Learning Efficiency Scale

From a view of a learner, learning efficiency means spending less time, effort, and other personal resources acquiring a given set of information or skills. It also means gaining something of personal value in exchange for those resources. In this sense, teachers choose the level of learning efficiency that their students may earn. Teachers know that one way of presenting a lesson can take a few seconds, another way may take a few hours. They decide the timeframe, degree of difficulty a student will encounter, evaluation standards, etc. for each lesson. Mobile PCs offer ways for teachers to provide more efficient learning options for students.

After drafting a series of principles and checklists related to learning efficiency, I decided to see if I had enough material to draft a proof of concept learning efficiency scale. Here’s parts of that draft. I think enough exists at least for teachers to conduct mental experiments and informal evaluations of learning efficiency by learners and observers of lessons.

Learning Efficiency Scale

This Learning Efficiencies Scale (LES) yields a measure of instructional competence, e.g., power or proficiency. It provides a framework for students and school observers to rank the relative capacity of school lessons and instructional material to yield intended student behavior. Learning efficiencies describe which instruction assists a student to reach a learning criterion quicker, easier, or with less effort when compared with other possible ways of reaching the same criterion (Heiny, 2007).

Assumptions
LES uses the assumption that learning efficiencies reside with the instructor. That is, teachers or teaching material simplify presentations to the bare minimum number of steps and content necessary for a student to demonstrate learning each lesson. Instructional simplicity contrasts with other rating scales that use an assumption that learning efficiencies reside with the learner.

Scientific Principles
LES uses several enduring scientific principles that indicate state-of-the-art (SOTA) of instructional presentations that yield efficient learning. These principles have legacies that extend from millennia old instruction practices and 19th century studies of learning. Many instruction programs and materials use eclectic informal and unidentified mixtures of these principles. Names of SOTA principles derive from several theories:

Trial-and-error learning – as adapted for use with mobile PCs (Heiny, L., 2005) as Direct Learning in beginning academic subjects.

Stimulus-Response learning – as adapted to instruction-learning equations by numerous behavioral specialists in a wide range of academic subjects, and by educators mostly in rhetoric.

Two Choice Visual Discrimination Analysis – as adapted and elaborated beyond the Try Another Way technology.

Direct Instruction – based on ancient instruction practices used in many cultures to sustain social institutions, such as families, economies, polities, and religions; adapted into one of the most sophisticated and largest empirically tested, technically based instruction practices and curricula available from preschool through high school.

Each principle has a body of peer reviewed experimental study reports that offer technical definitions and procedures in order to replicate results and to apply these principles in other settings, such as schools and mobile PC learning venues.

So What?
Teachers already know these four principles, but have few guides for using them quickly and concisely to plan and conduct instruction.

LES brings into one instrument assessments of how use of these instructional principles influences academic behavior, specifically learning from a given lesson.

LES assesses how variations in use of technical details in the instruction-learning equation affect the likelihood of a student reaching a technical criterion for learning each lesson. These assessments allow instructors to make technical refinements to instruction and content in order to increase student learning rates.

Counterpoints
Some teachers hold that they must first consider the circumstances of a learner before they can select an instructional process or material for a lesson. They contend that they teach humans, not other animals, and therefore, must respect learners differently from applying scientific principles and predetermined procedures through lessons.

They also say that each learner must come to class ready to learn as defined by each teacher’s idea of readiness.

Some people hold that instruction is an art, an indefinable process with nuances acquired only through professional training and practice. Teaching is not a mechanical process. Many teachers consider it an insult to suggest that anyone can reduce instruction to several objectively observable and repeatable principles that affect student learning rates.

Also, teachers blog daily and comment frequently in professional association and union meetings about heavy workloads that detract from their best instruction. They say they know what’s best to do, but cannot do so, because they lack sufficient resources. They assert that non-educators cannot accurately assess their competence.

Hypothesis
The more efficient a lesson, the more time and resources the instructor has for another lesson, thus the higher the likelihood those students will learn more than students with less efficient lessons.

Who Cares?
Those seeking to increase student learning rates can use this scale or just its ideas to refine instruction and content without changing tangible, material resources available to either the instructor or learners.

The Scale
The Learning Efficiencies Scale measures and compares the relative amount of instructional resources used until a student fulfills the learning criterion for a lesson. Instructional resource use may occur through face-to-face, one-on-one, group, mobile PC or some assemblage of these instructional modes. In its most simple form, the fewer learner resources used to meet a learning criterion, the more efficient the learning.

The referent for judging the level of efficiency is what-is-possible according to scientific data. References do not account for state-of-practice and other reasons for lower than maximum assessments defined by the four scientific data principles.

LES reports instruction as Highly Efficient, Efficient, Normally Efficient, Less Efficient, or Inert (Laissez-faire) Efficiency.

The Highly Efficient learning assessment uses five stars to symbolize it, Efficient uses four stars, Normally Efficient uses three stars, Less Efficient uses two stars, and the Inert (Laissez-faire) Efficiencies learning assessment uses one star as its symbol.

Dimensions of instructional resources measured to categorize efficiency levels include: use of task sequencing; of forward and of backward chaining; of redundant cues; of behavioral reinforcers; of shape, color, size, and position; of clock time; and of number of instruction-trial blocks. Other resources may be added or substituted when data indicate their contributions to efficiencies.

Each resource has technical definitions and observable, countable indices that accumulate to rank an instructional lesson or material according to its learning efficiencies.

Star Ratings
The number of stars assigned to an efficiency level symbolizes the instructional capacity to yield efficient learning.

***** Highly Efficient instruction receives a Five Star Rating,
**** Efficient instruction receives Four Stars,
*** Normally Efficient receives Three Stars,
** Less Efficient receives Two Stars, and
* Inert / Laissez-faire receives One Star.

Scale Based Assessments
Several procedures exist for assessing learning efficiencies. They allow using this tool as a nominal level scale.

The easiest index of learning efficiency is to count the number of minutes (or seconds) that elapses from the beginning of instruction until all students meet criterion for learning that lesson. A lesson that takes five minutes to meet criterion is more efficient than one that takes 50 minutes to reach the same result.

A second index …

Please let me know, what value you find in this incomplete draft. What makes sense, what seems a stretch beyond available data, etc.

(I’ll edit this post and add more later.)