Emerging Technologies for Learning

From Emerging Technologies for Learning

Contents 1 Introduction 2 Distributed Knowledge & Cognition 3 Attrition 4 Limitless Dimensions of Learning 5 Connectivism and Networked Learning 6 What is connectivism 7 Expertise 8 References

Introduction

Over the last century, educator’s understanding of the process and act of learning has advanced considerably. In the early 20th century researchers viewed learning through the lens of behaviourism – relegating the inner workings of the mind to the status of a black box, seeking instead to focus on observable and manipulate-able external behaviours. This view served well the industrial age in which it developed – the information age was still decades in the future.

As researchers and educators probed more deeply into the process of learning, the weaknesses of behaviourism became evident. How can depth of understanding be discerned under a behaviourist model? How are emotions and learner motivation accounted for in behaviourism? Since the mid 20th century, cognitivism and constructivism have developed as learning theories to address the weaknesses of behaviourism. In the last decade learning sciences have advanced sufficiently to provide educators with a fairly well developed body of research, that can be used as a guide in making decisions about developing learning activities and approaches for effective learner engagement. Learning sciences are an interdisciplinary science, bringing "together researchers in psychology, education, computer science, and anthropology".^[1]

A review of existing literature on learning reveals four broad components and three distinct processes through which these components are enacted. The components (detailed in Image 6), include:

• Social. Learning is a social^[2] process. Knowledge is an emergent property of interactions between networks of learners.
• Situated. Learning occurs within particular situations or contexts. Both "learning and cognition...are fundamentally situated",^[3] raising the importance of educational activities mirroring actual situations of use.
• Reflective. Learners requires time to assimilate new information. Learners require the "opportunity to reflect on, defend, and share what they have learned if it is to become part of their available repertoire"^[4]
• Multi-faceted. Learning incorporates a range of theory, engagement, "tinkering" or bricolage, and active construction.^[5]

The social, situated, reflective, and multi-faceted aspects of learning are expressed through various educational approaches:

• Self-paced. Reflected in traditional distance education models relying on open enrolment
• Guided. Increased assistance (through tutors or instructors) provided to learners. May be self-paced in an open enrolment model or through a paced (fixed start/end date)
• Cohort. With peers - paced and guided

Social software can play an important role in self-directed distance education environments,^[6] allowing learners the freedom of self-paced instruction with the social support of contact with peers. Through forums, blogs, social networking tools such as ELGG, and others, a sense of connectedness to other learners can be fostered that is currently lacking in many distance education programs.

Distributed Knowledge & Cognition

"All the knowledge is in the connections" David Rumelhart

Knowledge is distributed across a network that includes people and objects. To navigate, make sense, and come to understand (even grow and advance) knowledge, the process of cognition is also distributed across networks,^[7] and includes "interactions between people and with resources and materials in the environment".^[8] Building an airplane is a complicated task, involving engineers, electricians, managers, and others. The capacity to build an airplane is realized when special knowledge domains and skills are connected.

Participatory sense-making – the view that learners coordinate activity in "interaction, whereby individual sense-making processes are affected and new domains of social sense-making can be generated that were not available to each individual on her own"^[9] - is particular valuable in a networked world. The personal network an individual has created (which can include blogs, trusted experts, communities, informal learning tools like online search) plays a vital role in his/her ability to make sense of changes and trends. The network, in essence, becomes a filtering agent assisting educators and learners to make sense of, and manage, the incessant waves generated by an increasing sea of information.

Attrition

Attrition – particularly in online and distance learning – may be minimized through increased attention on the components of effective learning. The importance of engagement ("creating habits of mind")^[10] and motivation cannot be overstated as foundational to learner retention. In traditional institutions, attrition can be reduced when students are academically and socially integrated with the institution of study^[11]. The need for social contact is arguably more important online that in regular face-to-face institutions. In addition to high levels of self-motivation, appropriate institutional support, and access to needed learning resources, distance (and online) learners need to "develop interpersonal relationships with peers, faculty, and staff".^[12] Existing centralized learning models (learning management systems) are conceptually mismatched to the distributed, social, situated, and personal agent views of learning. Social software may provide a better model for educators to consider, as it places greater emphasis on "self-governed, problem-based and collaborative learning processes".^[13]

Limitless Dimensions of Learning

The full spectrum of learning (Image 7) - formal, informal, simulation, mentoring, performance support, self-learning (awareness of self and thinking habits), and communities - must be attended to by the educational process. Learning as capacity-development emphasizes attention to each of these domains. An engineer working in a distributed team requires different learning assistance than a salesperson making contact with a new client. Classroom and course-based learning are only a single aspect of a broad spectrum of learning needs. To date, universities have focused on formal education. With increased attention, in corporations and society, being paid to lifelong learning, and with the advancement of prior learning assessment and recognition (PLAR), it is conceivable that universities will begin acknowledging a broader spectrum of learning experiences than they have in the past.

Connectivism and Networked Learning

"Only connect! That was the whole of her sermon. Only connect the prose and the passion, and both will be exalted, and human love will be seen at its height. Live in fragments no longer."
E.M. Forster, 1910

Given the increasingly complex world of information, and the social, multi-faceted dimensions of learning, it’s appropriate to address new views of learning and teaching.

Net pedagogy has been suggested as a means to consider the "changing landscape of teaching and learning online".^[14]

Connectivism^[15][16], has also been suggested as a model of learning in an age defined by networks.

Networks and connections are deceptively simple. It would not appear that the formation of a simple connection has the capacity to reverberate across a network, rewriting both form and function. And yet it does. Latent semantic analysis suggests that "people have much more knowledge than appears to be present in the information to which they have been exposed",^[17] or put another way, the addition of a new element of information yields a greater impact than what exists within the information itself.

New information (a node) creates a ripple effect altering the meaning of other nodes within a network. A new node of information results in new connections, which in turn results in new knowledge, and thereby increased understanding on the part of the learner. Knowledge is a function of connections and understanding is the emergent shape of the network.

What is connectivism

Connectivism is the view that knowledge and cognition are distributed across networks of people and technology and learning is the process of connecting, growing, and navigating those networks. What does it mean to say that learning is networked? Learning can be described as a network on three separate levels (see Image 8).

1. Neural level – the formation of neural connections as new stimuli, input, and experiences shape the physical development of the brain.^[18] Research suggests connections and networks are prominent in memory formation and activation.^[19] Knowledge and learning are not held at any particular point in the human brain. Instead, they are distributed across numerous sections. Knowledge is an emergent attribute of patterns of neural connectivity.
2. Conceptual level - within a discipline or field of knowledge. Key concepts of a field – those which are foundational to the knowledge of a discipline – are networked in structure.^[20] Novice learners seeking to develop advanced understanding of a discipline do so through the formation of conceptual connections similar to those held by experts within the field.
3. External. The formation of networks has been significantly aided through the development of participatory web technologies. Blogs, wikis, social bookmarking, and social networking sites, raise the capacity of individuals to connect with others, with experts, and with content. Understanding, in a networked sense, is an emergent element related to the shape and structure of the learner’s personal information and social networks. The development of RSS as a means of aggregating information and mashups as a means of combining information in various contexts, contributes to the external formation of networks which in turn assist learners in forming accurate conceptual relationships within the field. High levels of participation in social networks, especially with younger learners, "suggests new ways of thinking about the role of education".^[21]

While network attributes are similar in all three levels of networked learning, a node, however, differs in each instance. A node in a neural network is a neuron. In a conceptual network, a node is an idea or collection of ideas (networks can serve as nodes when connected to larger network structures). In an external network, a node is a person, an information source, or similar entity capable of accepting connections and thereby participating in a network.

Expertise

"More than anything else, being an educated person means being able to see connections so as to be able to make sense of the world and act within it in creative ways. All of the other qualities that I’ve just described—listening, reading, writing, talking, puzzle-solving, seeing the world through others’ eyes, empowering others, leading—every last one of these things is finally about connecting." William Cronon

Developing expertise requires sustained attention and focus, a concept at odds with the rapidly changing, sometimes transient relationships many individuals have with information. Educators must balance what is known about the development of expertise with the motivational aspects of new technologies and the innovative (sometimes motivating) uses of these tools. Expertise is "largely a matter of amassing considerable skills, knowledge, and mechanisms that monitor and control cognitive processes to perform a delimited set of tasks efficiently and effectively".^[22]

Is a simple connection sufficient? Numerous taxonomies (Fink, Wiggins, Bloom) indicate that knowledge and learning can be characterized by gradients, levels, and stages. Perhaps we have been conditioned to expect something as complex as learning to require a complex process or explanation. But what if forming a connection is enough? What if learning is as simple (for the purposes of most educators) of getting learners to form diverse networks representing divergent viewpoints and cultures? What if exposing learners to rich networks of content and conversation is sufficient? The learners will, after all, begin to "play", make sense, interact, and grow in knowledge and understanding.

A second component requires consideration: the depth and quality of learning in a network. Sometimes learning involves forming networks and connections at a basic Level (often with the intent of creating awareness of related fields which may impact our own area of expertise). This is weak tie learning. Learning in this instance is defined by creating connections to peripheral fields or simply interacting briefly with new information and then moving on. Strong tie learning, on the other hand, involves more time, effort, expertise, and sustained focus. Geetha Narayanan defines this as slow learning where emphasis shifts from speed to depth and wholeness of learning.^[23]

Sometimes educators want learners to gain an awareness of factors, other times we want them to interact with elements in order to understand deeply. Sometimes educators want learners to develop knowledge for foundation building. Different knowledge-network connections, defined by strength of the tie, result in different depth of learning. Perhaps "only connect" is still (almost 100 years later) a sufficient motto. Perhaps the elimination of barriers to connection is the greatest systemic challenges our institutions face. And the role of teaching is one of guiding, directing, and curating the quality of networks learners are forming.

References

1. ↑ Sawyer, R. K. (Ed.). (2005). The new science of learning. In The Cambridge handbook of learning sciences. Available from http://www.cambridge.org/us/catalogue/catalogue.asp?isbn=9780511217685
2. ↑ Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. New York: Cambridge University Press.
3. ↑ Seely Brown, J., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.
4. ↑ Merrill, D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43–59.
5. ↑ Papert, S. (1991). Situating constructionism. Retrieved February 20, 2009, from http://papert.org/articles/SituatingConstructionism.html
6. ↑ Anderson, T. (n.d.). Distance learning—Social software’s killer ap? Retrieved March 1, 2009, from http://www.unisa.edu.au/odlaaconference/PPDF2s/13%20odlaa%20-%20Anderson.pdf
7. ↑ Pea, R. D. (1993). Practices of distributed intelligence and designs for education. In G. Salomon (Ed.), Distributed cognitions (pp. 47–87). New York: Cambridge University Press.
8. ↑ Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 7(2), 174–196.
9. ↑ De Jaegher, H., & Di Paolo, E. (2007). Participatory sense-making: An enactive approach to social cognition. Phenomenology and the Cognitive Sciences, 6(4), 485–507.
10. ↑ National Survey of Student Engagement. (2008). Promoting engagement for all students: The imperative to look within. Retrieved March 1, 2009, from http://nsse.iub.edu/NSSE_2008_Results/docs/withhold/NSSE2008_Results_revised_11-14-2008.pdf
11. ↑ Tinto, V. (1982). Dropout from Higher Education: A Theoretical Synthesis of Recent Research Review of Educational Research, 45(1), 89-125
12. ↑ Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. Internet and Higher Education. 1-16.
13. ↑ Dalsgaard, C. (2006). Social software: E-learning beyond learning management systems. European Journal of Open, Distance and E-Learning. Retrieved February 20, 2009, from http://www.eurodl.org/materials/contrib/2006/Christian_Dalsgaard.htm
14. ↑ The net pedagogy portal http://www.thewebworks.bc.ca/netpedagogy/
15. ↑ Siemens, G. (2005). Connectivism: A learning theory for a digital age. International Journal of Instructional Technology and Distance Learning, 2(1). Retrieved February 20, 2009 from http://www.itdl.org/Journal/Jan_05/article01.htm
16. ↑ Learning Technologies Centre. (2008, November). Connectivism and connective knowledge online course. Retrieved March 1, 2009, from http://ltc.umanitoba.ca/wiki/Connectivism
17. ↑ Landauer, T. K., Dumais, S. T. (1997). A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge. Retrieved February 20, 2009 from http://lsa.colorado.edu/papers/plato/plato.annote.html.
18. ↑ Bechtel, W., & Abrahamsen, A. (2002). Connectionism and the mind: Parallel processing, dynamics, and evolution of networks (2nd ed.). Malden, MA: Blackwell.
19. ↑ Reder, L.M., Park, H., & Kieffaber, P.D. (2009). Memory systems do not divide on consciousness: Reinterpreting memory in terms of activation and binding. Psychological Bulletin, 135(1), 23-49
20. ↑ Novak, J. D., & Cañas, A. J. (2006b). The theory underlying concept maps and how to construct them. Retrieved December 26, 2007, from Institute for Human and Machine Cognition Web site: http://cmap.ihmc.us/Publications/ResearchPapers/TheoryCmaps/TheoryUnderlyingConceptMaps.htm
21. ↑ Ito, M., Horst, H., Bittanti, M., boyd, d., Herr-Stephenson, B., Patricia G. Lange, P. G., et al. (2008, November). Living and learning with new media: Summary of findings from the digital youth project. Retrieved February 20, 2009, from http://www.macfound.org/atf/cf/%7BB0386CE3-8B29-4162-8098-E466FB856794%7D/DML_ETHNOG_WHITEPAPER.PDF
22. ↑ Feltovich, P. J., Prietula, M. J., Ericsson, K. A. (2006). Studies of expertise from psychological perspectives. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), Cambridge handbook of expertise and expert performance (Paperback, pp. 41–67). London: Cambridge University Press.
23. ↑ Narayanan, G. (2007). A Dangerous but Powerful Idea - Counter Acceleration and Speed with Slowness and Wholeness. Retrieved on February 20, 2009 from http://kt.flexiblelearning.net.au/tkt2007/edition-13/narayaran/