Darrell A. Dromgoole, Associate Professor and Extension Specialist, Texas A&M AgriLife Extension Service.
Scott Cummings, Associate Department Head and Program Leader; Professor and Extension Specialist, Texas A&M AgriLife Extension Service.
In response to the Covid 19 pandemic Extension educators across the state increased their utilization of digital engagement to deliver program content. As digital engagement becomes more predominant as a means of delivering program content it is important to consider the theories and frameworks for online education to ensure the effectiveness of programs that incorporate digital engagement in some fashion. Last week we discussed behaviorism and cognitivism. In this installment of Next Step to Success we will discuss theoretical frameworks relevant to the pedagogical aspects of online education.
Just as no single learning theory has emerged for instruction in general, the same is true for online education. A number of theories have evolved, most of which derive from the major learning theories previously discussed. In this section, several theories will be examined in terms of their appropriateness for the online environment.
Similar to behaviorism and cognitivism was the work of several education theorists, including Lev Vygotsky, John Dewey, and Jean Piaget (Picciano, A., 2017). Their focus on social constructionism was to describe and explain teaching and learning as complex interactive social experiences between clientele and educator (Picciano, A., 2017). Vygotsky theorized that learning is problem solving and that the social construction of solutions to problems is the basis of the learning process (Picciano, A., 2017). Vygotsky described the learning process as the establishment of a “zone of proximal development” in which the teacher, the learner, and a problem to be solved exist (Picciano, A., 2017). In Extension educator the Extension educator provides a social environment in which the clientele can assemble or create with others the knowledge necessary to solve the problem. Similarly, John Dewey saw learning as a series of practical social experiences in which learners learn by doing, collaborating, and reflecting with others (Picciano, A., 2017). While developed in the early part of the 20th century, Dewey’s work is very much in evidence in a good deal of present-day social constructivist instructional design (Picciano, A., 2017). The use of reflective practice by both learner and teacher is a pedagogical cornerstone for interactive discussions that replaces straight lecturing, whether in a face-to-face or online environment (Picciano, A., 2017). Jean Piaget, whose background was in psychology and biology, based his learning theory on four stages of cognitive development that begin at birth and continue through one’s teen years and beyond (Picciano, A., 2017). Seymour Papert, in designing the Logo programming language, drew from Jean Piaget the concept of creating social, interactive microworlds or communities where children, under the guidance of a teacher, solve problems while examining social issues, mathematical and science equations, or case studies (Picciano, A., 2017). Papert’s approach of integrating computer technology into problem solving is easily applied to many facets of instructional design of online courses (Picciano, A., 2017).
A number of theories and models have roots in one or more of the above frameworks. In the latter part of the 20th century, the major learning theories, especially cognitive theory and social constructivism, began to overlap (Picciano, A., 2017).. For example, Wenger and Lave (1991) and Wenger (1998) promoted concepts such as “communities of practice” and situated learning. Their position was that learning involves a deepening process situated in, and derived from, participation in a learning community of practice. Their work is very evident in many studies, including those related to online education (Picciano, A., 2017).
Information processing learning theory is a variation of cognitivism that views the human mind as a system that processes information according to a set of logical rules (Picciano, A., 2017). In it, the mind is frequently compared to a computer that follows a set of rules or program (Picciano, A., 2017). Research using this perspective attempts to describe and explain changes in the mental processes and strategies that lead to greater cognitive competence as children develop. Richard Atkinson and Richard Shiffrin (1968) are generally credited with proposing the first information processing model that deals with how students acquire, encode, store (in short-term or long-term memory), and retrieve information.
One of the more popular and controversial theories relates to learning styles and suggests that individuals learn differently depending upon their inclinations and personalities. Carl Jung argued that individual personality types influence various elements of human behavior, including learning. Jung’s theory focuses on four basic psychological dimensions (Picciano, A., 2017):
While each unique dimension can influence an individual learning style, it is likely that learning styles are based on a combination of these dimensions (Picciano, A., 2017). For example, a learning style might include elements of extroversion, sensation, feeling, and perception as personality dimensions. Many educators are familiar with the Myers-Briggs Type Inventory (MBTI) which has been used for decades to assist in determining personality types, including how personality relates to student learning (Picciano, A., 2017). The MBTI is based extensively on Jung’s theories and has been used to predict and develop different teaching methods and environments and to predict individual patterns of mental functioning, such as information processing, idea development, and judgment formation (Picciano, A., 2017). It can also be used to predict patterns of attitudes and interests that influence an individual’s preferred learning environment and to predict a person’s disposition to pursue certain learning circumstances and avoid others (Picciano, A., 2017). Lin, Cranton & Bridglall (2005) indicated that much of the work of Carl Jung and the MBTI is applicable to learning environments, whether face-to-face or online. For example, the extrovert may prefer active, highly collaborative environments while the introvert would prefer less interaction and less collaboration (Picciano, A., 2017). This suggests that instruction should be designed to allow both types of individuals—the outgoing social organize as well as the introspective reflective observer—to succeed (Picciano, A., 2017).
Howard Gardner (1983) has developed a theory of “multiple intelligences” that proposes that intelligece is not merely a singular entity but consists of multiple entities used by individuals in different proportions to understand and to learn about the world (Picciano, A., 2017). Gardner has identified nine basic intelligences: linguistic, logical/mathematical, spatial, musical, bodily kinesthetic, interpersonal, intrapersonal, naturalistic, and existential (see Figure 1). Gardner’s theory has received criticism from both psychologists and educators who view these “intelligences” as talents, personality traits, and abilities (Picciano, A., 2017). His work has also been questioned by those who propose that there is, in fact, a root or base intelligence that drives the other “intelligences.” Gardner does not necessarily disagree with this latter position but maintains that other intelligences can be viewed as main branches off the base root intelligence (Picciano, A., 2017). This theory has important pedagogical implications and suggests the design of multiple learning modalities that allow learners to engage in ways they prefer, according to their interest or ability, and to challenge them to learn in other ways that are less related to their preferences, interests, or abilities (Picciano, A., 2017).. Gardner’s work also addresses the common concern that too much teaching and learning is linguistically based (reading, writing, and speaking) and that the other intelligences are underutilized (Picciano, A., 2017).
Modern neuroscience research also suggests that students learn in different ways depending upon a number of factors including age, learning stimuli, and the pace of instruction (Picciano, A., 2017). Willingham (2008) suggests that learning is a dynamic process that may evolve and change from one classroom to another, from one subject to another, and from one day to another (Picciano, A., 2017). This research also supports the concept that multiple intelligences and mental abilities do not exist as mere “yes/no” entities but within ranges which the mind blends in a manner consistent with the way it responds and learns from the external environment and instructional stimuli (Picciano, A., 2017). Conceptually, this suggests a framework for a multimodal instructional design that relies on a variety of pedagogical techniques, delivery approaches, and media (Picciano, A., 2017).
In addition to these theories, Malcom Knowles (1998) warrants consideration as the individual who distinguished between andragogy (adult learning) and pedagogy (child learning) (Picciano, A., 2017). Adults, whether seeking to enhance their professional skills or to improve their quality of life, learn differently than children (Picciano, A., 2017). Courses designed for adults should tap into their social contexts and experiences (Picciano, A., 2017). Knowles’ insights are especially important for Extension education, where online technology is used for adult clientele in traditional Extension educational programs and continuing education programs, competency-based learning, and career/professional development.
Figure 1. Gardner’s Multiples Intelligences.
The “community of inquiry” model for online learning environments developed by Garrison, Anderson & Archer (2001) is based on the concept of three distinct “presences”: cognitive, social, and teaching (see Figure 2). While recognizing the overlap and relationship among the three components, Anderson, Rourke, Garrison, and Archer (2001) recommended further research on each component. Their model supports the design of online and blended courses as active learning environments or communities dependent on instructors and clientele sharing ideas, information, and opinions (Picciano, A., 2017). Of particular note is that “presence” is a social experience and manifests itself through interactions among clientele and clientele (Picciano, A., 2017). The community of inquiry has become one of the more popular models for online and blended courses that are designed to be highly interactive among clientele and faculty using discussion boards, blogs, wikis, and videoconferencing (Picciano, A., 2017).
Figure 2. Community of Inquiry (Garrison, Anderson, Garrison and Archer, 2000).
George Siemens (2004) has been the main proponent of connectivism, a learning model that recognizes major shifts in the way knowledge and information flows, increases, and changes because of immense data communications networks (Picciano, A., 2017). Internet technology has moved learning from internal, individualistic activities to group activities (Picciano, A., 2017). In developing the theory, Siemens acknowledged the work of Alberto Barabasi and the power of networks (Picciano, A., 2017). He also referenced an article written by Karen Stephensen (1998) entitled “What Knowledge Tears Apart, Networks Make Whole,” which accurately identified how large-scale networks become indispensable in helping people and organizations manage data and information. Siemens describes connectivism as:
the integration of principles explored by chaos, network, and complexity and self organization theories [where] learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more and are more important than our current state of knowing” (Siemens, 2004).
Siemens noted that connectivism as a theory is driven by the dynamic of information flow (Picciano, A., 2017). Students need to understand, and be provided with, experiences in navigating and recognizing constantly shifting and evolving information (Picciano, A., 2017). Siemens proposed eight principles of connectivism (see Figure 3). Connectivism is particularly appropriate for courses with very high enrollments and where the learning goal or objective is to develop and create knowledge rather than to disseminate it (Picciano, A., 2017).
Figure 3. Siemens’ Eight Principles of Connectivism.
In future Next Step to Success blogs we will continue to discuss educational theories and models that influence the effectiveness of online education.
Anderson, T., Rourke, L., Garrison, D.R, and Archer, W. (2001). Assessing social presence in asynchronous text-based computer conferencing. Journal of Asynchronous Learning Networks, 5(2) Retrieved from: http://immagic.com/eLibrary/ARCHIVES/GENERAL/ATHAB_CA/Anderson.pdf.
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In Spence, K. W., & Spence, J. T. The psychology of learning and motivation (Volume 2). New York: Academic Press. pp. 89–195.
Anderson, L., & Krathwohl, D. (2001). A taxonomy for learning, teaching, and assessing, Abridged Edition. Boston, MA: Allyn and Bacon.
Barabasi, A. (2002). Linked: The new science of networks. Cambridge, MA: Perseus Publishing.
Dewey, J. (1916). Democracy and education. New York: The Free Press.
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books.
Jung,C. Psychological types. Original in German. Zurich: Rascher Verlag. (1921). There are a number of English translations.
Knowles, M., Holton, E. & Swanson, R. (1998) The adult learner (5th Edition). Houston: Butterworth-Heinemann Publishers.
Picciano, A. (2017). Theories and frameworks for online education: Seeking an integrated model. Online Learning, 21(3), 166-190. doi: 10.24059/olj.v21i3.1225.
Siemens, G. (2004). Connectivism: A learning theory for the digital age. Paper retrieved from: http://www.elearnspace.org/Articles/connectivism.htm.
Stephenson, K., (1998). Internal Communication, No. 36: What Knowledge Tears Apart, Networks Make Whole. Retrieved from http://www.netform.com/html/icf.pdf.
Wenger, E. & Lave, J. (1991). Situated learning: Legitimate peripheral participation (Learning in doing: Social, cognitive and computational Perspectives. Cambridge: Cambridge University Press.
Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge: Cambridge University Press.
Willingham, D. (2008). What is developmentally appropriate? American Educator, 32(2), pp. 34-39.