Knowledge Acquisition Theory
1
Running head: INSTRUCTIONAL COMMUNICATION MATTERS
Instructional communication matters: Refinement of measures of perceived cognitive
learning from a Knowledge Acquisition Theory perspective
Robert J. Trader, PHD
October 11, 2008
McDaniel College
Dr. Robert J. Trader
405 Pleasanton Road, A32
Westminster, MD 21157
Email: rtrader@mcdaniel.edu
Phone: 859-227-6703
Knowledge Acquisition Theory
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Abstract
This study provides further evidence that Knowledge Acquisition Theory’s receiver
oriented message perspective provides valuable insights into instructional
communication that were previously unavailable. This study tests three new
measures of perceived cognitive learning. Results of this test indicate that the Data
Obtained, Information Obtained, and Knowledge Obtained Measures were sensitive
enough to represent these constructs in the minds of two distinct groups of learners.
The study also provides evidence that message clarity and message relevance are
more complex constructs than had previously been explored within instructional
communication research. Knowledge Acquisition Theory accounts for 58-84% of the
variance in undergraduate perceived cognitive learning.
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Knowledge Acquisition Theory
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Instructional communication matters: Refinement of measures of perceived cognitive
learning from a Knowledge Acquisition Theory perspective
Introduction
Cognitive learning is rather poorly measured and defined within instructional
communication research. In fact, a clear vision of what higher education is for seems
to be generally lacking (Sprague, 1992, 1993). In order to correct this lack of clarity
in definitions for higher education and cognitive learning, this study introduces
Knowledge Acquisition Theory (KAT) and new measures of cognitive learning based
upon the definition of cognitive learning proposed in Knowledge Acquisition Theory.
The dominant paradigm in instructional communication research holds that
communication in the classroom is important because the difference between
knowing and teaching is communication (Hurt, Scott, & McCroskey, 1978). From the
perspective of this paradigm, higher education is teachers pouring their accumulated
knowledge into the waiting and empty minds of students via communication in the
classroom. Unsurprisingly, the measure associated with cognitive learning as defined
within the dominant paradigm is the “learning loss” measure (Chesebro & McCroskey,
2000; Richmond, McCroskey, Kearney & Plax, 1987). Consistent with the view of
higher education as a funnel system from teachers to students emphasizing mimicry
and memorization, the learning loss measure attributes student learning to the
presence or absence of an “ideal” instructor. Also consistent with this view, the
instructional technique attracting the most research attention is the most teacher
centered instructional method—the lecture.
Message variables such as “clarity” and “relevance” also reflect the teacher
centered perspective of the dominant paradigm. “Clarity” is “teacher clarity” defined
as “a cluster of teacher behaviors that result in learners gaining knowledge or
understanding of a topic, if they possess adequate interest, aptitude, opportunity,
Knowledge Acquisition Theory
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and time” (Cruickshank & Kennedy, 1986, p. 43). Relevance is the ability of teachers
to persuade students that course content is somehow relevant to students’ lives
(Frymier & Shulman, 1995; Frymier & Houser, 1998; Frymier, Schulman, & Houser,
1996) regardless of whether said content is truly in some way relevant.
In contrast to the teacher centered traditional view of instructional
communication, Knowledge Acquisition Theory adopts a receiver-oriented message
perspective to understand the cognitive learning process within the higher education
course. Communication in the classroom from a KAT perspective is more than just
teacher talk. Students are exposed to a wide variety of messages within and outside
of a higher education course and these messages can be more or less relevant to
different aspects of a student’s life. These messages range from interactions with
teachers and classmates to interactions with textbooks and other learning materials.
While lectures have certainly not disappeared from higher education classrooms,
other teaching and learning methods such as discussions are often designed into a
course to facilitate some aspect of cognitive learning. It is not so much that lectures
are outdated or that discussions are somehow inherently better (or worse) than
lectures, it is more that different message dissemination techniques serve different
functions. These functions are contingent upon beliefs about what higher education is
designed to accomplish.
Higher education does not occur within a vacuum. Rather, higher education
reflects the needs and goals of society, government, and industry. The Commission
on the Future of Higher Education in their 2006 report states, “With too few
exceptions, higher education has yet to address the fundamental issues of how
academic programs and institutions must be transformed to serve the changing
needs of a knowledge economy” (p. 25). The 2007 Education Criteria for
Performance Excellence put forth by the Baldridge National Quality Program of the
National Institute of Standards and Technology proposes that higher education
Knowledge Acquisition Theory
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institutes can meet the demands of a knowledge driven society by adopting a
learner-centered approach focusing on active learning and the development of
problem-solving skills. The Boyer report (1998) stresses the need for integrating
student and faculty research in order to better develop student problem solving skills.
Ultimately, the needs and demands of life in the 21st century suggest that the goal of
higher education is to produce knowledge workers: people who are able to identify
problems and work out solutions to those problems.
The smorgasbord approach to higher education in which students sample
tidbits of information from a variety of disciplines in order to acquire a well rounded
“liberal” education is somewhat outmoded. This model was put into a place during a
time period when exposure to information was extremely limited. As little as 30
years ago, information exposure within a higher education context was restricted
pretty much to courses students took and access to information outside of class was
restricted to what was available in the institution’s academic library or the media
(assuming that a student utilized either one of these sources). Students in the 21st
century face an overload of information. There is an overload of information and data
flooding our media, and creating congestion on our information superhighway, the
Internet.
The challenge is no longer gaining access to information. The challenge is to find
and access quality information, to make sense out of this information, and to work
out viable solutions to problems based on the highest quality information available.
We need people who can synthesize information, create order out of the current
chaos, and make connections between the information available (much of which lies
dormant) and the problems that face us. Simply stated, our knowledge driven
society creates a demand for creative problem solvers who know how to transform
data into information into knowledge.
Knowledge Acquisition Theory
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KAT addresses the process for developing problem-solving skills. First, KAT
building off of Bloom’s (1956) taxonomy of cognitive learning defines cognitive
learning as a three part process: 1) acquiring data (encoding and memorization), 2)
acquiring information (comparing and contrasting data, analyzing and synthesizing
data; critical thinking), and finally 3) acquiring knowledge (testing what is believed
to be true, applying information to the solution of problems and measuring the
success of this application). Different types of interactions with messages are likely
to produce different gains in data, information, or knowledge. KAT posits that the
quality of lectures, notetaking, highlighting, listening, and reading (data acquisition
behaviors) predict increases in data acquisition. Information is acquired through
some combination of discussion, writing essays, and debating (information
acquisition behaviors). Finally, knowledge is acquired through doing research,
through creating finished products, and through successful completion of problem
solving exercises (knowledge acquisition behaviors). The three happen in sequence.
One cannot have knowledge without having acquired information, nor information
without having acquired data. The real challenge is the quality of the interaction
students have with instructional messages.
First, data must be attended. Gibson (1950) points out that in order for a
stimulus to be responded to, the stimulus must first be perceived. Scholars from the
field of communication echo Gibson’s sentiment. Donohew, Palmgreen, and Duncan
(1980) and later Donohew, Palmgreen, and Lorch (1994) in their Activation Model of
Information Exposure (also Donohew et al., 1991, 1998) advance and offer
considerable support for the claim that persuasion begins with attention. What is
persuasion other than changes in affect, cognition, and/or behavior and hence
learning? In Knowledge Acquisition Theory (KAT), data must first be attended in
order for decoding to ensue. At the minimum, in order for data to be attended within
a higher education course, a lecture must be listened to or a textbook must be read.
Knowledge Acquisition Theory
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While these behaviors do not guarantee that data has been attended, performance of
these behaviors increases the probability that data (and hence information and
knowledge) will be acquired.
Second, data must be remembered. If data are not remembered, they cannot be
processed. In other words, data must be stored long enough in memory for data to
be able to be analyzed, evaluated, compared, contrasted, and integrated with other
data. Behaviors related to positive increases in recall and retention include
highlighting key data, notetaking, outlining, summarizing, and discussion (Doctorow,
Marks, & Wittrock, 1978; Hooper, Sales, & Rysavy, 1994; Taylor & Berkowitz, 1980;
Wittrock & Alesandrini, 1980). Again, performance of these behaviors increases the
probability that data (and hence information) will be recalled, but does not guarantee
data (or information or knowledge) acquisition.
The first and second steps when combined equal decoding. In other words,
decoding combines attending data with the lowest level of Bloom’s (1956) taxonomy
of cognitive learning objectives, remembering data (Anderson & Krathwohl, 2001).
Decoding is simply data acquisition, and there is a final step necessary for data to be
transformed into information.
The final step for data to be transformed into information is information
processing. Information processing is roughly equivalent to the revised higher order
cognitive learning objectives of Bloom’s taxonomy (Anderson & Krathwohl, 2001)
namely analyzing, evaluating, synthesizing, and integrating. While these cognitive
learning objectives are loosely based on Bloom’s taxonomy, they in no way follow
Bloom’s prescribed order since these actions can occur simultaneously as well as in
tandem. Behaviors associated with this final step include: a) critical thinking, b)
summarizing, and c) thinking deeply about the relationships between data. Yet again,
performance of these behaviors increases the probability that data will be
Knowledge Acquisition Theory
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transformed into information, but does not guarantee data (or information)
acquisition.
For information to be transformed into knowledge, data must first be acquired
through decoding and data must be processed into information. Thus, knowledge
acquisition is dependent on data and information acquisition. However, in order for
information to be transformed into knowledge, information must be usable to
students. There is a difference between knowing and doing. Data and information are
known (decoded and analyzed). However, until data as information becomes usable
and thus the basis for action, data as information is not knowledge. Usable
information (knowledge) can be applied to problem solving or future learning.
Behaviors likely to support this final transformation based partially on
recommendations for higher education reform of research universities in the Boyer
Report (1998) include: 1) doing applied research, 2) creating finished products, 3)
giving presentations, 4) leading discussions, and 5) providing systematic accounts of
how information could be used to solve hypothetical problems. Of course, performing
these activities in no way guarantees that information will be transformed into
knowledge. However, performing these activities increases the probability that
information will be transformed into knowledge (usable information).
In reverse engineering the challenge of student acquisition of knowledge in the
higher education context, it becomes apparent that the knowledge acquisition
behaviors described above are all commonly associated with communication. For
data acquisition, the communication is predominantly receptive. Students listen to or
read other people’s generated messages. For information acquisition, the
communication is predominantly interactive. Messages are exchanged between
instructors and students and/or between students and other students. For knowledge
acquisition, the behavior is predominantly productive. Students become the
producers of messages. Thus, for students to acquire knowledge, they have to move
Knowledge Acquisition Theory
9
from rather passive message receivers to active message producers. Messages are,
of course, the central communication science construct (Powers, 1995). This being
the case and since messages are central to data, information, and knowledge
acquisition, the next step is to consider how the characteristics of messages relate to
student knowledge acquisition behaviors in the higher education context.
To understand how messages and their design relate to student behavior,
Knowledge Acquisition Theory (KAT) builds on Zipf’s principle of least effort (1949).
The principle of least effort states that human beings have a tendency to lower their
goals if meeting a higher goal requires more effort than the person currently wishes
to make. For example, a student may knowingly turn in a lower quality paper if
submitting a higher quality paper requires more effort than the student is current
willing to make. Knowledge Acquisition Theory argues that messages can be
designed and that interactions with messages can be designed to maximize
outcomes while seemingly minimizing efforts.
It seems obvious that students who make minimal effort to acquire data,
information and/or knowledge are less likely to have obtained that data, information,
and/or knowledge or to be able to utilize the information they have for the solving of
problems. In fact, research in persuasion suggests that positive or negative attitude
change is contingent upon the depth of information processing (Cacioppo & Petty,
1984; Kruglanski, 1989, 1990; Kruglanski & Orehek, 2007; Kruglanski & Thompson,
1999; Petty & Wegner, 1999). In other word, people have an internal monologue,
and when people are exposed to some information, this monologue elaborates upon
some physical or conceptual aspect of the message. According to the Elaboration
Likelihood Model, people elaborate (the internal monologue focuses) on one of two
sets of cues: a) central cues such as the argument and the evidence supporting the
argument or b) peripheral cues such as appearance, credibility, or other cues not
Knowledge Acquisition Theory 10
directly related to the argument that people can still give themselves messages
about via their internal monologue.
For example, when listening to a speech, audience members may focus on the
appearance of the speaker (i.e. “This person looks intelligent and trustworthy. This
person seems knowledgeable. So, I’ll accept what they say, at least for now.”). This
is an example of the peripheral route to persuasion. Or, an audience member could
focus on what the speaker is saying (i.e. “I read a book about this once that
completely agrees with what this woman is saying. As a matter of fact, that last
statement makes complete sense.”). This would be an example of the central route
to persuasion. The ELM claims that deeper processing, central route processing, is
more persuasive. In other words, the attitudinal change is more resistance to
subsequent change when people process messages using the central route. If this is
indeed the case, then we need to have our students deeply process instructional
messages. Unfortunately, the ELM offers little advice on how to construct messages
to encourage deep processing. The ELM does suggest that people with more
background knowledge on a topic tend to process messages centrally and more
deeply.
Messages can be designed to make interaction with those messages appear
easier or more appealing and this is likely to encourage depth of processing. The two
variables suggested by the instructional communication research literature for
making messages appear easier or more appealing are clarity and relevance. A clear
message psychologically appears easier to understand, and thus students will more
likely process a clear message deeply. A relevant message is more likely to motivate
one to more deeply process that message since the message is more appealing, and
potentially has a higher sense of importance. As stated earlier, existing instructional
communication research has focused exclusively on teacher clarity and the ability of
teachers to persuade students that content is relevant.
Knowledge Acquisition Theory 11
Knowledge Acquisition Theory refines the clarity and relevance constructs. A
higher education course consists of a variety of messages including: 1) what
textbooks say (Textbook Clarity), 2) what instructors say (Instructor Clarity), 3)
rules for class interaction and for evaluating progress (Procedural Clarity), and 4) the
overall goals of the course usually in the form of a syllabus (Course Clarity).
Relevant messages can be messages given in a course that are relevant to a
student’s 1) present life (Primal Relevance), 2) possible future life (Distal Relevance),
and/or 3) that have already proven useful (Generic Relevance). Trader’s (2007)
unpublished dissertation asks the question which of these dimensions of clarity
and/or relevance best predict student perceptions of knowledge acquired from the
completion of a college course. The results showed that Generic Relevance, Course
Clarity, Primal Relevance, Distal Relevance, and Textbook Clarity significantly
predicted student perceptions of knowledge acquisition. In fact, these variables
accounted for 63% of the variance in student perceptions of knowledge obtained
from completion of an undergraduate course.
In Trader’s (2007) unpublished dissertation, the measure used for cognitive
learning failed to make the distinction between data, information, and/or knowledge
obtained from successful completion of an undergraduate course. The current study
offers new measures of data, information, and knowledge obtained. Thus, the first
research question this study asks is:
RQ1: Do the Data Obtained Measure (DOM), Information Obtained Measure (IOM),
and Knowledge Obtained Measure adequately capture these constructs?
Also, in Trader’s (2007) unpublished dissertation, Procedural Clarity and
Presentation Clarity dropped out of the final model failing to be significant predictors
of student perceived cognitive learning. Does this still hold true when cognitive
learning is divided into data obtained, information obtained, and knowledge
obtained?
Knowledge Acquisition Theory 12
RQ2: Which message variables (out of the four dimensions of clarity and the three
dimensions of relevance) best predict data obtained?
RQ3: Which message variables (out of the four dimensions of clarity and the three
dimensions of relevance) best predict information obtained?
RQ4: Which message variables (out of the four dimensions of clarity and the three
dimensions of relevance) best predict knowledge obtained?
Knowledge Acquisition Theory 13
Methods
In order to answer the research questions, data for this study were obtained
using an online survey program. An Independent Samples T-Test was used to
answer Research Question 1. Research Questions 2-4 were answered using stepwise
regression.
Sampling
Students enrolled in upper level biology and communication courses at a small
private liberal arts college on the east coast were asked to fill in the online survey.
These students received no credit or incentives for participating in the study.
Participation was completely voluntary and anonymous. This study had 56
participants, 34 females and 32 males. Twenty of these students were biology
majors and 36 were Communication majors. There were 11 sophomores, 34 juniors,
and 21 seniors.
Procedures
Students whose last names began with A, C, E, G, I, K, M, O, Q, S, U, W, or Y
were asked to consider the course from which they felt they had learned the most
while attending college (n = 32). And students whose last names began with B, D, F,
H, J, L, N, P, R, T, V, X, or Z were asked to consider the course from which they felt
they had learned the least while attending college (n = 24). Students were asked to
verify whether they were considering the course from which they had learned the
least or the course from which they ad learned the most. Students were then asked
to fill in their answers to several Likert scales as honestly as possible.
Measures
The measures used in this study were: 1) Data Obtained Measure (DOM),
Information Obtained Measure (IOM), and Knowledge Obtained Measure (KOM) to
capture student perceptions of cognitive learning, 2) Textbook Clarity Measure (TCM),
Instructor Clarity Measure (ICM), Procedural Clarity Measure (PCM), and Course
Knowledge Acquisition Theory 14
Clarity Measure (CCM) to measure student perceptions of message clarity, and finally
3) Primal Relevance Measure (PRM), Distal Relevance Measure (DRM), and the
Generic Relevance Measure (GRM) to measure student perceptions of message
relevance. The clarity and relevance measures were used in Trader’s (2007)
dissertation, but the three measures of perceived cognitive learning are new.
Measures of Perceived Cognitive Learning
Cognitive learning is measured in a variety of ways. One method is to give
students a test. This test is course specific and cannot be used as a measure of
cognitive learning in other courses. Realizing the limitations with measuring cognitive
learning in this way, instructional communication scholars devised a measure of
cognitive learning that is not course specific. This measure, called the “learning loss”
measure, consists of the following two questions on a 0-9 scale: 1) “How much did
you learn in class” and 2) “How much do you think you could have learned in the
class had you had an ideal instructor” (Richmond, McCroskey, Kearney & Plax, 1987).
The first item is subtracted from the second in order to compute the perceived
learning loss.
Chesebro and McCroskey (2000) claim that the learning loss measure is valid,
and its use has been supported in one comparative experimental research study.
Though this measure is convenient and can be applied to any classroom context, it
oversimplifies perceptions of cognitive learning (Witt, Wheeless, & Allen, 2004) and
posits student cognitive learning as the result of the efforts or existence of a
perceived “ideal” instructor. While this approach is consistent with the traditional
knowledge transfer model and its emphasis on pedagogy, it is an inadequate
measure for KAT in which the burden for knowledge acquisition is largely placed on
the student.
Henning in his unpublished dissertation (2006-2007) offers the following measure
of perceived cognitive learning to replace the learning loss measure. Henning’s
Knowledge Acquisition Theory 15
measure, the perceived Cognitive Learning Aptitude Measure (CLAM), is an 8-item 5point Likert-type scale derived from Bloom’s (1956) original conceptualization of
cognitive learning. The measure has a 1-factor structure and a strong reliability of
items (α = .928), and in Henning’s study explains 66.58% of the variance in
perceived cognitive learning aptitude.
While Henning’s Cognitive Learning Aptitude Measure is a strong measure of
student perceptions of cognitive learning in a general sense, Trader’s (2007)
unpublished dissertation created and adopted the Knowledge Gained Inventory to
more fully capture student perceptions of knowledge gained from undergraduate
course. Henning’s Cognitive Learning Aptitude Measure includes items about linking
course content to other course content, but not to past or projected learning
experiences and objectives. Knowledge gain is partly a question of knowing and
partly a question of doing something with the information known such as using it in
present or future learning endeavors. The Knowledge Gained Inventory (KGI)
included items to capture both knowing and doing. In Trader’s (2007) unpublished
dissertation, the reliability for KGI using Cronbach’s alpha was .89 with an
Eigenvalue of 4.29. KGI explained 62% of student perceptions of knowledge
acquisition in that study. However, KGI fails to capture the data obtained,
information obtained, and knowledge obtained distinctions in cognitive learning
proposed in KAT.
Last spring, a focus group of 5 undergraduate students met to discuss cognitive
learning as defined within Knowledge Acquisition Theory. For one hour this group
discussed data obtained, information obtained, and knowledge obtained and how
these could be measured. The group generated a consensual list of five items for
each aspect of cognitive learning that the group believed captured the meaning of
these terms. All of the resultant scales in this study related to perceived cognitive
learning are unidimensional. All scales in this study are 5-point Likert scales. The
Knowledge Acquisition Theory 16
items and their factor loadings for each of the perceived cognitive learning scales are
as follows:
Table 1: Data Obtained Measure (n = 55,  = .95, Eigenvalue = 4.212 with 84% of
the variance explained)
Item
I remember key words from the course.
I remember key facts from the course.
I can recall the main points from the course.
I can recall the main themes from the course.
I can identify important terms from the course.
Loading
.902
.949
.931
.889
.918
Table 2: Information Obtained Measure (n = 56,  = .95, Eigenvalue = 4.159 with
83% of the variance explained)
Item
I can see how the ideas from the course fit together.
I can evaluate the pros and cons of what I learned.
I can look at the course topic from multiple perspectives.
I can distinguish similarities and differences between sections of the
course.
I can summarize course materials.
Loading
.937
.915
.934
.918
.854
Table 3: Knowledge Obtained Measure (n = 55,  = .97, Eigenvalue = 4.461 with
89% of the variance explained)
Item
I can use information from this course to solve problems.
The information is usable.
I can apply what I learned in this course to understanding my experiences.
I can use course concepts to help solve problems.
I can apply what I have learned to future learning activities.
Loading
.953
.930
.948
.963
.927
Measurement of Content Relevance
The Primal Content Relevance Measure, the Distal Content Relevance Measure,
and the Generic Content Relevance Measure are derived from Frymier and Shulman’s
(1995) Content Relevance Scale ( = .88). The problem with Frymier and Shulman’s
measure is that it emphasizes teacher behaviors rather than potential relevance of
content to student learners and fails to indicate clearly the way in which content can
be relevant to students. In fact, Frymier and Shulman’s scale with its emphasis on
examples is more closely related to instructor clarity than relevance.
Knowledge Acquisition Theory 17
Relevance in KAT is primarily the degree of utility of course content, and has a
temporal dimension in that content can be relevant to a learner’s past experiences
(has already shown relevance), present life, and/or future experiences or
expectations. Further, content can be relevant to a learner’s life outside of a course
and to a learner’s perceived future goals as well as relevant to all people.
In order to tap into the above mentioned dimensions of content relevance, the
following three measures were created. The Primal Relevance Measure consists of
three items to capture student perceptions of the relevance and applicability course
content had to their present lives. The Cronbach’s alpha reliability for the scale in
this study is .94 with an Eigenvalue of 2.679 and 89% of the variance explained. In
Trader’s (2007) unpublished dissertation, the reliability was .90. The factor loadings
for the Primal Relevance Measure items are as follows:
Table 4: Primal Relevance Measure (n = 56,  = .94, Eigenvalue = 2.679 with 89%
of the variance explained)
Item
The course topic was relevant to my life at that time.
I could apply the course content to problems in my life at that time.
The content applied to my own life at that time.
Loading
.943
.948
.944
The Distal Relevance Measure consists of four items that capture student
perceptions of potential relevance and applicability of course content to future life,
career, and learning. The Cronbach’s alpha reliability for the scale in this study is .94
with an Eigenvalue of 3.523 and 88% of the variance explained. In Trader’s (2007)
unpublished dissertation, the reliability of the scale was .90. Factor loadings for the
items are as follows:
Table 5: Distal Relevance Measure (n = 56,  = .94, Eigenvalue = 3.523 with 88% of
the variance explained)
Item
I believed the course content would help me find a job.
I thought I might need the information/skills from this course someday.
I believed the knowledge I gained in this course would help me with other
courses.
Loading
.927
.963
.941
Knowledge Acquisition Theory 18
I believed the course content would help me in my future life.
.922
The Generic Relevance Measure contains six items asking about student
perceptions of the relevance of course content to understanding of the world in which
they live. Several items ask if content has already proven useful for work, life, and
learning. And, one item asks if everyone needs to know the content. The Cronbach’s
alpha for the Generic Relevance Measure in the present study is .95 with an
Eigenvalue of 4.285 and 72% of the variance accounted for. In Trader’s (2007)
unpublished dissertation, the reliability was .89. Factor loadings for the items of the
Generic Relevance Measure are as follows:
Table 6: Generic Relevance Measure (n = 56,  = .95, Eigenvalue = 4.285 with 72%
of the variance explained)
Item
I understand why the content of the course was important.
The course content was similar to my own experiences.
Since taking the course, I have a better understanding of the world I live
in.
I have used knowledge gained from this course in my other courses.
I have used knowledge gained from this course outside of school in my
work or internship.
Everyone needs to know the content in this course.
Loading
.874
.816
.789
.868
.913
.803
Measurement of Message Clarity
Instructional message clarity in KAT is divided into four measures: textbook
clarity, presentation clarity, procedural clarity, and course clarity. The Textbook
Clarity Measure is based on Chesebro and McCroskey's (1998) Teacher Clarity Short
Inventory ( = .92). “My textbook” was used in place of “my instructor”. Several
items were moved to other scales. And, several items were added to more clearly
specify the clarity of textbooks. The most important aspects of textbook clarity as
revealed in the research on clarity are: 1) clearly presenting concepts, 2) content
organization, 3) having clear objectives, 4) use of examples, 5) readability, and 6)
inclusion of multiple entry points for finding information. Textbooks that include
Knowledge Acquisition Theory 19
these things are more likely to be perceived as high in clarity, and those that do not
as low in clarity.
The Textbook Clarity Measure used in this study consists of seven items reflecting
the important aspects of textbook clarity listed in the preceding paragraph. The
Cronbach’s alpha reliability for the Textbook Clarity Measure is .94 with an
Eigenvalue of 5.1 with 73% of the variance accounted for. In Trader’s (2007)
unpublished dissertation, the reliability was .91. Factor loadings for the Textbook
Clarity Measure items are as follows:
Table 7: Textbook Clarity Measure (n = 55,  = .94, Eigenvalue = 5.1 with 73% of
the variance explained)
Item
My textbook clearly defined major concepts.
In general, I understood the textbook.
The objectives for each chapter in the textbook were clear.
My textbook was well organized.
My textbook provided clear and relevant examples.
My textbook used relevant graphics to explain key concepts.
The textbook had a good index or glossary to find necessary information.
Loading
.861
.827
.924
.905
.850
.793
.806
The Instructor Clarity Measure and the Procedural Clarity Measure are based on
Simonds’ (1997) Teacher Clarity Scale ( = .93) with the two subscales (content
clarity and procedural clarity) having  reliabilities of .88 each. Important aspects of
presentation clarity are: 1) use of examples, 2) use of visual aids to clarify
explanations, 3) use of previews and summaries, 4) stressing and defining main
points, 5) staying on topic, and 6) having clear objectives. Inclusion of these aspects
is likely to lead to higher perceptions of presentation clarity.
The Instructor Clarity Measure used in this study consists of ten items. The
Cronbach’s alpha for the Presentation Clarity Measure is .94 with an Eigenvalue of
6.604 with 66% of the variance accounted for. In Trader’s (2007) unpublished
dissertation, the reliability was .95. Factor loadings for the measure are as follows:
Knowledge Acquisition Theory 20
Table 8: Instructor Clarity Measure (n = 54,  = .94, Eigenvalue = 6.604 with
66% of the variance explained)
Item
My instructor
My instructor
My instructor
My instructor
class.
My instructor
My instructor
My instructor
My instructor
My instructor
presented.
My instructor
was clear when presenting content.
used examples when presenting content.
related examples to the concept being discussed.
used the board, transparencies, or other visual aids during
Loading
.913
.935
.873
.536
gave previews of material to be covered.
gave summaries when presenting content.
stressed important points.
stayed on topic.
clearly explained the objectives for the content being
.754
.841
.787
.724
.913
defined major/new concepts.
.878
Item 4 had a factor loading below the .6 level. The item was kept for theoretical
reasons.
The important aspects of procedural clarity are: 1) having clear goals and
objectives, 2) having clear assessments and procedures for assessment, 3) checking
student understanding, 4) providing feedback, and 5) having clear classroom policies
and consequences for violation. The Procedural Clarity Measure used in this study
consists of ten items. The Cronbach’s alpha reliability for the Procedural Clarity
Measure is .96 with an Eigenvalue of 7.373 and 74% of the variance accounted for.
In Trader’s (2007) unpublished dissertation, the reliability was .95. Factor loadings
for the Procedural Clarity Measure items are as follows:
Table 9: Procedural Clarity Measure (n = 56,  = .96, Eigenvalue = 7.373 with 74%
of the variance explained)
Item
My instructor
My instructor
My instructor
My instructor
My instructor
My instructor
My instructor
My instructor
My instructor
My instructor
violation.
communicated classroom processes and expectations clearly.
described assignments and how they should be done.
asked if we knew what to do and how to do it.
prepared us for the tasks we would be doing next.
pointed out practical applications for coursework.
prepared students for exams.
explained how we should prepare for an exam.
provided students with feedback of how well they were doing.
provided rules and standards for satisfactory performance.
communicated classroom policies and consequences for
Loading
.882
.889
.899
.926
.758
.875
.872
.815
.860
.797
Knowledge Acquisition Theory 21
Finally, the Course Clarity Measure is a blend of the most relevant items from the
Chesebro and McCrockey clarity scale and the two Simonds’ scales mentioned above
applied to the clarity of courses as a whole. Aspects of course clarity include: 1) clear
organization, 2) clear goals and objectives, 3) integration of parts and whole, 4) a
clear syllabus, 5) clear instructor expectations, and 6) clear relationship between
assessment and content. The Course Clarity Measure used in this study consists of
ten items reflecting the six aspects listed in the preceding sentence. Cronbach’s
alpha reliability for the Course Clarity Measure is .94 with an Eigenvalue of 7.618 and
76% of the variance accounted for. In Trader’s unpublished dissertation, the
reliability was .95. Factor loadings for the Course Clarity Measure items are as
follows:
Table 10: Course Clarity Measure (n = 53,  = .94, Eigenvalue = 7.618 with 76%
of the variance explained)
Item
The course was well organized.
I understood the purpose or goal of the course.
The different parts of the course contributed to my understanding of the
course as a whole.
The different parts of the course were good examples of the course's main
goal or purpose.
The syllabus was clear.
The syllabus outlined the content of the course well.
I knew what the instructor expected of me in this course.
Testing reflected what I was supposed to have learned in the course.
The course was what I expected it to be.
The course was well integrated.
Loading
.743
.752
.854
The above measures were used to test the research questions of this study.
Results of those tests are reported in the next section.
.861
.825
.836
.725
.768
.766
.798
Knowledge Acquisition Theory 22
Results
Research Question 1 asks whether the Data Obtained, Information Obtained, and
Knowledge Obtained Measures adequately capture these distinctions. This was tested
using an Independent Samples T-Test. Group 1 consists of students considering the
course from which they felt they had learned the most and Group 2 consists of
students considering the course from which they have learned the least. If the Data
Obtained, Information Obtained, and Knowledge Obtained Measures show significant
differences between the two groups, then it seems likely that the measures
adequately capture these distinctions in perceived cognitive learning.
A t-test indicates that Group 1 (M = 4.23, SD = .52) and Group 2 (M = 2.58, SD
= 1.06) significantly differ in their perception of data obtained from an
undergraduate course [t(54) = 7.682, p < .001]. A t-test indicates that Group 1 (M
= 4.26, SD = .51) and Group 2 (M = 2.41, SD = .93) significantly differ in their
perception of information obtained from an undergraduate course [t(54) = 9.521, p
< .001]. Finally, a t-test indicates that Group 1 (M = 4.30, SD = .52) and Group 2
(M = 1.94, SD = .71) significantly differ in their perception of data obtained from an
undergraduate course [t(54) = 14.32, p < .001].
Research Questions 2-4 asked which of the 4 dimensions of perceived message
clarity and the 3 dimensions of perceived message relevance best predict student
perceptions of data obtained, information obtained, and knowledge obtained from
successful completion of an undergraduate course. Stepwise regression as opposed
to hierarchical linear regression was used to test this since no theoretical differences
are proposed for message characteristics in KAT.
Results (adjusted R-Square = .60, F (2, 53) = 68.810, p < .001) indicate that
Generic Relevance (β = .693, t = 7.667, p < .001) and Textbook Clarity (β = .203, t
= 2.247, p < .029) are the significant predictors of Data Obtained. Primal Relevance
(β = -.114, t = -.613, p = N.S.), Distal Relevance (β = -.280, t = -1.766, p = N.S.),
Knowledge Acquisition Theory 23
Instructor Clarity (β = .173, t = 1.538, p = N.S.), Procedural Clarity (β = .115, t =
1.089, p = N.S.), and Course Clarity (β = .177, t = 1.532, p = N.S.) are not
significant.
Results of the stepwise regression to test RQ2 (adjusted R-Square = .70, F (3,
52) = 42.790, p < .001) indicate that Generic Relevance (β = .533, t = 5.260, p
< .001), Course Clarity (β = .320, t = 3.195, p = .002), and Textbook Clarity (β
= .163, t = 2.099, p = .041) are significant predictors of perceived Information
Obtained in this study. Primal Relevance (β = .013, t = .082, p = N.S.), Distal
Relevance (β = -.203, t = -1.425, p = N.S.), Instructor Clarity (β = .105, t = .579, p
= N.S.), and Procedural Clarity (β = -.008, t = -.052, p = N.S.) are nonsignificant.
Stepwise regression was also run on the data as above with the inclusion of the
Data Obtained Measure since data acquisition is a prerequisite for information
acquisition. Results (adjusted R-Square = .83, F(3, 52) = 90.188, p < .001) indicate
that Data Obtained (β = .602, t = 6.991, p < .001), Course Clarity (β = .218, t =
2.845, p = .006), and Generic Relevance (β = .195, t = 2.2122, p = .039) are
significant predictors of Information Obtained. Primal Relevance (β = .026, t = .218,
p = N.S.), Distal Relevance (β = -.071, t = -.649, p = N.S.), Textbook Clarity (β
= .051, t = .836, p = N.S.), Instructor Clarity (β = .051, t = .375, p = N.S.), and
Procedural Clarity (β = .008, t = .068, p = N.S.) are nonsignificant.
Finally, stepwise regression was also used to test RQ3. Results (adjusted RSquare = .74, F(2, 53) = 80.174, p < .001) indicate that Generic Relevance (β
= .666, t = 7.252, p < .001) and Course Clarity (β = .226, t = 2.896, p = .005) are
significant predictors of Knowledge Obtained. Primal Relevance (β = .186, t = 1.280,
p = N.S.), Distal Relevance (β = .065, t = .490, p = N.S.), Textbook Clarity (β = .059, t = -.829, p = N.S.), Instructor Clarity (β = -.041, t = -.244, p = N.S.), and
Procedural Clarity (β = -.067, t = -.478, p = N.S.) are nonsignificant.
Knowledge Acquisition Theory 24
Since data is a prerequisite for information and information is a prerequisite for
knowledge, stepwise regression with the addition of Data Obtained and Information
Obtained was once again calculated on the data. Results (adjusted R-Square = .84,
F(3, 52) = 95.216, p < .001) indicate that Information Obtained (β = .618, t =
6.674, p < .001), Generic Relevance (β = .397, t = 4.467, p < .001), and Textbook
Clarity (β = -.156, t = -2.642, p = .011) significantly predict Knowledge Obtained.
Primal Relevance (β = .127, t = 1.104, p = N.S.), Distal Relevance (β = .156, t =
1.497, p = N.S.), Instructor Clarity (β = .036, t = .460, p = N.S.), Course Clarity (β
= .088, t = 1.102, p = N.S.), Procedural Clarity (β = .031, t = .444, p = N.S.), and
Data Obtained (β = .081, t = .687, p = N.S.) are nonsignificant.
Knowledge Acquisition Theory 25
Discussion
The results of the present study are encouraging. A receiver oriented message
perspective offers insights into perceived cognitive learning in the higher education
course that were not previously available.
First, the results of the Independent Samples T-Test comparing students
considering either the course they thought they had learned the most from or the
course they thought they had learned the least from provides considerable support
for the claim that the Data Obtained, Information Obtained, and Knowledge Obtained
Measures do indeed measure what they were designed to measure. One expects that
in any course students will remember certain words, facts, and even some
arguments for or against an idea. Even in the Data Obtained and Information
Obtained cases, there were statistically significant differences in what students
believed they had learned in the most and least conditions. The measures were
sensitive enough to capture these differences. The difference in student perceptions
of cognitive learning was of course biggest in relation to perceptions of Knowledge
Obtained since knowledge is much more dependent upon scaffolding.
Next, the results indicated considerable support for the claims that 1) different
aspects of message clarity and message relevance are important during different
phases in the knowledge acquisition process, 2) knowledge acquisition is a building
process (knowledge builds off of prior knowledge), 3) that instructional
communication is more than mere teacher talk, and 4) that there is a generation
effect.
Generic Relevance is significant in the results for every regression run in this
study. Generic Relevance is content that has already proven useful in a student's life.
Obviously, knowledge builds upon prior knowledge and it is thus important to provide
students with knowledge upon which to build. Solutions that have worked in the past
are likely to be applied again in the future. However, this approach to problem
Knowledge Acquisition Theory 26
solving can be problematic. There is the danger of trained incapacity, for example, in
which solutions to problems are chosen based on convenience (the method of
tenacity and least effort) and lack of recognition of viable alternatives (people are
blinded by their prior training). In other words, people are trained to repeat the
same mistakes from the past over and over again.
The results suggest that it may be necessary to leave a certain amount of
ambiguity in our messages in order to ensure that student thinking about problems
goes beyond the obvious solution. This is especially true in regards to Knowledge
Obtained. Textbook Clarity is negative and significant in the final regression equation.
Textbook Clarity, Instructor Clarity, and Procedural Clarity are negative but
nonsignificant in the fourth regression equation. This suggests that too much clarity
stifles creativity and fails to stimulate the brain enough to think outside the box. This
also provides support for a generation effect.
A generation effect is the idea that the brain requires stimulation through
omitting a certain amount of information that the brain then fills in. In other words,
giving the complete answer is less stimulating than giving a partial answer. Robust
self-generation effects have been found within a wide variety of contexts such as to
increase reading comprehension and retention (Doctorow, Marks, & Wittrock, 1978;
Hooper, Sales, & Rysavy, 1994; Taylor & Berkowitz, 1980; Wittrock & Alesandrini,
1980); the solving of mathematical problems (Lawson & Chinnappan, 1994;
McNamara & Healy, 1995a, 1995b, 2000); the retention of nonwords (Begg, Snider,
Foley, & Goddard, 1989; Brooks, Dansereau, Holley, & Spurlin, 1983; Foos, Mora, &
Tkacz, 1994; Frase & Schwartz, 1975; Jacoby, 1978; Johns & Swanson, 1988;
Nairne & Widner, 1987; Watkins & Sechler, 1988); the recall of advertising product
information (Reardon & Moore, 1996; Sengupta & Gorn, 2002); and even the recall
of answers to trivia questions (deWinstanley, 1995; Pesta, Sanders, & Murphy,
1999; Peynircioglu & Mungan, 1993). Clarity is a double edged sword. Sometimes it
Knowledge Acquisition Theory 27
is good to be clear and sometimes it is necessary to be ambiguous in order to
stimulate learner minds.
In this study, clarity seems to be important in data and information acquisition.
In particular, it seems that clear textbooks and clear course goals aid students in the
acquisition of unfamiliar data and information. Textbooks provide the bulk of
messages with which students interact and course goals frame the meaning of those
messages. However, if students feel that the answers are too clear cut, that there is
no room for improvement or no need for further inquiry into a topic, then students
are less likely to feel like knowledge has been gained.
Finally, prior instructional communication research has focused almost exclusively
upon “teacher clarity” when considering the role message characteristics play in
student cognitive learning. Findings in this study as well as in Trader’s (2007)
unpublished dissertation suggest that Instructor Clarity is not an important factor in
student perceived cognitive learning. Perhaps, it would be better to design
undergraduate courses in which the onus of learning is placed on the students rather
than on the instructor. While it will always be necessary to have instructors who have
sufficient content knowledge by which to select course content to expose students to
and sufficient knowledge of learning processes that inform appropriate interactions
with content during the appropriate stage in the learning process, learning is
predominantly an individual struggle.
Of course, there are several limitations to this study that suggest cautious
interpretation of the results. First off, the sample size is rather small thus increasing
the risks of Type II errors. Second, the sample is a convenience sample and there is
no way to determine if the participants were representative of all undergraduates.
The study took place among biology and communication majors at a small liberal
arts college, and thus there may be some unique qualities among these people and
Knowledge Acquisition Theory 28
the types of courses they have experienced. However, the results of this present
study are in alignment with the results from Trader’s (2007) unpublished dissertation.
The results of this study are encouraging and suggest that a receiver oriented
message perspective can yield insights into instructional communication that were
not previously possible. First off, Knowledge Acquisition Theory provides us with a
theoretical basis for understanding how instructional messages and student
interactions with content can be optimized in relation to student cognitive learning.
The surprising thing in this study is that despite the small sample size, KAT accounts
for 58% of the variance in perceived data obtained from an undergraduate course,
70-83% of the variance in perceived information obtained, and 74-84% of the
variance in perceived knowledge obtained from successful completion of an
undergraduate course.
Though the results of this study are encouraging, there is still much work to be
done. Other message variables such as message complexity remain unexplored. It is
also unclear if Knowledge Acquisition Theory holds true within a variety of content
domains or within other educational contexts. The author of this study sincerely
hopes that other instructional communication scholars will adopt a receiver oriented
message perspective in their future research endeavors.
Knowledge Acquisition Theory 29
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