students can demonstrate mastery as quickly as they choose to. The path to actual progress becomes clear and students’ lives become increasingly oriented toward real learning.
2. Mastery-based learning in action
Continuous assessment allows mastery-based learning — a teaching methodology premised on the idea that progress should be based on mastery instead of seat time — to be implemented.
Students who are struggling will not automatically advance before they have a chance to master the material at hand. On the other hand, advanced students can progress through material at their own pace and remain engaged by pursuing more challenging work as they pass out of the basics. In this sense, students cannot be satisfied with their achievements relative to others; they are encouraged to seek their own course and take responsibility for their learning.
3. Increased self-awareness for students
Time and again, we encounter evidence that self-awareness — understanding of how one feels, thinks, and learns (also known as metacognition) — is one of the most significant factors in professional and personal success. The renowned psychologist, Howard Gardner has argued that self-knowledge — “intrapersonal skill” — is one of the eight defining types of intelligence (the others being “linguistic,” “logical-mathematical,” “naturalist,” “bodily-kinesthetic,” “spatial,” “musical,” and “interpersonal”). The more continuously we assess students and provide feedback, the more knowledge they can gain about themselves — what it takes for them to master something, how they can approach problems differently, what their blind spots are, and how to eliminate them.
4. Greater insight into student needs for teachers
One of the biggest challenges facing schools and administrators today is the growing diversity of the students within their population–and a correspondingly increased diversity of needs to consider. Some struggle because English is not their first language; others have difficulty with focus or organization. Others may be particularly weak in some area but possess unusual strengths in another (which the existing curriculum may not take into account).
As every teacher knows, classroom management is a consummate juggling act. To remain attentive to the needs of all students, teachers must engage the more advanced students while helping the struggling ones catch up. At any given point in a lesson, a teacher must decide whether to move through the material aggressively and add more challenges and twists to the problems presented, or build in more of cushion for those who are confused. Any one of these strategies, including “teaching to the middle,” is bound to leave some students bored or confused.
Blended learning solutions that offer an analytics dashboard supported by continuous assessment give both students and teachers more freedom in this respect: students move through coursework at their own pace and teachers retain control over the classroom while gaining insight into the learning process. A teacher might discover through analytics that a student who is weak with math word problems is struggling because he has difficulty with reading comprehension; that teacher can then coach him through material that improves his grasp of syntax and vocabulary. Another student who understands mathematical concepts but has trouble with carelessness in arithmetic can receive feedback about how to develop stronger estimation abilities or check work once completed.
5. Discovery of the interrelatedness of concepts and subject domains for content creators
Continuous assessment generates a good deal of data around the efficacy of learning content and methodology. When we are able to analyze learning patterns around various concepts in a granular way, teachers, publishers, and administrators can uncover interdisciplinary relationships between subject domains and concepts. We might discover that effective remediation in a subject requires attention to another subject or that the root of common misunderstandings within a subject is something altogether unexpected.
For instance, we might uncover a relationship between quantitative/logical skill and English composition. We might discover that a specific order of teaching subjects (or even concepts) is remarkably effective — that logic and foreign language or fractions and musical harmony should be taught side by side, for example.
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]]>Partners can implement recommendations and predictive analytics in a way that best serves their students and instructors. It’s exciting to watch their visions come to life as they develop products using the Knewton API.
Some partners are taking advantage of Knewton’s infrastructure to integrate adaptive learning into brand-new digital learning products.
Triumph Learning, for example, is building Get Waggle — a competency-based, CCSS-aligned product for grades 3-8 with some fun engagement elements, like a “lift meter” which shows students their progress around skills, standards, and goals, while also rewarding grit and performance.
Foreign language publisher Lelivrescolaire is working with Knewton to build an adaptive mobile French grammar app for middle school students, featuring a personalized study center. Lelivrescolaire is leveraging Knewton’s partnership with Gutenberg Technology, an end-to-end digital publishing platform, to seamlessly integrate Knewton recommendations.
Publishers like Cambridge Learning and Macmillan Education are also using Knewton technology to build new digital products; stay tuned for updates!
Other partners are using Knewton to enhance proven solutions. In Pearson MyLab with Knewton Adaptive Learning, students in reading, economics, math, and writing courses use an “Adaptive Study Plan” to suggest what to study next, in addition to an existing syllabus. See how it works.
In Pearson Mastering with Knewton Adaptive Learning, students follow a manually assigned assessment with an “adaptive follow-up” assignment — a dynamic set of activities to help in specific areas. This is an innovative new strategy for science education; Mastering includes courses in chemistry, biology, physics, anatomy & physiology, and other subjects. Watch how it works.
Another existing integration is Houghton Mifflin Harcourt’s Personal Math Trainer Powered by Knewton, a Pre-K-8 automatic intervention and acceleration tool that uses Knewton to create a progressive, personalized learning experience. Personal Math Trainer helps teachers emphasize depth of understanding and problem-solving skills in the classroom.
Students and teachers are using some of these products today. Others are still being built. Many others haven’t been publicly announced yet. For those that have been released, we’re paying close attention to feedback from students and teachers. Pedagogy, user experience, content — these are our partners’ areas of expertise, and the decisions made in these arenas are theirs and theirs alone. But we’re eager to learn more about how personalized recommendations and predictive analytics work best for different populations of students and teachers, and to apply these insights in ways that benefit the whole education ecosystem.
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]]>I often get asked about Knewton’s choice to be a technology infrastructure provider rather than an app developer. Why build a platform to help others make adaptive apps? Why not just make them ourselves?
Focus
In Knewton’s early days, we actually did build apps (for test prep and college math readiness). This was never our ultimate goal, but we needed proof-of-concepts for what was — our adaptive infrastructure platform.
Knewton’s infrastructures enable proficiency-based adaptive learning. We can measure not only what students did, but also what they know, how prepared they are, and how their abilities evolve. We never thought of ourselves as experts in how to design apps. We want to support those experts.
Supporting Innovation
There’s a remarkable amount of innovation in the edtech space. Other organizations will experiment with new types of digital materials and experiences. Knewton’s infrastructure helps support this creativity — allowing others to focus on their core competencies (content development, user experience, pedagogy, distribution) while still providing proficiency-data-driven personalization to students.
“Warm Starts”
As a platform, we can provide students using Knewton-powered apps with features that wouldn’t be possible in the closed-app environment that has heretofore dominated the education industry.
Today, students walk into classrooms each September as if they were just born. Teachers must learn everything about them from scratch. Knewton-powered apps change this, allowing each student to start courses “warm” by connecting his or her learning history to every app. Instructors see students’ proficiency in individual concepts, how they study, and how well different strategies work for them. Students get learning products that provide the exact material they need, when they need it. This learning history stretches across grades and subjects, for every Knewton-powered app — helping all students maximize their potential.
Network Effects
The vast number of students across Knewton-powered products produce potent network effects. Knewton uses the combined power of every student’s anonymized data to help every other student learn more deeply and effectively. Our API partners’ products share the benefits of these network effects.
The Knewton team comprises many former teachers, content developers, and instructional designers, in addition to software engineers and data scientists. We recognize the fundamental importance of pedagogy, content, teachers, and user interfaces. Our platform is just one part of this larger ecosystem.
Early on, Knewton decided to focus on the science of digital education — to devote the time and technical expertise to building an adaptive learning infrastructure — to make it easier for our users to perfect their art of producing world-class education apps.
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]]>The post Knerds on the Board: What is Adaptive Learning? appeared first on .
]]>In today’s age of big data, words and phrases like “adaptive learning,” “personalization” and “differentiation” are getting tossed around with increasing frequency. What exactly do these terms mean and to what extent do they overlap?
To learn more about Knewton Adaptive Learning, download the whitepaper here.
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]]>Here’s an example of how this works. Suppose that a student at the beginning of her first Knewton-enhanced course is struggling with a word problem which involves calculating the area of a triangle. Assume we know nothing about the student aside from this fact. (This is an uncommon scenario — students who have made any progress in a Knewton-enhanced course or who have taken previous Knewton-enhanced courses will have already generated proficiency data that can help inform recommendations.) Knewton must determine why the student is getting this exercise wrong, so that we can recommend content that helps her learn the skills and concepts required to solve this problem.
Specifically, we must ask the following question: what is preventing the student from solving the triangle problem? There are several possibilities. It may be the case that she doesn’t know how to calculate the area of a triangle. Perhaps she struggles to read and interpret word problems. Maybe the base and the height of the triangle are given as decimals and she doesn’t know how to multiply decimals. There is even the possibility that she doesn’t know how to multiply integers!
It might also be the case that she can find the area of a hundred triangles with her eyes closed while she taps her head, rubs her belly and hops on one foot, and that she’s simply distracted by a computer game that she’s toggling to and from as her teacher wanders in and out of eyeshot of her computer screen. This last possibility is an important one. However, for the purposes of this example, let’s assume that the student is engaged and that her difficulty stems from the fact that she doesn’t understand one or more of the skills or concepts I described above. As you’ll recall if you’ve read one of our posts on knowledge graphs or checked out our white paper, we refer to these concepts as prerequisites.
Let’s list the prerequisites for the triangle word problem again:
Using circles to represent the concepts and arrows to represent the prerequisite relationships, we can draw a diagram:
After we’ve identified the prerequisites, we must then assess the student’s proficiency in these areas so that we can recommend content that helps her learn the necessary concepts to solve the triangle word problem. For example, if she does poorly in assessments on calculating the area of a triangle, we can recommend additional content that helps her master this concept. But where do we start? Should we start by giving her an assessment on prerequisite 3 (Calculate the area of a triangle), or should we start with something more basic, like prerequisite 1 (Multiply integers)?
As you ponder this question, you might notice that the prerequisites in this example are not necessarily independent of one another. For example, it is unlikely that the student can multiply decimals if she cannot multiply integers. In fact, the content in this course that is associated with multiplying decimals expects and assumes that the student is able to multiply integers. In other words, 1 is a prerequisite for 2! Furthermore, prerequisite 1 (Multiply integers) is only important to the triangle problem as it relates to prerequisite 2 (Multiply decimals). In this case, we say that prerequisite 2 subsumes prerequisite 1 because it transmits the knowledge from 1 that is required to solve the triangle problem.
We can adjust our diagram to reflect this as follows:
How does this observation about the relationship between prerequisites 1 and 2 help us determine what the student does and does not know? Let’s imagine that we give the student an assessment on prerequisite 1 (Multiply integers) and she aces it. All we can say is that she’s proficient in prerequisite 1. However, what if we give her an assessment on prerequisite 2 (Multiply decimals) and she aces that? Since we know that the assessments for prerequisite 2 expect and assume that the student is proficient in prerequisite 1, then based on her performance in prerequisite 2, we can be fairly confident that she is proficient in both 1 and 2. Conversely, if she fails prerequisite 1 (Multiply integers), it’s probably safe to say that she is not proficient in prerequisite 2 (Multiply decimals) either. In other words, we can estimate her proficiency in certain concepts without having to directly assess her on them.
There is also the case that the student knows how to multiply integers but does not know how to multiply decimals. We can only determine this by assessing her on both concepts, and therefore, the subsumption relationship does not help us in this scenario. We can, however, use information about how similar students performed in the past to help us identify this scenario. (In a future blog post, we’ll expand on network effects, or how we utilize data about other students’ activity to inform the recommendations we generate for each individual student.)
The example above involves just a few concepts, but for a typical Knewton-enhanced course, we map out the relationships between hundreds of concepts. Knowing what concepts subsume other concepts allows us to eliminate concepts that we think students are already proficient in and more quickly hone in on what they should study to meet their goals. It’s like when the blocks disappear in Tetris!
To summarize: rather than assessing a student on every single prerequisite concept, we can use our understanding of the content — specifically, the relationships that exist between the concepts — to make intelligent inferences about what the student does and does not know. As a result, we can generate recommendations that lead to a more efficient use of the student’s time and energy.
In this post, I’ve described one way that we use our understanding of content to make learning more efficient and effective for our students. In a future blog post, I’ll talk about how we use goals that are defined by students and instructors to help us generate better recommendations.
For more from the Knewton Adaptive Instruction team, check out Jesse Sternberg’s post on the cross-disciplinary approach of the Knewton knowledge graph and Matt Busick’s post on the power of a knowledge graph.
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]]>Mrs. T is also aware that the problems on this quiz call upon and use previous skills in her course, skills she refers to as the section’s prerequisites. For example, a section from Chapter 9 introduced word problems involving perimeter, a Chapter 11 section covered how to interpret histograms and charts, a Chapter 12 topic provided a basic understanding of functions, and an earlier section in Chapter 13 covered combining like terms.
Mrs. T wants to help Stu review these prerequisite skills, so she examines her gradebook to see what scores he received in each topic. He seemed to do fairly well (over 80%) on the quiz from Chapter 9 on perimeter word problems and the quiz from Chapter 12 on functions, but he struggled (lower than 60%) with the section from Chapter 11 on interpreting histograms and charts and the earlier section in Chapter 13 on combining like terms.
Mrs. T creates a packet of review materials and puts together a remedial quiz containing questions from those sections in Chapters 11 and 13. Her hope is that after Stu has gained proficiency in the prerequisite skills, he will better understand the Chapter 13 material and be ready to re-tackle the quiz on adding and subtracting polynomials.
This sort of personalized remediation is indeed carried out by the best teachers in our schools, but it can be especially difficult if the student continues to fail in upcoming topics, and it becomes a Herculean, if not an impossible, task if the teacher has to carry it out for all the students in all her classes.
In a Knewton-powered adaptive course, the intricate scenario just outlined happens at the click of a button — thanks in part to the Knewton knowledge graph. The knowledge graph is a cross-disciplinary graph of academic concepts; within the graph, concepts have prerequisite relationships that help define a student’s path through the course.
When a student fails a topic in a Knewton-powered course, he or she is instantly remediated with prerequisite skills, prioritized on the strength of their relationships to the topic at hand and on the student’s demonstrated strengths and weaknesses. This frees Mrs. T from the administrative work of locating all prerequisite skills and correlating them with each student’s past performance. She now has more time to orchestrate classroom activities, introduce creative group work, or sit down with each student to address their misconceptions and encourage them through their frustrations.
Knewton’s knowledge graphs, carefully constructed by subject matter experts, incorporate the connections identified by experienced teachers into the course itself. Learning is by nature an extremely interrelated activity, and with a knowledge graph an adaptive platform can take full advantage of those connections when scaffolding students and guiding them toward mastery.
As more and more students progress through a Knewton course, the strength of these connections are refined over time. We may find that some prerequisite skills are rarely helpful, or only helpful to certain types of students with identified weaknesses, while others are extremely effective as skills to review before students return to a failed topic. The goal of such data-driven analytics is to mimic in real time, on a large scale, the sort of intuition a great teacher develops over his or her career. (For more on how Knewton uses student performance data to improve its recommendations over time, download the Knewton adaptive learning white paper).
Creating unique study plans for each student in a class would be incredibly time-consuming for teachers. By mapping each course to a continuously refined knowledge graph, Knewton does this automatically, ensuring that no student is left behind.
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]]>Knewton is working hard to solve this problem. In particular, we on the Knewton Adaptive Instruction Team have worked to create a system that assesses the needs of each individual student and serves him or her the learning experience he or she needs at exactly the right time.
To do this, we use the Knewton knowledge graph, a cross-disciplinary graph of academic concepts. Within the knowledge graph, concepts have prerequisite relationships that help define a student’s path through the course. Special relationships that define content as either “instructional” or “assessment” determine what kind of content to deliver to students at any given point. Knewton recommendations steer students on personalized and even cross-disciplinary paths on the Knowledge Graph towards ultimate learning objectives based on both what they know and how they learn. (For more about how cross-disciplinary learning paths are enhanced by continuous adaptivity and network effects, download our white paper on the science behind recommendation.)
Our team is always working to enhance the graph’s capacity to make fine-tuned recommendations for all the courses it powers. Often, this simply involves helping partners build graphs that better represent their content, but sometimes it can involve making changes to the nature of the graphing process itself on our end in order to ensure that the process is attuned to and capable of capturing the idiosyncrasies of a range of content domains.
Recently, we have used the latter process to help solve another challenge that inhibits learning in today’s classrooms. Since each subject in a traditional school requires a different teacher with the correct area of expertise, the various subjects are presented to students as being far more distinct and separate from each other than they actually are. Extensive studies show that students benefit in many ways from a cross-disciplinary approach, but practical concerns often get in the way. A history teacher might notice that her students’ essays suffer more from a lack of basic writing skills than from a lack of understanding of the historical facts, but she can’t suspend her own curriculum to teach those skills (even if she’s qualified to do so) and she can’t ask the English teacher to revisit them in his class, because he has to get his class through Hamlet by the end of the week. (It is true that some schools are advancing cross-disciplinary instruction and encouraging teachers of different disciplines to plan their curricula together; however, these schools are the exception, not the rule.)
Knewton’s goal is to link multiple subjects into one huge knowledge graph, rather than creating several separate ones in parallel. Generally, the process of graphing involves asking ourselves questions about the content at hand. If we’re looking at a section of a history book, for example, we would ask ourselves what other historical facts and concepts a student must understand in order to contextualize the new content. To create an interdisciplinary graph, we would ask questions like, “What reading level is necessary to parse out all the important details in this section?” and “What understanding of fractions and percentages is necessary to read the pie chart on page 145?”
The goal of this approach is to ensure that students possess the skills and knowledge they need to tackle the learning experience we recommend for them. We also hope that this will prove to be a helpful tool for educators to develop holistic curricula and collaborate effectively with their peers teaching other subjects. We hope that those who have not begun to approach learning in this way will now find it easier to begin to do so and that those who have already been struggling with these issues will see us a valuable ally.
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]]>The post How Adaptive Learning Can Help Students Think About Meaning appeared first on .
]]>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:
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.
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.
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.
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.
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.
To learn more about Knewton adaptive learning, download the whitepaper here.
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]]>The post Why Students Don’t Like School, and What Adaptive Learning Can Do About It (Part 4) appeared first on .
]]>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.
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:
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.
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.
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.
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.
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]]>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.
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.
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.
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:
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.
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