Connected Teaching and Learning

Barnett, J., Mcpherson, V., & Sandieson, R. M. (2013). Connected teaching and learning: The uses and implications of connectivism in an online class. Australasian Journal of Educational Technology, 29(5), 685-698.

Article 1 Title: Connected teaching and learning: The uses and implications of connectivism in an online class

Broad Topic: The authors are exploring the use of connectivism by implementing the theory in an online program at a Canadian university. The instructor employed a new paradigm in that he had the students teach one module of their choosing during the course. The results are discussed, coded, and salient points are discussed that provide important information pertaining to the practice of Connectivism.

Theoretical frameworks used to ground the piece: Multiple works are referenced throughout the article but the following are the most influential as it relates to the authors task: 1) Siemens and Downes seminal article on connectivism, as well as supporting articles by the theorist, 2) Ireland’s article titled Situating connectivism, 3) Dunaway’s Connectivism: learning theory, and pedagogical practice for networked information landscapes, and 4) Mackey & Jacobson’s Reframing information literacy as metaliteracy.

Seminal constructs or concepts in the article: The article refers to the traditional approaches to teaching and learning as the canonical methods. These methods, behaviorism, cognitivism, and constructivism could not explain the types of learning that was occurring as technologies emerged; hence, a new approach was needed to explain the experiences garnered in the virtual environment. “Students with the ability to direct their own learning, pulling information from a variety of human and other resources, were not necessarily operating in behaviorist or cognitivist frameworks. Furthermore, their learning had more to do with the integration and critical analysis of, and contribution to, disparate resources than simple existential constructions of meaning. So, constructivism could not precisely explain such learning, either” (p. 685). Due to this breakdown in knowledge construction, connectivism emerged as a way to explain learning within a networked environment. As it relates to education, “connectivism incorporates many of the understandings of the canonical approaches. We can glimpse a facet of behaviorism in the connectivist requirement that to know something, one must be able to do it; it shares the notions of neural networks with cognitivism; and it supports the group and community notions of social constructivism and transformational teaching” (p. 686).

Broad research or theory objectives including hypotheses, propositions, etc.: The implication of connectivism within the educational environment is the purposeful connections that are established and accessible. Learning adds to these connections which can create change within the established network. Strong and fast connections are pertinent to the learner, yet it is important that “links to older sources are not lost as new links are made. The world may know more every day, but it does so only if it holds onto its history” (p. 688). In terms of this article, the authors posit that due to the connectivist nature, the participants, and the instructor both own and share information collectively. This means that the instructor needs to evaluate the connections they have established and determine their strength as it relates to the importance in terms of the material being taught. Simply put, connectivism as a teaching method is not a “stand and deliver” methodology. It is more of an influential method of facilitating connections within the class network. “Most of all, teaching becomes the process of helping students and society to critically examine connections in their courses and in their present and future lives” (p. 689).

Research methodology: The purpose of the article is to implement connectivism within a course at a university in Canada. The instructor, and 17 students, were tasked with teaching one module of the course. This created 18 week-long modules for a ten-week course; so, for some weeks, two modules were offered (see screenshot below of the course calendar). The authors’ goals were threefold in that they wanted 1) to describe this particular course as a model of connectivism in itself, 2) to make inferences based upon our evolving understanding about connectivism as a learning model, and 3) to analyze the implications of this course as a learning model through the lens of our experiences in it. “In order to create both trustworthiness and authenticity in the data collection, we decided to chat online with each other in a series of online conversations over the course of two months and these conversations would comprise the data collected” (p. 691). From these conversations, the data emerged, and each researcher coded the data alone while making a dictionary of the codes created. This allowed them to then compare their work with one another and create a list of codes that represented their thoughts on the course experience.

Findings, implications, conclusions: Four main ideas emerged from the data: Learning theory; course content, context, or progress; student experience; and instruction. For the sake of brevity, the following statements sum up the authors’ findings as it relates to each idea:

  • Connectivism as a learning theory occurs across both brains and artificial intelligences; learning occurred as a group working together; top-down approach may be better in certain situations due to the amount of knowledge the instructor possess; democratic network of learning.
  • Course content was decided on the instructor wanting to implement a social networking form of teaching; a lot of planning went into design; student apprehension; student flexibility.
  • Students’ experience was described as comfortable due to the personal experience it provided; voicing opinions was appreciated; time-commitment requirements were needed; unsure on how their design and teaching would go; discussion forums were lively.
  • The instructor experienced a loss of control due to the course design; share power; need institutional freedom for a pure connectivist course to work.

Prescriptive and descriptive contributions: In terms of application, this attempt at implementing a pure connectivist course design was encouraging. The instructor yielded his authority and prior knowledge construct in order to test out a group-led form of education. Essentially, the instructor merged his role with that of that student and “shared power for content definition and creation as well as course moderation with the students enrolled in the course” (p. 696). Through this attempt, students were able to experience a different role by moderating discussions and creating relevant background material. One point to note was that for this experience to be “more” connectivist in nature, the students/instructor could have negotiated on which topics were to be taught, the value in which the connections established, as well as establishing a learning community for those who wanted to participate after the course.

Critique of ideas and research: A critique that could be leveled against this idea is the lack of structure or organization that is likely to arise when attempting this process with more complicated courses. I believe there is always a need for a top-down approach when it comes to dispersing information, i.e., sharing wisdom so to speak. If every course were to follow a democratic approach similar to what the article referenced, then much of the course content would be haggled and debated throughout the academic semester with little progress toward knowledge consumption. The idea of having students teach certain materials is appealing and would provide a different perspective for all parties. However, I think that a de facto leader (teacher) should provide a stabilizing and structuring presence throughout the course.

Relevant gaps and research opportunities: In particular, future researchers should focus on the following questions when deciding whether to implement Connectivism in their classroom:

  • If content is decided collectively, who decides the assessment portion of the course?
  • Would formative assessment hold more weight in this design than summative? Is the process more important than the result?
  • Is there room for a pure connectivist experience in which the content is determined as the nodes are established?
  • Is learning in general the goal or is network establishment via searching the point of such a theory?

Connectivism for Writing Pedagogy: Strategic Networked Approaches to Promote International Collaborations and Intercultural Learning

Vas, R., Weber, C., & Gkoumas, D. (2018). Implementing connectivism by semantic technologies for self-directed learning. International Journal of Manpower, 39(8), 1032–1046. https://doi.org/10.1108/IJM-10-2018-0330

Broad Topic: As with the other topics, this paper covers the connectivism theory and how it can be applied cross-culturally through three learning experiences.

Theoretical frameworks used to ground the piece: George Siemens and Stephen Downes’ 2005 initial paper on connectivism is the foundational educational theory. Also, the authors draw inspiration from the following articles: 1) An evaluation of structural model for independent learning through connectivism theory and web 2.0 towards students’ achievement, 2) Using connectivism theory and technology for knowledge creation in cross-cultural communication, 3) Study on college English teaching interaction and teaching practice based on connectivism from the neurocognitive perspective, and 4) Intercultural rhetoric and professional communication: Technological advances and organizational behavior. These articles form the basis for the researcher’s guiding principle in that learning is guided via a range of technologies and, in keeping with the technological times, a new learning theory that capitalizes on this trend is needed.

Seminal constructs or concepts in the article: Referencing Siemens’ and Downes’ paper, the founding authors created six stages of connectivist learning: Awareness & receptivity, Connection forming, Contribution & involvement, Pattern recognition, Meaning making, and Praxis. These six stages form the nexus of how connectivism works with learning. In Stage 1, students acquire basic skills, then begin to learn, use, and acquire new tools in their newly formed personal networks (Stage 2). Stage 3, Contribution & Involvement, students become more comfortable in their networks and make their presence known to others; essentially, a more noticed connection. Stage 4 involves the student being an active contributor, while Stage 5 sees the learner forming opinions and determining the meaning of patters. Lastly, Stage 6 is a reflective process in which the student is actively reengaging and recreating their personal networks to best suit their learning needs. The core of connectivism revolves around knowledge being distributed across networks and that learning happens when students are able to transverse those established networks. “The role of technology in connectivism is to be enablers of new opportunities—by opening doors and creating pathways to new information sources and maintaining those connections” (p. 3). The researchers’ purpose in creating the Trans-Atlantic and Pacific Project (TAPP), describing Personal Learning Networks (PLN’s), and Fabric of Digital Life (Fabric), is to highlight the success of incorporating networking in a writing pedagogy. Through these examples, their hope is to establish research-based practices that employs the connectivist learning principle.

Broad research or theory objectives including hypotheses, propositions, etc.: The learning theory, connectivism, was born out of the technological and networked environment that has existed since the late 80’s. Founded in 2005, this theory states that learning is enhanced through society and technology. Patterns to learning are formed and learning is directly influenced by the networks that are established throughout society. The “node” that the learner connects with is a deciding factor in what information is established and how information flows. In this article, the authors are instructors who teach writing and communication. Their purpose in designing the soon to be mentioned projects, was to “examples gather attention on theory-driven practices in our local contexts that take advantage of available tools and resources. Our goal is to use these experiences as a basis for promoting connectivist learning, mindfully designed and carefully deployed” (p. 5).

Research methodology: In this study, the researchers showcased the success of connectivism is three areas of evidence-backed practice: Trans-Atlantic and Pacific Project (TAPP), Personal Learning Networks (PLN), and the Fabric of Digital Life (Fabric). TAPP emphasized multiple stages in the connectivist theory by allowing students to partner with students at a university in Spain. Through oral presentations, reflections, and establishing commonalities, students were able to form connection nodes, establish connections via a chosen technology, learn about different viewpoints, and finally unify their thoughts by reflecting on their experience. In the PLN example, “student development of visualizations of their individual learning networks provides a means to both document their awareness and receptivity to connections and also begin to sort and filter the enormous flows of information available” (p. 11). The instructors allowed the students to design a visualization as a way to introduce themselves to their Spanish partner. The differing designs emphasized Stages 4 & 5 in the connectivism process by allowing the students to focus on the connections they formed via a diagram. Through learning networks, the students were able to witness firsthand the construction and facilitation of connectivism. Lastly, the students utilized a digital archive and increased their knowledge on digital literacy and by determining what contributions would be accepted onto the platform. This allowed the students to further examine the interconnected networks that broadened their learning experience by reflecting on the awareness needed in making decisions that affect their learning.

Findings, implications, conclusions: The researchers concluded that through the aforementioned examples, the students gained an intercultural competence in collaborating with other nationalities, formed new connections, and learned from their communication on how to best proceed with their course assignments. Also, by growing their learning network, the students were able to decipher and filter through flows of information that illustrated their unique cultures and individuality. This allowed the students to reflect on their purposeful connections and taught them the value of modifying connections as they deemed fit. Finally, when working with the digital archive known as Fabric, students were able to modify and contribute content that increased their digital literacy and showcased their understanding of emerging patterns in a networked society.

Prescriptive and descriptive contributions: While connectivism as an educational theory is still being debated, the researchers were able to demonstrate three evidence-based practices that, according to the six stages of connectivist learning, met the required threshold. I also felt like the teacher’s methodology of requiring oral presentations and reflections were a great way to determine competency and learning outcomes. The students were able to demonstrate problem-solving and critical thinking skills in a cross-cultural learning environment that demonstrated learning “on the fly” instead of traditional assessments. In terms of learning technologies, this methodology is similar to defending one’s portfolio or presenting a paper and/or original research. By engaging in the aforementioned communicative practices, they further increased their connections and learned to expand their social circles; both of which are important when pursuing a career. The students learned the value of networking in a global society, formed their own opinions, and contributed to a learning environment all while engaging the resources around them.

Critique of ideas and research:  As it relates to this article, my critique would point to the principle in which the research stands upon; connectivism. As with other learning theories that are well-established, connectivism does not have a well-founded literature in which to compare the different practices that it might apply. Similarly, it could be argued that this theory is not really a theory and merely should serve as a supporting role to other established paradigms. I do agree that learning occurs via networked systems, and I appreciate the founding researchers work in creating a theory to explain our digital age, yet I think that instead of creating new theories of learning we should potentially integrate the ones we have. Could we not take the best of constructivism, behaviorism, cognitivism, social learning, connectivism, etc., and produce a theory that is relevant to the age in which we live? Why must we deconstruct in order to mesh with our new reality?

Relevant gaps and research opportunities: Connectivism requires the students to be motivated and self-directed in the pursuit of knowledge. Well, I have witnessed (and been guilty myself) students who need the teacher to drive the course content while holding the learner responsible for the knowledge that is attempting to be shared. Be it formative assessments or recitation of speeches, there exists a place in education for a top-down approach. Secondly, the rise of MOOCs in the mid 2010’s is very applicable to this theory. Content is organized and distributed via a network and the student is largely responsible for driving the knowledge engine. The jury is still out as to whether MOOCs will persist in the future (though they haven’t fared very well as of now). I suppose a full-on approach to testing this theory would be to apply it directly to a fully online course. While we have seen evidence-based practices in terms of projects (as witnessed in this investigation), it would be interesting to apply the six stages to multiple courses that are being taught online and see how the learner responds. Would they direct their learning towards their interests, or flounder without direction?

Implementing Connectivism by Semantic Technologies for Self-Directed Learning

Tham, J., Duin, A. H., Veeramoothoo, S. (Chakrika), & Fuglsby, B. J. (2021). Connectivism for writing pedagogy: Strategic networked approaches to promote international collaborations and intercultural learning. Computers and Composition, 60. https://doi.org/10.1016/j.compcom.2021.102643

Broad Topic: This article is seeking to determine if a measure can be created as it relates to the connectivism learning theory and the learners’ determination of connected concepts.

Theoretical frameworks used to ground the piece: “Connectivism describes knowledge as a set of symbolic mental constructs and learning as the process of accessing and storing these symbolic representations in the learner’s memory” (p. 1034). The researchers are trying to employ a network connected ontology to describe how connectivism can be measured. In short, how can learning performance be determined through connections that are formed when attempting to learn a subject? The use of ontologies as a knowledge representation would allow a visual graphic in which the interlinking concepts can be connected, thus describing the relationships between areas of interest.

The authors are seeking to answer two questions as it relates to concept importance: 1) How well connected are the concepts in a network, and 2) how necessary is a single concept for understanding connected concepts based on its underlying semantics? Essentially, how well do learners perceive the information they are receiving? The authors, with the assistance of an ontology engineer, determined that the level of “need” and “detail” would be the two deciding factors when detailing the determination of concept importance. A complex mathematical formula is created that is beyond the scope of this investigation, yet the relationships within the ontology are numerically defined. “Taking into consideration the type and number of connections for concepts, the concept importance measure is a suitable measure to rate the potential of concepts in a domain to explore new information in line with the idea of connectivism” (p.1039).

Seminal constructs or concepts in the article: George Siemens’ paper, Connectivism: A learning theory for the digital age, serves as the seminal construct for this paper. His work established the theory and laid the framework for other researchers. The researchers are also using the following articles to base their work on: Vas, R. (2007), “Educational ontology and knowledge testing,” as well as Weber and Vas (2016), “Applying connectivism? Does the connectivity of concepts make a difference for learning and assessment?” In applying these articles, the researchers are interested in the self-directed learning that many learners apply when gaining new information. They note that learning is not an isolated process, and much information is gained via the use of various communication techniques. Knowledge objects, and the technology environments where information is stored, would benefit from a schema that categorizes the ontology in a personalized manner. “Even if the implications of connectivism on individual learning is still unexplored; it is expected that better information access supports learning and a measure explaining the importance of concepts in information sharing helps to gain better insight into the learning process” (p. 1035).

Broad research or theory objectives including hypotheses, propositions, etc.: The researchers are interested in exploring the concepts deemed important by the learner, thus determining the domain ontology of the learner. “Accordingly, the measure integrates a set of factors: the number of connections of a concept and an interpretation of the semantics of the domain ontology. These factors – composing the measure – are together called the “importance dimensions” (p. 1037). The researchers use a multiple-choice exam in hopes of mapping the importance measure of each topic explored. Their goal is to document the various topics that are visited by the learner as they study for a domain-specific exam. The knowledge network that is created seeks to determine if the concept importance measure forms a correlation between performance on test questions of the same subject.

Research methodology: Data was collected in 2016 and 2017 from an undergraduate course in management information systems. In 2016, 267 students for both mid-term and final-term examinations were sampled, and in 2017, 278 students for the mid-term and 276 students for the final-term made up the population of students. The mid-term exam consisted of 20 questions while the final was comprised of 30 questions. The 2016 exams (mid and final) covered 56 concepts and 246 total questions. Similarly, the 2017 exams covered 49 concepts and 217 total questions. Two professors and the ontology engineer selected the concepts for the methodology at which point they then screened each question and matched it with a concept. A subontology was extracted and used to calculate the importance value for each concept. A graph was created that separates the ontology into smaller clusters that allows for better visualization of the concepts (graph to follow paragraph). “The size of the node visualizes the degree of the concept importance, and the color of the nodes, references each to one of the nine calculated modularity classes. Only the concepts that were matched against the examination questions are shown with labels” (p. 1040).

Findings, implications, conclusions: The goal of the process was to see if concept importance could be a predictor to how well the learner answered test questions. The researchers used a linear regression model as well as a quadratic function to see if concept importance could be a predictor test competency. No significant results could be found even though trends were determined for all aggregation levels. To explain this occurrence, the researchers suggested the “small number of concepts and observations for each individual and the randomization of the questions” (p. 1041). Variations at the concept level resulted in an unsteady trend that led to outliers affecting the data points at the class level. For the 2016 data, the p-value for the mid-term was .18 above the .05 threshold, while the final p-value was .9 above the .05 mark (each was stated as non-significant for the regression model used). Likewise, the 2017 data resulted in a p-value .31 above the .05 threshold for the mid-term and .44 for the final (both are non-significant for this model). Multiple reasons for these results are given by the researchers but for the purpose of this study, only a few will be mentioned: 1) recorded observations are limited, 2) unequal questions as it relates to associated questions, 3) unequal domain level coverage, 4) controlled environment in terms of test-taking yet uncontrolled in terms of test-prep, and 5) different learning environments and how the learner perceives information.

Prescriptive and descriptive contributions: I found it interesting that the researchers were attempting to find a measure on how the learner deems important concept connections. They also noted in their research that “no single strand of theories can currently fill the gap between the ideas of connectivism and its implementation or derive implications on the learning process” (p. 1043). This seems to point to a need for connectivism to be further researched in terms of application, and whether the theory can stand on its own in terms of an educational learning principle. While they did not find any significant results, their work is encouraging in that given a larger sample size with more variables (test questions), results might indicate a different finding. In terms of our learning technologies, I feel like the researchers were trying a novel approach in attempting to measure concepts and their importance to the learner via a relatively new (and largely ungrounded) theory. The idea of connectivism appeals to me due to its core premise in that learning is made through connections that are established between nodes. Yet, like the authors in this study, the impact of this theory needs to be vetted and more firmly established before we as a field accept it within the learning community.

Critique of ideas and research: A few points to note: Students learn in different environments and with various technology; the concept important measure is subjective and not well grounded; information exploration is not well-defined in the research or within the connectivism theory. These three areas, alongside the other points I mentioned in the findings, lead me to believe that an attempt to map an ontology with the learning experience would prove to be a mammoth effort due to the individualistic nature of learning and the replication needed to provide support for the study.

Relevant gaps and research opportunities: This study serves as a great starting point for future researchers in attempting to find a measurable observable involving the connectivism theory. While the researchers noted their lack of significant results, credit to them in attempting to find a way to link a theory with an applicable measure. The trends that were noticed, higher importance to lower passing of concepts in the mid-term, while lower importance to higher passing of concepts for the final, indicate a need for starting points in what is deemed fundamental knowledge. The authors noted that highly difficult objects are challenging to learn, yet the performance related to the objects increase over time. More research needs to be done on the concept importance measure and if it is valid in various projects.