Final 2008

THE EFFECTS OF SUPERVISORY SUPPORT, AGE AND GENDER ON SELF
EFFICACY AND METACOGNITIVE ACTIVITY IN A LEARNER
CONTROLLED TRAINING ENVIRONMENT
A Dissertation
Presented to the Faculty of the College of Business Administration
Touro University International
In Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy in Business Administration
By
James V. Polizzi
October 2008

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Copyright by James V. Polizzi
2008
All rights reserved

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BIOGRAPHICAL SKETCH
James Polizzi earned a Bachelors of Business Administration (Marketing) from The City
College of New York in 1966. He received a Masters of Business Administration
(Management) from Wagner College in 1996.
He received a Doctor of Philosophy, Business Administration from Touro University
International in 2008.
He is currently an instructor in the Management Department at Berkeley College, New
York City and Online Campuses and President of The Aegis Group – a strategic
consultancy.

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DEDICATION
I dedicate this dissertation to my wife, Josephine. Her continuous support, understanding
and encouragement gave me the will to finally complete this endeavor.

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ACKNOWLEDGEMENTS
I would like to thank SimuLearn, Inc for their permission to use the Virtual Leader
leadership training software in the conduct of this study. Particular thanks to Mr. Pierre
Thiault for his advice and continuous support for this project.

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Table of Contents
Page
List of Figures................................................................................................................... vii
List of Tables ..................................................................................................................viiii
Abstract………………………………………………………………………………...…ix
CHAPTER I: INTRODUCTION........................................................................................ 1
Problem Background .............................................................................................. 1
Identification of the Issues...................................................................................... 7
CHAPTER II: LITERATURE REVIEW ........................................................................... 9
Learning Theory...................................................................................................... 9
Metacognition and Training.................................................................................. 13
Learner Controlled Training and Metacognitive Interventions ............................ 18
Motivation and Self Efficacy................................................................................ 23
Age, Gender and Computer Self Efficacy ............................................................ 26
Supervisory Support.............................................................................................. 28
CHAPTER III: METHODOLOGY.................................................................................. 35
Research Design.................................................................................................... 36
Operationalization of Variables ............................................................................ 37
Sample................................................................................................................... 39
Procedure .............................................................................................................. 39
Research Questions............................................................................................... 40
Data Analysis Plan................................................................................................ 42
CHAPTER IV: ANALYSIS OF DATA........................................................................... 47
Preliminary Analyses............................................................................................ 48

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Research Questions............................................................................................... 54
Research Question 1 ................................................................................. 55
CHAPTER V: DISCUSSION AND IMPLICATIONS.................................................... 65
Summary of Findings............................................................................................ 65
Implications........................................................................................................... 68
Metacognitive Activity for Children Versus Adults................................. 68
Gender, Age, and Computer Self-Efficacy............................................... 69
Supervisory Support and Self-Efficacy .................................................... 70
Metacognitive Activity and Self-Efficacy ................................................ 71
Recommendations for Future Research................................................................ 72
Recommendations for Practice ............................................................................. 75
Conclusions........................................................................................................... 77
References......................................................................................................................... 79
Appendix A: Demographic Survey................................................................................... 88
Appendix B: Learner Control Self Efficacy Scale............................................................ 89
Appendix C: Computer Self-Efficacy Scale..................................................................... 91
Appendix D: Supervisory Support Scale.......................................................................... 95
Appendix E: Metacognitive Activity Scale ...................................................................... 96
Appendix F: Full Regression Results for Path Models..................................................... 98

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List of Figures
Page
Figure 1. Path Model for Research Questions 1-4 ............................................................46
Figure 2. Path Model for Research Question 5 .................................................................46
Figure 3. Path Model for Research Questions 1 and 2 with Regression Coefficients.......55
Figure 4. Path Model for Research Question 3 with Regression Coefficients for
Males..................................................................................................................................57
Females ..............................................................................................................................57
Younger Participants..........................................................................................................60
Figure 7. Path Model for Research Question 4 with Regression Coefficients for Older
Participants.........................................................................................................................61
Figure 8. Path Model for Research Question 5 with Regression Coefficients..................63

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List of Tables
Page
Table 1. Descriptive Statistics for Sample Demographic Characteristic (N=120)...........49
Table 2. Descriptive Statistics for the Composite Measures (N=120)..............................50
Table 3. Correlations Between Composite Measures (N=120) ........................................51
Table 4. Correlations Between Composite Measures as a Function of Gender
(N=120) ............................................................................................................................52
Table 5. Correlations Between Composite Measures as a Function of Age Group
(N=120) .............................................................................................................................53

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ABSTRACT
THE EFFECTS OF SUPERVISORY SUPPORT, AGE AND GENDER ON SELF
EFFICACY AND METACOGNITIVE ACTIVITY IN A LEARNER CONTROLLED
TRAINING ENVIRONMENT
James V. Polizzi, Ph.D.
Touro University International 2008
The increase in costs and frequency of training have driven U.S. businesses to a
greater use of learner controlled training, i.e. training delivered in the absence of a live
instructor. Success in learning complex material has been positively related to
metacognitive activity, yet learner controlled training may present unique challenges to
the formation of learning strategies. This study investigated the relationships between
employee self efficacy, computer self efficacy, supervisory support, gender and age and
their effect on metacognitive activity. The research was conducted during
organizationally sponsored, learner controlled training among adults. The study results
suggest a positive role for supervisory support on self-efficacy and metacognitive
activity. Metacognitive activity increased with higher levels of learner control self
efficacy which, in turn, was associated with higher levels of computer self efficacy.

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CHAPTER I: INTRODUCTION
Problem Background
As organizational efforts to improve productivity increase, employee training has
become an even more critical element of firm activities. Importantly, in addition to
productivity, the very nature of the business organization is shifting. As projected by the
RAND Corporation (2004), the required skills for a productive workforce in the 21st
century will include: problem solving skills, communication and collaborative ability.
The emergence of a knowledge-based workforce demands that education and training
become a continuous process throughout the life course, involving training and retraining
that continue well past initial entry into the labor market. Technology-mediated learning
is a promising tool for life-long learning, both on the job and through traditional public
and private education and training institutions. (RAND, 2004)
The American Society for Training and Development (ASTD; 2008) estimates
2006 learning and development spending for U.S. firms at $129.6 billion. Expenditures
per employee have risen to $1,040 in 2006, approximately 2% above 2004. Together,
managerial and executive development training totaled more learning content in 2004
than technology, business processes and industry-specific content (ASTD, 2004). A key
indicator of the trends in business organizations is the increasing use of terms such as
“workforce development” and “organizational effectiveness” as part of the titles of in-
house trainers and the establishment of a “Chief Learning Officer” (Rodriquez, 2005).
Human Resources Focus (“Despite Economy,” 2004) noted some significant
trends in training budgets and the nature of training methodologies: U.S. companies spent
more money on training, provided more hours of training and increased use of technology

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for training in 2002 versus 2001. Training delivered via learning technologies increased
to 15.4% in 2002, from 10.5% in 2001; while training delivered via a traditional
classroom technique declined to 72%, versus 77% in 2001. More recently, 2004 saw 50%
of technology based delivery in an online format, with 75% of online learning classified
as “self-paced” (ASTD, 2004).
According to ASTD (2004), organizations with high levels of investment in
training aligned learning with business needs and achieved efficiency and effectiveness in
the learning function. Collins and Clark (2003) found that human resource practices (i.e.
training) were found to be positively correlated with creating organizational competitive
advantages. The increase in use of technology to deliver training, coupled with the
concurrent decline in traditional instructor-led training has been facilitated by the
widespread use of desktop computers and near universal access to the World Wide Web
in U.S. firms. Additionally, the rising costs of training have stimulated a move to more
efficient methods of delivering training in organizations.
Training without a live instructor encompasses many methods of instruction,
either as single or mixed method approaches, including Web based training, Intranet
training programs, and CD-ROM. Collectively called learner controlled training
(Schmidt, 2003), the benefits of self-pacing, flexible access and lower costs are driving
more firms to increase use of this design in training programs. The increased availability
of interactive training designs gives individuals increased control over the pace, sequence
and time spent on training (Tannenbaum, 1992).
Research on learner controlled training has shown generally positive, but mixed
results. Learners who are allowed to choose the sequence of learning, content, and time in

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study have reported positive attitudes towards training and improved outcomes (e.g.,
Brown, 2001; Kinzie & Sullivan, 1989; Milheim & Martin, 1991; Morrison, Ross, &
Baldwin, 1992). Despite these observed advantages, increased learner control has not
shown consistent improvements in post training performance. Brown (2001) found that
learner control is associated with a number of negative processes, including lower time
on task and inadequate learning strategies. Eom and Reiser (2000) found a poorer posttest
result for learner control subjects than for program control subjects. Pollock and Sullivan
(1990) found that students with no control over practice had higher posttest scores than
students given control over the amount of practice. Gist, Schwoerer and Rosen (1989)
found that a modeling approach resulted in higher performance levels than computer-
assisted instruction among managerial trainees. Hannafin and Sullivan (1996) found that
learner ability affected the amount of control students applied in a learner controlled
program.
In a review of interactive learning environments (Aleven, Stahl, Schworm,
Fischer & Wallace, 2003), the authors identify the need for further investigation of the
effects of the context in which learning occurs. Specifically, they identify the physical,
social and institutional environment as factors potentially affecting the learning process.
Thus, an important area for research is to identify the variables that might influence
learners in learner controlled training. One result of these findings has been an increased
focus on the learner as an active participant in the learning process. A significant
conclusion is that not all learners are capable of successfully directing their learning; they
fail to take advantage of the control they are given.

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A review of the literature suggests that learner controlled training can be
improved by incorporating learning theory to increase the understanding of how people
learn. Many employees, especially older workers, approach these nontraditional training
environments with some trepidation and are easily frustrated with the inability to directly
question a live instructor. Thus, training design can be improved by understanding the
attitudes and capabilities of workers to utilize the technology. Most empirical studies of
learner control have been conducted among students in academic environments.
Developing a greater understanding of adult learners in an organizational setting can
improve the effectiveness of training.
A significant criticism of learner controlled training has been the lack of teaching
of higher order cognitive skills. Live instruction has traditionally been viewed as the best
method for communicating these skills. A growing number of training designs utilize in-
training interventions to guide the learner in the cognitive processes need to master the
training material. These can be broadly classified as metacognitive interventions;
explained as guiding the learner to think about their thinking process. Metacognitive
interventions have been associated with positive outcomes in learner controlled training
environments and holds promise for improving organizational training designs (Schmidt,
2003). Antecedents of metacognitive activity have been identified as learner mastery and
performance orientations, where performance orientations include the learner’s
motivation and perception of self efficacy (Schmidt).
Training outcomes can also be influenced by the potential effects of an
employee’s perception of their ability to learn without a live instructor. This general
construct of perception of ability is identified as self efficacy (Bandura, 1986). Self

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efficacy refers to an individual’s belief in his/her ability to accomplish a given task
(Bandura). Self efficacy relates to effort expended on a task, and persistence in achieving
a positive task result (Gist & Mitchell, 1992). In a training environment, self efficacy
beliefs are likely to contribute positively to successful outcomes. Research has
consistently demonstrated a positive relationship between self efficacy, positive
motivation and learning (e.g. Gist, Stevens & Bavetta, 1991; Martocchio, 1994; Mathieu,
Tannenbaum & Salas, 1992). However, the literature reveals very few studies of the
relationships between self efficacy and metacognitive interventions in business
organizations (e.g., Schmidt, 2003). The first question raised here, then, is whether an
adult learner’s self efficacy perception affects metacognitive activity in a learner
controlled training environment in the context of a business organization.
While Bandura (1986) posited an individual’s behavior as a result of external
approval or disapproval, the landmark Ohio State Leadership research (Halpin & Winer,
1957) studied the link between supervisory behavior and subordinate attitudes and
performance. The external influences the employee observes – namely, the attitude of
their supervisor for this alternative training design can affect perceptions of self efficacy
(e.g., Bandura, 1986; Illeris, 2003; Martocchio, 1992).
To summarize, two variables may affect the level of metacognitive activity in a
learner controlled training environment: the learner’s perception of self-efficacy in the
absence of a live instructor, and the degree to which the individual’s supervisor exhibits
support/trust for the learner in learner controlled training. The nature of the delivery of
learner controlled training in the contemporary organization (e.g., via desktop
computers), raises the possibility that two further variables may affect learning: age and

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gender. Given recent trends, we can presume continued and increasing use of computers
to deliver training, thus examination of these variables may prove useful.
It is a general perception among those in business and in the general population
that older workers are less comfortable and less proficient than younger workers in the
skillful use of desktop computers. Specifically, older users were found to have low
confidence in their ability to use computer technology (Comber, Hargreaves & Dorn,
1997). We can speculate that older workers will exhibit lower self efficacy in training
delivered by computer than training delivered with traditional instructor-led training.
While gender has generally been shown to have mixed effects on computer
competency (e.g., Ford, Miller & Moss, 2001; Henry & Stone, 1999); the differences in
masculine sex role traits (e.g., independence, assertiveness and competitiveness) and
feminine sex role traits (e.g., dependence and interpersonal relationships) may have an
effect in a learner controlled training environment where the absence of a live instructor
prevents interaction. Gilley (2002) reports that females do not perceive themselves as
manipulators of computer technology; but merely as end users of pre-designed programs.
The American Association of University Women (AAUW; 2000) studies found that
females have been encouraged to participate in computer technology through the use of
productivity software such as graphics programs, databases, page layouts, and so forth,
whereas males are more adventurous in their learning with respect to computer
technology. Gender differences in computer self efficacy is revealed in Brosnan’s (1998)
study which found that 64% of females agreed that computing was a “male activity” and
that “men were better at computing than women” (1998, p. 63). Gender, therefore, may

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have an effect on a learner’s perception of self efficacy in training delivered in the
absence of a live instructor.
Identification of the Issues
The increasing complexity of job requirements has fostered a continuing increase
in training activities and related costs for U.S. firms. Training designs have evolved to
meet these challenges by increasing use of training without a live instructor and are
increasingly utilizing in-learning interventions to improve acquisition of thinking skills
(metacognitive activity). There is empirical evidence that increased metacognitive
activity improves the effectiveness of learner controlled training, yet not all learners show
consistently positive results. What, then, are the key variables affecting metacognitive
activity? Self efficacy perceptions and supervisory support have been shown to affect
learning outcomes; with self efficacy viewed as both an independent variable affecting
learning outcomes and a dependent variable affected by supervisory support. With the
trend to even greater use of learner controlled training, age and gender are two
moderating variables important to evaluate when researching self efficacy in this
environment.
The research questions guiding this study are:
1. Is supervisory support related to learner control self efficacy and computer
self-efficacy in a learner controlled training environment?
2. Are computer self-efficacy or learner control self-efficacy related to
metacognitive activity in a learner controlled training environment?

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3. Do the relationships between supervisory support, learner control self-
efficacy, and metacognitive activity vary as a function of the gender of the
learner?
4. Do the relationships between supervisory support, learner control self-
efficacy, and metacognitive activity vary as a function of the age of the
learner?
5. Does computer self-efficacy have an effect on learner control self-efficacy
which subsequently has an effect on metacognitive activity?
In order to address these questions, a quantitative study to test the effects of supervisory
support, age and gender on an adult learner’s perception of self-efficacy and
metacognitive activity when metacognitive interventions are utilized in a learner
controlled training environment was performed. The results of this study provide insights
potentially valuable in improving the effectiveness of learner controlled training.

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CHAPTER II: LITERATURE REVIEW
This section will review and apply the significant theories and empirical research
encompassing learning theory, metacognitive processes, an individual’s perception of
self-efficacy, the effects of supervisory influences, and experience with metacognitive
interventions as a training strategy. This review will also establish the relationship of
metacognitive activity to potential successful training outcomes and metacognitive
interventions as a factor which improves individual metacognitive activities. Individual
self-efficacy will be shown to be influenced by supervisory support for the specific
training activity. The present study examined the interaction of the internal effects of
metacognition and self-efficacy, as well as the external influence of supervisory support
on self efficacy in a learner controlled training environment.
Learning Theory
Skinner (1968, 1969) proposed that learning is a result of patterns of behavior
developed as a response to a stimulus. Skinner built on Watson’s work from the
beginning of the twentieth century using an empirical approach with animals, termed
stimulus-response behaviorism (DeMar, 2004). Ultimately termed classical behaviorism,
the theory viewed learning as changing the behaviors of individuals, sometimes through
trial and error experiences until a positive reinforcement was obtained (Semple, 2000).
Skinner’s experiments led him to modify Watson’s original view of behavior by adding
the concept of intermediary purposefulness to the stimulus – response formula (DeMar).
This concept is now described as operant conditioning, i.e. people behave in a particular
way because of the past consequences of that behavior, and thus one acts in expectation
of a certain outcome (DeMar). Skinner’s research with rats showed that punishment

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halted a previously rewarded behavior almost immediately; but previously rewarded
behavior continued for some time when only the reward was withheld (Naik, 2004).
Behaviorists embrace four main steps regarding learning: first, each step should
be brief and follow from previously learned behavior; second, behavior is shaped by the
pattern of reinforcements, so learning should be regularly rewarded; third, provide
immediate feedback; fourth, the learner should be given direction to the most successful
path (Semple, 2000). Behaviorist theories of learning led to the introduction of
“programmed learning” (also programmed instruction) by machines in the 1950’s and
1960’s (Semple). In a learning environment, behaviorism relies on an instructor centered
approach where the learner is largely passive and controlled by the instructor’s processes
(Constructivist Learning Theory, n.d.).
Constructivist Learning Theory (n.d.) views learning differently from the
behaviorist stimulus-response phenomena. Constructivism posits the concepts of self-
regulation and acquisition of conceptual cognitive structure through reflection and
abstract thought (Constructivist Learning Theory, n.d.). Two major themes of
constructivism relate to how people learn: order and self (Mahoney, n.d.). Mahoney
explains that order reflects a person’s activities devoted to establishing a pattern to prior
experiences using emotional “meaning-making” processes (p.3). Constructivists further
posit that the organization of activity is fundamentally self-referent and self-repeating;
people continually experience and monitor their sense of personal identity (Mahoney).
Flavell (1977) posits that a person’s knowledge affects and is affected by how one
perceives things; and how one classifies and conceptualizes influences the way a person
reasons about those things. Cognition can be described as a system of “interacting

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processes which generate, code, transform and otherwise manipulate information”
(Flavell, p. 14). Viewed more narrowly, cognition addresses physical and mathematical
objects while social cognition concerns human affairs and social interactions (Flavell).
Social cognition explains that courses of action are chosen as a result of a person’s
perceived capabilities and sustained partly on the basis of expected outcomes (Bandura,
1986).
In expanding the constructivist learning theory, Bandura (1986) explains that, in
the social cognitive view, humans are not driven solely by inner forces or by external
stimuli. Rather, the interaction of behavior, cognitive and personal factors, and
environmental events describe a model of reciprocity of these elements that seeks to
explain human functioning (Bandura). Each of these factors can be of different strengths
and can occur at different times. The influence of any factor can take time to develop and
to trigger a reciprocal influence.
Bandura (1986) describes the nature of social cognition, and its differences from
Behaviorism, in terms of “capabilities” (p. 18-21). Symbolizing capability refers to the
human capacity to transform experiences into internal models that serve as guides for
future action (Bandura). This suggests that experience mediates the classical stimulus-
response view. Forethought capability is explained (Bandura) as the use of a visualized
future which is affected by goals and potential courses of action; suggesting that
stimulus-response is also mediated by anticipated future outcomes – not necessarily an
immediate outcome. Bandura also posits an external influence on learning: vicarious
capability, i.e. the ability to learn through observation of actions of others and
consequences of those actions. Self-regulatory capabilities are, perhaps, central to social

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cognitive theory (Bandura). Behavior is motivated and regulated by a person’s internal
goals and standards as well as their assessment of their performance towards those goals
(Bandura). Thus, self-produced influences mediate the stimulus-response model. Bandura
describes the distinctively human characteristic of self-reflective capability:
This (self-reflective capability) enables people to analyze their experiences and to
think about their own thought processes. By reflecting on their varied experiences
and on what they know, they can derive generic knowledge about themselves and
the world around them. People not only gain understanding though reflection,
they evaluate and alter their own thinking. In verifying thought through self-
reflective means, they monitor their ideas, act on them or predict occurrences
from them, judge the adequacy of their thoughts from the results, and change
them accordingly. (p.21)
With regard to the nature of cognitive and personal factors, Wood and Bandura (1989a)
discuss the role of cognitive, vicarious, self-regulatory and self-reflective processes as
central to people’s behavior in organizations. Wood and Bandura explain that people
develop competencies through behavior modeling, cultivation of beliefs in their
capabilities, and enhancement of motivation through goals.
Gagne´ and Briggs (1974) describe the act of learning as composed of three
internal states: information, intellectual skills and strategies. Information can be stored in
memory for retrieval as required or accessed directly as in printed directions. Intellectual
skills are described as the ability to learn new things based upon cues that must be
previously learned and recalled. A learning situation often requires the use of strategies
for learning and remembering. These strategies are very general and apply to a wide
range of learning situations. Referred to as “self-management” (Gagne´ & Briggs, 1974
p. 9), the concept embodies a learner’s individual process for solving problems and
recalling previously learned methods of cognitive paths.

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Variations in adult learning – both inter personal and intra-personal – have been
attributed to differences in prior knowledge, cognitive processes, and learning and
memory strategies (Weinert & Kluwe, 1987). The identification and explanation of the
role of learning strategies in organizational training are examined in detail in the
following section - Metacognition and training.
Metacognition and Training
In general, Metacognitive theory focuses on first, the awareness and management
of one’s thinking; second, differences in self-efficacy perceptions; third, knowledge and
knowledge and development of thinking strategies from one’s experiences and fourth,
strategic thinking (Paris & Winograd, 1990). Cognitive strategy is an internal skill in
which the learner consciously or unconsciously selects a mode of thinking about and
solving a problem. The object of the skill is to manage thinking behavior (Gagne´ &
Briggs, 1974). The quality of one’s cognitive strategies affects the degree of creativity,
fluency and criticality of the learning process (Bruner, Goodnow & Austin, 1956, Gagne´
& Briggs).
Flavell is most often cited as the developer of original propositions about what are
called metacognitive processes. Flavell (1976) attempted to explain why children could
not solve problems although they were given correct solution procedures. He believed
that this was “the central problem in learning and development, namely, how and under
what conditions the individual assembles, coordinates or integrates his already existing
knowledge and skills into new functional organizations” (p. 231). In examining the
inability of children to solve problems consistently, Flavell posed two questions: “what
problem-adaptive things might they be failing to do, or what problem mal-adaptive things

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might they be doing instead?” (p. 232). From these questions, he developed the construct
of metacognition. Flavell described the construct as follows:
Metacognition refers to one’s knowledge concerning one’s own cognitive
processes and products or anything related to them, e.g., the learning-relevant
properties of information or data . . . Metacognition refers, among other things, to
the active monitoring and consequent regulation and orchestration of these
processes in relation to the cognitive objects or data on which they bear, usually
in the service of some concrete goal or objective. (p.232)
Flavell (1979) explained metacognitive experiences as “any conscious cognitive
or affective experiences that accompany and pertain to any intellectual enterprise” (p.
906). These experiences are conscious and are generally accompanied by emotions such
as anxiety, feeling of knowing, or judgments of learning. Flavell (1987) explained
metacognitive experiences with the following:
If one suddenly has the anxious feeling that one is not understanding something
and wants and needs to understand it, that feeling would be a metacognitive
experience. One is having a metacognitive experience whenever one has the
feeling that something is hard to perceive, comprehend, remember or solve; if
there is a feeling that one is far from the cognitive goal; if the feeling exists that
one is, in fact, just about to reach the cognitive goal; or if one has the sense that
the material is getting easier or more difficult that it was a moment ago. (p. 24)
Metacognitive experiences aid in the assessment of metacognitive goals, modification of
metacognitive knowledge and in the utilization of strategies (Flavell, 1979).
Flavell (1979) developed a model of metacognition and cognitive monitoring that
contained four classes of cognitive phenomena: metacognitive knowledge, metacognitive
experiences, tasks and actions (strategies). Flavell described metacognitive knowledge as
“that segment of your stored world knowledge that has to do with people as cognitive
creatures and with their diverse tasks, goals, actions, and experiences” (p. 906).
Metacognitive knowledge consisted of three factors: (a) person, (b) task and (c) strategy.

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The person factor of metacognitive knowledge concerns knowledge and beliefs
about one’s self and others as cognitive processors. Flavell (1987) identified three
subcategories of the person factor: intraindividual, interindividual and universal.
Intraindividual knowledge relates to the variation in interests, propensities and aptitudes.
Interindividual knowledge concerns comparisons between persons. Universal knowledge
is concerned with “intuitions about the way the human mind works – knowledge of such
universal mental phenomena” (Flavell, p. 22).
The task factor of metacognitive knowledge relates to the availability of
information and the use of that information in the context of task demands or goals.
According to Flavell (1987), the task factor concerns how information “affects and
constrains how one should deal with it” (p. 22). Flavell explains that if information is
very difficult, one proceeds slowly and carefully to insure deep and comprehensive
understanding. The strategy variable concerns “what strategies (means, processes, and
actions) are likely to be effective in achieving what subgoals and goals in what sorts of
cognitive undertakings” (Flavell, 1979, p. 907). In 1982, Kluwe expanded the concept by
identifying two common attributes of metacognitive activities: the subject has some
knowledge of his own thinking and the subject may monitor and regulate the course of
his own thinking.
Metacognition has been defined in various ways by different researchers;
however, the various approaches contain the following concepts: knowledge of one’s
knowledge, thought processes, and cognitive and affective states; the ability to
consciously and deliberately monitor and regulate one’s knowledge, processes, and
cognitive and affective states (Hacker, 2003, p. 6). Metacognition can also be explained

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as the ability to control one’s cognitive processes, viewed as self-regulation (Livingston,
1997) or self-management (Gagne´ & Briggs, 1974).
Metacognitive skill has been found to distinguish successful learners from
unsuccessful learners (Tannenbaum & Yuki, 1992). Metacognitive interventions (in-
learning training strategies) have been found to increase the amount and accuracy of
learner’s knowledge and to improve strategies for allocating time and effort (Schmidt &
Ford, 2003). In one study among students (Relan, 1995) subjects receiving learning
strategy training in a computer based instruction environment performed better in posttest
results than those who received no strategy training.
The increased availability of interactive training designs gives individuals
increased control over the pace, sequence and time spent on training (Tannenbaum &
Yuki, 1992). Brown (2001) concludes that learner choices regarding study time and
practice positively affected knowledge acquisition in a computer based training program.
Learners with increased control can consciously tailor training, leading them to learn the
task more effectively (Ford, Smith, Weissbein, Gully & Salas, 1998). However, Brown
also found that learner control is associated with a number of negative processes,
including lower time on task and inadequate learning strategies.
Gagne´ and Briggs (1974) proposed that cognitive strategies can be learned by
organizing external interventions that foster the development of internal processes. They
posit a design whereby “favorable conditions” (1974, p. 72) must be designed and present
for instruction in cognitive strategy development. Those conditions suggest that in order
to learn to think, a learner must be guided into opportunities to think.

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Glaser and Pellegrino (1987) suggest that the improvement of the skills of
learning will take place through the development of procedural (problem-solving)
knowledge. While their research attempted to identify cognitive components of
performance on tasks used to assess aptitude, their ultimate goal was to use the
knowledge gained to design instruction to directly or indirectly teach the processes that
facilitate learning. This research is one of the early investigations into what is now known
as metacognitive intervention in training design. The authors analyzed the processes used
by high and low performing individuals and concluded that the problem solving strategies
employed differed for each group. An oral problem solving technique was employed to
identify the processes used by each group in a standardized analogy test. The findings
suggest that high-ability individuals limit their approach to a few plausible mathematical
relationships; whereas, the low-ability individuals do not solve analogies with a
systematic approach. The implications which can be drawn involve the possibility of
influencing mental processing skills by teaching individuals to employ better methods of
searching memory and seeking connections.
Livingston (1997) posits that learners with greater metacognitive abilities tend to
be more successful in their cognitive activities and that individuals can learn how to
improve cognitive activities. Metacognition can enable learners to gain greater benefit
from instruction and influences the use and maintenance of cognitive strategies
(Livingston). Cognitive Strategy Instruction is a technique that emphasizes the
development of thinking skills and processes as a means to enhance learning
(Livingston).

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Schmidt and Ford (2003) studied the effect of trainee characteristics on
metacognitive activity in a learner controlled training environment and found that
metacognitive activity was mediated by the level of the trainee’s goal avoidance
orientation. The implication of this finding for the present study is that the outcomes of
metacognitive interventions are not consistently positive among all learners, but that
individual differences may account for variations in metacognitive activity. These
individual differences may include trainee motivation and perceptions of self-efficacy.
Flavell (1979) explains that metacognitive processes can lead a learner to select, evaluate
and revise cognitive strategies with regard to a learner’s ability and interest in what is
being learned.
Relevant to this current study, metacognition had been linked to self-efficacy
perceptions early in Flavell’s (1987) thinking about the construct. Flavell referred to
metacognition as having a psychological space. He hypothesized the interactions that
may link metacognition and other constructs. The constructs included “executive
processes; formal operations; consciousness; social cognition; self-efficacy; self-
regulation; reflective self-awareness and the concept of psychological self or
psychological subject” (Flavell, p. 25).
Much of the empirical research on interventions has been conducted among
children and/or in academic settings, suggesting the opportunity for a more generalizable
study among adult learners in an organizational training environment.
Learner Controlled Training and Metacognitive Interventions
Learner controlled training has increased in usage as a result of widespread
availability of workplace and personal computers (Schroeder, 1994). Learner control

19
refers to the degree to which learners are able to choose the method, timing, practice and
feedback during training (Milheim & Martin, 1991). A major advantage of learner
controlled training over traditional forms of training is its potential to allow trainees to
proceed through training at their own rate, controlled by their own needs and preferences
(Eom & Reiser, 2000).
However, empirical research as shown mixed results for learner controlled
training (Eom & Reiser, 2000). This section will briefly present the underlying theory
and research for learner controlled training, leading to the assessment that metacognitive
interventions may have the potential to improve outcomes in learner controlled training
environments.
Hilgard and Bower (1966) explain that the beginnings of learner controlled
training, then referred to as “programmed learning”, emerged from a behaviorist
perspective. Programmed instruction is characterized by having information broken down
into smaller, simpler groups of information. While considered a more effective teaching
method than historical methods, several weaknesses emerge: behavioral models of
programmed instruction isolate factual information thus, learners learn in isolation, not in
the context of the interrelationships of the material (Hilgard & Bower). The programmed
instruction technique evolved into many tools, the most common today called Computer-
Assisted Instruction (CAI), a technique which can be designed to incorporate both the
original behaviorist view of learning (e.g., Skinner, 1968, 1969) and the more widely
accepted cognitive view (e.g., Bandura, 1986). Learner controlled training does not
presume the use of the computer as the training delivery method, but the computer’s near

20
total availability in organizations today has made its use more widespread than traditional
paper, audio and video techniques.
Eom and Reiser (2000) posit that the conflicting or mixed results of learner
control are possibly due to the characteristics of the learner population. Kinzie (1990)
found that the degree of experience and comfort with learner control instruction
influences the effectiveness of the instruction. Kinzie, Sullivan and Berdel (1988) found
that pre-test reading levels were a more significant predictor of performance than the
level of learner control and called for an examination of self regulatory skills in learner
controlled environments.
Bandura (1986) describes the widely accepted view that social learning practices
are improved by structuring the learning environment in such a way as to allow learners
to judge themselves in reference to their own capabilities and standards, rather than in
comparison with others.
Self regulatory skills are a learner characteristic that may affect a learner’s ability
to benefit from learner controlled instruction (Armstrong, 1989; Eom & Reiser, 2000).
Self regulated learning strategies have been defined as metacognitive, motivational, and
behavioral techniques that a learner can use to control his or her learning process
(Zimmerman & Martinez-Pons, 1988). Eom and Reiser (2000) explain that intrinsic
motivation and self efficacy have an impact on self regulated learning. Boekaerts (1995)
indicated that self regulated learning strategies involve affective variables (e.g., anxiety)
as well as cognitive variables. Bandura (1986) viewed self regulation as composed of
multiple processes such as self observation, self judgment and self reaction. Motivational
factors such as attribution and self efficacy influence self regulated learning strategies;

21
thus self regulated learners can be considered self motivated and are self directed in a
metacognitive sense as well (Eom & Reiser, 2000). Jegede, Taplin, Fan, Chan and Yum
(1999) found a higher level of use of metacognitive strategies among students describing
themselves as high achievers in a learner controlled environment.
Computer based training designs allow users to exert significant control over
sequence of learning, content and pace of instruction (Bell & Kozlowski, 2002). In a
review of the literature examining effectiveness of learner control in CAI, Lunts (2002)
reports that the amount of learner control affects the effectiveness of the method, with
greater control associated with improved creativity and learner initiative. Lunts further
reports that, generally, the literature suggests that learner control is a useful tool for
adapting a learning environment to students’ needs. Perceived learner control positively
affects motivation and the amount of effort invested in the learning task (Perez, Kester &
Van Merrienboer, n.d.). Eom and Reiser (2000) explain that poor performance under
learner control appears due to the learners’ failure to use effective learning strategies and
poor metacognitive skills. However, when learner control is supplemented with in
learning interventions, individual performance increases (Bell & Kozlowski). In his
summary of five meta-analyses of the impact of technology on student achievement,
Schacter (1999) reports that CAI, integrated learning systems and instruction in higher
order thinking show positive gains in researcher constructed tests, standardized tests and
national tests.
Gagne´ (1977), in reporting a series of experiments of in-training interventions ,
proposed that learners are able to exercise more successful control over their own
learning process by using a cognitive strategy that is presented to them during the

22
learning experience or by using a cognitive strategy that may have previously been
learned. The use of frequent numerical or technical questions interspersed in a long
reading passage resulted in an improved retention of the information compared to those
not exposed to interruptive questions. Gagne´ (1977) suggests that the question
interventions had the effect of “activating a strategy of attending” (p. 168) to the facts to
be learned. This anticipated Flavell’s (1979) theory of metacognitive processes and the
use of in-training interventions to stimulate a learner’s ability to increase learning
effectiveness.
Watson (n.d.) reported significant positive performance improvement among
students receiving metacognitive prompts during a computer based learned controlled
tutorial. Embedded metacognitive training resulted in a significant increase in
performance versus both strategy training and a no-training control group among primary
school students (Mevarech, 1999). Hill and Hannafin (1997) report improvements in
posttest performance as a result of embedded cues. Metacognitive training for math
students resulted in increased performance versus traditional learning methods in a two-
year study among eighth-grade students (Mevarech & Kramarski, (2003).
As with metacognitive studies, much of the empirical research on learner control
has focused on students in a school learning environment. In fact, this situation led Lunts
(2002) to characterize learner control research as “excessively targeting younger and
inexperienced learners” (p. 68). Lunts further implies that learner control should have a
greater chance for success with adult learners, as they are likely to be more motivated and
able to comprehend the higher order skills (versus factual information) contained in many
organizational training programs.

23
Motivation and Self Efficacy
Motivation has been described as a cognitive process which directs choices
among alternative paths of voluntary actions (Vroom, 1964). A number of theorists have
explained motivation in terms of the expectancy-valence model (Atkinson, 1964;
Fishbein, 1967; Vroom). This model suggests that one’s degree of motivation is
dependent upon both the belief that specific actions will produce particular outcomes and
the value of those outcomes to the individual. Valence is described as the anticipated
satisfaction (positive or negative) of an outcome, whereas value refers to the actual
satisfaction derived. A learner’s perception of self-efficacy can be measured in terms of
their judgments of capabilities and the strength of that belief (Bandura, 2003).
Bandura (1988) joins motivation and self-efficacy as follows:
People’s beliefs in their capabilities affect their motivation as well as the activities
they undertake. Significant human accomplishments require perseverant effort. It
is renewed effort in the face of difficulties and setbacks that usually brings
success. … The important matter is not that difficulties arouse self-doubt –which
is a natural immediate reaction – but the recovery from difficulties. Some people
quickly recover their self-confidence; others lose faith in their capabilities. It is
resiliency of self-belief that counts. (p.282)
Evaluations of self-efficacy affect an individual’s initiation of behavior, the amount of
effort to be expended, and the duration of that effort in the face of disconfirming evidence
(Bandura, 1977). Wood and Bandura (1989a) explain self-efficacy as a regulatory
mechanism affecting motivation:
There is a difference between possessing skills and being able to use them well
and consistently under difficult circumstances. To be successful, one not only
must possess the required skills, but also a resilient self-belief in one’s capabilities
to exercise control over events to accomplish desired goals. People with the same
skills may, therefore, perform poorly, adequately, or extraordinarily, depending
on whether their self-beliefs of efficacy enhance or impair their motivation and
problem-solving efforts. (p. 364)

24
Self-efficacy, as explained by Bandura (1986), mediates the relationship between one’s
knowledge and actions. Knowledge and skills are needed, but insufficient alone for
successful performance. People often perform at less than optimum levels, although they
know the correct actions because their self-efficacy perceptions affect their actions.
Bandura (1988) lists four sources of perceived self-efficacy: mastery experiences,
vicarious experience, social persuasion, and physiological state. Mastery experiences,
also called success experiences, help an individual gain a sense of capability. When an
individual achieves success through sustained effort, setbacks and failures can be
managed more easily. Individuals partly judge their capabilities through comparison with
others by observing them through vicarious experiences. Self-efficacy beliefs can also be
affected by modeling – access to successful models can increase an individual’s
perception of self-efficacy. Conversely, observing others’ failures despite high efforts can
lower an individual’s perception of probable success. Social persuasion concerns the
impact of the opinions of others regarding the individual’s likelihood of successfully
completing a task. Realistic encouragement can lead to greater individual effort. The
concept of physiological state also affects an individual’s perception of self-efficacy.
Emotional arousal and tension can signal a possible poor performance. Particularly in
strength-related activities, individuals judge their possible efficacy in terms of perceived
fatigue levels, and presence/absence of pain.
Relevant to this study, the effect of social persuasion, particularly from one’s
organizational supervisor can be a key determinant of an individual’s perception of self-
efficacy as they begin a training task. Bandura’s (1988) key point on this factor is that
individuals who have a strong belief in their efficacy work, think and behave differently

25
than those who doubt their capabilities and that social persuasion, e.g. supervisory
behavior, can be a factor in an individual’s perception of self-efficacy. This view of
supervisory support as an independent variable affecting self-efficacy is explained and
elaborated upon in depth in the following section: Supervisory Support.
Self-efficacy is learner’s judgment of their capability to perform actions related to
training (Hill & Hannafin, 1997). Self-efficacy beliefs affect activities through cognitive,
motivational and decisional processes (Bandura & Locke, 2003). In his elaboration of
Kolb’s Learning Cycle model, Vince (1998) proposes that learner anxiety; fear and doubt
at the start of a learning process can either promote or discourage learning. Learner
anxiety in training may impact learning and is likely to be negatively associated with
learning (Warr & Bunce, 1995).
Bandura and Wood (1989) found that a learner’s perception of efficacy, in this
case, achievable standards of performance in operating a simulated firm, affected use of
strategically effective thinking. Results indicated both an initial higher level of strategic
thinking and subsequent increased use of strategic thinking for individuals with highest
perceived initial self-efficacy.
The positive expectation of other organizational members may result in improved
performance (the Pygmalion effect); and self-efficacy can be positively affected through
the persuasive effect of the other organizational members (Gist, 1987). Supervisors and
organizations are clear sources of support for employees and affect employee
commitment to organizational activities (Stinglhamber& Vandenberghe, 2003).
The instructional processes involved in training should increase trainee self-
efficacy and improve expectations that the training will have a positive outcome

26
(Tannenbaum & Yuki, 1992). Employees who begin training with the belief that they are
able to successfully learn the content are likely to have more successful training
experiences (Tannenbaum & Yuki). Martocchio (1994) found a significant decline in
anxiety when trainees began training with the belief that they could build on their present
abilities. A key issue, therefore, emerging from this review is whether the level of
metacognitive activity in a non-academic, learner controlled training environment is
influenced by the trainee’s perception of self-efficacy.
Age, Gender and Computer Self Efficacy
Additional variables may have an effect on self efficacy perceptions in a learner
controlled training environment: age, gender and computer self efficacy. While
demographic characteristics have been studied as variables in training studies, they most
often have been viewed as statistical control variables. The two most frequently studied
variables have been age and gender (Colquitt, LePine & Noe, 2000). In their meta
analytic path analysis of training motivation, Colquitt et al. found that older trainees
demonstrated lower motivation, learning and self efficacy. Maurer, Weiss and Barbeite
(2003) reported that older workers had lower self efficacy with regard to learning abilities
and cognitive processes. Other empirical studies have reported a negative relationship
between age and learning (Gist, Rosen & Schwoerer, 1988; Martocchio, 1994).
Age effects were noted in a study of computer attitudes (Czaja & Sharit, 1998), as
older adults reported less comfort, less competence and less control over computers than
did younger adults. Similarly, Henderson, Deane, Barrelle and Mahar (1995) found that
older users have low confidence in their ability to use computer technology. Comber,
Colley, Hargreaves and Dorn (1997) found that older employees demonstrated less

27
interest in and poor attitudes towards computer-based training. Thus, the age of the
employee may affect self efficacy in training delivered solely by computer.
Examining self efficacy with regard to gender, Choi (2004) reports that masculine
sex role traits are strongly related to independence, assertiveness and competitiveness,
while feminine sex role traits are related to dependence and interpersonal relationships.
Thus, gender may have an effect on self efficacy perceptions in training that is
accomplished on an individual basis in the absence of a live instructor. Studies of the
effect of gender on computer self efficacy have shown mixed results. Qutami and Abu-
Jaber (1997) reported that male and female college students performed equally in
computer skills training, while Comber et al. (1997) reported lower computer self
assurance for females than males. In independent tasks involving computer based Internet
use, Ford et al. (2001) found that females studied exhibited poorer performance and
lower self efficacy than males on most tasks, but no difference in overall self efficacy
related to computer use. Henry and Stone (1999) found that females had lower computer
self efficacy and lower outcome expectancy than males in using computer systems at
work. Pajares (2002), however, concludes that gender differences in self efficacy can be
eliminated or minimized when employees receive unequivocal feedback about their
capabilities as well as progress in learning.
The above suggests that age and gender could be important variables in the study
of self efficacy in learner controlled training. These were examined as moderating
variables in the research design. Since age and gender are expected to influence
metacognitive activity, self efficacy and supervisor support, the relevant research
questions appear in the next section.

28
Supervisory Support
While it would appear natural for an individual to assume responsibility for his or
her own learning, this would unreasonably dismiss the influence of the social
environment. Gagne´ (1977) explains the important effect of events in the external
environment on what and how learning takes place. From early infancy and throughout
adulthood, individuals are subject to the influences of others (parents, peers, teachers and
supervisors) on learning.
Bandura (1986) lays a theoretical foundation for the effect of external influences
on personal effort:
“People who are persuaded verbally that they posses the capabilities to master
given tasks are likely to mobilize greater sustained effort than if they harbor self-doubts
and dwell on personal deficiencies” (p.231).
In a study of pretraining motivation (Facteau, Dobbins, Russell, Ladd & Kudish,
1995) found that supervisory support was positively related to training motivation;
whereas, peer support, subordinate support and top management support were negatively
related to motivation.
Wood and Bandura (1989b) showed that interpretation of personal efficacy can
affect performance and that perceptions of efficacy can be affected by external factors.
Wood and Bandura’s study induced conceptions of ability among two groups of MBA
students by instructing one group that decision-making skills were acquirable through
practice (acquirable skill condition), while the second group was instructed that decision-
making reflected basic cognitive capacities already possessed (entity condition). The
sample did not differ in pretest perceived self-efficacy. The findings provided evidence
that the conception of ability has substantial impact on self-regulatory behaviors.

29
Understanding ability as an acquirable skill resulted in a highly resilient sense of
self-efficacy and high performance outcomes among that group of students. Conversely,
the group in the entity condition viewed substandard performance as due to their own
limitations and performance declined as the decision-making tasks became more complex
and difficult. Relevant to the present study was the finding that the use of analytic
strategies for decisions also varied by group. That is, the acquirable skill group developed
and successfully used strategies to improve performance, with the entity group failing to
successfully develop and utilize strategies. Thus, perceptions of efficacy affect learning
strategies as well as task performance. Jacobs, Prentice-Dunn, and Rogers (1984)
demonstrated that efficacy beliefs can be artificially altered, with subject performance
consistent with the level of efficacy imposed from the outside. Bandura and Locke (2003)
posited that competencies can be can be increased by instilling a strong sense of learning
efficacy.
The effects of supervisor behavior on subordinate attitudes and behavior was the
subject of the Ohio State Leadership Studies. Halpin and Winer (1957) identified two
independent dimensions of leader behavior: Consideration and Initiating Structure.
Consideration encompasses friendship, mutual trust and respect as aspects of supervisory
behavior towards subordinates. Initiating Structure refers to the organization and
definition of subordinate activities. Subordinate satisfaction has been found to be related
to supervisory consideration in a number of studies in the 1950’s (Fleishman, 1957;
Halpin & Winer, 1957; Halpin, 1957).
Bandura (1986) offers a social psychologist’s explanation of the relationship of
subordinate satisfaction and supervisory behavior. Bandura explains that, in human

30
development, physically rewarding events often are accompanied by expressions of
interest and approval of others, while non-rewarding events are associated with
disapproval. People choose particular actions for approval and avoid actions which elicit
disapproval. Thus, the predictive value of the social reactions of others serves as an
incentive for a person’s actions. Bandura (1986) stated:
The approval or disapproval of those who can exercise reward and punishment
power has more influence on one’s actions than similar expressions by those who
cannot affect one’s life....It is difficult to conceive of a society populated with
people who are completely unmoved by the respect, approval and reproof of
others (p. 235)
In 1961, Likert found large differences between satisfied and dissatisfied work
groups’ reporting of supervisory behaviors. For example, 61% of employees with
favorable attitudes reported that their supervisor recommends promotions, transfers and
pay increases while only 22% of employees with unfavorable attitudes reported that
particular supervisory behavior. This pattern of relationships between positive employee
attitudes and supportive supervisory behavior was consistent throughout the study. In
their meta-analysis of organizational behavior modification, Stajkovic and Luthans
(1997) found that social rewards, such as recognition and attention, were statistically
equal to financial rewards in generating increased task performance in both
manufacturing and service organizations.
Feedback from authority figures can be viewed as a form of persuasion that
affects motivation (Latham & Locke, 1991). Perceptions of a task environment can be
influenced by verbal or written persuasion from others in the social environment
(Martocchio, 1992). Supervisory cues have been found to affect employee intrinsic and
extrinsic satisfaction in a task environment (Griffin, 1983). Learners exhibit greater

31
effort and are more likely to succeed if they receive encouragement from other
organizational members (Wood & Bandura, 1989a). Perceptions of positive supervisory
support have been linked to increased trainee motivation prior to training (Cohen, 1990).
Supervisory support for training has been positively associated with successful learning
transfer (Huczynski & Lewis, 1980). Managerial knowledge of the benefits of online
training and interest in implementation fosters faster and more effective implementation
of online training designs (Newton, Hase, & Ellis, 2002).
Gist and Mitchell (1992) explain that self-efficacy is an individual’s judgment of
perceived capability to perform a specific task and that, in an organizational context,
information obtained from the individual, the task itself and others in the organizational
environment may affect the individual’s assessment of capability. The authors further
propose a model of the formation of self-efficacy that contains three broad categories of
factors: analysis of task requirements, assessment of personal and situational resources,
and attributional analysis of experience. Within attributional experience, verbal
persuasion cues may include feedback about an individual’s abilities. Gist and Mitchell
develop the concept of pure persuasion, that is, the use of emotional and cognitive
arguments to convince an individual that he or she can perform a task at a given level.
While the authors hold that this concept may result in more weakly held efficacy beliefs,
there is a clear potential for impact on efficacy beliefs.
Gist and Mitchell (1992) further propose that one’s judgment of self-efficacy is
composed of variable and stable components and that equal self-efficacy judgments may
result in unequal performance due to the individual differences in variable and stable
levels. In this research, therefore, it is hypothesized that a worker’s level of self-efficacy

32
is affected by the variability in social persuasion – operationalized in this study as
supervisory support.
Age and gender are expected to have a moderating effect in this study. To address
this, gender was examined in the context of both perceived supervisory support and its
effect on self efficacy and self efficacy and its effect on metacognitive activity.
The above review suggests the following research questions and relevant
hypotheses:
The first research question is: Is supervisory support related to learner control
self efficacy and computer self-efficacy in a learner controlled training environment?
Two research hypotheses were examined in addressing this question:
H1: There is a positive relationship between supervisory support and computer
self-efficacy in a learner controlled training environment.
H2: There is a positive relationship between supervisory support and learner
control self-efficacy in a learner controlled training environment.
The second research question is: Are computer self-efficacy or learner control
self-efficacy related to metacognitive activity in a learner controlled training
environment? The corresponding research hypotheses are:
H3: There is a positive relationship between computer self-efficacy and
metacognitive activity in a learner controlled training environment.
H4: There is a positive relationship between learner control self-efficacy and

33
The third research question is: Do the relationships between supervisory support,
learner control self-efficacy, and metacognitive activity vary as a function of the gender
of the learner? The corresponding research hypotheses are:
H5: The relationship between supervisory support and computer self-efficacy
varies by gender.
H6: The relationship between supervisory support and learner control self-efficacy
varies by gender.
H7: The relationship between computer self-efficacy and metacognitive activity
varies by gender.
H8: The relationship between learner control self-efficacy and metacognitive
activity varies by gender.
The fourth research question is: Do the relationships between supervisory
support, learner control self-efficacy, and metacognitive activity vary as a function of the
age of the learner? The corresponding research hypotheses are:
varies by age.
H10: The relationship between supervisory support and learner control self-
efficacy varies by age.
varies by age.
activity varies by age.

34
The fifth research question is: Does computer self-efficacy have an effect on
learner control self-efficacy which subsequently has an effect on metacognitive activity?
The research hypothesis is:
H13: Computer self-efficacy has a positive, indirect effect on metacognitive
activity through learner control self-efficacy.

35
CHAPTER III: METHODOLOGY
The literature review above identified a number of limitations of the existing
theory and empirical research on metacognitive interventions. While it is not feasible to
address all of these limitations in this study, the focus is to understand the relationships of
the variables in an organizational environment. The generalizability of findings to
organizational training is the primary limitation of prior studies. The majority of research
with metacognitive interventions has been among children and young adults in
educational settings. Indeed, the theoretical foundations of the construct by Flavell (1976,
1977, 1979 & 1987) are almost totally based on observations and research among pre-
adult populations. In this study, pre-adult is defined as individuals who are primary or
secondary school students. The present study explored the effect of variables on
metacognitive activity among adult learners in an organizationally sponsored setting.
A second limitation of prior research has been the limited consideration of the
role of self efficacy in the learner’s approach to learner controlled training. The
influences of trainee motivation and self efficacy have been virtually ignored in past
studies of learner control. This study attempted to identify the relationship between two
types of self efficacy (i.e. computer self-efficacy and learner control self-efficacy) and
metacognitive activity in a learner controlled training environment. Further, the influence
of supervisory support on self efficacy perceptions, while reasonably well researched, has
not been extensively examined in the context of training. Finally, age and gender in
learner controlled training have been almost universally viewed as control variables;

36
whereas this study examined them as moderating variables. That is, rather than merely
controlling for age and gender, their specific effects were examined.
Research Design
This was a survey-based field study designed to gather data on metacognitive
activity, learner control self efficacy, computer self-efficacy, and supervisory support in a
learner controlled training environment. Age and gender were assessed and examined as
potential moderating variables. The study is a non-experimental design; the data support
associational inferences among the variables, but not causal relationships.
The study was conducted among managers whose graduate school education is
being fully or partially sponsored by their employer. Embedded in the graduate school
curriculum is the Virtual Leader© program from Simulearn Inc. The philosophy behind
Virtual Leader is that leadership is a complex skill that can become intuitive through
practice. The program provides applied training focusing on situational awareness, group
dynamics, and managing and empowering others. The Virtual Leader program is
designed as a learner controlled training simulation with managers accessing the program
at their convenience over the 8-10 hour total training content. The Virtual Leader
program is a CD-ROM based training tool where managers utilize a traditional
desktop/laptop computer located on the organization’s premises or at the educational
institution.
The program contains three distinct sections: Leadership Fundamentals, Learning
the Principles and Applying the Principles. The final section consists of six simulated
meeting scenarios where the manager is placed in the position of a manager conducting a
meeting in a graphic interface. The manager uses the principles from the preceding

37
sections to manage the simulated participants to a successful meeting outcome. The
simulated meeting can be halted to allow the manager to review either the Fundamentals
or Principles sections preceding the meeting simulation. In this manner, managers can
alter their leadership strategies during the simulation to adapt to changing meeting
conditions.
Hacker (2003) defined metacognitive activity as “knowledge of one’s knowledge,
thought processes, and cognitive and affective states; the ability to consciously and
deliberately monitor and regulate one’s knowledge, processes, and cognitive and
affective states” (p. 6). Metacognitive activity within Virtual Leader is engendered
through on-screen progress bars detailing emotional conditions of the meeting
participants in real time, options for agreeing or disagreeing with opinions from the
simulated participants, changing the topic under discussion, and providing praise or
criticism for any participant. Feedback to the trainee’s responses (from the simulated
participants) is immediate, allowing the trainee to continue or change the strategy being
utilized.
Operationalization of Variables
A short demographic survey (Appendix A) was administered to the respondents at
the beginning of the study. Gender was assessed as male or female. Age was assessed as
a continuous variable. Ethnicity was assessed as White, Black, Hispanic, American
Indian (or Eskimo or Aleut), Asian (or Pacific Islander) or other.
Learner control self efficacy was measured using a pre-training, self-administered
questionnaire consisting of seven items to assess a learner’s confidence to perform
successfully on this task (see Appendix B). Learners rated their self efficacy on a five

38
point Likert-type scale ranging from strongly disagree to strongly agree. The instrument
is a selection of appropriate items from previous instruments used to collect self efficacy
data (Ford et al., 1998; Gist et al., 1989; Gist, 1989; Tannenbaum & Yuki, 1992). The
internal reliability of these instruments ranged from .88 to .90 (Cronbach’s α).
Computer self-efficacy was measured using a modification of the Computer Self-
Efficacy scale (Computer Self-Efficacy Survey, n.d.). The instrument (see Appendix C)
uses a five-point Likert-type scale ranging from Very Little Confidence to Quite a Lot of
Confidence. The internal reliability of the section used is .94 (Cronbach’s α).
Supervisor support was measured using a pre-training, self-administered
questionnaire consisting of five items to assess the learner’s perception of supervisory
support for the learner in a learner controlled training environment. The instrument (see
Appendix D) consisted of a selection of questions from Jiang and Klein’s (2000) scale
measuring supervisor support, internal reliability of entire instrument was α =.83. This
questionnaire was administered after the individual completed the initial self efficacy
questionnaire to minimize possible bias caused by the order of the information sought.
For example, one could reasonably speculate that asking questions about supervisory
support initially could impel the respondent to answer subsequent questions about self
efficacy in a manner artificially consistent with prior answers. Answering the questions in
an order other than intended, therefore, threatens both the reliability and the validity of
the instrument.
Metacognitive activity was measured using a post-training, self-administered
questionnaire. The training activity was administered in a learner controlled environment
which utilizes metacognitive interventions. A selection of questions appropriate for this

39
study were taken from Ford et al.’s (1998) instrument measuring metacognitive activity
(see Appendix E). Internal reliability for the entire instrument was α =.83. Learners
reported their metacognitive activities for each of ten items on a five point Likert-type
scale ranging from strongly disagree to strongly agree.
Sample
This study was conducted among managers whose graduate school business
education is being fully or partially sponsored by their employer. Questionnaires were
distributed to geographically dispersed graduate business schools across the United
States. The Virtual Leader leadership training program is used by these schools as part of
the leadership curriculum. While all students are required to participate in the training,
only those managers with organizational sponsorship were included in the data for this
study. The study ran for about three months; yielding a sample size of 120 qualified
subjects.
Procedure
The study consisted of pre and post training questionnaires administered
immediately prior to and upon completion of the leadership training session. The four
questionnaires were assembled in a set (Computer Self Efficacy, Learner Control Self
Efficacy, Supervisory Support and Metacognitive Activity), stapled and each set
numbered for identification. Oral and/or written instructions were given to the course
instructor indicating that the respondents should complete the measures in the order of
the stapled packet. All pages within a set contained the same identification number so
that the data for each individual can be identified, even if the pages become separated.

40
The course instructors distributed and collected the questionnaires during the
Virtual Leader training session. All instructors were briefed on the study and were able to
administer the questionnaires and answer any procedural questions. The instructors
mailed the completed questionnaires to the researcher.
All data were collected anonymously with age, gender and ethnicity collected on
the cover page containing instructions before the first (learner control and computer self
efficacy) questions are completed. Respondents were asked to complete the first
questionnaire (containing the demographic items, learner control self-efficacy, and
computer self-efficacy scales) before the training begins; respondents were then asked to
complete the final questionnaire (containing the supervisory support and metacognitive
activity scales) immediately upon completion of the Virtual Leader © training.
Research Questions
The current study addressed five research questions incorporating the variables of
learner control self-efficacy, computer self-efficacy, supervisory support, metacognitive
activity, age, and gender. The first four research questions are based on the model shown
in Figure 1. The first research question is: Is supervisory support related to learner
control self efficacy and computer self-efficacy in a learner controlled training
environment?
Two research hypotheses were examined in addressing this question:
H1: There is a positive relationship between supervisory support and computer
self-efficacy in a learner controlled training environment.
H2: There is a positive relationship between supervisory support and learner
control self-efficacy in a learner controlled training environment.

41
The second research question is: Are computer self-efficacy or learner control
self-efficacy related to metacognitive activity in a learner controlled training
environment? The corresponding research hypotheses are:
H3: There is a positive relationship between computer self-efficacy and
H4: There is a positive relationship between learner control self-efficacy and
The third research question is: Do the relationships between supervisory support,
learner control self-efficacy, and metacognitive activity vary as a function of the gender
of the learner? The corresponding research hypotheses are:
varies by gender.
H6: The relationship between supervisory support and learner control self-efficacy
varies by gender.
varies by gender.
activity varies by gender.
The fourth research question is: Do the relationships between supervisory
support, learner control self-efficacy, and metacognitive activity vary as a function of the
age of the learner? The corresponding research hypotheses are:
varies by age.

42
H10: The relationship between supervisory support and learner control self-
efficacy varies by age.
varies by age.
activity varies by age.
The fifth research question is based on the model shown in Figure 2. In this
model, computer self-efficacy was hypothesized to have an effect on learner control self-
efficacy, which subsequently has an effect on metacognitive activity. The research
hypothesis is:
H13: Computer self-efficacy has a positive, indirect effect on metacognitive
activity through learner control self-efficacy.
Data Analysis Plan
The current study employed both descriptive and inferential statistical techniques.
Descriptive statistics consisted of (a) a description of the sample and (b) a description of
the scores on the four primary scales of interest (i.e. supervisory support, computer self-
efficacy, learner control self-efficacy, and metacognitive activity). The description of the
sample consisted of frequencies and relative frequencies for gender, age group, and
ethnicity. The description of scores on the supervisory support, learner control self-
efficacy, computer self-efficacy, and metacognitive activity scales consisted of means
and standard deviations, as well as an assessment of reliability. Reliability was assessed
with Cronbach’s α (internal consistency).

43
All inferential analyses consisted of two-tailed tests and an α level of .05. Two-
tailed tests were appropriate for this analysis as the directions of the relationships were
unknown, although where one-tailed tests were required to obtain statistical significance,
the results of these tests are reported as well. Initially, the correlations between the
supervisory support, learner control self-efficacy, computer self-efficacy, and
metacognitive activity were computed for the entire sample, and then separately for
males and females and for younger and older individuals. Then, a series of path models
were examined in which perceived supervisory support is specified as a predictor of
learner control self-efficacy and computer self-efficacy, which in turn are specified as
predictors of metacognitive activity.
Path analysis is essentially a combination of several simultaneous regression
analyses where selected variables can serve as both predictors and outcomes. In the
current study, learner control self-efficacy and computer self-efficacy took on this dual
role, serving as outcomes of supervisory support and predictors of metacognitive activity,
as shown in Figure 1. The hypotheses of the current study were tested by examining the
statistical significance of the standardized regression coefficients (β) for each variable. Z
scores express value in terms of how many standard deviations it is from the mean for
that set of data. It is useful when comparing results from variables with different scales as
is the case in the current study.
The first hypothesis is: There is a positive relationship between supervisory
support and computer self-efficacy in a learner controlled training environment. The
effect marked ‘a’ in Figure 1 represents the relationship between supervisory support and
computer self-efficacy. The relationship between supervisory support and computer self-

44
efficacy was tested via the statistical significance of the standardized regression
coefficient (β) calculated for the effect marked ‘a’. The second hypothesis is: There is a
positive relationship between supervisory support and learner control self-efficacy in a
learner controlled training environment. The relationship between supervisory support
and learner control self-efficacy is represented by the effect marked ‘b’ in Figure 1, and
this relationship was tested in the same manner as the first hypothesis.
The third hypothesis is: There is a positive relationship between computer self-
efficacy and metacognitive activity in a learner controlled training environment. This
relationship is represented by the effect marked ‘c’ in Figure 1. The fourth hypothesis is:
There is a positive relationship between learner control self-efficacy and metacognitive
activity in a learner controlled training environment, and this relationship is represented
by the effect marked ‘d’ in Figure 1. To test these relationships for statistical significance,
the standardized regression coefficients (β) associated with the effects marked ‘c’ and ‘d’
were calculated.
The fifth, sixth, seventh, and eighth hypothesis relate to the effect that gender may
have on the relationships between the four primary variables in the current study (i.e.
supervisory support, computer self-efficacy, learner control self-efficacy, and
metacognitive activity). These hypotheses state that the standardized regression
coefficients labeled ‘a’ ‘b’ ‘c’ and ‘d’ in Figure 1 are not the same for males and females.
To test for differences in these relationships between males and females, a path model
was computed in which the effects marked ‘a’ ‘b’ ‘c’ and ‘d’ were computed separately
for males and females. Then, the fit of this model was compared to the fit of models in
which the effects marked ‘a’ ‘b’ ‘c’ and ‘d’ were computed to be equal for males and

45
females. Four models were computed, with the effects marked ‘a’ ‘b’ ‘c’ and ‘d’
computed to be equal between males and females one at a time. If the model in which
the effect is computed to be equal for males and females fits significantly worse than the
model in which the effects are computed separately for males and females (as determined
by the χ2
difference test), then the hypotheses related to gender effects will be supported.
The ninth, tenth, eleventh, and twelfth hypotheses relate to differences in the
relationships between the primary variables in the current study as a function of age
group (younger individuals versus older individuals). The method of testing these
hypotheses was identical to the method used to test the fifth through eighth hypotheses.
That is, models in which the effects marked ‘a’ through ‘d’ in Figure 1 will be compared
to models in which these effects are computed to be equal across the two groups, and the
statistical significance of the difference in fit between the two models will be determined
via the χ2
difference test.
The thirteenth hypothesis deals with the positive, indirect effect of computer self-
efficacy on metacognitive activity through learner control self-efficacy. The statistical
significance of the indirect effects was computed via the Sobol z test (Sobol, 1982). The
Sobol test involves computing the product of the two unstandardized regression
coefficients (the effects marked ‘a’ and ‘b’ in Figure 2) and determining if that product is
larger than would be expected if there was no indirect effect.

46
Figure 1. Path Model for Research Questions 1-4
c
b d
Figure 2. Path Model for Research Question 5
a b
Computer
Self-Efficacy
Supervisory
Support
Learner Control
Self- Efficacy
Metacognitive
Activity
a
Computer
Self-Efficacy
Learner Control
Self- Efficacy
Metacognitive
Activity

47
CHAPTER IV: ANALYSIS OF DATA
Preliminary analyses consisted of (a) a description of the sample and (b) a
description of the scores on the four primary scales of interest (i.e. supervisory support,
computer self-efficacy, learner control self-efficacy, and metacognitive activity). The
description of scores on the supervisory support, learner control self-efficacy, computer
self-efficacy, and metacognitive activity scales consisted of means and standard
deviations, as well as an assessment of reliability. Reliability was assessed with
Cronbach’s α (internal consistency).
All inferential analyses consisted of two-tailed tests and an α level of .05. Two-
tailed tests were appropriate for this analysis as the directions of the relationships were
unknown, although where one-tailed tests would have been significant, they will be
reported along with the two-tailed test results. Initially, the correlations between the
supervisory support, learner control self-efficacy, computer self-efficacy, and
metacognitive activity were computed for the entire sample, using AMOS, and then
separately for males and females and for younger and older individuals. Then, a series of
path models were examined in which perceived supervisory support is specified as a
predictor of learner control self-efficacy and computer self-efficacy, which in turn are
specified as predictors of metacognitive activity.
Path analysis is essentially a combination of several simultaneous regression
analyses where selected variables can serve as both predictors and outcomes. In the
current study, learner control self-efficacy and computer self-efficacy occupied this dual
role, serving as outcomes of supervisory support and predictors of metacognitive activity.

48
The hypothesized relationships in the current study were tested by examining the
statistical significance of the standardized regression coefficients (β) for each effect. The
use of standardized regression coefficients is conventional in path analysis and multiple
regression. The standardized regression coefficients also have the advantage of providing
information on the effect size associated with each relationship, as they are interpretable
as the change in the criterion variable that results from an increase of one in the predictor
variable, when both variables have been converted to a common metric (i.e. z scores).
Z scores express value in terms of how many standard deviations it is from the mean for
that set of data. It is useful when comparing results from variables with different scales as
was the case in the current study.
The thirteenth hypothesis deals with the indirect effect of computer self-efficacy
on metacognitive activity through learner control self-efficacy. The statistical
significance of the indirect effects was computed via the Sobol z test (Sobol, 1982). The
Sobol test involves computing the product of the two unstandardized regression
coefficients and determining if that product is larger than would be expected if there was
no indirect effect.
Preliminary Analyses
Descriptive statistics for the sample demographic characteristics are shown in
Table 1 and Table 2. Two-thirds of the sample (66.7%) was male, and White participants
formed the largest ethnic group (78.3%). The age of the participants ranged from 21 to
62 with a mean of 30.33 years (SD=7.64 years). Table 2 shows the descriptive statistics
for the four composite measures in the current study.

49
Table 1
Descriptive Statistics for Sample Demographic Characteristic (N=120)
Frequency Percentage
Gender
Female 40 33.3
Male 80 66.7
Ethnicity
White 94 78.3
Hispanic 13 10.8
Black 7 5.8
Asian 5 4.2
Refused 1 .8
Mean SD
Age 30.33 7.64

50
Table 2
Descriptive Statistics for the Composite Measures (N=120)
Number
of Items Minimum Maximum Mean SD α
Computer Self-
Efficacy
32 73 159 130.57 16.26 .96
Learner Self-
Efficacy
7 17 35 26.09 3.96 .91
Supervisor
Support
4 5 20 13.44 3.46 .89
Metacognitive
Activity
8 19 40 33.03 4.11 .88
Cronbach’s α reliability coefficients were computed and were high for each of the
four scales (ranging from .88 for metacognitive activity to .96 for computer self-
efficacy). Table 3 shows the correlations between the four composite measures for the
total sample. Computer self-efficacy was positively correlated with both learner control
self-efficacy (r=.49, p<.001) and metacognitive activity (r=.26, p<.01) but not with
supervisory support. Learner self-efficacy was also correlated with supervisory support
(r=.22, p<.05) and metacognitive activity (r=.34, p<.001). In addition, supervisory
support was positively correlated with metacognitive activity (r=.18, p<.05).

51
Table 3
Correlations Between Composite Measures (N=120)
Computer
Self-Efficacy
Learner Self-
Efficacy
Supervisor
Support
Metacognitive
Activity
Computer Self-Efficacy 1.00
Learner Self-Efficacy .49*** 1.00
Supervisory Support .13 .22* 1.00
Metacognitive Activity .26** .34*** .18* 1.00
*p<.05, **p<.01, ***p<.001
Table 4 shows the correlations between the four composite measures, but this
time the correlations were computed separately for males and females. For females, the
only statistically significant correlation was between learner control self-efficacy and
metacognitive activity (r=.49, p<.01). Among males, the correlations between computer
self-efficacy and learner control self-efficacy (r=.68, p<.001), supervisory support
(r=.23, p<.01), and metacognitive activity (r=.31, p<.01) were all statistically
significant. In addition, learner control self-efficacy was positively correlated with
supervisory support (r=.23, p<.05) and metacognitive activity (r=.27, p<.05). The
relationship between metacognitive activity and supervisory support was not statistically
significant (but the correlation of .20 would have been statistically significant using a
one-tailed test). Although the fact that there were twice as many males as females
resulted in higher power for the statistical significance of these correlations for males, the

52
correlations for males are larger in size as well with the exception of the correlation
between learner control self-efficacy and metacognitive activity (which was larger for
females) and between supervisory support and metacognitive activity (which was
statistically non-significant and identical for both males and females).
Table 4
Correlations Between Composite Measures as a Function of Gender (N=120)
Computer
Self-Efficacy
Learner Self-
Efficacy
Supervisor
Support
Metacognitive
Activity
Females (n=40)
Learner Self-Efficacy .23 1.00
Supervisory Support .04 .18 1.00
Metacognitive Activity .16 .49** .20 1.00
Males (n=80)
Supervisory Support .23* .23* 1.00
Metacognitive Activity .31** .27* .20 1.00
*p<.05, **p<.01, ***p<.001

53
In order to examine the correlations between the four composite variables as a
function of age, the sample was split into those 28 and younger (n=62, 51.7%) and those
29 or older (n=58, 48.3%). This split was chosen primarily to achieve an approximately
equal sample size for the two groups while providing a younger sample whose work
experience almost certainly includes computer use (i.e. workforce entry after 1998).
Table 5 shows the correlations between the four composite measures for the younger age
group and for the older age group.
Table 5
Correlations Between Composite Measures as a Function of Age Group (N=120)
Computer
Self-Efficacy
Learner Self-
Efficacy
Supervisor
Support
Metacognitive
Activity
Younger Respondents (28 years old and younger, n=62)
Supervisory Support .09 .30* 1.00
Metacognitive Activity .31* .37** .21 1.00
Older Respondents (29 years old and older, n=58)
Learner Self-Efficacy .26* 1.00
Supervisory Support .19 .15 1.00
Metacognitive Activity .24 .33* .16 1.00
*p<.05, **p<.01, ***p<.001

54
For the younger respondents, computer self-efficacy was positively correlated
with both learner control self-efficacy (r=.68, p<.001) and metacognitive activity (r=.31,
p<.05). In addition, learner control self-efficacy was positively correlated with both
supervisory support (r=.30, p<.05) and metacognitive activity (r=.37, p<.01). Among
the older respondents, computer self-efficacy was again correlated with learner control
self-efficacy (r=.26, p<.05), but not with metacognitive activity (although the correlation
of .24 would have been statistically significant using a one-tailed test). Learner control
self-efficacy, on the other hand, was positively correlated with metacognitive activity
(r=.33, p<.05) but not with supervisory support, and supervisory support and
metacognitive activity were not correlated. Therefore, it appears that the correlations
among the four measures tended to be higher for younger respondents than for older
respondents.
Research Questions
The research questions were addressed using path analysis. Initially, the model
presented in Figure 1 of Chapter 3 was computed, and the resulting standardized
regression coefficients and R2
values are shown in Figure 3 (with full regression results
shown in Appendix F). The answers to the first two research questions are derived from
the coefficients of this model.

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