Why We Learn

Paas, Tuovinen, van Merrienober, and Darabi (2005) draw on research related mental efficiency and the tenets of Keller’s ARCS model of motivation to arrive at a rather startling deduction; the idea that the results of the mental efficiency calculation, when plotted on a Cartesian axis, provide insight into a learner’s involvement as well as the instructional efficiency of a learning experience. The primary assumption underlying their work is that motivation (or involvement), mental effort, and performance are positively related, i.e., if one of these increases, the others do as well.

Paas et al. use the term “instructional involvement” (I) to refer to their motivational construct. The most interesting result of their assumption is that the “neutral” condition for instructional involvement runs perpendicular to the line representing “zero efficiency”. These two lines and the corresponding regions are combined in the image below.

The red area represents the area that, presumably, should be targeted by the instructional designer. But where should one focus, assuming they have the ability to dynamically monitor mental effort and performance. Further, are instructional experiences within one area of this continuum more beneficial based on the goals of instruction? What sort of trade-offs should one expect if choosing to target point A rather than point C? Does point E represent the “best of both worlds”?

Measures of motivation are numerous: self-efficacy, goal orientation, and attribution theory are three of the most prevalent. Paas et al. don’t propose that their construct replaces these ideas, but that it provides an overall measure of involvement. They leave it to researchers of motivation to determine the underlying reasons why a learner’s involvement is at a measured level.

It seems to me that education’s ultimate goal is transfer. Ironically, efficient instruction, a primary aim of instructional design, could prevent one from reaching this goal. Instructional designs represent the way in which its creator/designer views, perceives, and/or understands the content of study. Efficient instruction prescribes a narrow (single) path from what the learner knows to what the learner should know. Varying instructional techniques or utilizing complex (dynamic) methods of task selection, while increasing efficiency, is not likely to improve transfer. The path from which the (dynamically-selected) task originates is still narrow and pre-defined by the design.

Tangentially related information, and recollection of experiences (even mistakes), are useful when recalling information. Misperceptions, once corrected, can serve as points of activation (as episodic or autobiographical memories). These experiences and “ways of organizing” are likely beneficial in transfer situations, but they’re idiosyncratic. The teacher or designer’s idiosyncrasies, integrated into an instructional design, are not as likely to be assimilated into a learners schemata because the learner is not their “owner”. How do we construct experiences that facilitate the generation of these idiosyncrasies?

Research on feedback suggests that learners pay great attention when their misconceptions are challenged, and even greater attention when they find their internal “calibration”, or their ability to assess their expertise within a domain, to be inaccurate. These situations are more likely to occur when learners are not led stepwise from point A to point B. That is to say that instructional experiences that result in, but then alleviate cognitive dissonance (see Piaget’s disequilibrium) might be more likely to produce diverse and wide-ranging schema.

Much of the work on instructional efficiency has been completed within the field of research related to cognitive load. Cognitive load theory (CLT) prescribes the presentation of learning tasks matching complexity to learner expertise, so as to ensure that working memory capacity is not overloaded during instruction. More advanced studies vary task complexity based on (a.) performance, (b.) mental effort, or (c.) mental efficiency (calculated using the first two values). Randomly presenting problem states, assuming immediate feedback (corrective or explanatory in nature) is provided, may facilitate the construction more complex schema, consequently increasing performance in transfer situations.

Cognitive Load

Cognitive load theory (CLT) is a theory rooted in the idea that working memory is a limited capacity store within which processing occurs. More broadly, cognitive load theory provides evidence for why specific learning supports / designs are efficient. Essentially, this line of research looks at ways in which instructional design elements might facilitate or serve as an impediment to learning. A primarily tenet of CLT is that there is a dynamic relationship between learner expertise and the amount and/or type of support that should be provided. There are three types of cognitive load, illustrated graphically below.

Individuals possess various levels of background knowledge and unique sets of learning strategies. These qualities interact with the content-to-be-learned resulting in intrinsic load, or load that results from the relative complexity of the content. The use of the word relative is key, as simple content to which a learner has not been exposed is likely to result in high levels of load. Conversely, complex derivations of formulas my produce low levels of intrinsic load to a mathematician.

Extraneous load is used to refer to the load resulting from the instructional design. That is to say that the way in which instruction unfolds might require additional, unproductive processing on the part of the learner. There are a variety of conditions that have been demonstrated to produce such effects, many within the field of multimedia. Cognitive load theory urges the instructional designer take every precaution to minimize extraneous load.

The third category of load is termed germane load, originating from the idea that if intrinsic load can be decreased (through chunking, the use of advanced organizers, etc.), and extraneous load is minimized through wise instructional design (easier said than done, specifically at the individual level), the designer may impose additional load relevant to the topic of study. More precisely, germane load is load that reinforces the construction and automation (automatic processing) of schemas (organized networks of thought).

Instructional Efficiency

Cognitive load theorists have developed a formula for determining “instructional efficiency”. This is a relative measure utilizing (most often) two variables, standardized measures of learner effort (often self-reported on a likert scale, either during the training phase or the testing phase) and performance. The difference between these two values results in either a positive or negative number, ranging from -1 to +1. Often, this quantity is divided by the root of 2 so that it might be plotted.

It is interesting to note that researchers use the terms “mental efficiency” and “instructional efficiency” interchangeably. For example, Paas et al. (2003) introduces the quantity using mental efficiency, but then uses the terms “high-instructional efficiency” and “low-instructional efficiency” when referring to positions on the Cartesian axis, reproduced below for the reader’s convenience.

This interchangeability indicates the perspective of the researchers, specifically the implicit assumption that the work required to construct various instructional experiences is constant, or of no concern. CLT research provides powerful prescriptions for instructional design, supported by approximately twenty years of academic research. However, as with much of the instructional design research, the needs of their most important audience member, the classroom teacher, are not addressed. More precisely, the “efficiency” of the ID process is not a component of the efficiency equation.

By disregarding the mental effort required to create instructional designs / experiences of varying complexity, researchers miss a chance to provide practitioners meaningful information. More precisely, the classroom teacher might want to be able to quantify, at least generally, what sort of “payoff” they might expect by devoting additional effort and time to the construction of instructional experiences to comply with the tenets of cognitive load theory.

A Teacher’s Perspective

As stated previously, the entire body of work related to instructional efficiency is focused on the student’s perspective; the goal is to find instructional strategies that produce the greatest gains while decreasing cognitive demands. However, the preparatory (design) work required to construct such experiences, the background knowledge and the corresponding mental effort (and time) required, has been neglected. How might we relate the input (design of instruction) to the output (student performance) of instruction?

A chemistry teacher who must teach students how to write chemical formulas for ionic compounds can pursue this goal in a variety of ways. Assuming they are experts in the domain, they may decide to prepare very little in terms of materials and assessment of students’ background knowledge, use lecture and guided practice, and materials from the textbook. This strategy requires very little preparatory time, but mastery is likely to take longer.

Alternatively, the teacher might develop some materials on their own and administer a pre-assessment. Maybe they decide to construct worked examples and partially completed problems, spending a day or so on each as they work towards the guided practice. In this situation, guided practice may begin several days into the unit, rather than on day one as we might expect in the first example.

Finally, the instructor may commit even more time and effort, pre-assessing learners and developing materials, possibly creating color-coded manipulatives and a corresponding activity to use after and introductory lecture, and periodically for reinforcement / remediation as students move on to examine worked examples and partially completed problems. They may continue to assess students’ knowledge periodically throughout the instructional experience in order to tailor instruction to individual needs. We might think of this as the instructor assuming more of the “mental load” or doing more of the “work”, reducing the burden on students.

Cost-Effectiveness Analysis?

The classroom instructor is interested in more than the efficiency of an instructional design. They’re also interested in the design-time to teaching-time ratio (often reported to be very high for complex designs), and the relative efficiency of instruction from the teacher’s perspective. Teachers, it seems, perform an informal cost-effectiveness analysis utilizing these sorts of variables as inputs. Cost-effectiveness analysis, different from cost-benefit analysis which is tied to actual financial costs, was developed by the military and is often used in the health care field. The general formula for determining the cost-effectiveness ratio is:

Costs for the instructional process might be described by values of “effort-time”; the product of self-reported mental effort (as used by researchers when describing instructional efficiency) and time. Individual measures for effort-time could be determined for the design phase, the instructional phase, and the learning phase. The instructional phase and the learning phase refer to the same period of time but differ in perspective; the instructional phase uses the effort value for the instructor, the learning phase uses the effort value from the learners.

The effects of the instructional process could be represented by student performance. Alternatively, the previously described “instructional efficiency” might serve as a measure of effect, but using this value would result in students’ self-reported mental effort values appearing in both the numerator and the denominator of the equation. A example of what the cost-effectiveness ratio for the instructional process, using performance as the measure of effect, is provided below.

In this equation, ET represents the product of mental effort and time. The subscript “D” represents the design phase, I represents instruction, and L represents learning. It would be interesting to use this conceptualization, or something similar, to evaluate a variety of approaches to classroom instruction – similar to those described in the examples above.

Summary

My question is a practical one: how much time and effort is saved, in the planning and instructional stages, by implementing the tenets of CLT (or any instructional design paradigm for that matter)? As the prescriptions coming from the academic community become increasingly complex, the classroom teacher struggles to keep up. Practically speaking, there is only so much one individual can know and do. It is surprising, and maybe a bit revealing, that instructional designs are not evaluated in this way.

One might argue that good teachers should be striving to reduce cognitive load regardless or any formal or informal cost-effectiveness analysis, or that over time instructors will become better at generating good designs and will accumulate ideal instructional designs for different lessons These are valid arguments. However, each new class presents a different composition of learners, meaning that although generated instructional materials can be reused, analyses would have to be completed to accurately implement the design.

In the end, the best argument for conducting studies such as the one proposed here is the fact that it does not exist. Providing teachers with guidelines related to design paradigms – what they should expect to commit, in terms of time and effort, and the corresponding benefits from implementation – are legitimate goals and might lead to revisions to designs that make their adoption more plausible in the school environment.

Overview

Feedback and Self-Regulated Learning: A Theoretical Synthesis, written by Deborah L. Butler and Philip H. Winne in 1995, is a popular piece of academic literature. Google Scholar indicates that it has been cited by 1183 works. Its importance is likely a result of that way in which it views the nearly universally reported benefits of feedback through the prism of self-regulation. Essentially, the authors suggest a shift akin to the evolution from behaviorism to cognitivist ideas. Whereas the former focused exclusively on input and output, the latter posits that an idiosyncratic interpretation of stimuli must be considered. In this case, feedback serves as the stimuli to which the individual responds.

Feedback can be classified in a variety of ways; by timing (immediate or delayed), type (corrective, suggestive, elaborative), and by origin (external or internal). The authors also describe “cognitive feedback’’, relating perceived cues to use learning strategies and potential achievement. Five products of feedback are presented.

1. Confirm conceptual understanding
2. Add to conceptual understanding
3. Overwrite misunderstanding
4. “Tune’’ understanding
5. Restructure schemata (when entire conceptualization is wrong)

One of the primary points that the authors make is that all feedback is filtered through a wide array of preexisting learner qualities, e.g., domain specific history, goal orientation, epistemological beliefs, and efficacy. It is these qualities that lead to the afore mentioned “idiosyncratic interpretations’’ of feedback. So, while these may be the potential results of feedback, the way in which external feedback is filtered by the learner determines the magnitude (or existence) of the intended effect.

At the center of Butler and Winne’s argument is the idea of calibration, which refers to the monitoring of one’s ability to predict their own understanding. It is the degree to which feedback disconfirms one’s certitude in their ability to make such predictions that results in deep processing of feedback. More generally, this work suggests that one is more apt to work to resolve deficiencies in their ability to know themselves then to know an answer. In fact, the authors suggest that “calibration’’ is what’s monitored, rather than knowledge. According to Butler and Winne, the best feedback,

informs students about their monitoring of learning needs (achievement relative to goals in prior phases of engagement) and guides them in how to achieve learning objectives (cognitive engagement by applying tactics and strategies (p. 273).

So, feedback should be focused on helping students become calibrated, while simultaneously providing cues as to what types of learning strategies are likely to be most beneficial for specific learning tasks or domains. This differs from corrective feedback and questioning often employed when a student responds to a query incorrectly.

What This Means for Teachers

Feedback is effective. One need only review John Hattie’s table of effect sizes for support of its use in the classroom. However, this research points to the more complex nature of the interpretation of feedback by learners. Learners bring a set of preexisting beliefs and a history of experiences related to topics of study to the classroom. These inform they way in which students set and pursue goals. The combination of student history and goal orientations results in a unique interpretation of feedback. More importantly, internal feedback — also related to this personal history — in many ways supersedes external feedback’s ability to shape student performance.

Certainly, internal feedback alters external feedback’s effect. Awareness of this reality, and the idea that the primary goal of feedback should be to improve students’ internal “calibration’’ are the primary takeaways for the classroom instructor. Understanding each student’s history in the domain of study provides the foundation, while reflective writing tasks and short surveys might provide useful information as well. As with most tasks related to the interaction of humans, the interpretation of feedback is complex, and difficult (or at least time consuming) to do at the group level.

I’ve just received four questions that I must address as the primary component of comprehensive exams at Kent State University. I have eight weeks to answer these completely, after which they will be evaluated. If I’m successful, I will then be asked to defend my responses in person.

Research Methods in Educational Psychology and Instructional Technology

One of the things that may distinguish one discipline from another is the research that is done. The research may differ in the content, in the questions asked, and in the methods used. Instructional Technology and Educational Psychology are closely related disciplines in many ways, but they are also different. Choose a topic that is likely of interest to each field. Now design two studies, one that looks at the topic from an Educational Psychology perspective and one that does so from an Instructional Technology perspective. Explain how and why the two studies are different. What do these differences imply for the two disciplines?

Cognitive Load Theory in Designing Instruction

The basics of cognitive load theory seem well-established at this point, and it appears that instruction that fails to minimize extraneous cognitive load is likely to be less effective than instruction that takes this theory into account. First, explain the theory and its application clearly and succinctly. Then go beyond it. Specifically, it is unlikely to that cognitive load is the only factor that determines what and how much people learn from instruction, multimedia, and other elements. Describe another key factor that you believe, from your reading of the literature, has important effects on learning. Now consider how those two factors may interact in instructional settings. Describe a study that would illuminate these interactions and provide practical guidance in the design of instruction. You may narrow this down to a specific type of instruction, setting, population, or other factors.

The Application of Motivation Theory to Education

Discuss the current trends in contemporary motivation theory in the context of education. Compare and contrast motivation theories, such as need for achievement, attribution theory, achievement goal theory and theories of self-regulated learning. Make certain that your discussion focuses on theoretical developments that have stemmed from correcting earlier theoretical misconstructions. In your response be sure to cite research, which discusses each theoretical perspective’s take on achievement outcomes. Present some directions for the future related to the development of motivation theories and educational practices.

Instructional Design for Maximizing the Effectiveness of Technology

Many of us think that instructional design is one of the key foundations of instructional technology in general. In the current explosion of interest in and use of technology in education and training at all levels, it often appears that the focus is almost exclusively on the technology, with relatively little attention paid to instructional design and especially to the systematic processes of ID usually used within the field. There may be many reasons for that. Discuss the barriers that exist to applying instructional design procedures to improving the effectiveness of technology in schools, universities, or companies. Describe in some detail an approach to instructional design that would help teachers and others maximize the effectiveness of technology use in education. You may choose the setting, grade level, and other contextual factors that you concentrate on here. The approach may be one that is already in the ID literature or one that you have developed (with reference to the ID and related literatures).