International Journal
of
Self-Directed Learning®
Volume 9, Number 1
Spring 2012
The International Journal of Self-Directed Learning (ISSN 1934-3701) is published
biannually by the International Society ...
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012
  i
International Journal of Self-Direct...
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012
  ii
Preface
Quoting from our purpose st...
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012
  iii
__________________________________...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Autonomous Learning and Reciprocal Causation
International Journal of Self-Directed Learning Volume 9, Number 1, Spring 20...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Towards a Distance Learning Environment that Supports Learner Self-Direction
International Journal of Self-Directed Learni...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Learning Success Factors Across Course Delivery Formats
International Journal of Self-Directed Learning Volume 9, Number 1...
Ponton and car self directed
Ponton and car self directed
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Ponton and car self directed
Ponton and car self directed
Ponton and car self directed
Ponton and car self directed
Ponton and car self directed
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Ponton and car self directed

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  • 1. International Journal of Self-Directed Learning® Volume 9, Number 1 Spring 2012
  • 2. The International Journal of Self-Directed Learning (ISSN 1934-3701) is published biannually by the International Society for Self-Directed Learning. It is a refereed, electronic journal founded to disseminate scholarly papers that document research, theory, or innovative or exemplary practice in self-directed learning. Submission guidelines can be found at http://www.sdlglobal.com SUBSCRIPTION or BACK COPY ORDERS: Contact: International Journal of Self-Directed Learning 7339 Reserve Creek Drive, Port Saint Lucie, FL 34986 issdl.sdlglobal@gmail.com © 2012, International Society for Self-Directed Learning. All rights reserved. No portion of this journal may be reproduced without written consent. Exceptions are limited to copying as permitted by Sections 107 (“fair use”) and 108 (“libraries and archives”) of the U. S. Copyright Law. To obtain permission for article reproduction, contact the editors at: International Journal of Self-Directed Learning issdl.sdlglobal@gmail.com Cover design by Gabrielle Consulting
  • 3. International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   i International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012 EDITOR Lucy Madsen Guglielmino, Florida Atlantic University (Emeritus) EDITOR EMERITUS Huey B. Long, University of Oklahoma (Emeritus) EDITORIAL BOARD Naomi Boyer, Polk State College Ralph G. Brockett, University of Tennessee Robert J. Bulik, University of Texas Academy of Health Science Education (Emeritus) Rosemary Caffarella, Cornell University Philippe Carré, Université Paris Ouest Nanterre La Défense, France Gary J. Confessore George Washington University (Emeritus) Richard E. Durr, OnLine Training Institute Brian Findley, Palm Beach State College Paul J. Guglielmino, Florida Atlantic University Joan H. Hanor, California State University San Marcos Roger Hiemstra, Syracuse University (Emeritus) Waynne James, University of South Florida Carol Kasworm, North Carolina State University William J. Kops, University of Manitoba, Canada Theresa N. Liddell, School District of Palm Beach County Patricia A. Maher, University of South Florida Sharan Merriam, University of Georgia (Emeritus) Magdalena Mo Ching Mok, The Hong Kong Institute of Education Albertina Oliveira, University of Coimbra, Portugal EunMi Park, Johns Hopkins University School of Medicine Janet Piskurich, Paul L. Foster Medical School, Texas Tech George Piskurich, ACS, a Xerox Company Michael K. Ponton, Regent University Kathleen B. Rager, University of Oklahoma Thomas G. Reio, Jr., Florida International University Karen Wilson Scott, Idaho State University Peter L. Zsiga, St. Lucie County Schools, Indian River State College Editorial Associate: Elizabeth G. Swann Webmaster: Richard E. Durr, Online Training Institute
  • 4. International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   ii Preface Quoting from our purpose statement, the International Society for Self-Directed Learning is “dedicated to the promotion of self-directed lifelong learning and to the encouragement and dissemination of continued research on self-directed learning both within and outside of institutional contexts: in childhood education, higher education, adult education, training and human resource development, as well as informal and non-formal contexts” (http://sdlglobal.com/aboutusSDL.php). This journal, as one of the major efforts of the ISSDL, seeks to publish quality pieces reporting on both research and practice pertinent to the furtherance of self-direction in learning and the adoption of practices supportive of self-directed learning by organizations and institutions. As our larger world and our everyday lives become increasingly complex and rich with information and technology, opportunity and challenge, the attitudes, aptitudes, and behaviors associated with self-direction in learning become ever more vital--and organizations and institutions have a responsibility to provide supportive environments for their growth. Self-directed, lifelong learners are better suited to survive and thrive in complex environments. This issue offers a wealth of new perspectives on self-direction in learning, ranging from the theoretical to the applied, from explication of terminology to large-sample research, from efforts to enhance self-directed learning in an early learning center to an institution of higher education. Beginning the issue, Ponton and Carr offer a detailed explication of triadic reciprocal causation as presented in Bandura (1986. 1989). Based on the recognition that autonomous learning and self-directed learning involve a “reciprocity of interaction between the learner, his or her learning behaviors, and the environment,” their microanalysis of the possible interactions expands our understanding of this vital concept. Continuing in the theoretical realm, Jezegou builds on her 15 years of previous research on self-directed learning in adult distance education at the Ecole Supérieure des Mines de Nantes and in the research team Apprenance et formation at Paris Ouest University in Nanterre-La Défense (France). After a careful review of foundational concepts, she proposes a model of presence in distance education as supportive of online learners’ success. Boyer and Usinger detail a large quantitative study designed to gather information to support strategic planning at a state college with an open-access policy, particularly in the area of distance education programming. Presenting information about the constructs that appear to impact student success across delivery formats, they offer insights that can be helpful for any organization or institution offering distance learning opportunities. The last article in this issue addresses our earliest educational experiences in institutions. Mettler and Korte have many years of experience in nurturing self-direction in learning at the Early Learning Center at Jefferson County Open School in Colorado, the same school complex that provided the inspiration for Posner’s Lives of Passion, School of Hope (2009). They suggest and document four essential elements of a learning environment conducive to self-directed learning that have application far beyond the early learning classroom. Lucy Madsen Guglielmino, Editor
  • 5. International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   iii ______________________________________________________________________ International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012 CONTENTS Preface ii Autonomous Learning and Triadic Reciprocal Causation: A Theoretical Discussion Michael K. Ponton and Paul B. Carr 1 Towards A Distance Learning Environment That Supports Learner Self-Direction: The Model Of Presence Annie Jézégou 11 Tracking Pathways To Success: Identifying Learning Success Factors Across Course Delivery Formats Naomi Boyer and Peter Usinger 24 The Early Learning Center at Jefferson Open School: (Re)Discovering the Joy of Learning Ana Mettler and Mary Korte 38
  • 6. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   1 AUTONOMOUS LEARNING AND TRIADIC RECIPROCAL CAUSATION: A THEORETICAL DISCUSSION Michael K. Ponton and Paul B. Carr Essential to social cognitive theory is the notion of triadic reciprocal causation through which human functioning is understood by considering interactions between the person, behavior, and the environment. Due to the lack of a balanced discussion of autonomous learning through the lens of reciprocal determinism in the literature, the purpose of this article is to offer such a discussion that highlights how autonomous learning—like any domain of human functioning—can only be adequately understood by considering the reciprocity of interaction between the learner, his or her learning behaviors, and the environment. Social cognitive theory (SCT, Bandura, 1986) supports an emergent interactive view of personal agency (Bandura, 1989) in which human functioning is described by the reciprocal interplay of three constituent factors—person, environment, and behavior—referred to as triadic reciprocal causation. Bandura (1986) asserted the following: “progress in understanding how personal factors affect actions and situations is best advanced through the microanalysis of interactive processes” (p. 28); therefore, understanding any domain of intentional action (i.e., personal agency) requires an analysis of not only these factors but also their interaction. For more than 10 years, SCT has been used as a theoretical framework for developing new conceptualizations of autonomous learning as well as self-directed learning (cf. Ponton, 1999; Ponton, 2009; Ponton & Carr, 1999; Ponton & Carr, 2000; Ponton, Derrick, & Carr, 2005; Ponton & Rhea, 2006). Thus, triadic reciprocal causation (TRC) has been an explicit part of this emerging literature. Unfortunately, the use of the behavioral model of Fishbein and Ajzen (1975; cf. Ponton & Carr, 1999) at various international meetings by these same theorists in order to describe the conative roles of desire (cf. Meyer, 2001), resourcefulness (Carr, 1999), initiative (Ponton, 1999), and persistence (Derrick, 2001) with respect to autonomous learning (cf. Confessore, 1991) has created a seeming overemphasis on the person-behavior interaction (i.e., learner autonomy vis-à-vis autonomous learning) at the expense of the
  • 7. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   2 other two (i.e., person-environment and behavior-environment interactions). The purpose of this article is to discuss all three interactions in greater detail and outline not only bidirectional influences but also mediating paths. Background Social cognitive theory supports an agentive view of human activity, portraying people as proactive, intentional initiators of their actions and thoughtful self-reflectors of associated consequences. Unlike earlier theories of psychology that either discount the role of thinking on action (i.e., radical behaviorism) or the environment on action (i.e., radical cognitivism), SCT recognizes that the exhibition of agency depends upon the reciprocal interplay of all three of the following determinants: person (cognitive, affective, conative, and biological aspects), behavior, and environment (Bandura, 1986). These interacting factors constitute a model referred to as triadic reciprocal causation (see Figure 1). These three factors influence each other bidirectionally and interact to varying degrees dependent upon temporal and situational factors that include subjective perceptions and objective environments. Thus, causation describes mutual influence rather than a certainty of outcome. Figure 1. A model of the three interacting determinants of human behavior (Bandura, 1986, p. 24). In 1999, Ponton defined autonomous learning as follows: “an agentive learning process in which the conative factors of desire, initiative, resourcefulness, and persistence are manifest” (p. xiii); these four factors were proposed by Confessore in 1991 as salient to autonomous learning. Ponton (2009) later asserted that “personal agency in autonomous learning can be manifest in imposed, selected, or created learning environments and exercised via collective, proxy, or individual agency” (p. 70). As a manifestation of personal agency, the phenomenon of autonomous learning can only be adequately understood by an analysis of the interactions associated with the TRC model. The importance of using agency theory to understand autonomous learning is based upon the premise that autonomous learning refers to “purposeful, intentional learning” (Ponton & Rhea, 2006, p. 45) directed toward learning goals of personal ENVIRONMENT PERSON BEHAVIOR
  • 8. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   3 value. Personal agency is exercised whenever one uses forethought to motivate and guide action, acts intentionally in activating plans, regulates action toward goal accomplishment, and reflects upon actions and consequences to both learn and motivate future action (cf. Bandura, 2006). The motivating dynamics associated with forethought are explained by expectancy value theory (Atkinson, 1964; Vroom, 1964) and goal theory (Locke & Latham, 1990); the dynamics associated with self-reflection are explained by attribution theory (Weiner, 1985). Thus, when a person believes that learning represents an appropriate path to obtain a valued outcome, establishes a learning plan and goal to reach this outcome, is motivated to engage in the plan and pursue the goal based upon perceived valence in relation to other desirable outcomes as well as perceived capability to be successful in the learning, and intentionally acts with manifest resourcefulness, initiative, and persistence, then one is engaging in an autonomous learning activity. Note that the “plan” can be deciding to pay attention to a facet of an imposed environment, select aspects of the environment that support learning, or create entirely new environments; however, agency requires such intentional forethought regardless of the plan’s complexity. In addition, personal agency is exercised whether the learning activity is created by oneself (individual agency), by working with others (collective agency), or by someone else who the agent deems to have salient knowledge and skills (proxy agency) because it is the agent who intentionally acts regardless of the mode through which the agency is exercised (Bandura, 2006). (Note: In 2009, Ponton argued that self-directed learning occurs when the agent uses individual agency to create and direct learning activities in contrast to the multiple modes of agency and varied forms of the environment that can be used in autonomous learning.) In 1999, Bussey and Bandura asserted the following: In the agentic sociocognitive view…people are self-organizing, proactive, self- reflective, and self-regulating, and not just reactive organisms shaped and shepherded by external events. The capacity to exercise control over one’s thought processes, motivation, affect, and action operates through mechanisms of personal agency. Among the mechanisms of agency, none is more central or pervasive than people’s beliefs in their capabilities to produce given levels of attainments. Unless people believe they can produce desired effects by their actions, they have little incentive to act or to persevere in the face of difficulties. Perceived efficacy is, therefore, the foundation of human agency. (p. 691) Motivational considerations such as value expectancies and causal and effort attributions do not result in actual motivation to engage in a given activity unless beliefs in personal capability—i.e., self-efficacy—are strong (Bandura, 1997). In general, people do not choose to engage in perceived futile endeavors; therefore, preferential activities transform into chosen pursuits based upon a strong sense of efficacy. Using the self-reflective capability of personal agency, self-efficacy is based upon appraisals of four sources of information: enactive mastery experiences, verbal
  • 9. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   4 persuasion, vicarious experiences, and physiological/emotive arousals (Bandura, 1997). The most authentic mechanism in building a strong sense of efficacy occurs when previous successful performances are personally attributed to ability rather than luck or the assistance from others (i.e., mastery experiences). Self-efficacy can also be strengthened when the valued opinions of others communicate to the agent that he or she has requisite capability (i.e., verbal persuasion). SCT also recognizes the power of observational learning particularly when personal experiences are lacking; therefore, people appraise their own capabilities by watching models deemed as similar (i.e., vicarious experiences) as suggested by the expression “if that person can do it, so can I.” Finally, interpretations of somatic feedback can be used to strengthen efficacy provided such feedback is interpreted as a natural, epiphenomenal reaction based upon the task at hand or as a temporary indicant of expanding capability. Note that the locus of information associated with enactive mastery experiences and physiological/emotive arousals is behavior whereas the locus for verbal persuasion and vicarious experiences is the environment; however, it is the person who receives and interprets this information thereby formulating beliefs in personal efficacy. The environment includes objective and subjective aspects and can be shaped dynamically or statically. The objective environment includes the people, natural and manmade structures, and social systems that surround us; the subjective environment includes how we perceive the world around us. Both environments influence how we think, feel, and behave and can either facilitate or impede desired courses of action. In addition, environments can be proactively created (i.e., dynamically shaped via intentional thought or action) or reactively realized (i.e., statically shaped as a response to who we are). In the latter case, the environment refers to the social environment that reacts to one’s physical characteristics, status, or any other known characteristics; the environmental reaction occurs without purposeful causal action by the person (Bandura, 1986). A given person’s social environment includes those people who have chosen to be part of this environment and to interact in a manner influenced by their understanding of this person. Discussion The literature presented provides many salient constructs related to human functioning in general and autonomous learning in particular. However, such functioning is the result of a dynamic interplay of the TRC determinants. SCT rejects the notion that any human activity can be understood by either focusing on any subset of these determinants (e.g., a study of only the person, environment, or behavior) or considering a subset of interactions. The development of a complete picture of autonomous learning requires a complete discussion of this interplay in light of the aforementioned constructs. Direct Effects With three determinants, the TRC model provides six direct effects (see Figure 2). Using the theoretical ideas presented, each direct effect can be described as follows:
  • 10. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   5 Figure 2. Six possible direct effects associated with the TRC model. 1. P à B: Motivational considerations coupled with self-efficacy provide the motivation for the agent to engage in autonomous learning in pursuit of new knowledge or skills. Example: a person anticipates satisfaction from learning more about a particular topic (i.e., a motivational consideration), decides that requisite capability exists to learn about this topic via a particular learning activity (i.e., an efficacy appraisal), and participates in this learning activity. 2. B à P: Autonomous learning leads to outcomes that inform motivational considerations as well as provides information (i.e., mastery experiences and physiological/emotive arousals) that informs efficacy beliefs; autonomous learning also leads to new knowledge or skills. Example: a person experiences a great deal of satisfaction from the learning associated with a learning activity (i.e., informs motivation) and believes that requisite capability to learn further from this activity is present (i.e., informs efficacy). 3. B à E: The autonomous learner focuses on aspects of an imposed environment or selects/creates an environment via individual, proxy, or collective agency conducive to autonomous learning. Example: a person selects a college course designed by a professor (i.e., a learning activity created via proxy agency). 4. E à B: The environment either facilitates or impedes autonomous learning. Example: a tutor selected by a student helps the student to learn. 5. P à E: Personal characteristics affect social environments (i.e., those persons, which include models and persuaders, who choose to surround the agent as well as the manner in which they behave and the information that they convey). Example: a famous person 6 5 4 3 2 1 ENVIRONMENT PERSON BEHAVIOR
  • 11. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   6 enters a room filled with people and affects their behavior by his or her physical qualities and reputed characteristics. 6. E à P: Social experiences influence values and expectations; events influence time and situationally dependent motivational considerations; verbal persuasion influences efficacy beliefs. Example: A lull in familial activities on a Saturday afternoon motivates a mother to engage in an hour of reading about a topic of interest (i.e., time and situationally dependent motivation). Mediating Processes An extension of the direct effects, six complete (i.e., full cycle associated with the TRC model) mediating processes can also be described: 1. P à E à B à P: Personal characteristics affect social environments that can facilitate or impede autonomous learning, thereby producing within the agent (a) outcomes that inform motivational considerations, (b) efficacy information, and (c) new knowledge or skills. 2. P à B à E à P: The agent intentionally engages in autonomous learning via an environment conducive to learning. This learning activity, when observed, affects others who provide verbal persuasion that influence the agent’s efficacy beliefs. In addition, the autonomous learning activity is placed temporally and situationally among other activities, thereby affecting motivational considerations within the agent. 3. E à B à P à E: The environment facilitates/impedes autonomous learning, thereby changing the agent in a manner that affects those who surround the agent. 4. E à P à B à E: The environment influences the motivation to engage in autonomous learning that involves focusing on aspects of an imposed environment or selecting/creating environments conducive to learning. In addition, this learning activity, when observed, affects others. 5. B à P à E à B: Autonomous learning produces outcomes, efficacy information, and new knowledge or skills that create observable changes in the agent so that others choose to facilitate or impede autonomous learning. 6. B à E à P à B: The agent selects or creates an environment conducive to learning. This learning activity, when observed, affects others and the manner in which they interact with the agent. In addition, the autonomous learning activity is placed temporally and
  • 12. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   7 situationally among other activities, thereby affecting motivational considerations within the agent that influence future participation in autonomous learning. Rebounding Processes Direct and mediating effects also provide for “rebounding” processes. Three examples: 1. E à B à P à B: The environment facilitates/impedes autonomous learning and creates outcomes that are desirable or undesirable, thereby changing the agent in a manner that affects how the agent intentionally engages in autonomous learning. 2. P à B à E à B: The agent intentionally engages in autonomous learning by selecting or creating an environment conducive to learning. This learning activity, when observed, affects others who can facilitate or impede autonomous learning directly. 3. E à P à B à P: The environment presents to the person a repertoire of people from which the agent chooses and appraises a model in order to inform efficacy beliefs (i.e., vicarious experience). The previous description of various interactions is not an attempt to offer any particular path analytic model but rather an attempt to reveal the rich complexity of implications associated with the TRC model that has no “beginning” or “ending” point; human action can be catalyzed in a multitude of ways that vary in time due to personological, environmental, or behavioral dynamics. It is quite likely that there are additional psychosocial constructs as well as different interpretations of the interactions highlighted that can be used to continue this discussion. In addition, further discussion can consider both interdeterminant interactions (e.g., many actions co-vary as do “situational happenings,” Bandura, 1986, p. 25) as well as temporal dynamics (i.e., interactions may be immediate or separated greatly in time, Bandura, 1986). To learn means to change with respect to acquiring knowledge or skills, thereby influencing—in addition to being influenced by—how one thinks, feels, and acts; however, understanding the phenomenon of learning cannot be understood outside of context. That is, when there is agency in learning (i.e., intentional learning under the learner’s control), there must be a consideration of the following questions: (a) why learn? (b) what to learn? (c) when to learn? (d) how much to learn? (e) how long to learn? and (f) how to learn? All of these questions are answered by an agent who already has developed in a unique manner based upon previous learning. But previous learning is dependent upon interactions between the agent and the environment as well as his or her behaviors; the focus of analysis is not merely on the agent. We learn from others either by direct instruction or by observation, we learn from ourselves as we make sense of our actions, and the manner and degree that we
  • 13. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   8 are able to learn from others or ourselves is influenced by how much we have already learned from others or ourselves. Personal development—learning—cannot be understood by focusing solely on the person despite the fact that this is where learning occurs (i.e., no one can learn for someone else). Similarly, the answers to the aforementioned questions cannot be understood by a singular personological focus. A simple conclusion of motivation theory is that at any given instant we do what we are most motivated to do; however, life is a series of instants in which there is great variation in the things we do resulting from the vacillations of our motivation. As we age, we have a relatively stable value system resulting from our previous learning, but we also have temporally unstable situational factors that interact with our value system, thereby influencing our motivation to act at any given moment. The varied answers to these questions—particularly the first five—are a result of this dynamic interplay. The sixth question—how to learn—introduces the varied modes of agency through which our personal agency can be exercised. When we are motivated to learn, we can allow others to create our learning activity (i.e., proxy agency), work with others to create a learning activity (i.e., collective agency), or create a learning activity all on our own (i.e., individual agency); however, regardless of the mode, all three are catalyzed by our personal agency to intentionally learn something of personal value. The particular mode that is chosen, though, is based upon a consideration of not only utility (i.e., how well the mode may help us learn) but also self-efficacy; that is, we must believe that we are capable of enacting the mode to create an effective learning activity. As already discussed, the strengthening of efficacy appraisals is rooted in the interplay of the person, environment, and behaviors. This article represents an attempt to offer a more complete discussion of the vast complexity associated with the phenomenon of agency in general and autonomous learning in particular as suggested by the TRC model (cf. Ponton & Rhea, 2006). A conclusion, however, should not be that to understand autonomous learning one must understand every conceivable construct and every conceivable interaction. There are likely a limited set of constructs and interactions that offer the greatest predictive power and explanatory utility to both understanding and, ultimately, facilitating autonomous learning; and specific environments (e.g., a given educational or corporate setting) may offer controls for certain constructs and interactions that promote parsimonious models with limited application. For all theory building, however, interactions associated with the TRC model’s three determinants should be considered to as great an extent as is reasonable in order to capture the rich complexity of human agency. References Atkinson, J. W. (1964). An introduction to motivation. Princeton, NJ: D. Van Nostrand. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
  • 14. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   9 Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44(9), 1175-1184. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman and Company. Bandura, A. (2006). Toward a psychology of human agency. Perspectives on Psychological Science, 1(2), 164-180. Bussey, K., & Bandura, A. (1999). Social cognitive theory of gender development and differentiation. Psychological Review, 106(4), 676-713. Carr, P. B. (1999). The measurement of resourcefulness intentions in the adult autonomous learner. Dissertation Abstracts International, 60, 3849. Confessore, G. J. (1991). Human behavior as a construct for assessing Guglielmino’s Self-Directed Learning Readiness Scale: Pragmatism revisted. In H. B. Long & Associates (Eds.), Self-directed learning: Consensus & conflict (pp. 123- 146). Norman, OK: Oklahoma Research Center for Continuing Professional and Higher Education of the University of Oklahoma. Derrick, M. G. (2001). The measurement of an adult’s intention to exhibit persistence in autonomous learning. Dissertation Abstracts International, 62, 2533. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Locke, E. A., & Latham, G. P. (1990). A theory of goal setting & task performance. Englewood Cliffs, NJ: Prentice Hall. Meyer, D. T. (2001). The measurement of intentional behavior as a prerequisite to autonomous learning. Dissertation Abstracts International, 61, 4697. Ponton, M. K. (1999). The measurement of an adult’s intention to exhibit personal initiative in autonomous learning. Dissertation Abstracts International, 60, 3933. Ponton, M. K. (2009). An agentic perspective constrasting autonomous learning with self-directed learning. In M. G. Derrick & M. K. Ponton (Eds.), Emerging directions in self-directed learning (pp. 65-76). Chicago, IL: Discovery Association Publishing House. Ponton, M. K., & Carr, P. B. (1999). A quasi-linear behavioral model and an application to self-directed learning (NASA Technical Memorandum 209094). Hampton, VA: NASA Langley Research Center. Ponton, M. K., & Carr, P. B. (2000). Understanding and promoting autonomy in self- directed learning. Current Research in Social Psychology, 5(19). Retrieved from http://www.uiowa.edu/~grpproc Ponton, M. K., Derrick, M. G., & Carr, P. B. (2005). The relationship between resourcefulness and persistence in adult autonomous learning. Adult Education Quarterly, 55(2), 116-128. Ponton, M. K., & Rhea, N. E. (2006). Autonomous learning from a social cognitive perspective. New Horizons in Adult Education and Human Resource Development, 20(2), 38-49. Vroom, V. H. (1964). Work and motivation. New York, NY: John Wiley & Sons. Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92(4), 548-573.
  • 15. Autonomous Learning and Reciprocal Causation International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   10 ___________________________________ Michael Ponton (michpon@regent.edu) is Professor of Education at Regent University. His research interest is in the development of a better understanding of the role of human agency in learning. Paul Carr (paulca2@regent.edu) is Professor of Global Leadership and Entrepreneurship at Regent University. His research interests are in resourcefulness in learning, adult learning, and autonomous learning. Author Note A preliminary version of this article was presented at the 26th International Self-Directed Learning Symposium, Cocoa Beach, FL, February 8-11, 2012. Correspondence concerning t his article should be addressed to Michael K. Ponton.
  • 16. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   11 TOWARDS A DISTANCE LEARNING ENVIRONMENT THAT SUPPORTS LEARNER SELF-DIRECTION: THE MODEL OF PRESENCE Annie Jézégou This article presents the essential features of a model of presence in e- learning (Jézégou, 2012). It describes the three dimensions of the model and shows how they are related to one another. The article clarifies one of the main hypotheses of this model: that distance education environments with a high level of presence support learner self- direction. This general hypothesis is developed by separating it into two sub-hypotheses, respectively linked to one of the two dimensions of the socio-cognitive concept of self-direction (Carré, Jézégou, Kaplan, Cyrot, & Denoyel, 2011): self-determined motivation and self- regulation. In France, a trend in research on autoformation (self-directed learning) focuses on “open distance learning environments” designed and implemented by training centers in companies, adult education providers, or institutions of higher education (Carré et al., 2011). These environments can be e-learning environments (the most prevalent), multimedia resource centers, spaces for individualized training, or blended learning environments. For several years, the purpose of French research has been to develop a theoretical framework for distance learning environments that support the learner’s self-direction. This framework, which is still under development, identifies several educational dimensions conducive to learner self-direction. These dimensions are particularly linked to the French work on the notion of openness (Collectif de Chasseneuil, 2001; Collectif du Moulin, 2002; Jézégou, 2005) and on a proposed model of présence in e-learning (Jézégou, 2012). This presence, which is potentially measurable, results from certain forms of social interaction between teacher and learners, and between learners when they are engaged in a distance collaboration in a digital communication space. These spaces are materialized using tools such as web telephony, online chat and virtual classrooms (synchronous communication tools) and / or email, forums and wikis (asynchronous communication tools). More abstractly, they are associated with intellectual universes shared and supported by social interactions, some of which can generate presence within these digital spaces (Garrison & Anderson, 2003; Garrison & Arbaugh, 2007; Jézégou, 2012).
  • 17. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   12 This article describes the essential features of the model of presence in e- learning (Jézégou, 2012) and the possible effects of presence, as modeled here, on learner self-direction. First, it briefly outlines the theoretical framework of the personal dynamic of self-direction inspired by Bandura’s socio-cognitive paradigm (1986), self-determination theory (Deci & Ryan, 2000) and Zimmerman’s triadic model of self-regulation (2002). Secondly, the three dimensions of the proposed model of presence are related to one another and the main hypothesis derived from this relation--that a high level of presence supports learner self-direction--is presented. Two sub-hypotheses of the effect of presence on learner self-determination and self- regulation follow. The direction for future empirical research is indicated in the conclusion. The Double Dimension Of Learner Self-Direction: Theoretical Framework For nearly thirty years, the theory of self-directed learning has been the subject of much research, following the pioneering work of Tough (1971), Knowles (1975), Long (1975), Hiemstra (1976), and Guglielmino (1978). The emerging French research on self-directed learning attributes to the concept of learner’s self-direction a double dimension, within a socio-cognitive perspective (Brewer, 2009 ; Carré, 2003, Carré et al., 2011; Cosnefroy, 2011, Jézégou, 2010a). The first dimension is self- determined motivation (an autonomous, authentic free will to learn) while the second one is self-regulation (the exercise of agentic, self-controlled learning activity). The term double is used because of an interdependent relationship between these two dimensions (Carré, 2003; Cosnefroy, 2011; Deci & Ryan, 2000; Schunk & Zimmerman, 2007). A high level of initial motivation is necessary to involve oneself in an activity to achieve a personal goal, as is self-regulation of the different aspects of the conduct of this initial activity. Self-regulated processes are important in maintaining this motivation during the activity. This motivation is both the source and a consequence of these processes. Self-direction is a socio-cognitive concept. Socio-cognitive theory (Bandura, 1986) takes the position that human behaviors are not primarily influenced by environmental determinants as stipulated in the behaviorist approach or the determinist current in sociology. Nor do they depend solely on internal or personal determinants as stated in current dispositionalist psychology. According to the socio- cognitive paradigm, these behaviors are part of a system of reciprocal causality between three types of determinants: personal determinants (P), environmental determinants (E), and behavioral determinants (B). Interactions between these three types of determinants are subject to reciprocal causality, and are in continuous interaction in variable and contingent importances to conditions, activities and temporalities. The weight of these determinants is not always the same, nor do they necessarily act at the same time. However, the development or modification of one of them will cause a change in the system of their interaction, as circumstances vary from one individual to another.
  • 18. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   13 Figure 1. The model of triadic reciprocal causality (Bandura, 1986). The learner’s self-direction follows this reciprocal causality. In its broadest sense, it can be defined as the psychological control exercised by the learner on his training and learning (Long, 1989). This self-direction or psychological control is not directly observable. It manifests itself through those learner behaviors whose characteristic is to be both self-determined and self-regulated (Carré, 2003; Cosnefroy, 2011; Jézégou, 2010a). Some environmental factors (determinants) can promote or hinder these behaviors (Deci & Ryan, 2000; Hiemstra, 2000; Vallerand, Carbonneau, & Lafrenière, 2009; Zimmerman, 2000). Self-Determined Motivation According to the motivational theory of self-determination (Deci & Ryan, 1985), self-determined behaviors are linked to personal choice of activity. They are expressed through proactive and future oriented behavior. People who exhibit such behaviors can have three specific forms of motivation: intrinsic motivation, integrated motivation and identified motivation. The intrinsically motivated people are the most self-determined. They choose to engage in or lead an activity for the fun, interest or stimulation it provides. Those having an integrated motivation choose to engage in an activity, not for pleasure, but in order to follow their personal beliefs. By contrast, people with identified motivation press themselves into engagement with an activity because of external influences from their environment. This third form of motivation can sometimes also be qualified as self-determined because it may be linked to a personal choice, but the resulting behaviors are less self-determined than in the two previous forms. Thus, the behaviors resulting from these three forms of motivation have different levels of self-determination. Intrinsic motivation has the most positive impact on cognitive, behavioral, and emotional aspects of learning, followed by integrated motivation, then identified motivation. These effects decrease with the degree of extrinsic determination (Deci & Ryan, 2008; Laguardia & Ryan, 2000; Vallerand et al., 2009). According to the theory of self-determination, behaviors resulting from self- determined motivation are driven by the quest for satisfaction of three basic psychological needs: the need for (a) autonomy, for (b) competency, and for (c) social P E B
  • 19. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   14 belonging. These three needs are universal and occur in all human activities, including learning (Deci & Ryan, 2000). The need for autonomy refers to the need to feel free to make choices, to be responsible for actions and decisions. The need for competency is defined as the need for the ability to have satisfying interactions with the environment and to take charge personally of one’s actions. The need for social belonging is the need to feel accepted by others and to maintain good relationships with them. According to the theory of self-determination, all humans try to satisfy these three needs in interactions with their environments. The environmental factors that afford people opportunities to satisfy these three needs facilitate self-determined motivation (intrinsic, integrated and identified motivation), whereas those that thwart satisfaction of these needs hinder it (Deci & Ryan, 2008; Laguardia & Ryan, 2000; Vallerand et al., 2009). Self-Regulated Learning Even learners with self-determined motivation can have difficulties in directing their own learning. Another dimension is important: the ability to develop effective strategies of self-regulation to succeed in learning. In its broadest sense, self-regulation in learning refers to the control the learner exercises on his (her) own cognitive processes (Boekaerts, Pintrich, & Ziedner, 2000; Corno, 2001; Cosnefroy, 2011; Schunk & Zimmerman, 2007). The socio-cognitive research on self-regulation identifies three forms of control (Zimmerman, 2002). Each one refers to a specific form of self-regulation: 1. Covert self-regulation refers to control exerted by the learner on his or her emotional, socio-emotional and motivational states. 2. Behavioral self-regulation refers to control the learner exercises over his (her) learning behaviors. 3. Environmental self-regulation refers to control the learner exercises on the various components of his (her) educational environment. These three forms of self-regulation are subject to reciprocal causality (Zimmerman, 2002) as illustrated in Figure 2. Zimmerman (2000) proposes that learners better achieve personal goals by self-regulating their behavioral, environmental, and covert processes in a coordinated fashion. Each of the three forms of the self-regulated process is divided into three cycle phases: performance, forethought, and self-reflection, as depicted in figure 3. First, the learner sets a personal learning goal and plans a strategy to attain this goal. This forethought phase precedes learning. As was noted earlier, learners do not engage in goal setting and strategic planning unless they are personally motivated. This strategy is then applied and observed, and its implementation modified (performance phase). Finally, performance and satisfaction are self-evaluated, attributing causal significance to the results. The self-regulatory approach may be modified during subsequent efforts to learn and perform. This self-reflection phase will, in turn, influence the forethought phase of the next episode of learning; this cycle continues indefinitely.
  • 20. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   15 Figure 2. The triadic forms of self-regulation (Zimmerman, 2002). Figure 3. Cyclical phases of self-regulation (Zimmerman, 2000). According to the socio-cognitive theory (Bandura, 1999; Schunk & Zimmerman, 2007), certain environmental factors sustain the process of self-regulation, helping the learner to exercise control linked to the three forms of self-regulation previously described. Learner characteristics Learning environment Learner behaviors Behavioral self-regulation comportementale Environmental self-regulation Covert self-regulation use of strategy by the learner retroactive loop Performance Forethought Self-reflection
  • 21. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   16 Possible Effects Of A High Level Of Presence On Learner Self-Direction Building on this theoretical base, the second section of the article describes the essential features of a proposed model of presence in e-learning (Jézégou, 2012), including its three dimensions and how they relate to one another; and presents a general hypothesis linked to learner self-direction in online environments. This theoretical hypothesis is that a high level of presence, as an environmental factor, promotes the learner's self-directed behaviors. At the current stage of the work on this model of presence, this hypothesis is separated into two sub-hypotheses, each linked to a dimension of the concept of self-direction, self-determined motivation and self- regulation. Essential Features Of The Model Of Presence In E-Learning The model of presence asserts that certain forms of social interactions between teacher and learners, and among learners engaged in distance collaboration create presence within the digital communication space. This presence fosters the emergence and the development of an online community of inquiry. In its broadest sense, a community of inquiry is a relatively flexible social organization that is directed towards the resolution of a problematic situation such as dealing with a doubt about a given topic, reacting to an unexpected event, or completing a project (Deledalle, 1998; Dewey, 1938, Favre, 2006; Jézégou, 2010b). The members of this community build a collective experience that allows them to reach their goal while pursuing their own personal objectives (Deale & Charlier, 2006; Dillenbourg, Poirier, & Carles, 2003; Henri & Lundgren-Cayrol, 2001; Jézégou, 2010b). The North American model of community of inquiry in e-learning also asserts this general position (Garrison & Anderson, 2003; Garrison & Arbaugh, 2007; Garrison & Archer, 2007). However, the French model addresses the notion of presence in e-learning from a different angle, defining and characterizing it differently (Jézégou, 2012). This difference is linked to two fundamental aspects of the model. The model of presence in e-learning places greater emphasis on the notion of transaction derived from the philosophy of pragmatism (Dewey & Bentley, 1949) and, in contrast with the North American model, is also affiliated with the European approach to socio-constructivism (Bourgeois & Nizet, 1997; Darnon, Butera, & Mugny, 2008; Monteil, 1987; Perret-Clermont & Nicolet, 2002). Both approaches share a focus on the notion of "contradictory" collaboration (Baudrit, 2008; Damon & Phelps 1989; Jézégou 2012). In its broadest sense, collaboration is characterized by the equal status of group members, their participation in social interactions, and the sharing of a jointly defined set of activities in solving a problematic situation (Dillenbourg, Poirier, & Carles 2003; Henri & Lundgren-Cayrol 2001; Jézégou 2010b). "Contradictory" collaboration emphasizes the positive role played by transactions on individual and collective construction of knowledge. Transactions are social interactions that include confrontations between different points of view, mutual adjustments, negotiations, and deliberations (Dewey & Bentley, 1949; Lipman, 1995). The European socio-
  • 22. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   17 constructivist approach places greater emphasis on the expression of divergent opinions and the confrontation between different points of view on the learning process. According to Dewey and Bentley (1949), the transactions are manifested in jointly-led activities and in a common method of practice of inquiry. This practice would be the best way to clarify the situation, to solve the problem, and to justify the solutions. It can be considered a scientific process in which the results are generated in an "experimental" manner as assumptions are revised in the light of experience and deliberation. The practice of inquiry unfolds in four stages (Dewey & Bentley, 1949). A working definition of the problem posed by the situation is first devised. The situation is observed and analyzed to abstract and refine the problem and understand its specific character and causes. In the second stage, hypothetical actions that may solve the problem are formulated and compared. The goal is to determine which hypothesis seems likely to offer the most satisfactory solution without losing sight of the complexities of the situation. The third stage is to test the hypothesis to see if it offers an effective solution to the problem. The final stage involves a critical analysis of the three previous stages of the investigation, whose aim is to assess the practical consequences of testing and the results obtained. This concluding stage may redefine the situation or communicate the results of the completed investigation in a mutual and transparent way. The notion of transaction and the method of practical inquiry are at the heart of the model of presence in e-learning proposed (illustrated in Figure 4). Figure 4. The tridimensional model of presence in e-learning (Jézégou, 2012). Socio-cognitive presence + + Socio-­‐affective   presence     + Global presence + Pedagogical presence
  • 23. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   18 In this model, presence is defined as follows (Jézégou, 2012): Presence in E-learning results from certain forms of social interactions between teacher and learners, but also among learners themselves when they are engaged in distance collaboration within the digital space of communication. These social interactions are simultaneously: 1. transactions between learners during the inquiry; in other words, social interactions involving a confrontation of individual views, with mutual adjustments, negotiations and deliberation about how to solve shared problems (socio-cognitive presence); 2. interactions that create a socio-emotional climate conducive to transactions between learners; in other words, social interactions that are symmetrical and amiable (socio-affective presence); 3. interactions of coordination, animation, and moderation that the teacher maintains with the learners to support the transactions between learners while contributing to a conducive socio-emotional climate (pedagogical presence). This definition describes each aspect of the three dimensions of the model: (a) socio- cognitive presence, (b) socio-affective presence and (c) pedagogical presence. This model supports the principle that presence in e-learning is the result of these three dimensions; therefore, the greater the level of each, the greater the overall presence. The model formulates the general theoretical hypothesis that a high level of presence in e-learning, as an environmental factor, supports the learner’s self-direction. Two Sub-Hypotheses: The Mediating Role Played By Two Psychological Needs The model of presence in e-learning proposes to separate this general hypothesis into two specific sub-hypotheses. First, a high level of presence in e- learning can promote learners’ self-determined behaviors, mainly through its impact on the satisfaction of their need for social belonging. The indirect influence of a high level of presence on the learner’s self-determined behaviors is depicted in Figure 5. Figure 5. The indirect influence of a high level of presence on the learner’s self- determined behaviors. Satisfaction of learner’s need for social belonging charateristics High level of presence Learner’s self- determined behaviors 1 2 3 2
  • 24. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   19 This sub-hypothesis is based on the fact that presence in e-learning, as modeled here, fosters the emergence and development of an online community of inquiry. This is particularly the case when the level of presence is high. High levels of presence can respond to the learner's need for social belonging in the online community. In other words, presence can contribute to feelings of being respected, understood, and accepted by peers while maintaining authentic and constructive relationships with them. This feeling of involvement with others can stimulate the learner’s self- determined behaviors. The pleasure, interest and stimulation aroused by collaboration in solving a shared problem at distance can promote intrinsic motivation. The learner can also contribute to the community in an intense and authentic way as a result of personal convictions (integrated motivation). The second sub-hypothesis is that a high level of presence in e-learning can promote the learner's self-regulated behaviors, mainly through its impact on the satisfaction of his or her need for competency. The indirect influence of a high level of presence on the learner’s self-regulated behaviors is depicted in Figure 6. The model of presence in e-learning proposes that the group constructs the distance collaborative experience that enables it to reach a shared goal of solving a problematic situation linked to the formalization and implementation of solutions. This collaborative experience is based on the practice of inquiry, requiring each learner in the group to begin a process of self-regulation. Such self-regulation is begun by choosing and defining the shared goal of resolving a problem situation to be analyzed collectively. The self-regulation process underlies the conception and experimentation that determines the chosen strategy towards resolution of a hypothesis as well as the monitoring of this strategy. These two stages of the practice of inquiry require each learner in the group to enter the first two phases of the process of self- regulation: of forethought, and of performance. Finally, this practice obliges learners to make a contribution to the analysis and evaluation of their experience, and its effects upon them. This stage of the practice of inquiry stimulates the self-reflection phase of the process of self-regulation. Figure 6. The indirect influence of a high level of presence on the learner’s self- regulated behaviors. Satisfaction of learner’s need for competency belonging charateristics High level of presence Learner’s self- regulated behaviors 1 2 3 2
  • 25. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   20 Participation in such a collaborative experience will not be natural or automatic for all of the learners in the group, especially when they are separated geographically (Deale & Charlier, 2006; Dillenbourg et al., 2003; Henri & Lundgren-Cayrol, 2001). They may feel unable to begin a process of self-regulation. This feeling may be heightened when they realize that the process requires control of emotion and motivation (internal self-regulation), control of collaborative behaviors (behavioral self-regulation), and control of the spatio-temporal, human and technological aspects of the collaboration (environmental self-regulation). So, a high level of presence (socio-cognitive, socio-affective, and pedagogical) in the digital communication space, maintained throughout the duration of the inquiry, can help the learner to develop efficient self-regulating behaviors. It can validate the learner's own satisfaction with striving towards competence, notably in collaborating at distance with others, but also while self-regulating the different aspects of his study and learning. In summary, a high level of presence in e-learning would help to satisfy two psychological needs of the learner: the need for social belonging and the need for competency. It would then exercise an indirect influence on the development of self- directed behavior(s) with the characteristics of being both self-determined and self- regulated. Thus a high level of presence in e-learning would promote learner self- direction. Conclusion This article has described the essential features of a model of presence in e-learning and the possible effects of presence, as modeled here, on learner’s self-direction. This model can be applied to any form of distance education, although e-learning environments are the most prevalent in this field. Empirical research is needed to confirm the relevance of this model and identify its strengths and vulnerabilities. This empirical research will help to refine and verify the general theoretical hypothesis presented in this article. To test this hypothesis, a matrix of indicators of presence will be constructed and a protocol for assessing the level of presence existing within an environment of e- learning will be developed. This development process, which will involve multiple researchers, will be described in a future paper. With the help of other experts on self- directed learning, qualitative empirical research will be conducted on students enrolled in e-learning environments. The goal of the research is to verify the hypothesis that a high level of presence supports learner’s self-direction. Another perspective is to relate the model of presence to the previous works on the notion of openness in distance learning environments (Jézégou, 2005, 2008, 2010c). The research indicates that high levels of openness and freedom of choice for learners as they structure their learning environments promote self-direction. This research program will contribute to a theoretical framework for distance learning environments that support learner’s self-direction.
  • 26. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   21 References Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice - Hall. Bandura, A. (1999). Social cognitive theory of personality. In L. Pervin & O. John (Eds.), Handbook of personality (2nd ed.) New York: NY: Guilford. Baudrit, A. (2008). L’apprentissage collaboratif: Plus qu’une méthode collective ? Bruxelles, Belgium: De Boëck. Boekaerts, P. R., Pintrich, P. R., & Zeidner, M. (Eds.). (2000). Handbook of self- regulation. London, England: Academic Press. Bourgeois, E., & Nizet, J. (1997). Apprentissage et formation des adultes. Paris, France: Presses Universitaires de France. Brewer, S. (2009). Articuler dispositions des apprenants et dispositifs de formation : Perspectives d’un linguiste en éducation. In G. Lameul, A. Jézégou, & A. F. Tollat (Eds.), Articuler dispositifs de formation et dispositions des apprenants (pp. 45-70). Lyon, France: Chronique Sociale. Carré, P. (2003). La double dimension de l’apprentissage autodirigé. Contribution à une théorie du sujet apprenant. Revue Canadienne pour l’étude de l’éducation des adultes, 17, 66 - 91. Carré, P., Jézégou, A., Kaplan, J., Cyrot, P., & Denoyel, N. (2011). “L’autoformation”: The state of research on self-directed learning in France. International Journal of Self-Directed Learning, 8(1), 7 - 17. Collectif de Chasseneuil. (2001). Accompagner les formations ouvertes. Paris, France: L’Harmattan. Collectif du Moulin. (2002). Intégrer les formations ouvertes. Paris, France: L’Harmattan. Corno, L. (2001). Volitional aspects of self-regulated learning. In B. Zimmerman & D. Schunk (Eds.), Self-regulated learning and academic achievement. Mahwah, NJ: Lawrence Erlbaum, 191 - 225. Cosnefroy, L. (2011). L’apprentissage autorégulé: Entre cognition et motivation. Grenoble, France: PUG. Damon, W., & Phelps, E. (1989). Critical distinctions among three approaches to peer education. International Journal of Educational Research, 13(1), 9-19. Darnon, C., Butera, F., & Mugny, G. (2008). Des conflits pour apprendre. Grenoble, France: PUG. Deale, A., & Charlier, B. (Eds.). (2006). Comprendre les communautés virtuelles d’enseignants: Pratiques et recherches. Paris, France: L’Harmattan. Deci, E., & Ryan, R. (1985). Intrinsic motivation and self-determination in human behavior. New York, NY: Plenum. Deci, E., & Ryan, R. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227-268. Deci, E., & Ryan, R. (2008). Favoriser la motivation optimale et la santé mentale dans les divers milieux de vie. Canadian Psychology / Psychologie Canadienne, 49(1), 24-34.
  • 27. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   22 Deledalle, G. (1998). La philosophie Américaine. Bruxelles, Belgium: De Boëck Université. Dillenbourg, P., Poirier, C., & Carles, L. (2003). Communautés virtuelles d’apprentissage: E-jargon ou nouveau paradigme? In A. Taurisson & A. Sentini (Eds.), Pédagogie.net. Montréal, Canada: Presses Universitaires. Dewey, J. (1938). Experience and education. New York, NY: Collier Books. Dewey, J., & Bentley, A. F. (1949). Knowing and the known. In J. A. Boydston (1989) (Ed.), John Dewey: The later works. 1925–1953 (Vol.16, pp. 2-294). Carbondale, IL: Southern Illinois University Press. Favre, M. 2006. Qu’est-ce la problématisation ? L’apport de John Dewey. In M. Favre & E. Vellas (Eds.), Situations de formation et problématisation (pp. 17-30). Bruxelles, Belgium: De Boeck. Garrison, D. R., & Anderson, T. (2003). E-learning in the 21st century. A framework for research and practice. New York, NY: Routledge. Garrison, D. R., & Arbaugh, J. B. (2007). Researching the community of inquiry framework: Review, issues, and future directions. The Internet and Higher Education. 10(3), 157 - 172. Garrrison, D. R., & Archer, W. (2007). A theory of community of inquiry. In M. G. Moore (Ed.), Handbook of distance education. (2nd ed., pp. 77-88). Mahwah: NJ: Laurence Erlbaum Associates. Guglielmino, L. M. (1978). Development of the Self-Directed Learning Readiness Scale. Dissertation Abstracts International, 38, 6467A. Henri, F., & Lundgren-Cayrol, K. (2001). Apprentissage collaboratif à distance. Sainte Foy: Presses Universitaires du Québec. Hiemstra, R. (1976). Lifelong learning. Lincoln, NE: Professional Educators Publications. Hiemstra, R. (2000). Self-directed learning: The personal responsibility model. In G. Straka (Ed.), Conceptions of self-directed learning: Theoretical and conceptional considerations (pp. 93-108). Berlin, Germany: Waxmann. Jézégou, A. (2005). Formation ouvertes: Libertés de choix et autodirection de l’apprenant. Paris: L’Harmattan. Jézégou, A. (2008). Apprentissage autodirigé et formation à distance. Distances et savoirs, 6(3), 343-364. Jézégou, A. (2010a). Diriger par soi-même sa formation et ses apprentissages. In B. Raucent, C. Verzat, & L. Villeuneuve (Eds.), Accompagner les étudiants (pp. 53-85). Bruxelles, Belgium: De Boëck Université. Jézégou, A. (2010b). Community of inquiry in e-learning: A critical analysis of the Garrison and Anderson model. Journal of Distance Education 24(3), 12-29. Retrieved from http://www.jofde.ca/index.php/jde/article/view/707 Jézégou, A. (2010c). Le dispositif GEODE pour évaluer l’ouverture d’un environnement éducatif. Journal of Distance Education 24(2), 83-108. Retrieved from http://www.jofde.ca/index.php/jde/article/view/62 Jézégou, A. (2012). Presence in e-learning: Theoretical model and perspectives for research. Journal of Distance Education 26(2). Retrieved from http://www.jofde.ca/index.php/jde/article/view/809
  • 28. Towards a Distance Learning Environment that Supports Learner Self-Direction International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   23 Knowles, M. (1975). Self-directed learning: A guide for learners and teachers. New York, NY: Association Press. Laguardia, J., & Ryan, R. (2000). Buts personnels, besoins psychologiques fondamentaux et bien être: Théorie de l’autodétermination et application. Revue Québécoise de Psychologie, 21(2), 281-303. Lipman, M. (1995). A l’école de la pensée. Bruxelles, Belgium: De Boeck & Larcier. Long, H. B. (1975). Independent study in the education of Colonial adults. Journal of Research and Development in Education, 8, 54-65. Long, H. B. (1989). Self-directed learning: Emerging theory and practice. Norman, OK: University of Oklahoma. Monteil, J. (1987). A propos du conflit socio-cognitif: D’une heuristique fondamentale à une possible opérationnalisation. In J. Beauvois, R. Joule, & J. Monteil (Eds.), Perspectives cognitives et conduites sociales. Théories implicites et conflits cognitifs (Vol. 1, pp. 199-210). Paris, France: Cousset DelVal. Perret-Clermont, A. N., & Nicolet, M. (Eds.). (2002). Interagir et connaître. Enjeux et régulations sociales dans le développement cognitif. Paris, France: L’Harmattan. Schunk, D., & Zimmerman, B. (Eds.) (2007). Motivation and self-regulated learning: Theory, research and applications. New York, NY: Lawrence Erlbaum Associates. Tough, A. (1971). The adult’s learning projects: A fresh approach to theory and practice in adult learning. Toronto: Ontario Institute for Studies in Education. Valllerand, R. (2000). Deci and Ryan‘s self-determination theory: A view from the hierarchical model of intrinsic and extrinsic motivation. Psychological Inquiry, 11, 312-318. Vallerand, R., Carbonneau, N., & Lafrenière, M. C. (2009). La théorie de l’autodétermination et le modèle hiérarchique de la motivation intrinsèque et extrinsèque. In P. Carré & P. Caspar (Eds.), Traité de psychologie de la motivation (pp. 47-66). Paris, France: Dunod. Zimmerman, B. (2000). Self-regulatory cycles of learning. In G. Straka (Ed.), Conceptions of self-directed learning : Theoretical and conceptional considerations (pp. 221-234). Berlin, Germany: Waxmann. Zimmerman, B. (2002). Efficacité perçue et autorégulation des apprentissages durant les études: Une vision cyclique. In P. Carré, & A. Moisan (Eds.), La formation autodirigée. Aspects psychologiques et pédagogiques (pp. 69-88). Paris, France: L’Harmattan. __________________________________ Annie Jézégou (annie.jezegou@mines-nantes.fr*) is researcher in Education at the Ecole Supérieure des Mines de Nantes (France). Her laboratory is the CREAD (Centre de Recherche sur l'Education, les Apprentissages et la Didactique) at the European University of Rennes 2 (France). She has been working for close to 15 years on the topic of self-directed learning in adult distance education and has produced books and numerous articles.
  • 29. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   24 TRACKING PATHWAYS TO SUCCESS: IDENTIFYING LEARNING SUCCESS FACTORS ACROSS COURSE DELIVERY FORMATS Naomi Boyer and Peter Usinger This exploratory study supports strategic planning efforts, with a focus on student self-direction, to improve the success of an academic institution’s distance education programming, to moderate the gap in failure and withdrawal rates between course delivery formats, and to provide tailored support mechanisms to the diverse student population the college serves. The constructs that impact course success were investigated through use of the Motivated Strategies for Learning Questionnaire (MSLQ) with 570 students enrolled in various course delivery formats. The open-access opportunity for students to enroll in college provides a gateway for many who otherwise would not have the option, due to financial constraints, previous academic record, or life conditions, to participate in higher education. Perhaps tied to the limitations that may have restricted post-secondary enrollment options or linked to other variables, successful retention of students and completion of two and four years degrees remains a national concern. The rate of program completion within a traditional timeframe for community college students is close to 50% (Goldrick-Rab, 2010). Course completion rates vary based upon subject matter and other variables; however, course delivery via online courses appears to have a strong impact on retention. There is a 10-20% increase in withdrawals and failures in online courses (Doherty, 2006; Herbert, 2006). Retention and Success in Online Courses Despite the fact that learning in online courses has not been found to have significantly different outcomes in terms of grades and student satisfaction, the presented research appears to only be focused on course completers, who are traditionally “well-prepared and motivated students” (Jaggars & Bailey, 2010, p. 11). Those students who do not persist in online courses are influenced by a number of variables including technological competency, previous experience with online courses, personal life commitments, demographic variables, prior educational success, e-learning quality, and individual characteristics (Harrell & Bower, 2012; Ho, Kuo, &
  • 30. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   25 Lin, 2009; Nora & Snyder, 2008; Yen & Lui, 2009). Harrell and Bower (2012) identified a three-variable model that was useful in predicting community college student persistence in online courses that included auditory learning style score, GPA, and basic computer skills. Self-Directed Learning and Success in Online Environments A number of researchers indicate that self-directed learning habits contribute to online success and satisfaction, as well (Ho, et al., 2009; Yen & Liu, 2009). It has been suggested that promotion of “better self-directed learning habits, meta-cognitive skills, and online collaborative behaviors” will facilitate adult learners’ levels of “e- learning readiness” (Ho, et al., 2009). In addition, it has been noted that online learning requires a “fairly high degree of self-motivation, self-direction, and self- discipline” (Moore, 1987) and course success in online environments is linked to concepts of independence, self-direction, or autonomy (Guglielmino & Guglielmino, 2002). Yen and Lui (2009) link higher learner autonomy with completion of community college online courses and overall success in grades; yet there is a gap in the literature in regard to persistence in online learning and interventions for community college students (Nash, 2008). Motivation has been noted as one of the most important components of online educational success, particularly as it is linked to the reasons for choosing to do a task and individual beliefs about the ability to perform a task (Yukselturk & Bulut, 2007). Self-direction in learning is a complex concept with a variety of aspects and associated constructs. In practice, self-direction involves shifting the responsibility for the learning activity from an external source such as teacher to the individual learner, with the learner assuming some level of control and active engagement with the learning process. Whether this assumption of control takes the form of behavioral activities such as planning objectives, identifying resources, setting timelines, developing products, and authenticating learning or through the process of discovery and exploration, the individual learner is central to the acquisition of knowledge. A number of factors have been identified as contributing to self-direction. Stockdale (Stockdale & Brockett, 2011) identified initiative, control, self-efficacy, and motivation as part of her instrument, the PRO-SDLS, as contributing to self-direction. Others have included level of autonomy, self-regulation, time management, self- control, person and social responsibility as factors relating to self-direction (Li, Wright, Rukavina, & Pickering, 2008; Pajares, 2002). In addition, the Self-Directed Learning Readiness Scale includes the constructs of love of learning, self-concept as an effective independent learner, view of learning as a beneficial process, initiative in learning, self-understanding, and acceptance of responsibility for one’s own learning (McCune, Guglielmino, & Garcia, 1990). Autonomy, as measured by the Learner Autonomy Profile, includes four factors: desire to learn, learner resourcefulness, learner initiative, and learner persistence (Confessore & Park, 2004).
  • 31. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   26 Purpose Student success and completion in online higher education courses is an ongoing concern noted throughout the distance learning literature. In order to increase retention and student success it is critical to identify inhibiting factors. The purpose of this paper is to investigate the constructs that impact course success in different delivery formats. It is hypothesized that those constructs linked to higher levels of self-direction (i.e. intrinsic goal orientation, control beliefs about learning, self- efficacy for learning and performance, meta-cognitive self-regulation, effort regulation, and help seeking) will correlate with student success and/or course completion. The study will examine the following questions: 1. What differences between students selecting different course delivery formats exist in factors typically associated with self-directed learning behaviors and meta-cognitive strategies that lead to higher course success and persistence rates? 2. What variations in self-directed learning and meta-cognitive success strategies exist within courses of the same subject domain and across different disciplines, and to what extent are these variations confounded with particular course delivery formats? 3. What relationships (correlations) exist among selected MSLQ constructs, and how robust are these relationships across different sets of student characteristics (e.g., demographics), course characteristics (e.g., Math vs. English), and delivery formats? 4. What predictive pathways (via multivariate regression modeling) can be established (if any) that are able to explain how certain self-directed learning behaviors and meta-cognitive strategies can lead to improved course success/completion rates (and perhaps to subsequently higher college success)? This exploratory study was conducted in a state college with an open-access policy. It aims to support the strategic planning efforts to improve the success of distance education programming, to assist with moderating the gap in failure and withdrawal rates between course delivery formats, and to provide more tailored support mechanisms to the diverse student population the college serves. In addition, the analysis is expected to provide a multivariate model designed to describe cause- effect relationships between key MSLQ constructs and student learning outcomes within and across the academic disciplines involved in the study. In the process, the researchers also hope to be able to disaggregate the MSLQ into a chain of constructs that can effectively predict a significant portion of self-directed learning success for different student populations. Method During the fall term of 2011, Polk State College administered a slightly shortened version of the Motivated Strategies for Learning Questionnaire (MSLQ) by Pintrich and DeGroot (1990) to a self-selected set of undergraduate students enrolled
  • 32. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   27 in college-level and developmental education courses. The selection criteria for the courses involved a multi-year review of online course success rates and flagged those courses for participation in the study that had shown consistently high failure and withdrawal rates and typically registered students for more than one section per term. The questionnaire was made available as an online survey (Zoomerang) to students enrolled in all three delivery types of these courses: distance learning, hybrid/blended, and face-to-face formats. The purpose of this paper is to investigate the constructs that impact course success. It is hypothesized that those constructs linked to higher levels of self-direction (i.e. intrinsic goal orientation, control beliefs about learning, self-efficacy for learning and performance, meta-cognitive self- regulation, effort regulation, and help seeking) will correlate with student success and/or course completion. Sample/Population The described study was conducted within the context of a four-year state college, a teaching institution with a mission of access, low-cost instruction, development of talent to support local workforce needs, and AA, AS, and Baccalaureate degree completion. The institution had total enrollment of 11,775 in the Fall 2011 term. The study sample was drawn from students enrolled in 15 courses covering the following areas of curriculum: Developmental Math and Writing, Mathematics and Statistics, English Composition, Earth Sciences, Psychology, Medical Terminology, and Nursing. One technical course, OST1142 (Keyboarding) was excluded from the discipline-specific clusters later in the analysis. Emails were sent to 4,860 students with instructions and a link to complete the instrument. A follow-up email was sent to students approximately two weeks later, and faculty were asked to encourage students to complete the inventory. In total, 570 students completed the inventory, representing a response rate of 11.7%. The distribution of responses across courses and the number of associated sections is shown in Table 1. Instrumentation The Motivated Strategies for Learning Questionnaire (MSLQ) was utilized as the primary tool for gaining information about the students’ value, expectancy, and affect for learning (Pintrich, Smith, Garcia, and Mckeachie, 1991). The MSLQ has a number of scales that align to the general concept of self-direction and self-regulation of learning and covers many of the constructs associated with self-direction in learning. In addition, the MSLQ had become a formative assessment tool of choice for a number of faculty teaching Student Learning Success and Developmental Math courses to facilitate their support for student learning behaviors that lead to higher academic success rates. The MSLQ includes a motivation section and learning strategies section. The measure includes the following scales and sub-scales: motivation-intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs, self- efficacy for learning and performance, test anxiety; and learning strategies scales: rehearsal, elaboration, organization, critical thinking, metacognitive self-regulation,
  • 33. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   28 Table 1. Student Participation Across Courses Course Traditional Delivery Format Hybrid Delivery Format Online Delivery Format Unknown Format Number of Sections Number of Students Number of Sections Number of Students Number of Sections Number of Students Number of Students ENC0025 16 33 0 0 1 2 7 ENC1101 30 41 0 0 4 5 6 ENC1102 21 39 3 7 3 5 7 ESC1000 10 16 0 0 3 6 3 HSC1531 3 5 0 0 4 8 1 MAC1105 24 43 0 0 2 4 5 MAT0018 28 46 0 0 0 0 6 MAT0028 32 51 0 0 0 0 9 MAT1033 32 52 0 0 2 5 5 MGF1106 9 25 0 0 1 2 4 NUR1033C 2 8 0 0 0 0 1 NUR1211C 2 15 0 0 0 0 0 NUR2421C 3 10 0 0 0 0 1 OST1142 0 0 0 0 1 4 1 PSY2012 21 33 0 0 6 12 6 STA2023 12 26 0 0 2 2 3 time and study environment management, effort regulation, peer learning, and help seeking (Duncan & McKeachie, 2005). The motivation section assesses students’ “goals and value beliefs for a course, their beliefs about their skill to succeed in a course, and their anxiety about tests in a course” (Pintrich, et al., 1991, p. 3). The learning strategies section includes cognitive and metacognitive strategies and student management of different resources (Pintrich, et al., 1991). The questionnaire was made available as an online survey (Zoomerang) to students enrolled in all three delivery types of these courses, distance learning, hybrid/blended, and face-to-face formats. While all MSLQ scales were utilized to relate to the behaviors of self-direction in online courses, some of those that did not display significant correlations with course success were considerably shortened to reduce the time students needed to complete the questionnaire. While the instrument’s primary focus is motivation and learning strategies, a number of the integrated constructs can provide information about self-directed behaviors and then be utilized to consider issues relating to student success and retention in online courses. Of the 15 scales of the MSLQ, five constructs were included completely, five almost completely, while five constructs were represented partially, leading to a total number of 61 out of the original 81 questions (excluding demographic items). One of the eliminated survey items was excluded from the Time/Study Environment Management scale (“I attend class regularly.”)
  • 34. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   29 since it wasn’t applicable to the online learning environment in the given form. A summary of the scale differences between the original MSLQ and the version used in this study is shown in Table 2, along with the reliability estimates (Cronbach’s alpha). The instrument took approximately 15 minutes to complete. Major Findings and Conclusions Overall, and as indicated by the comparative Cronbach’s alpha values in Table 2, all MSLQ constructs in this study showed the same robust reliability and replicable factorial patterns that validated the original scale design (Duncan & McKeachie, 2005; Pintrich, et al., 1991). Similarly, zero-order correlations between the different motivational and cognitive scales replicate the MSLQ auto-correlation patterns established by the same studies or subsequent reviews. Table 2. MSLQ Item by Construct Comparison MSLQ Scale Definition Original MSLQ Applied in Study Items α Items Α Motivational Constructs 1. Intrinsic Goal Orientation 4 0.74 4 0.72 2. Extrinsic Goal Orientation 4 0.62 2 0.62 3. Task Value 6 0.90 2 0.86 4. Control of Learning Beliefs 4 0.68 3 0.70 5. Self-Efficacy for Learning & Performance 8 0.93 8 0.95 6. Test Anxiety 5 0.80 4 0.75 Learning Strategies Constructs 1. Rehearsal 4 0.69 2 0.69 2. Elaboration 6 0.75 2 0.59 3. Organization 4 0.64 2 0.58 4. Critical Thinking 5 0.80 5 0.79 5. Metacognitive Self-Regulation 12 0.79 10 0.83 6. Time/Study Environmental Management 8 0.76 7 0.80 7. Effort Regulation 4 0.69 4 0.71 8. Peer Learning 3 0.76 3 0.78 9. Help Seeking 4 0.52 3 0.73 Total Items in Questionnaire 81 61 While those results and the relatively high response rate were encouraging, some aspects of the remaining analysis had to be postponed until the next questionnaire submission since the participation by students in online and hybrid classes did not allow for a more detailed/disaggregated analysis.
  • 35. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   30 The exploration of response-differences between course types, course-delivery formats, and interactions between academic discipline and delivery format was hampered by the fact that only 62 students (or about 10.9% of the sample) that participated in the study were enrolled in hybrid and online courses. In addition, 65 participants (or 11.4% of the sample) selected not to provide an accurate student ID and had to be excluded from core sections of the analysis. Despite those challenges, the data analysis provided interesting insights into the relationships between the motivational factors and learning strategies and the accomplished course outcomes. Table 3 displays the correlations between the respective MSLQ scales and achieved course grade (A = 5; B = 4; C = 3; D = 2; F/W=1), course success (1 = passed; 0 = failed/withdrew), and delivery format of the course (1 = Face-to-Face; 2 = Hybrid/Online). Table 3. Correlations between MSLQ Scales, Course Outcomes and Delivery Method Pearson Correlation Coefficients (N=502) Grades Success Method Motivational Constructs 1. Intrinsic Goal Orientation 0.13* 0.10* -0.01 2. Extrinsic Goal Orientation 0.02 0.02 -0.08 3. Task Value 0.16*** 0.11** 0.00 4. Control of Learning Beliefs 0.19*** 0.13** 0.04 5. Self-Efficacy for Learning & Performance 0.33*** 0.26*** 0.02 6. Test Anxiety -0.20*** -0.14** -0.02 Learning Strategies Constructs 1. Rehearsal 0.01 0.02 -0.03 2. Elaboration 0.03 0.00 0.00 3. Organization 0.05 0.04 -0.06 4. Critical Thinking -0.02 -0.02 0.09 5. Metacognitive Self-Regulation 0.00 -0.02 0.01 6. Time/Study Environmental Management 0.12** 0.04 0.00 7. Effort Regulation 0.17*** 0.11* 0.02 8. Peer Learning -0.02 0.02 -0.18*** 9. Help Seeking -0.03 0.02 -0.26*** Note: *p < .05. **p < .01. ***p < .001. While motivational constructs show similar correlation patterns indicated by previous studies, the scales associated with learning strategies show only weak relationships with grades or successful course completion. Among motivational factors, intrinsic goal orientation, self-efficacy, and test anxiety have been traditionally
  • 36. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   31 the most powerful factors of academic success, and the study data supports those findings to a large degree. There are several indicators of challenges that underprepared students face when starting their postsecondary career aspirations. The absence of any significant relationship between course outcomes and metacognitive self-regulation, combined with the fact that the student sample seems to rely mostly on effort learning strategies, appears to have little or nothing to do with the subsequent students’ course grades/success. About half of the students participating in this study have been enrolled in developmental education or entry-level college courses, and more than 40% are part- time students. Studies conducted, for example, by the Community College Research Center (Jaggars & Bailey, 2010) in the traditional community college environment have indicated that the lack of successful learning strategies, particularly for part-time students, is a key contributor to student course failures, while adding significant time to degree completions. With currently 90% of the College’s FTIC enrollment not college-ready in all core placement areas, these results speak for a strong need to advance the teaching of successful learning strategies into the First-Year curriculum and the associated learning support environments. The last column of Table 3 addresses one of the main questions raised at the onset of this study and concerns the differences between students selecting different course delivery formats and if there are significant relationships between factors typically associated with self-directed learning behaviors and meta-cognitive strategies that lead to higher course success and persistence rates. Interestingly, the only two MSLQ constructs that display significant correlations with the course delivery method have also no relationship to student success or course grades. For students enrolling in online classes, the opportunities for Peer Learning and Help Seeking that involve support mechanisms common to the traditional face-to- face environment are not easily replicable in a virtual learning space. Thus, it is not surprising that online students show a significantly lower degree of activities associated with those two constructs. That these factors do nor seem to directly impact either course success or grades (at least in the statistical analysis presented) could easily lead to the conclusion that they are, perhaps, not sufficiently important and that students are obviously aware of the facts and learn to adjust accordingly. However, this could be a precarious conclusion since it would bypass the impact peer learning and help seeking has on other academic success factors. Instead, it should point toward the enhanced support needed in the online learning environment to compensate for the corresponding lack of personal, learning-centered transactional opportunity. In other terms, even if these factors don’t appear to directly impact course outcomes, they might significantly mitigate the circumstances under which course success is achieved. Additional research is required to assess the degree this mitigation is aligned with a motivational pathway model proposed by Connell, Spencer, and Aber (1994). Another question raised concerned the variations in self-directed learning and meta-cognitive success strategies that exist within courses of the same subject domain and across different disciplines, and to what extent these variations are confounded with particular course delivery formats. While the sample constraints do not allow for a course-level exploration of factors, the sample sizes for academic-area-specific
  • 37. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   32 analyses are sufficient enough to at least establish a baseline for four areas, English, Math, Nursing, and Science. The correlations between MSLQ factors and course outcomes pertinent to each area are shown in Table 4. Based on course enrollment, four academic area clusters were formed: English, with 152 respondents; Math, with 288 respondents; Science, with 90 respondents; and Nursing, with 35 students completing the MSLQ. While the different sub-sample sizes are strongly affecting the statistical comparability of the significance levels associated with the correlations presented, the data still show a high level of instrument immanent sensitivity to the specificities of the various disciplines. In our sample, students’ course success in English is largely influenced by the extent to which they are able to manage their time and study environment. In addition, those who want to achieve a better grade display higher levels of self-efficacy and effort. All other factors had basically no influence on the academic outcomes for this subgroup. In contrast, student success in mathematics shows significant correlations with motivational constructs (Control Beliefs, Self-Efficacy, and Test-Anxiety), while learning strategies, in the form of higher effort regulation, only seem to matter when it comes to achieving better grades. In addition, Intrinsic Goal Orientation and Task Values play a supportive role as the motivational underpinnings that help in securing a higher grade. Particularly noteworthy is the highly negative impact of test anxiety on mathematics course outcomes; no other academic area comes even close on the level of impact produced by this factor, an outcome that mirrors the results of many studies in this area. Unfortunately, the variety of different types of scientific disciplines across the many areas of social and natural sciences from which student participation in the study originated was coupled with an equally rich variety of responses across the 15 constructs of the questionnaire. As a result, the only significant correlation for this academic area we can report is the relationship between good time and study management of students and their final grade. The nursing cohort, on the other hand, shows a completely different pattern, and one that is most aligned with the correlations between original MSLQ constructs and course grades. Within the traditional community college environment, nursing students have been long viewed as the top achievers, a group that displays the utmost dedication to the motivation and behaviors it takes to graduate and succeed in licensure exams. The magnitude of relationships between MSLQ factors and course outcomes shown in Table 4 underlines this dedication, but also the sensitivity of the instrument to the different learning environments and student cohorts. This becomes particularly visible when comparing the correlations between grades and the factors of Peer Learning and Help Seeking. Both seem to be a natural expression of the collaborative behaviors relevant in a nursing environment, but they only show high correlations for this group and display no course-level impact relevance across the other academic domains involved in this study.
  • 38. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   33 Table 4. Correlation between MSLQ Scales and Course Outcomes by Academic Area Pearson Correlation Coefficients English (N=152) Math (N=288) Science (N=90) Nursing (N=35) Grades Success Grades Success Grades Success Grades Success Motivational Constructs 1. Intrinsic Goal Orientation 0.02 -0.02 0.14* 0.11 -0.04 -0.04 0.17 0.19 2. Extrinsic Goal Orientation -0.01 -0.04 0.05 0.08 0.03 0.06 0.16 -0.06 3. Task Value 0.00 -0.08 0.13* 0.09 0.16 0.08 0.15 -0.05 4. Control of Learning Beliefs 0.08 -0.05 0.20** 0.16* 0.06 -0.01 0.28 0.07 5. Self-Efficacy Learn. & Perf. 0.18* -0.05 0.38*** 0.32*** 0.20 0.13 0.24 0.17 6. Test Anxiety -0.01 -0.02 -0.29*** -0.20** -0.08 0.04 -0.05 0.17 Learning Strategies 1. Rehearsal -0.02 0.03 -0.01 0.05 0.05 -0.04 0.36* 0.32 2. Elaboration 0.06 -0.03 -0.04 -0.04 0.07 -0.04 0.28 0.20 3. Organization 0.06 0.03 0.03 0.05 0.14 -0.01 0.25 0.10 4. Critical Thinking 0.03 -0.07 -0.09 -0.04 -0.14 -0.19 0.13 0.03 5. Metacognitive Self-Regulation 0.03 -0.02 -0.03 0.00 0.07 -0.13 0.33 0.18 6. Time/Study Management 0.26** 0.24** 0.06 0.00 0.27* 0.00 0.41* 0.30 7. Effort Regulation 0.20** 0.15 0.19** 0.10 0.09 0.01 0.47** 0.42* 8. Peer Learning -0.09 -0.02 -0.07 -0.01 0.08 -0.01 0.34 0.19 9. Help Seeking 0.01 0.04 -0.08 0.02 0.07 -0.05 0.20 0.09 Note: *p < .05. **p < .01. ***p < .001. As indicated earlier, one of the main concerns that triggered our research is the need for a clear understanding of student success factors related to instructional and general academic support. The absence of any significant correlation between Metacognitive Self-Regulation and most other Learning Strategies-related factors with course-level outcomes certainly emphasizes the fact that underprepared high-school
  • 39. Learning Success Factors Across Course Delivery Formats International Journal of Self-Directed Learning Volume 9, Number 1, Spring 2012   34 students do not transform magically into postsecondary success stories; they need the college’s environment and support to help them gain the necessary skills to master the challenges they face. The need for additional support for developmental students becomes even more obvious when we assemble all MSLQ constructs to carry out a multivariate regression analysis in assessing the combined impact of motivational factors and learning strategies on course outcomes achieved by participating students across all academic disciplines involved in this study. Table 5 details the results of this analysis, Table 5. Multivariate Regression Analyses Multivariate Regression Analyses Grades Success F-Value Pr > F R- Square F-Value Pr > F R- Square Model 6.15 <.0001 0.16 3.55 <.0001 0.10 Parameter Estimates t-Value Pr > |t| St-B t-Value Pr > |t| St-B Intrinsic Goal Orientation -1.33 0.1838 -0.08434 -0.69 0.4930 -0.04503 Extrinsic Goal Orientation -0.39 0.6940 -0.01845 -0.10 0.9166 -0.00509 Task Value 1.22 0.2247 0.06795 0.26 0.7967 0.01493 Control of Learning Beliefs -1.67 0.0958 -0.11134 -2.02 0.0434 -0.13996 Self-Efficacy for Learning & Performance 5.70 <.0001 0.42505 5.07 <.0001 0.39189 Test Anxiety -1.90 0.0577 -0.09207 -0.99 0.3212 -0.04977 Rehearsal 0.34 0.7307 0.02127 1.05 0.2956 0.06701 Elaboration -0.20 0.8401 -0.01278 -1.04 0.2984 -0.06825 Organization 0.11 0.9155 0.00657 0.25 0.8051 0.01582 Critical Thinking -0.84 0.4004 -0.04790 -0.80 0.4257 -0.04700 Metacognitive Self-Regulation -0.71 0.4796 -0.05465 -0.73 0.4641 -0.05864 Time/Study Environmental Management 1.97 0.0489 0.12231 0.54 0.5888 0.03471 Effort Regulation 0.31 0.7572 0.01957 0.39 0.6944 0.02576 Peer Learning -0.49 0.6210 -0.02886 -0.51 0.6130 -0.03059 Help Seeking -0.54 0.5873 -0.03169 0.69 0.4907 0.04169