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THE EFFECTS OF SUPERVISORY SUPPORT, AGE AND GENDER ON SELF
EFFICACY AND METACOGNITIVE ACTIVITY IN A LEARNER
CONTROLLED TRA...
ii
Copyright by James V. Polizzi
2008
All rights reserved
ii
BIOGRAPHICAL SKETCH
James Polizzi earned a Bachelors of Business Administration (Marketing) from The City
College of Ne...
iii
DEDICATION
I dedicate this dissertation to my wife, Josephine. Her continuous support, understanding
and encouragement...
iv
ACKNOWLEDGEMENTS
I would like to thank SimuLearn, Inc for their permission to use the Virtual Leader
leadership trainin...
v
Table of Contents
Page
List of Figures.....................................................................................
vi
Research Questions............................................................................................... 54
Re...
vii
List of Figures
Page
Figure 1. Path Model for Research Questions 1-4 ....................................................
viii
List of Tables
Page
Table 1. Descriptive Statistics for Sample Demographic Characteristic (N=120)...........49
Table ...
ix
ABSTRACT
THE EFFECTS OF SUPERVISORY SUPPORT, AGE AND GENDER ON SELF
EFFICACY AND METACOGNITIVE ACTIVITY IN A LEARNER CO...
1
CHAPTER I: INTRODUCTION
Problem Background
As organizational efforts to improve productivity increase, employee training...
2
for training in 2002 versus 2001. Training delivered via learning technologies increased
to 15.4% in 2002, from 10.5% in...
3
study have reported positive attitudes towards training and improved outcomes (e.g.,
Brown, 2001; Kinzie & Sullivan, 198...
4
A review of the literature suggests that learner controlled training can be
improved by incorporating learning theory to...
5
efficacy refers to an individual’s belief in his/her ability to accomplish a given task
(Bandura). Self efficacy relates...
6
gender. Given recent trends, we can presume continued and increasing use of computers
to deliver training, thus examinat...
7
have an effect on a learner’s perception of self efficacy in training delivered in the
absence of a live instructor.
Ide...
8
3. Do the relationships between supervisory support, learner control self-
efficacy, and metacognitive activity vary as ...
9
CHAPTER II: LITERATURE REVIEW
This section will review and apply the significant theories and empirical research
encompa...
10
halted a previously rewarded behavior almost immediately; but previously rewarded
behavior continued for some time when...
11
processes which generate, code, transform and otherwise manipulate information”
(Flavell, p. 14). Viewed more narrowly,...
12
cognitive theory (Bandura). Behavior is motivated and regulated by a person’s internal
goals and standards as well as t...
13
Variations in adult learning – both inter personal and intra-personal – have been
attributed to differences in prior kn...
14
might they be doing instead?” (p. 232). From these questions, he developed the construct
of metacognition. Flavell desc...
15
The person factor of metacognitive knowledge concerns knowledge and beliefs
about one’s self and others as cognitive pr...
16
as the ability to control one’s cognitive processes, viewed as self-regulation (Livingston,
1997) or self-management (G...
17
Glaser and Pellegrino (1987) suggest that the improvement of the skills of
learning will take place through the develop...
18
Schmidt and Ford (2003) studied the effect of trainee characteristics on
metacognitive activity in a learner controlled...
19
refers to the degree to which learners are able to choose the method, timing, practice and
feedback during training (Mi...
20
total availability in organizations today has made its use more widespread than traditional
paper, audio and video tech...
21
thus self regulated learners can be considered self motivated and are self directed in a
metacognitive sense as well (E...
22
learning experience or by using a cognitive strategy that may have previously been
learned. The use of frequent numeric...
23
Motivation and Self Efficacy
Motivation has been described as a cognitive process which directs choices
among alternati...
24
Self-efficacy, as explained by Bandura (1986), mediates the relationship between one’s
knowledge and actions. Knowledge...
25
than those who doubt their capabilities and that social persuasion, e.g. supervisory
behavior, can be a factor in an in...
26
(Tannenbaum & Yuki, 1992). Employees who begin training with the belief that they are
able to successfully learn the co...
27
interest in and poor attitudes towards computer-based training. Thus, the age of the
employee may affect self efficacy ...
28
Supervisory Support
While it would appear natural for an individual to assume responsibility for his or
her own learnin...
29
Understanding ability as an acquirable skill resulted in a highly resilient sense of
self-efficacy and high performance...
30
development, physically rewarding events often are accompanied by expressions of
interest and approval of others, while...
31
effort and are more likely to succeed if they receive encouragement from other
organizational members (Wood & Bandura, ...
32
is affected by the variability in social persuasion – operationalized in this study as
supervisory support.
Age and gen...
33
The third research question is: Do the relationships between supervisory support,
learner control self-efficacy, and me...
34
The fifth research question is: Does computer self-efficacy have an effect on
learner control self-efficacy which subse...
35
CHAPTER III: METHODOLOGY
The literature review above identified a number of limitations of the existing
theory and empi...
36
whereas this study examined them as moderating variables. That is, rather than merely
controlling for age and gender, t...
37
sections to manage the simulated participants to a successful meeting outcome. The
simulated meeting can be halted to a...
38
point Likert-type scale ranging from strongly disagree to strongly agree. The instrument
is a selection of appropriate ...
39
study were taken from Ford et al.’s (1998) instrument measuring metacognitive activity
(see Appendix E). Internal relia...
40
The course instructors distributed and collected the questionnaires during the
Virtual Leader training session. All ins...
41
The second research question is: Are computer self-efficacy or learner control
self-efficacy related to metacognitive a...
42
H10: The relationship between supervisory support and learner control self-
efficacy varies by age.
H11: The relationsh...
43
All inferential analyses consisted of two-tailed tests and an α level of .05. Two-
tailed tests were appropriate for th...
44
efficacy was tested via the statistical significance of the standardized regression
coefficient (β) calculated for the ...
45
females. Four models were computed, with the effects marked ‘a’ ‘b’ ‘c’ and ‘d’
computed to be equal between males and ...
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-Ef...
47
CHAPTER IV: ANALYSIS OF DATA
Preliminary analyses consisted of (a) a description of the sample and (b) a
description of...
48
The hypothesized relationships in the current study were tested by examining the
statistical significance of the standa...
49
Table 1
Descriptive Statistics for Sample Demographic Characteristic (N=120)
Frequency Percentage
Gender
Female 40 33.3...
50
Table 2
Descriptive Statistics for the Composite Measures (N=120)
Number
of Items Minimum Maximum Mean SD α
Computer Se...
51
Table 3
Correlations Between Composite Measures (N=120)
Computer
Self-Efficacy
Learner Self-
Efficacy
Supervisor
Suppor...
52
correlations for males are larger in size as well with the exception of the correlation
between learner control self-ef...
53
In order to examine the correlations between the four composite variables as a
function of age, the sample was split in...
54
For the younger respondents, computer self-efficacy was positively correlated
with both learner control self-efficacy (...
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  1. 1. 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
  2. 2. ii Copyright by James V. Polizzi 2008 All rights reserved
  3. 3. ii 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.
  4. 4. iii DEDICATION I dedicate this dissertation to my wife, Josephine. Her continuous support, understanding and encouragement gave me the will to finally complete this endeavor.
  5. 5. iv 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.
  6. 6. v 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
  7. 7. vi Research Questions............................................................................................... 54 Research Question 1 ................................................................................. 55 Research Question 2 ................................................................................. 56 Research Question 3 ................................................................................. 56 Research Question 4 ................................................................................. 60 Research Question 5 ................................................................................. 63 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
  8. 8. vii 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 Figure 5. Path Model for Research Question 3 with Regression Coefficients for Females ..............................................................................................................................57 Figure 6. Path Model for Research Question 4 with Regression Coefficients for 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
  9. 9. viii 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
  10. 10. ix 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.
  11. 11. 1 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
  12. 12. 2 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
  13. 13. 3 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.
  14. 14. 4 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
  15. 15. 5 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
  16. 16. 6 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
  17. 17. 7 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?
  18. 18. 8 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.
  19. 19. 9 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
  20. 20. 10 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
  21. 21. 11 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
  22. 22. 12 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.
  23. 23. 13 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
  24. 24. 14 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.
  25. 25. 15 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
  26. 26. 16 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.
  27. 27. 17 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).
  28. 28. 18 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
  29. 29. 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
  30. 30. 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;
  31. 31. 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
  32. 32. 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.
  33. 33. 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)
  34. 34. 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
  35. 35. 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
  36. 36. 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
  37. 37. 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.
  38. 38. 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.
  39. 39. 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
  40. 40. 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
  41. 41. 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
  42. 42. 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 metacognitive activity in a learner controlled training environment.
  43. 43. 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: H9: The relationship between supervisory support and computer self-efficacy varies by age. H10: The relationship between supervisory support and learner control self- efficacy varies by age. H11: The relationship between computer self-efficacy and metacognitive activity varies by age. H12: The relationship between learner control self-efficacy and metacognitive activity varies by age.
  44. 44. 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.
  45. 45. 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;
  46. 46. 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
  47. 47. 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
  48. 48. 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
  49. 49. 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.
  50. 50. 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.
  51. 51. 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 metacognitive activity in a learner controlled training environment. H4: There is a positive relationship between learner control self-efficacy and metacognitive activity in a learner controlled training environment. 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: H9: The relationship between supervisory support and computer self-efficacy varies by age.
  52. 52. 42 H10: The relationship between supervisory support and learner control self- efficacy varies by age. H11: The relationship between computer self-efficacy and metacognitive activity varies by age. H12: The relationship between learner control self-efficacy and metacognitive 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).
  53. 53. 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-
  54. 54. 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
  55. 55. 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.
  56. 56. 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
  57. 57. 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.
  58. 58. 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.
  59. 59. 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
  60. 60. 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).
  61. 61. 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
  62. 62. 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) Computer Self-Efficacy 1.00 Learner Self-Efficacy .23 1.00 Supervisory Support .04 .18 1.00 Metacognitive Activity .16 .49** .20 1.00 Males (n=80) Computer Self-Efficacy 1.00 Learner Self-Efficacy .68*** 1.00 Supervisory Support .23* .23* 1.00 Metacognitive Activity .31** .27* .20 1.00 *p<.05, **p<.01, ***p<.001
  63. 63. 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) Computer Self-Efficacy 1.00 Learner Self-Efficacy .68*** 1.00 Supervisory Support .09 .30* 1.00 Metacognitive Activity .31* .37** .21 1.00 Older Respondents (29 years old and older, n=58) Computer Self-Efficacy 1.00 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
  64. 64. 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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