Cognitive Load and Instructional Efficiency

by ajc

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.

Cognitive Load Types

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.

Instructional Efficiency

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.

Instructional Efficiency Graph

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:

Cost-effectiveness ratio

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.

Cost-effectiveness for the instructional process

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.


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.