Leading Change in Digital Learning Environments

Leading Change in Digital Learning Environments
Leading Change in Digital Learning Environments (Kuipers, 2020) – Click to view full size

 

Change is interconnected. Systems theory asserts that “a change in any part of the system creates change throughout the system” (Biech, 2007). This cascade of cause-and-effect suggests that for an organizational change to be successful, it needs to account for many interconnected elements. My infographic identifies six elements that I believe are essential for leading change in digital learning environments (Kuipers, 2020). On its own, this infographic is not a step-by-step model for change, but highlights valuable questions for leaders to consider when planning for change.

Before looking at how to implement change, leaders need to be able to answer why: Why change, why this change, and why now? Answering these questions can help leaders to shape an informational strategy for change (Biech, 2007). In my infographic, I’ve identified readiness and relevancy as the first gears to begin turning (Kuipers, 2020). Technology evolves in a continuous hype cycle (Gartner, n.d.), and digital learning environments are no exception. For the teachers and students within these environments to support a proposed change, they need to feel it is necessary and relevant (Weiner, 2009). “Problems arise when some feel committed to implementation but others do not” (p. 2). The first step for leading change should be to assess how ready an organization is to change.

A sense of personal relevancy is also essential for members of an organization to value a change and feel committed to it. When undergoing a fundamental shift in thinking, a colleague of mine noted the impact of running professional development to share the research and evidence for that change (R. Parker, personal communication, February 21, 2020). Sharing the underlying rationale gives people time to make personal connections to the evidence and discover how it is relevant to them. Building this sense of readiness and relevancy begins turning the gears towards a shared vision for change.

Leading change requires creating a vision and empowering others to act on it. This shared vision often involves a cultural shift and change in language, forming an attitudinal strategy for change (Biech, 2007). In my infographic, vision is the largest and most interconnected gear (Kuipers, 2020), which serves to illustrate the central role it plays in several models for change (Jick & Kanter, 1992; Kotter, 1998; Lecke, 2003; as cited in Al-Haddad & Kotnour, 2015). In an interview, a colleague expressed how a shift in language was essential for creating a shared vision (R. Parker, personal communication, February 21, 2020). Within his context, leadership took on the roles of thought-leader and provocateur, providing opportunities for staff to encounter and grapple with new perspectives. This form of attitudinal strategy aims to “change mindsets and, as a result, change behavior” (Biech, 2007).

Along with a shared vision, members of an organization need to be empowered to act on that vision (Kotter, 1996). In a digital learning environment, this may mean facilitative empowerment such as resources and technology (Biech, 2007), but also organizational empowerment through adaptive leadership to be a change-agent among peers (Khan, 2017). With a shared vision and empowerment within an organization, change can begin to pick up momentum.

Successful change requires time and the ability to gain momentum within an organization. Where vision and empowerment can create an attitudinal strategy for change, time and momentum can facilitate it. Creating this facilitative strategy “depends on a shared responsibility and the involvement of everyone in the organization” (Biech, 2007). When implementing a disruptive change to a school’s timetable, a colleague described how they gained momentum through in-person communication, both internal with staff and external with the community (M. Brown, February 21, 2020). In his context, leadership also needed to overcome resistance, which meant ensuring staff and students had resources and support to “pace [their] lessons, assignments and expectations” based on the changed timetable. In my interviews, colleagues often referred to this momentum as “buy-in”: a shared vision may get the gears turning, but without time and support, a change is unlikely to pick up momentum within an organization.

Managing successful change requires not only a plan but also an understanding of the interconnectedness of that plan. Can leaders create a shared vision without time? Will a new platform pick up momentum if it is irrelevant? By approaching change through a systems theory perspective, leaders can consider the people, technologies, and behaviours that are affected by their change (Biech, 2007). My infographic aims to provoke thought and ask questions, the answers to which may help leaders plan for more successful changes in their digital learning environments.

References

Al-Haddad, S., & Kotnour, T. (2015). Integrating the organizational change literature: A model for successful change. Journal of Organizational Change Management, 28(2), 234–262. https://doi.org/10.1108/JOCM-11-2013-0215

Biech, E. (2007). Models for Change. In Thriving Through Change: A Leader’s Practical Guide to Change Mastery. Alexandria, VA: American Society for Training and Development. Retrieved from https://ezproxy.royalroads.ca/sso/skillport?context=22651

Gartner. (n.d.). Gartner Hype Cycle | Hype Cycle Research Methodology [Website]. Retrieved from https://www.gartner.com/en/research/methodologies/gartner-hype-cycle

Khan, N. (2017). Adaptive or transactional leadership in current higher education: A brief comparison. International Review of Research in Open and Distance Learning, 18(3), 178–183. https://doi.org/10.19173/irrodl.v18i3.3294

Kotter, J. P. (1996). Leading change. Boston, MA: Harvard Business School Press. Kouzes, JM, & Posner, BZ (2002). The leadership challenge. San Francisco, CA.

Kuipers, S. (2020). Leading change in digital learning environments [Infographic]. Retrieved from https://malat-webspace.royalroads.ca/rru0128/wp-content/uploads/sites/158/2020/02/Assignment1-Visual.png

Weiner, B. J. (2009). A theory of organizational readiness for change. Implementation Science, 4(1), 1–9. https://doi.org/10.1186/1748-5908-4-67

Leading Change Through Subtraction

Many changes in modern schools are driven by the imperative to introduce new technologies. However, not all improvements are made by adding. What does change look like when we consider the subtraction of technology from a school? One of the most thoughtful organizational changes I have read about recently was an article by Ross Parker (2020) regarding the evolving technology policy at International College Hong Kong (ICHK). In “Can We Stop Software From Eating School?,” Parker expresses a growing concern over device use in schools, and the decision to reclaim “some of the quiet space commandeered by digital technology” (para. 14). His article builds a narrative of why ICHK decided to restrict the use of devices on campus, and how leading this scale of change took careful consideration and planning.

Subtracting technology from modern schools is not an easy change, and it “swims upstream” from the prevailing trend. At ICHK, leadership “asked [them]selves how [they] could orchestrate a sea change, without coming across as a bunch of old, irrelevant reactionary Luddites” (Parker, 2020, para. 13). Applying Al-Haddad and Kotnour’s (2015) taxonomy, the change in technology policy at ICHK represents a large-scale long-term change, which required internal alignment of the change type and change methods employed. Although it’s not apparent if a specific change method was used, it is evident that this change was made through a holistic approach. Leadership spent “9 months of intense discussion, drafting, consultation, introspection and iterative improvement” (Parker, 2020, para. 13), which is congruent with Kotter’s focus on Leading Change through a shared vision and strategy (Kotter, 1996, as cited in Al-Haddad & Kotnour, 2015). The result was a conscientious cultural change in the school that focused on subtraction.

Organizations that see change as addition without subtraction may end up with a soup of educational technology, seasoned with policies and chunky add-ons. This soup is the is the exact situation that many digital learning platforms end up in. Feldstein (2017) shares a cautionary narrative of adding, adding, and adding features to educational apps. Particularly adding features that are redundant and overlap with other systems. He terms this effect “Feldstein’s Law: Any educational app that is actively developed for long enough and has a large enough user base will become indistinguishable from a badly designed LMS” (para. 19). As a software developer working with educational technology, I have seen this runaway addition of features in several projects. Faced with a “a sense of urgency as emerging technical practices … challenge the traditional academic processes” (Udas, 2008, para. 2) the response is often to continue adding one new idea to the next.

Change is not just addition. It can—and vitally, should—include subtraction. The direction of a change should be considered along side Al-Haddad and Kotnour’s (2015) change types of scale and duration. Leaders looking to make change in their organizations can make equally powerful impacts by subtracting rather than adding: perhaps phasing out a technology, scaling back on an initiative, or pruning an unwieldy policy.

References

Al-Haddad, S., & Kotnour, T. (2015). Integrating the organizational change literature: A model for successful change. Journal of Organizational Change Management, 28(2), 234–262. https://doi.org/10.1108/JOCM-11-2013-0215

Feldstein, M. (2017). A flexible, interoperable digital learning platform: Are we there yet? [Blog post]. Retrieved from http://eliterate.us/flexible-interoperable-digital-learning-platform-yet

Parker, R. (February 10, 2020). Can we stop software from eating school? [Blog post]. Retrieved from https://medium.com/@rossdotparker/can-we-stop-software-from-eating-school-640a0e05ec4c

Udas, K. (June 30, 2018). Distributed learning environments and OER: The change management challenge. [Blog post]. Retrieved from https://web.archive.org/web/20160309200155

Attribution

Photo by Pixabay on Pexels

Digital Leadership in Open Source

What does leadership look like when participation is voluntary? This question came to mind as I was recently reading about leadership attributes. In one of my professional contexts, I work as a maintainer and community leader for an open source school platform called Gibbon. In this open source context, the people who choose to contribute their time and expertise to build the project are there of their own volition. I wondered: what attributes of leadership are the most essential in this situation? Throughout my readings, I used this question as a lens to examine and consider digital leadership in an open source context.

Trust is a cornerstone of open source communities, since many members join the community as strangers. In a blog post about digital leadership, Sheninger (2014) states that “it all begins with trust” (para. 6). He urges digital leaders to “give up control” in order to “unleash creativity and passion” in others (para. 6). This can be a difficult yet essential step for open source leaders. At some point, there’s too much work to be done by one person, and a leader needs to share the load. However, since members there there voluntarily and many have never met in person, it can be a tricky position to trust them, and in turn be trusted by them. In this way, trust is a two-fold attribute: both trusting—the capacity to place belief and reliance in others, and trustworthiness—the “ability to be relied on as honest or truthful” (“Trustworthiness”, n.d.). Kouzes and Posner (2011) suggest “the simple truth is that trusting other people encourages them to trust you, and distrusting others makes them more likely to distrust you” (p. 78). With this leadership attribute in mind, it may not be possible to build an open source community without some fundamental level of trust.

Leadership in an open source context should also be adaptive and flexible. Khan (2017) highlights how adaptive leadership provides a greater responsiveness towards change and increased motivation in followers. Her research finds that adaptive leadership is beneficial “in complex situations where the leader-follower relationship is attended to, but so are all environmental, cultural, and societal factors that will affect leaders and followers” (p. 180). Open source communities are fundamentally complex: their members may be anywhere in the world, speak different languages, and have different values. Paying attention to the leader-follower relationship in an open source community is also crucial because the organization structure may not follow a standard top-down hierarchy. Transactional reward-based leadership may be less effective because community members are already participating voluntarily, and their motivation is likely to be intrinsic rather than extrinsic. Adaptive leadership, with its flexibility and responsiveness towards complex factors, becomes an essential approach for digital leadership in open source communities.

Transparency and communication may also be essential leadership attributes for situations where participation is voluntary. Sheninger (2014) numbers communication as the first of seven Pillars of Digital Leadership in Education. He states that “digital leadership is about engaging all stakeholders in two-way communication” (para. 9). I think two-way communication is a logical foundation for open source communities: leaders may not see much headway by giving directives or commands one-way. “Static, one-way methods such as newsletters and websites [no longer] suffice” (Sheninger, 2014, para. 9). Community members are there voluntarily, and their motivation to contribute is likely tied to their having a voice in the project. For open source leaders to build a thriving community, they may need to build channels of communication that foster active two-way participation in the project.

What other leadership attributes are essential to a context where community members are voluntary, distributed globally, and motivated intrinsically? As I continue to research leadership and change in this course I hope to revisit the ideas in this blog post, and I’m curious to hear what leadership attributes my cohort members might suggest adding to this list.

References

Khan, N. (2017). Adaptive or transactional leadership in current higher education: A brief comparison. International Review of Research in Open and Distance Learning, 18(3), 178–183. https://doi.org/10.19173/irrodl.v18i3.3294

Kouzes, J. M., & Posner, B. Z. (2011). Engender Trust. In Credibility: How leaders gain and lose it, why people demand it. San Francisco, Calif.: Jossey-Bass. Retrieved from https://ezproxy.royalroads.ca/sso/skillport?context=43184

Sheninger, E. (2014). Pillars of Digital Leadership. International Center for Leadership in Education, 4. Retrieved from http://www.leadered.com/pdf/LeadingintheDigitalAge_11.14.pdf

Trustworthiness. (n.d.). In Lexico by Oxford University Press (OUP). Retrieved from https://www.lexico.com/en/definition/trustworthiness

Attribution
Image by MetsikGarden from Pixabay

Tech Ed 101: Technological Reproduction

Technology and civilization have stepped together through history, so much so that they are often equated as the same thing. When we look back through history, we’re often looking back at the progress of “technological evolution” (Dron, 2014, p. 241). Humans are so adept and noteworthy for their technological creations that the philosopher Marshall McLuhan famously suggests “that humans might be the ‘sex organs of the machine world’” (McLuhan, 1964, as cited in Dron, 2014, p. 240). Perhaps our technology classes in schools should add “Tech Ed”, and include a primer on technological reproduction. However humorous, the conflation of human evolution with technological evolution presents a conceptual problem: is every technology an advancement?

Understanding the difference between innovation and change is essential to understanding technological evolution. As with biological evolution, not every adaptation is beneficial: an organism’s environment will determine the “survival of the fittest” (Darwin, 1859). How do we determine what is fittest in a technological sense? Dron (2014) presents an overview of technological change in distance education. Among the change he studies are generational shifts, such as evolving pedagogies from behaviourist to constructivist models (p. 239). These shifts represent the environment for technology changing, and the fittest technologies would be the ones most adapted to the prevailing theories, ideas, and mindsets. However, even the metaphor of evolution suggests an overall advancement. In which ways can change be technological, but not innovative?

Perhaps innovation must represent a change in more than one aspect of an idea. The form of an object can change, but unless its purpose, intent, or mindset change, can it be called an innovation? For example, designing a better desk might be a classroom invention, but designing a teaching approach that doesn’t require desks may be a true innovation. Dron (2014) suggests that an important aspect of technology is the degree of choice it affords. Soft technologies allow greater choice and flexibility, whereas hard technologies limit choice (p. 241). “The more we embed processes and techniques in our tools, be they pedagogies or machine tools, the fewer choices are left to humans” (p. 242). By this definition, an innovation is something that moves on a continuum further towards being a soft technology. This suggests that innovation is a fundamental change, not just on the surface-level.

Can we actually define innovation? As Dron (2014) points out, even the concept of technology itself “is a slippery and evolving concept” (p. 239). The concept of innovation therefore remains even more elusive, but nonetheless important. As I teach young minds in my computer science class, I find myself wanting to add some more “Tech Ed” into the curriculum, and help foster a healthy skepticism of the world of technological innovation we live in.

References

Dron, J. (2014). Innovation and How we Change. Online Distance Education: Towards a Research Agenda, 237–265. Retrieved from http://books.google.co.za/books?hl=en&lr=&id=9dH9AwAAQBAJ&oi=fnd&pg=PA237&dq=water+taylor+and+francis&ots=FO3_1cbZvM&sig=bVYig9e3pS6iQrxQ64Vz9vB5Gzo

Darwin, C. (2004, Original work published 1859). On the origin of species, 1859. Routledge.

Attribution
Photo by Clint Patterson on Unsplash

Hype and Technology Acceptance

Hype can have positive and negative effects, yet I have always found the word to have a negative connotation. Gartner’s (2016) press release about emerging technology trends presents hype as a priority for business and innovation. The article stresses the importance of businesses chasing emerging technologies, lest they be left behind in the technological rat-race. As I read the article and examine the Gartner Hype Cycle (Gartner, n.d.), I am left wondering about the right side of the diagram: the “Slope of Enlightenment” and the “Plateau of Productivity” (Gartner, 2016, Figure 1). Do all these emerging technologies hit mainstream adoption and balance out in the middle, or do some never leave the “Trough of Disillusionment”? Hype, as a driver of progress and business, can be seen as a positive thing. Yet, hype can have an incredibly negative effect when it causes people to invest in technologies that never reach this theoretical plateau of productivity.

One place hype can have a notable effect is in technology acceptance models in schools. Reading the Gartner’s (2016) article reminded me of Dron’s (2014) article titled Innovation and How We Change. Dron suggests that “the uptake of technology is not simply a matter of whether people choose to use a technology but whether that technology actually has any real value” (p. 244). The “Peak of Inflated Expectations” in Gartner’s Hype Cycle certainly highlights that we often hype a technology long before it is proven to be useful. In my experience, this can have a negative impact on schools, who do not have the same funds as businesses to be chasing each new emerging technology. Taking time to analyze and consider a Technology Acceptance Model (TAM) can help technologies be “used, integrated, and absorbed into the educational system” (p. 243). However, even these models have been criticized as “idealized and empirically naïve” when applied to real-world contexts (Dron , 2014, p. 244).

In considering Gartner’s hype cycle, I wonder: are there ways that hype can have a positive effect on technology acceptance? Can hype help or hinder aspects of perceived usefulness and perceived ease of use that are central to the TAM approach?

References

Dron, J. (2014). Innovation and How we Change. Online Distance Education: Towards a Research Agenda, 237–265. Retrieved from http://books.google.co.za/books?hl=en&lr=&id=9dH9AwAAQBAJ&oi=fnd&pg=PA237&dq=water+taylor+and+francis&ots=FO3_1cbZvM&sig=bVYig9e3pS6iQrxQ64Vz9vB5Gzo

Gartner. (2016, August 16). Gartner’s 2016 Hype Cycle for Emerging Technologies Identifies Three Key Trends that Organizations Must Track to Gain Competitive Advantage. [Press Release]. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2016-08-16-gartners-2016-hype-cycle-for-emerging-technologies-identifies-three-key-trends-that-organizations-must-track-to-gain-competitive-advantage

Gartner. (n.d.). Gartner Hype Cycle | Hype Cycle Research Methodology [Website]. Retrieved from https://www.gartner.com/en/research/methodologies/gartner-hype-cycle

Attribution
Photo by Verena Yunita Yapi on Unsplash

An Ecosystem of Open Pedagogy

In my recent readings on open pedagogy and instructional design, an idea that resonated with me was that openness requires an ecosystem: the effort to create open resources and open pedagogy is just as critical as the effort to support, curate, and share those resources and practices. Bates (2019) argues that these resources “cannot successfully exist in a vacuum” (p. 594). He suggests that one reason OER has seen a slow adoption rate is the relative lack of supporting materials compared to commercial products. Developing open pedagogies is more than “licensing and content development” (p. 590); it requires an ecosystem of support. To thrive, openness needs people who not only plant the seeds but also nurture and cultivate the environment surrounding open practices and resources. In a blog post explaining the nature of the commons, Bollier (2011) aptly asserts that “there is no commons without commoning” (para. 5). Educators can work hard to create open resources, and they can choose to share them with permissive licenses. However, without a framework to help those resources grow, they may never have an opportunity to take root and see the light of day. To address this need, Stacey (2018) suggests that one area the Open Education Consortium could focus on is developing these support frameworks. He states that “simply having a community and pool of resources is not enough. There needs to be a set of protocols, values and norms devised by the community to manage its resources” (para. 8). In my tentative steps into the open pedagogy landscape, I have wondered where to begin. How do educators discover open pedagogies, let alone contribute to them? What are the frameworks that exist, and in which mediums? How do educators learn to become stewards of the commons? As advocates of openness embrace the tenants of “share alike”—planting the seeds of open content—we also need to be able to get our hands dirty, add some fertilizer, pull some weeds, and nurture the ecosystem of open education.

References

Bates, A.W. (2019). Teaching in a Digital Age – Second Edition. Vancouver, B.C.: Tony Bates Associates Ltd. Retrieved from https://pressbooks.bccampus.ca/teachinginadigitalagev2/

Bollier, D. (2011, July 15). The Commons, Short and Sweet [Blog post]. Retrieved from http://www.bollier.org/commons-short-and-sweet

Stacey, P. (2018, February 8). Global Education Commons Steward [Blog post]. Retrieved from https://edtechfrontier.com/2018/02/08/global-education-commons-steward/

Attribution
Photo by Francesco Gallarotti on Unsplash

The History of Programming Education: An Evolving Narrative of Why and How to Learn to Program

The modern world has become increasingly computerized. Microprocessor computers emerged in the ’60s and ’70s, moving gradually from universities into workplaces, then more rapidly into schools, into homes, and eventually into consumers’ pockets. Mirroring the emergence of computers has been the increasing need to program these devices. However, computer programming is widely considered a hard skill to learn (Mendelsohn, Green, & Brna, 1990; Guzdial, 2002; Kelleher & Pausch, 2005). To overcome this difficulty—both perceptual and technical—researchers and educators developed new educational programming languages to introduce computer code to people of all ages (Mendelsohn et al., 1990). The history of programming education is a narrative of answering two central questions: “Why learn to program?” and “how to learn to program?” Over the past six decades, many solutions have been created to answer the latter question of how to help novices learn programming concepts (Brusilovsky, Calabrese, Hvorecky, Kouchnirenko, & Miller, 1997; Kelleher & Pausch, 2005; Bau, Gray, Kelleher, Sheldon, & Turbak, 2017). However, the question of “why learn to program,” which was central to early research in programming education (Mendelsohn et al., 1990), has shifted in prominence over time. In our increasingly digital world, there has been “a global push to broaden participation in computer science” (Bau et al., 2017, p. 72). Yet, before focusing on how to make programming accessible to everyone, “one of the first questions that must be answered is why novices need to program” (Kelleher & Pausch, 2005, p. 84). The evolving relationship between these two questions, “why learn to program” and “how to learn to program,” has impacted the development of programming education over the past six decades.

The earliest educational programming languages of the ’60s and ’70s sought to introduce learners to the cognitively rewarding world of logic and problem-solving. Seymour Papert, an educator and computer scientist at the Massachusetts Institute of Technology (MIT), believed that learning to program was a way for students to express themselves and “[debug] their own thinking” (Guzdial, 2002, p. 3). The idea that “a programming language is … a medium that creates new ways of dealing with existing knowledge” was shared by several researchers at the time (Mendelsohn et al., 1990, p. 179). By focusing on the cognitive benefits of programming, the mechanics of how to program were secondary to the reason for learning to program. Mendelsohn et al., in researching the early uses of educational programming languages, frame the question of “why learn to program?” as a contrast between the goals of “programming to learn, or learning to program” (p. 179). Logo, the first educational programming language developed by Papert, Feuzeig, and Solomon, was an example of programming to learn: it helped children make cognitive connections between computer code and problem-solving situations, and could be used to “explore a wide variety of topics from mathematics and science to language and music” (Kelleher & Pausch, 2005, p. 113). Through this perspective, the question of “why learn to program?” was a driving force for early educational programming languages, superseding the question of “how to learn to program.” In the following decades, as computers became more central to our lives and our society, their increasing economic importance shifted the emphasis placed on these two questions.

In the ’80s and ’90s, the narrative for learning to program changed from a cognitive experience to an economic imperative, and approaches to programming education became more visual and more varied. Programming was no longer “an activity practiced only by the few who had access to the still-rare machines” (Guzdial, 2002, p. 2). With computers now in homes, schools, and workplaces, there was a new interest in “making programming accessible to a larger number of people” (Kelleher & Pausch, 2005, p. 83). New careers in technology and software development placed increased importance on the tools available to teach programming concepts. New techniques, such as mini-languages, created a simplified syntax for the express purpose of introducing novices to programming (Brusilovsky et al., 1997). The question of “why learn to program?” was still present, but a more prominent focus was placed on not only “how to learn to program,” but how these learning-languages transferred to the general-purpose languages of the computer industry (Mendelsohn et al., 1990; Brusilovsky et al., 1997; Guizdal, 2002). Through a systematic study of over fifty programming languages for novices, Kelleher and Pausch (2005) identified two primary objectives: those that “teach programming for its own sake” (p. 84), and those that “empower their users to create interesting programs” (p. 112). With programming becoming economically important for new careers and new avenues of research, the initial idea of programming as a “mental gymnasium” (Mendelsohn et al., 1990, p. 175) fell out of favour, replaced with the increasing need to learn programming for the sake of programming. With the proliferation of the Internet over the next two decades, the focus on how to program became even more prevalent.

The worldwide explosion of the Internet since the ’90s has increased interest in making programming accessible to everyone, and the question of “how to learn to program” has taken centre stage. Endeavours such as Code.org’s Hour of Code have created hundreds of apps and activities to introduce programming concepts to students around the world (Bau et al., 2017). Scratch, developed by the MIT Media Lab, and a spiritual successor to Logo, represents a block-based language; the latest evolution of how to learn to program. Block-based languages aim to lower the barriers to programming by offering a graphical syntax—reducing the need to memorize programming functions—as well as by offering the ability to experiment with code and remix it on-screen (Kelleher & Pausch, 2005; Bau et al., 2017). Research shows that these block-based languages do make programming easier to learn (Bau et al., 2017). However, the answer to the original question, “why learn to program,” has now become: because computers are everywhere. There is a global push to teach computer science concepts in schools, and some researchers suggest that “programming is still not nearly as widely learned as it should be” (Bau et al., 2017, p. 78). Nevertheless, as educational apps proliferate in classrooms around the world, it is essential to look back on the history of programming education and consider the purpose of these apps. Are schools teaching programming to offer cognitively rewarding activities to expand their students’ understanding of the world, or are they teaching programming intending to produce future programmers?

The history of programming education since the ’60s has demonstrated incredible ingenuity in answering the question of “how to learn to program.” Yet, throughout these evolving tools and techniques, continuing to ask the question of “why learn to program?” is equally imperative. This question, which motivated the creation of the first educational programming languages, seeks to understand the cognitive benefits of learning about logic and computational thinking. Several researchers and educators who have studied educational programming languages agree that the purpose for learning to program is an area that requires additional research (Mendelsohn et al., 1990; Guzdial, 2002; Bau et al., 2017). In the future narrative of programming education, the questions of “why learn to program?” and “how to learn to program?” must go hand-in-hand. We would be doing a grave disservice to future learners by asking one question without the other.

References

Bau, D., Gray, J., Kelleher, C., Sheldon, J., & Turbak, F. (2017). Learnable programming: Blocks and beyond. Communications of the ACM, 60(6), 72–80. https://doi.org/10.1145/3015455

Brusilovsky, P., Calabrese, E., Hvorecky, J., Kouchnirenko, A., & Miller, P. (1997). Mini-languages: a way to learn programming principles. Education and Information Technologies, 2(1), 65–83. https://doi.org/10.1023/A:1018636507883

Guzdial, M. (2004). Programming environments for novices. Computer Science Education Research, 127–154.

History of computing. (n.d.). In Wikipedia. Retrieved October 9, 2019, from https://en.wikipedia.org/wiki/History_of_computing

Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, 37(2), 83–137. https://doi.org/10.1145/1089733.1089734

Mendelsohn, P., Green, T. R. G., & Brna, P. (1990). Programming languages in education: The search for an easy start. Psychology of Programming, pp. 175–200. https://doi.org/10.1016/b978-0-12-350772-3.50016-1

Attribution

Photo by Clément H on Unsplash

How Media Affects Learning

Shared post between Laren Helfer, Sandra Kuipers, Kathy Moore, Mark Regan

Clark (1994) and Kozma (1994) see opposite sides of the issue regarding if and how media influences learning.  As a team, we were tasked with looking at what is happening in the field to see if or how media affects learning.  Here are four articles we found with our thoughts on the great debate between Clark and Kozma.

3 Ways Big Data is Changing Education Forever

https://www.entrepreneur.com/article/340087

Big data refers to large volumes of data bytes, which can be mined for information to provide a company with valuable, and otherwise inaccessible pieces of information about their customers. In 3 Ways Big Data is Changing Education Forever, Das (2019) describes how the affordances of big data can be applied to, and are impacting education. The nature of bytes existing as digital pieces of information, renders the impacts discussed by Das as relevant to education which has been delivered across a digital platform. Instruction delivered via traditional means would not generate bytes of information to analyze. If the digital platform (perhaps an LMS or a website) is understood to be the media of the instructional delivery, it would mean that it is the media itself, or the way by which the instruction is delivered and not the design of the instruction delivered by the media that is impacting education. That is, if the media was changed to a non-digital mode of delivery, any potential impacts of big data could not be realized. This is contrary to the Clark’s (1994) position that media does not influence learning; that it is merely a vehicle for delivering content, and that it is the design of the content that impacts learning.

Das (2019) points out that assessment and feedback are integral components of the learning process. When content is delivered via a digital media platform, big data can be used to illuminate elements about how a learner interacts with the content (e.g., how many times they return to certain pages, how long they view pages, how long it takes to answer questions, etc.). The analysis of this data can be applied to instructional design. The instructor can either provide the analysis as feedback to the student, modify subsequent instruction to better address learning needs, or even design automatic modifications into the software so that the digital course itself can modify the instruction to suit the individual learning needs it identifies. The bytes of data analyzed which enable these insights and interventions could not be obtained if the content was not delivered digitally. Therefore, digital media would be necessary to influence learning in exactly this way.

Clark (1994) challenges would-be critics of his arguments to consider; when media is being used instructionally, if there are any attributes of that “media that are not replaceable by a different set of media and attributes to achieve similar learning results for any given student and learning task” (p. 22). The potential of big data to afford enhanced assessment and feedback opportunities, relies on the attribute of digital media that it has the capacity to generate bytes of data. While this does not require only one specific type of software or platform be used to deliver content, it does implicate the choice of media as being an integral component as to whether or not the learning opportunities afforded by big data could be realized.

The Influences of Technology and Media on Learning Process

https://medium.com/@_mufarrohah/the-influences-of-technology-and-media-on-learning-process-de86ac9d7da6

In this article, the author seeks to explain the general concepts behind the pros and cons of media usages on learning. The article begins through reflection by explaining that technology is omni-present in many facets of learning and that the modern technology we see today, including computers and tablets, are changing the roles of both teachers and learners (Mufarroahah, 2016, para. 1). The article does justice to the dichotomy presented by Clark and Kozma. Kozma (1994) has made the case that media and learning are in a positive relationship, giving more opportunities for not only the learning environment itself, but the teaching process as well. Clark (1994) has taken a position that “there are no learning advantages from using technology and media in the learning process” (Mufarrohah, 2016, para. 3). The article in its conclusion is telling, in terms of what side the author leans in the great media debate. The author has sought to show the positive learning effects media in general can give the education community. Examples were presented such as Reeves’ (1998) cognitive tools reflection and beyond traditional teaching norms reflection, both of which point to the positive effects to which Kozma makes a case in his arguments. The author overall has presented both sides in an appropriate and fair manner, but leans to the side of Kozma  that media enhances the learning process and that there exists a positive relationship between them.

Make Personalized Learning a Reality for your Students

https://news.microsoft.com/apac/2019/05/02/make-personalized-learning-a-reality-for-your-students/

In this article, Microsoft presents a vision of personalized learning through collaboration tools, artificial intelligence, and immersive mixed reality. Images of touch-screen devices and colourful overlays of educational content embellish this message. Microsoft suggests that, for students to learn and thrive, they need the latest technologies: that these technologies “can transform a classroom” (Microsoft, 2019, para. 12) and “stimulate learning” (para. 10). The message conveyed is that personalization requires technology. Microsoft suggests that personalization “can be challenging for a teacher” (para. 8): why not solve these problems with artificial intelligence and machine learning? The article’s argument is backed with a glossy PDF of research by Microsoft and McKinsey, presenting data and infographics about the importance of social-emotional skills and critical thinking in future workplaces. Yet, this argument breaks down when critiqued against Clark’s (1994) argument of media vs. method. Do social-emotional skills and critical thinking require OneNote and Microsoft PowerPoint? Clark cautions that “we continue to invest heavily in expensive media in the hope that they will produce gains in learning” (para. 18). However, at the heart of learning is the method of instruction, and the method should not be confounded with the medium. Clark (1994) argues that “all methods required for learning can be delivered by a variety of media and media attributes” (para. 16). With Clark’s argument in mind, one shouldn’t discount educational technology either, yet it should be approached with a critical eye. McLuhan (1964) famously suggested that “the medium is the message,” which Kozma (1994) maintains and Clark disputes. As educators and technologists decide where they align in The Great Media Debate, it’s also important to ask: When does the message itself become lost behind the shiny touch-screen wifi-enabled augmented-reality medium?

Université de Montréal Opens Quebec’s First Virtual Reality Optometry Lab in Partnership with FYidoctors | Visique

https://www.newswire.ca/news-releases/universite-de-montreal-opens-quebec-s-first-virtual-reality-optometry-lab-in-partnership-with-fyidoctors-visique-831580808.html

This article introduces a new technology that the University of Montreal and FYidoctors | Visique are using to better the education of optometrists.  The media behind the technology is a simulation lab that provides students with experience in a virtual reality environment. The media allows students to work with real patient scenarios, but in the security of a simulated environment, where there is no risk to patient care.  Working in the lab provides students with the learning opportunity to experience everything from common to rare pathologies, allowing them to gain enough experience to be prepared to work on live patients.

The concept behind the lab goes against Clark’s (1994) position that media does not enhance learning.   Clark states “…computer simulation was used to teach students some skills required to fly a plane…people learned to fly planes before computers were developed and therefore the media attributes required to learn were obviously neither exclusive to computers nor necessary for learning to fly” (Clark, 1994, p. 11); however, just because learning once occurred without media does not mean that it cannot occur.  The media discussed in this article provides students with a learning experience that was not otherwise available, meaning that without this media their education would be missing a vital practical component. While optometrists did always receive the required education for the job, this media advances their learning, resulting in better optometrists. If the use of media enhances learning, then there is a strong relationship between the two.  As Kozma states, “[media will] advance the development of our field and contribute to the restructuring of schools and the improvement of education and training” (Kozma, 1994, p. 23), this concept makes media more than a learning tool, but a method critical to learning, which is applied by the simulation lab by the University of Montreal and FYidoctors | Visique.

 

References

Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21-29.

Kozma, R. B. (1994). Will media influence learning: Reframing the debate. Educational Technology Research and Development, 42(2), 7-19.

Microsoft. (2019, May 2). Make personalized learning a reality for your students. Retrieved from https://news.microsoft.com/apac/2019/05/02/make-personalized-learning-a-reality-for-your-students/

Mufarrohah, St. (2016, December 09). The influences of technology and media on learning processes [Blog Post]. Retrieved from https://medium.com/@_mufarrohah/the-influences-of-technology-and-media-on-learning-process-de86ac9d7da6

Reeves, T.C. (1998). The impact of media and technology in schools. The Journal of Art and Design Education, 4, 58-63. Retrieved from https://s3.amazonaws.com/academia.edu.documents/30758321/The_Impact_of_Media_by_Bertelsmann_Fdtn.pdf

Université de Montréal Opens Quebec’s First Virtual Reality Optometry Lab in Partnership with FYidoctors | Visique. (2019, October 3). Cision. Retrieved from https://www.newswire.ca/news-releases/universite-de-montreal-opens-quebec-s-first-virtual-reality-optometry-lab-in-partnership-with-fyidoctors-visique-831580808.html

Attribution

Photo by Giu Vicente on Unsplash

Explorations in Paneer and a Web of Life-long Learning

By Lisa Gates and Sandra Kuipers

At first blush, looking up a recipe for paneer (a soft cottage cheese) seems like a simple task, yielding straightforward results. While finding a good paneer recipe is easy, the task is more complex and involving than simply learning how it is made. The internet is abundantly full of information: recipes, regionality, commonality with other cuisines’ soft cheeses, and the history and etymology of paneer, making it a great example of a topic for life-long learning.

To explore the idea of abundant content online, we picked the topic of “how to make paneer”. We’re both passionate cooks, and paneer is something neither of us had made before and were both interested to learn more about. I (Sandra) love to make curries, but living in Asia it’s difficult to buy dairy products. Paneer is a “rich source of high quality animal protein, fat, minerals and vitamins” (Khan & Pal, 2011), so learning to make paneer would be a great way to add a healthy source of protein to my vegetarian curries. Paneer is delicious on its own and is often used as an ingredient in other dishes. Many of the initial recipes revealed have similar ingredients and methods, and a quick look at Wikipedia (“Paneer,” 2019) will show that there are many kinds of fresh cheeses that would be similar, if not the same as, paneer but from different places throughout the world.

Inspired by the availability of recipes, I (Lisa) decided to gather the ingredients and make a batch of paneer for dinner. Making paneer ended up taking much less time than looking for information about it did. Exploring paneer had me looking at a map of India to better understand parts of the country that my students are from, to find regionally specific recipes. I chose a recipe from Punjab that I may bring to a class potluck. Taking the learning and making it relevant to my life, with real world application and emphasis on learner construction (taking information and making one’s own meaning), including the shift from theoretical to practical experience (Ertmer & Newby, 1993) plants this exercise firmly as Constructivist in nature.

In the case of making paneer, online instructional content appears particularly well suited for short procedural tasks, such as a cooking recipe. Paneer can be made in 30 mins to 1 hour, something we didn’t know before starting this activity. The short duration of the learning process, as well as relatively few steps involved, suggests that using an online source of instruction would likely have a high degree of success. We wondered if longer more involved learning process may not see the same level of success, given the possibility of missing a step, or misunderstanding an instruction.

Our research into how to make paneer suggests that the availability of content online is a boon for life-long learning. Weller (2011) emphasizes that “learners need to be able to learn throughout their lives and to be able to learn about very niche subjects” (p. 228). In the case of learning how to make paneer, the abundance of content online makes it easy for someone interested in expanding their culinary repertoire to learn a new cooking process. They could be a professional looking to continuously improve their craft, or an individual interested in replicating their favourite dish. In each case, the availability of content outside of a formal learning setting enables individuals to engage in “innovative explorations, experimentations, and purposeful tinkerings” (Seely-Brown & Adler, 2008, as cited in Weller, 2011). These opportunities for informal exploration support the pursuit of life-long learning by providing just-in-time instructional content.

The knowledge of how to make paneer could be thought of as human knowledge, rather than academic knowledge or corporate knowledge. It is thought to originate in the Kusana and Saka Satavahana periods AD 75-300 (Khan & Pal, 2011), and may have begun as an oral body of knowledge, passed from family to family. The wide availability of recipes for how to make paneer online reflect this human origin: there is no copyright or patent that could be applied to this knowledge. We would confidently label this as “abundant content” based on Weller’s (2011) characteristics of a “pedagogy of abundance” (p. 229): content is free, abundant, and varied; sharing is easy and socially based; and content is user-generated. However, and abundance of content doesn’t guarantee success in learning.

Abundant content online can also be overwhelming. Weller (2011) expresses that an “excessive abundance constitutes a challenge” (p. 234), and requires different teaching and learning strategies. Learners facing an abundance of content need the skills to search and evaluate the material they find, such as general digital literacy skills and the ability to gauge the relevance of information found in searches. Basic digital literacy skills involve navigating the online environment, including the generation of relevant keywords for searches. Information evaluation, while not particularly challenging in the search of paneer recipes, can prove extremely important in other realms such as learning about science, geopolitical issues, or other life-long learning topics. The ability to discern real, well researched, peer-reviewed information can be paramount to one’s ability to navigate and understand the real world recognizing and avoiding the rabbit holes of conspiracy theories and junk science. Anderson and Dron (2014) emphasize that “there is a concern that ‘popular’ is not necessarily equal to ‘useful’”. They state:

Content is often curated, mashed-up, re-presented, and constructed or assembled by those in the network. This is a wonderful resource when seen as a co-constructed and emergent pattern of knowledge-building, but without the editorial control that a teacher or guide in a group provides, it can lead to network-think, a filter bubble in which social capital rather than pedagogy becomes the guiding principle. (p.140)

In our exploration of abundant content, we were easily able to find recipes for how to make paneer, and were even successful in creating a batch of paneer from scratch. However, throughout this exploration, we remain conscious of the different types of knowledge available online, and the possible pitfalls of abundant content. Some learning, such as short recipes and step-by-step instructions, may be better suited to online instruction than other types of learning. Our findings in this activity suggest that it’s important to understand Weller’s (2011) “pedagogy of abundance” (p. 229) when approaching learning online, and not make the assumption that abundant content automatically leads to successful learning.

References

Anderson, T., & Dron, J. (2014). Teaching Crowds: Learning and Social Media. https://doi.org/10.15215/aupress/9781927356807.01

Ertmer, P., & Newby, T. (2013). Behaviorism, Cognitivism, Constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43-71.

Khan, S. U., & Pal, M. A. (2011). Paneer production: A review. Journal of Food Science and Technology, 48(6), 645–660. https://doi.org/10.1007/s13197-011-0247-x

Weller, M. (2011). A pedagogy of abundance. Spanish Journal of Pedagogy, 249, 223–236.

 

Additional Information Sources

Understanding Learning through Constructivism

We are born into a complex world. It is a world governed by physical laws, social norms, societal expectations, cultural traditions, and family dynamics. Understanding this world is an equally complex process. Many of these rules are not black and white, and they are not spelled out in a handbook presented to each new member of our species. Learning is a fundamentally human process of unravelling the nuanced, interconnected, often confusing threads that make up our world. Through this process of navigating and unravelling complexity, we construct meaning. This meaning-making process is at the heart of constructivism, which emphasizes that “humans create meaning as opposed to acquiring it” (Ertmer & Newby, 2013, p. 55). I believe constructivism offers an invaluable approach to understanding how we learn and how we can share knowledge.

The feedback loop between what we experience and what we know is how we build mental models of the complex world around us. Piaget (1936) presents this process of constructing meaning as the theory of cognitive development, which forms the underpinnings of constructivism. When we’re young, we touch and probe the world around us, and construct our understanding based on how it reacts to our sticky fingers and inquisitive senses. As we get older, we touch and probe the world through interactions on a cognitive and social level. We ask questions, challenge assumptions, and construct cause-and-effect relationships in our understanding. However, rather than progressing developmentally through cognitive stages, Egan (1997) suggests that this progression occurs through the acquisition of cognitive tools: Somatic, Mythic, Romantic, Philosophic, and Ironic. As individuals construct meaning, they progress from a big, bold, black-and-white understanding of the world towards a more fine-grained and nuanced understanding of the many shades of grey in-between. This progression enables learners to continuously revise and interpret their knowledge by applying different cognitive tools to their experiences.

Each step of the learning process is reinforced through the many experiences that inform our ideas, and although these experiences may be similar, the meaning each learner constructs in their mind is unique. Ertmer and Newby (2013) express that “learners do not transfer knowledge from the external world into their memories; rather they build personal interpretations of the world based on individual experiences and interactions” (p. 55). Their exploration of constructivism as it relates to instructional design suggests that meaningful learning activities need to be rooted in real-world contexts, and that learning needs to be an active experience rather than a passive consumption of facts. This aspect of constructivism reinforces the belief that an instructional designer cannot transfer content to students through lessons, but can create situations in which students’ experiences allow them to solve problems and construct their own understanding.

A constructivist approach is particularly powerful when applied to the learning activities I design for my computer science classes. Jonassen, as cited in Merrill (2002), expresses the need for students to “learn domain content in order to solve the problem, rather than solving the problem as an application of learning” (p. 55). Rather than teaching variables, conditionals, loops, and arrays in a linear theory-driven approach, I can design more authentic opportunities to learn by creating real-world problems to solve. In my lessons, students aren’t given a step-by-step process to solve a problem. Instead, they work with a set of coding tools, their own understanding, and access to resources to add new concepts to their repertoire. Programming is highly feedback-oriented: try something, see if it works, debug, refine the approach, and continue experimenting until a solution is reached. Programming is also a highly creative process, and constructivism is well suited to “deal with complex and ill-structured problems” (Ertmer & Newby, 2013, p. 57). This problem-solving approach allows students to increasingly branch out from their familiar set of skills by tackling problems that require new perspectives and new skills. In a rapidly-evolving world where the technologies we use a decade from now may not exist yet, it will be essential for students to be able to approach problems where both the processes and the skills required to find a solution are unknown to them.

 

References

Egan, K. (1997). The educated mind: How cognitive tools shape our understanding. University of Chicago Press.

Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, Cognitivism, Constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71. Retrieved from https://onlinelibrary-wiley-com.ezproxy.royalroads.ca/doi/abs/10.1002/piq.21143

Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43–59. https://doi.org/10.1007/bf02505024

Piaget, J. (1936). Origins of intelligence in the child. London: Routledge & Kegan Paul.

 

Attribution

Photo by Jason Leung on Unsplash