Attract and Retain Learners with Digital Badges
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In the text-based learning arena, a consistent emphasis on clarity in communication is an absolute. If we assume that those charged with reading the litany of words sculpted into sentences will perceive what has been intended similarly—ie; all learners understand what has been said—we may be subjecting delivery of course material and expected results to mere chance.
It has been said that human beings were never born to read. In fact, reading was invented only a few thousand years ago. It has literally reorganized the functions of our brain and has served our ability to create, share thoughts and record history which are but a few of the advantages (Wolf, 2007). As the noted researcher Stanislas Dehaene has said, “decision makers in our education system swing back and forth with the changing winds of pedagogical reform, often blantanly ignoring how the brain actually learns to read” (Dehaene,2009).
It can be generally agreed upon that reading is an essential activity for the development of literacy and a host of other intellectual dividends. However, as I have discovered in my own academic journey, reading is a practice or a set of practices that, if honed and developed, may lead to the development of intellectual abilities and perhaps an aesthetic appreciation of literature (Smith, 1994).
The implication for design, the appropriate and careful analysis of semantics as it relates to intended meaning, as well as instructional strategies, cannot be overstated. The effort to achieve clarity must consider the fact that learners will represent a wide variety of reading practices which may or may not be conducive to deep learning produced as a result of effective interpretation.
Thus, as we analyze the abject need for clarity in communication with text as the primary source, we must also examine the confusion that words may bring to the reading-based learning environment. As an educator and developer of programs along with attendant courses, I have discovered that the well intentioned craft of conveyance through select words, phrases and sentences may send the intended message or messages reeling. I have found it absolutely necessary to ask three critical questions in my attempts to alleviate the confusion my assumptions may cause, or those of a secondary author whose ideas may in fact be quite foreign to me as well.
Three Critical Questions and Their Implications for Course Design
In the quest for written content to convey clear information that best allows for deep and meaningful learning, the following questions may best apply:
While applying the questions, it is important to remember that words are largely symbolic and may harbor a whole host of meanings applied by the reader.
Each question should beget a series of questions, inquiries and careful analysis of each area of communication. For example, we must concern ourselves with the relationship of the learning management system construct and its effect on the accuracy of informational intent (Shannon & Weaver, 1963). Instructional designers should incorporate an analysis of the potential impact that the desire for performance versus teaching for understanding may have, which is to say performance coming in the form of well intentioned but overwrought ancillaries, over-extended outcomes, external activities that possess little or no relevance, and other complicated tasks or less-than-useful elaborations of content (Ritchhart, 2015).
Conveying precise meaning in order to minimize confusion is paramount. It makes sense to be hyper-vigilant with not only the singular word, but also with what the potential result may be if we assess the level of logic and precision of meaning through the lens of lexical semantics. In other words, the course author must continuously assess what they’re intending to say, and how words, phrases and sentences they select may or may not provide for clarity on behalf of the learner. The author must also seek to delineate between the spoken and the written. It has been said that talking is the use of arbitrary vocal symbols to describe something. The transference of spoken ideas and information is simply not compressed on to a page when we are talking (Hall, 1959).
It has been said that there are two different types of learning, both of which may be products of two different methods of instruction. One type (or perhaps better stated as a result) is “surface learning,” whereby the student receives information imparted from the instructor and course content with the goal of producing results on an assessment through the recollection of facts. This results in little to no yield of understanding on behalf of the student, but the instructor has indeed performed by the standards of teaching by transmission. As Harold Gardner states in his book, The Unschooled Mind (1991), this leads to nothing more than superficial learning. If we are to seek what has been called, “deep learning”—the second type of learning—then course authors must accompany the development with the question, “Will what I am writing promulgate the development of understanding?”
It has been said that understanding is the result of many small performances of increasing complexity sewn together; it need not be served by assessments that are formalized and summative. The focus is to design, through the use of activities effectively described through words that serve to give the student the opportunity to manipulate, change, interact with and ultimately use the knowledge. How this may be done successfully is to incorporate a contextual analysis that involves what has been called, “probability of choice.”
Is There a Formula for Success?
The author must consider the macro-context of what is to be said using words, phrases and sentences. It has been said that information is defined as the logarithm of choices. Information delivered in a singular relay may lead to less confusion because the learner’s choice is limited. When we add additional relays of information, the learner is faced with an ever-increasing number of choices, augmented by the freedom to make certain choices. If this involves reading, the author as source of information delivers the intended message through a series of symbols, many of which may hold low or high correlation to the macro-intent of the course. The learner will now pick out one word after another, with the hope that there is an element of coherence between the macro context of the course, and the micro context of the message (Shannon & Weaver, 1963). Thus, as we analyze the need for clarity in communication with text as the primary source applied, we must also examine the confusion that words may bring to the reading-based learning environment and learn to apply a data-focused analysis that incorporates the use of information theory and attendant logarithmic formulas. Students are faced with words that may not be familiar with due to frequency of use; they will be either low or high frequency terms. In order to increase the probability of word recognition, it is imperative that we minimize reliance upon the content expert and those charged with drafting course content and incorporate a logarithmic approach using a computational model of word recognition, the Bayesian Reader. It has been said that the Bayesian Reader successfully simulates some of the most significant data on human reading and that model accounts for the “nature of the function relating word frequency to reaction time and identification threshold, the effects of neighborhood density and its interaction with frequency, and the variation in the pattern of neighborhood density effects seen in different experimental tasks” (Norris, 2006).
In order to successfully apply the theory, the designer, developer and content expert will need to apply terminology that has the highest probability of recognition and resist the temptation to use low-frequency lexical words, which may create an impediment to successful de-coding of the message. Low-frequency words may contribute or exacerbate present conditions of entropic behaviors in the online learning environment. In other words, they contribute to the “noise” that may distract and adversely affect persistence or an acceptable product that demonstrates accurate grasp of the information. In addition, a significant host of other variables intrinsic to the students such as learning stages, schema and reading habits, to cite but very few need to be applied to the formula.
As educators, our priority is to effectively deliver content that is constructed with the highest probability of understanding. In order to compliment any use of predictive analytics-based models, it is imperative that we employ word-focused analysis that may lead to enhanced clarity of content, persistence and academic success.
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Dehaene, S. (2009). Reading in the brain (1st ed., Vol. 1). New York, NY: Penguin.
Hall, E. T. (1959). The Silent Language. New York New York: Doubleday.
Ritchhart, R. (2015). Creating Cultures of Thinking. San Francisco: Jossey-Bass.
Shannon, C., & Weaver, W. (1963). The Mathematical Theory of Communication.Chicago: University of Illinois
Smith, M. C. (1994). What Do Adults Read and Why Does It Matter? Chicago: Mid-Western Educational Research Association.
Wolf, M. (2007). Proust and the Squid. New York, New York: Harper Collins.
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Author Perspective: Administrator