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We always tell our students that there are no shortcuts, that important ideas are nuanced, and that recognizing subtle distinctions is an essential critical-thinking skill. Mastery of a discipline, we know, requires careful study and necessarily slow, evolutionary changes in perspective.
Then we look around for the latest promising trend in teaching and jump in with both feet, expecting it to transform our students, our courses, and our outcomes. Alternatively, we sniff disdainfully at the current educational fad and proudly stand by the instructional traditions of our disciplines or institutions, secure in our knowledge that the “tried and true” has a wisdom of its own.
This reductive stance is a natural one. As university faculty who work within disciplines, we have each chosen a slice of human knowledge about which we are passionate, and we often settle on the most expedient (but sound) answer to the question of how to teach so that we can move on to the interesting issues and problems that led us to pursue academic careers in the first place. Further, the professional demands on us and the rewards for our work generally do not align with high levels of sustained effort invested in teaching.
However, what we tell students about mastering our respective disciplines are the same truths that apply to finding effective instructional strategies: The devil is always in the details, and nuance is critical. Yet in our desire to do right by our students and still invest the bulk of our efforts in teaching content, we put our faith in over-simplified generalizations that never seem to realize the full benefits that they promise.
There have been many sweeping statements made regarding the best ways to teach students in the 21st century. Two of the most au courant are “traditional lectures are ineffective” and “internet-based technologies help students learn.” There is empirical evidence to support the truth in each of these statements, true—but only if they meet specific parameters, which rarely carry over from their origins in educational research to guide their implementation in practice.
Are lectures bad for learning?
When we look beyond the rhetoric surrounding instructional practices to examine data, it turns out that bad lectures do limit students' learning and motivation. However, good lectures can be inspiring and have a positive—even transformative—impact on student outcomes. Given this unenlightening information, the real question becomes, “What differentiates a ‘good’ lecture from a ‘bad’ one?”
Good lectures share a number of key properties with any type of effective instruction. They begin by establishing the relevance of the material for students through explicit connections with their goals or interests. They activate prior knowledge by connecting new content with what students already know and understand or problems with which they are currently grappling. They present information in a clear and straightforward manner that does not require disproportionate effort to translate into terms and concepts meaningful to students. They limit the information presented to a small number of core ideas that are thoroughly but not redundantly explained.
Studies that systematically control the relevant features of lectures find significant learning benefits for students when these principles are implemented. However, the large-scale correlative studies of instructional format and student achievement that report negative outcomes for lectures do not control for or even ask about the presence or absence of these features. Thus it may be that the negative findings are a more accurate reflection of generally lackluster or ill-informed implementation of this teaching technique than a condemnation of the technique itself.
Of course, simply knowing or even applying these general principles for effective lecturing does not guarantee positive results. Students enter courses with differing backgrounds, levels of prior knowledge, goals, and interests. Given that each of the guidelines above explicitly frames practice in terms of characteristics that vary by learner, the underlying challenge is to find ways to connect with the broadest cross-section of students and find supplemental or alternate means of connecting with those who do not fit that mold.
Many instructors succeed at this through the use of assignments that require students to grapple with problems prior to the lecture. Others use “clickers” to stimulate engagement and structure situations in which the information presented is salient. However, the effective use of such practices involves understanding the students at whom the course is targeted.
Is technology good for learning?
Both the definitions and the uses of instructional technology are highly varied, so conversations about its benefits and limitations also tend to rely on overly broad generalizations. The two major foci of these discussions currently are game/simulation-based learning and so-called Web 2.0 technologies that allow users to interact with each other via the internet and to contribute content of various types directly to websites.
Advocates claim that these applications are important for improving student learning outcomes; they enhance relevance for students by engaging them through the generationally preferred medium of digital media and provide them with opportunities to actively engage with a course's content. While there are indeed instances where such benefits are realized, they are not reflected in comprehensive literature reviews or meta-analyses of the research. There is a simple explanation for this: not all uses of a technology are created equal.
The key features that drive engagement and learning pertain to the designs that underlie the technology rather than to the technology itself. When games and other digital learning environments are developed in accordance with principles of effective instruction, they achieve positive results. But they do not yield better results than less sophisticated instructional delivery systems that use the same instructional designs. Why? Because the active ingredients that affect students' learning are the same in both cases. One of the most durable descriptions of this phenomenon is Richard E. Clark's grocery truck metaphor: “Media are mere vehicles that deliver instruction but do not influence student achievement any more than the truck that delivers our groceries causes changes in our nutrition” (Clark, 1983, p. 445).
What the new media do offer are tools for interacting with instructors, peers, and content in ways that are not affordable or possible otherwise. When these interactions offer opportunities to observe or manipulate information and phenomena in meaningful ways, they can facilitate learning. Generally, the features that are most helpful for students include enabling the representation of concepts at multiple levels of abstraction (e.g., via concrete representation, abstract functional models or mathematical models), providing opportunities for more extensive practice than would otherwise be possible and offering immediate feedback to direct further learning efforts.
While they are potentially valuable learning tools, such technologies need to be designed in such a way that they are not confusing or overwhelming for the students who will use them. With any software, there is a learning curve for mastering the interface used to interact with it. To the extent that the interface functions in a standard way, students will be able to draw on previous technology experiences in using it. However, if it is significantly different from familiar interfaces, they will need to invest substantial effort in mastering its use before getting to content-related learning.
The greater the departure from familiar software environments, the steeper the learning curve. Thus the technology itself can act as a learning impediment for students with limited technology backgrounds. It may be the case that the potential learning benefits offered outweigh the cognitive costs, but it should not be assumed without evidence that this will be the case.
The role of cognition
There are two threads linking effective lectures and effective technology use. The first is consideration of what students bring to the table in terms of goals, interests, and prior knowledge. The second is the deliberate management of the opportunities for students to engage with content in order to focus their investment of mental effort on key ideas. In educational research, a powerful framework for considering these factors jointly is cognitive load theory (CLT).
When games and other digital learning environments are developed in accordance with principles of effective instruction, they achieve positive results. But they do not yield better results than less sophisticated instructional delivery systems that use the same instructional designs.
CLT operates under the central premise that learners are only capable of attending to a finite amount of information at a given time due to the limited capacity of the working (short-term) memory system. So it is necessary to carefully manage the flow of information with which learners must grapple.
It is likely that anyone who has taken an introductory course in educational or cognitive psychology will have heard of George Miller's (1956) “magical number” that people can only process seven information elements at a time, plus or minus two. However, what many people do not know is that this number is probably a substantial overestimate.
Miller obtained his finding by asking people to listen to strings of random numbers and recite them back as accurately as possible. These numbers were not linked to any context, and he assumed that they were ubiquitous placeholders for any type of information that people might need to process. What did not occur to Miller is that people use strings of numbers for many everyday tasks and have developed memory strategies to retain them. Think, for example, of how you remember a telephone number or your social security number; most people group the digits into two or three chunks (e.g., XXX-XXXX or XXX-XX-XXXX). It is these chunks that occupy space in working memory and help to organize the information so that it does not get lost. Subsequent research holds that the upper limit of our short-term memory is actually closer to four information pieces or chunks.
Given these tight bandwidth constraints, how do human beings handle any complex task—especially one that has more than four discrete elements? To simplify, we handle the task-relevant information much as we would a phone number: we divide it into meaningful units based on our knowledge of the content and task structure. The more knowledge we have about a task, situation, or content area, the more efficiently and adaptively we are able to map discrete pieces of information onto schemas.
These schemas are the abstract representations of our knowledge that serve as integrated templates for rapidly organizing the relevant facets of a situation. With deeper, more meaningful, and more interconnected knowledge, our schemas become more refined, nuanced, and capable of encoding increasing amounts of incoming information as a single chunk. Information that would occupy only one chunk for an advanced learner might be viewed by a novice as several discrete pieces of information.
Cognitive load is conceptualized as the number of separate chunks (schemas) processed concurrently in working memory while learning or performing a task, plus the resources necessary to process the interactions between them. Therefore a given learning task may impose different levels of cognitive load for different individuals based on their levels of relevant prior knowledge. Cognitive load is experienced as mental effort; novices need to invest a great deal of effort to accomplish a task that an expert might be able to handle with virtually none, because they lack sufficiently complex schemas.
When cognitive load (the information to be processed) exceeds working memory's capacity to process it, students have substantial difficulties. The most straightforward effect is that they are unable to learn or solve problems. However, other problematic outcomes can also occur. First, students may revert to using older or less effortful approaches to the problem that impose a less heavy load on working memory. This means that previously held misconceptions or erroneous approaches may be brought to bear, reinforcing knowledge that is counter to the material they are trying to learn.
Second, students may default to pursuing less effortful goals. In other words, they may procrastinate. In such situations, thinking about the whole of a complex task may be so overwhelming that students turn to more manageable activities: checking their email, cleaning their desks, or taking on whatever other chores do not exceed their processing ability. (Rumor has it that faculty have similar experiences.)
For this reason, one of the strategies for overcoming procrastination is to reduce the magnitude of a goal by breaking a large task into its component parts and dealing with only one piece at a time. This limits the complexity of the task faced, which reduces the cognitive load it imposes to manageable levels.
Managing cognitive load in teaching
In order to optimize the benefits of instruction, CLT prioritizes available information according to the type of cognitive load it imposes. Intrinsic load represents the inherent complexity of the material to be learned. The higher the number of components and the more those components interact, the greater the intrinsic load of the content. Extraneous load represents information in the instructional environment that occupies working memory space without contributing to comprehension or the successful solving of the problem presented. Germane load is the effort invested in the necessary instructional scaffolding and in learning concepts that facilitate further content learning.
Cognitive load is conceptualized as the number of separate chunks (schemas) processed concurrently in working memory while learning or performing a task, plus the resources necessary to process the interactions between them.
In this context, scaffolding refers to the cognitive support of learning that is provided during instruction. Just as a physical scaffold provides temporary support to a building that is under construction, with the intent that it will be removed when the structure is able to support itself, an instructional scaffold provides necessary cognitive assistance for learners until they are able to practice the full task without help. Extensive instruction typically provides multiple levels of support that are removed gradually to facilitate the ongoing development of proficiency. Processing the information provided as scaffolding imposes cognitive load. However, to the extent that it prevents the cognitive overload that would otherwise result for a learner struggling with new material, it is cost beneficial.
Thus, the three driving principles of CLT are: 1) present content to students with appropriate prior knowledge so that the intrinsic load of the material to be learned does not occupy all the available working memory, 2) eliminate extraneous load, and 3) judiciously impose germane load to support learning.
For any instructional situation, the goal is to ensure that intrinsic, extraneous, and germane load combined do not exceed working memory capacity. But how can we manage this? Although we do not control the innate complexity of the material we teach, we can assess the prior knowledge of our students to ensure they understand prerequisite concepts. If they have schemas in place to facilitate the processing of the new concept, their intrinsic load is lower than if they need to grapple with every nuance of the material without the benefit of appropriate chunking strategies.
This is an opportunity to effectively use technology. The use of “clickers” during lectures or short online assessments to be completed prior to attending class can provide a quick picture of which necessary elements students have in place before a new concept is introduced. If they lack the prerequisite knowledge, then the instructor should teach or provide that material first in order to prevent the advanced material from exceeding students' ability to process it.
The good news about extraneous load is that it should be eliminated whenever possible rather than managed. In fact, there are a number of simple and straightforward principles for doing so in instructional materials as well as in the classroom. Some have to do with the information presented. For example, ancillary information that is not directly on point should be eliminated.
This includes things like biographies of historic figures in science texts when the instructional objective is to teach a theory or procedure. While it may be an interesting human-interest story to consider whether or not an apple really fell on Newton's head, processing that information detracts from the working memory available to understand gravitational theory or how to solve problems using the law of gravity.
Other practices target the presentation of information. For example, it is better to integrate explanatory text into a diagram than to keep it separate, because the cognitive load of mentally integrating the information can be avoided when they are collocated. On the other hand, reading aloud the text that students are looking at forces redundant processing of the same information and impedes their ability to retain the material.
Because sensory information enters working memory through modality-specific pathways, which themselves have limited bandwidth, it is helpful for information to be distributed across modalities wherever possible. It is also helpful for all necessary information to enter working memory at approximately the same time. Thus, the first example uses linguistic and visual information together, which distributes the information across modalities and avoids the unnecessary load of holding the information from the diagram in working memory while searching for the appropriate text or vice versa. In contrast, the second example overloads the pathway that handles verbal information because it simultaneously delivers read and spoken information. It also requires that information from the text be held in working memory while the speech is processed, because people typically read to themselves much more quickly than words are read aloud.
Germane load is a highly complicated issue. Building scaffolds for learning imposes cognitive load. Novices being introduced to material for the first time need a great deal of explicit instruction, using very small chunks of information, to deeply process new information or problem-solving strategies. As they acquire more knowledge and skill, though, the external scaffolding which initially helped them becomes unnecessary and redundant. If such learning supports are not eliminated for those students, they cease to facilitate learning as germane load and begin to hinder it as extraneous load. This “expertise reversal effect” is the biggest challenge for developing effective instruction, because students do not all attain the same level of comprehension at the same time. What is germane and helpful load for one student may be extraneous and harmful for another.
Effective Practices
The keys to applying cognitive load theory effectively in a course are advance planning and the ongoing monitoring of students' progress. Because the central premise of CLT is to optimize the allocation of students' working memory resources for mastering particular information, it is vital to identify very specifically what the instructional objectives are for the course as a whole and for each class meeting or module. If we cannot be precise about what we want students to know and be able to do, we will not be able to structure their experiences to help them accomplish this.
Next, we need to sequence the objectives so as to present material in the order in which it is needed. If some topics build on others in the course, the prerequisite pieces should be taught before they are needed. For example, we should teach processes and procedures in the same sequence that students will perform them, so that work products from preceding steps can be used in subsequent steps. If the concepts, knowledge, or skills being taught do not have an inherent sequence, then it is generally most effective to order them from simplest to most complex.
Once we have figured out what content needs to be taught and the appropriate progression of topics, it is most helpful to students when we let them in on the secret. Trying to impose order on disconnected information is highly effortful. If we simply turn students loose on the material without presenting clearly what they should be trying to get from it and how it fits into the larger picture of the course's content, much of their cognitive resources will be allocated to figuring out what information is important (extraneous load) rather than focusing on constructing the knowledge necessary to meet our learning objectives.
Although the logic of the course content and sequence may be obvious to us as knowledgeable instructors and content experts, our students arrive without the benefit of the schemas we have developed. Regardless of their previous experiences (or perhaps because of them), they sincerely appreciate knowing up front what they will be learning, what is expected of them, how they will be assessed, and how all of these elements fit together. When these components of the course are unclear, students invest substantial effort in figuring them out. Further, they may reach incorrect conclusions, which leads to more extraneous effort as they work at cross purposes to the course.
Having mapped out the information in the course, we also need to determine how well students comprehend any knowledge on which later course content depends. This does not mean that we must burden our students (and ourselves) with exams or large assignments every week. Instead, we can use lightweight, rapid assessments that are not formally graded but are attuned to the key concepts upon which the new material draws.
These can include short online surveys on the content that must be submitted a few days before class, quick “check-in” conversations as class begins, or multiple-choice questions on key issues that students must respond to using personal response systems (“clickers”). These tools are most effective when students are accountable for submitting a response but not for the accuracy of their answers. The purpose is to inform the instruction we provide rather than to increase students' anxiety (i.e., emotionally invoked extraneous load) about not knowing a correct answer.
If students generally have a strong grasp of the prerequisite material, the likelihood of cognitive overload will be small, less scaffolding will be needed, and they can move directly into problem-solving. But if their understanding is weak, it will be important to review the prior material in detail, structure the new content as much as possible, and move slowly through it.
When introducing problem-solving procedures to novices, providing “worked examples” is a very helpful practice. This involves demonstrating and explaining the reasoning processes that are involved in solving a class of problems, using a representative example. This helps to manage cognitive load effectively in several ways. When a problem is taken on, there are two sources of potential load for a learner. The first is the need to structure the information provided to effectively frame and analyze the problem. The second is the application of appropriate problem-solving strategies. The worked example both demonstrates problem-framing and provides a concrete model of an appropriate problem-solving strategy.
[Students] sincerely appreciate knowing up front what they will be learning, what is expected of them, how they will be assessed, and how all of these elements fit together.
This reduces the degree of uncertainty under which the students are working on three fronts. First, it allows them to map concrete instances onto relevant schemas, facilitating effective chunking. Second, it reduces their reliance on highly effortful trial-and-error attempts to identify productive solutions, which substantially increase cognitive load and time spent without providing any learning advantage. Last, it breaks the procedure down into distinguishable steps that can be considered in smaller, more manageable chunks.
After walking through a full example, an excellent way to help students practice without getting overloaded is to provide a partially worked example and ask them to pick up where the completed part of the example leaves off. Having them practice the last steps first ensures that all aspects of the strategy to be learned are practiced. In complex, open-ended problems, students can get “off track” midway through an exercise and never have the opportunity to practice its later elements.
As students become proficient in the later steps, they can be given problems with fewer steps completed for them. In this way, instructors can effectively control the overall level of cognitive load imposed by the problem and ramp up to full problems after students have developed effective schemas and chunking strategies.
Practice makes perfect
As students encounter repeated instances of problem types during their learning, their strategies become more nuanced (to accommodate small differences between the problems) and less effortful to execute. As they practice, their skills require less and less conscious monitoring, which reduces the level of cognitive load that problem-solving imposes. This lets them efficiently address problems of increasing complexity. Experts are able to solve problems beyond the scope of what laymen can handle precisely because their core problem-solving procedures impose virtually no load on working memory. Therefore, they can assimilate very subtle nuances and much more complex problem features with their extra cognitive capacity.
The benefits of practice are just as powerful for teachers as they are for students. Teaching effectively and using cognitive load theory to guide practice is challenging. It requires the focused consideration of many details regarding our students, their knowledge, and our instructional goals. But with sustained effort, careful observations of what seems to yield more efficient and effective learning, and a willingness to make changes as necessary, these practices become less effortful. This frees up our own working memory resources to use for addressing both further complexities in addressing the learning needs of our students and the subtleties of our own disciplinary passions.
Resources
1. Bernard, R. M., Abrami, P.C., Lou, Y., Borokhovski, E., Wade, A., Wozney, L., Wallet, P. A., Fiset, M. and Huang, B. (2004) How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research 74:3, pp. 379-439.
2. Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A. and Bethel, E. C. (2009) A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research 79:3, pp. 1243-1289.
3. Clark, R. C., Nguyen, F. and Sweller, J. (2005) Efficiency in learning: Evidence-based guidelines to manage cognitive load, John Wiley & Sons, San Francisco.
4. Clark, R. E. (2001) Learning from media: Arguments, analysis, and evidence, Information Age Publishing, Charlotte, NC.
5. Cowan, N. (2000) The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences 24, pp. 87-185.
6. Feldon, D. F. (2007) Cognitive load in the classroom: The double-edged sword of automaticity. Educational Psychologist 42:3, pp. 123-137.
7. Kalyuga, S., Ayres, P., Chandler, P. and Sweller, J. (2003) The expertise reversal effect. Educational Psychologist 38:1, pp. 23-31.
8. Mayer, R. E. (2009) Multimedia learning, 2 Cambridge University Press, New York.
9. Miller, G. A. (1956) The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review 63, pp. 81-97.
10. Schwartz, D. L. and Bransford, J. D. (1998) A time for telling. Cognition & Instruction 16:4, pp. 475-522.
11. van Merriënboer, J. J. G. and Sweller, J. (2005) Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review 17:2, pp. 147-177.
David Feldon is an assistant professor of STEM education and educational psychology at the University of Virginia. His research examines the development of expertise in science, technology, engineering, and mathematics through a cognitive lens. He also studies the effects of expertise on instructors' abilities to teach effectively within their disciplines.
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