Category Archives: Uncategorized

Machine Intelligence and Intelligent Tutoring Systems (ITS)

My presentation on machine intelligence and intelligent tutoring systems. This presentation was a precursor to my paper on the same topic (available on this blog here and the book chapter I co-authored that is forthcoming in “Emerging Technologies and Pedagogies in the Curriculum” (Springer).

Science Literacy with Tim Caulfield

A whirlwind day with Tim, filming a series of five short videos on health research literacy. #BestDayEver #SciComm

Intelligent tutors and education 3.0/4.0: How can (should) machines teach, to meet  the needs of 21st century learners and a global society?

The idea of intelligent tutors is not new, nor is the technology. From the 1940’s, Alan Turing and other early innovators of computing systems envisioned these intelligent machines would be used to teach humans, with personalized learning being a specific pedagogical affordance of the technology (Ferster, 2014, 2017; Shute, 1994; Woolf, 2010). Historically, development and implementation of intelligent tutoring systems (ITS) has aimed towards mimicking or substituting for what has been considered the “gold standard” in education: one-on-one learner-teacher interaction (Baker, 2016; Ferster, 2014, 2017; Roll & Wylie, 2016; Woolf, 2010). This “gold standard” remains a persistent challenge within distance education (DE) contexts, though recent Internet communications technology (ICT) has helped solve some of these challenges, enabling more direct learner-teacher (and learner-learner) interaction (Garrison, Anderson & Archer, 2010; Simonson, Smaldino, Albright, & Zvacek, 2012; Woods & Baker, 2004). How might ITS build on this progress?

An important question that has shaped inquiry regarding the role of ITS and machine intelligence (MI)[1] in education and learning is, “Can machines teach?”, followed closely by the question “Should machines teach?” (Ferster, 2014, 2017). Challenges in answering these questions may stem from the embedded assumption: “Can (should) machines replace teachers?”. If we ignore this hidden question, and deal with the explicit question alone, the answer is conceptually straightforward: Yes, machines can and do (and, within the 21st century context, should) teach. Consider the question from a basic behaviourist perspective: Working one-to-one with an ITS, a student inputs an answer to the question/problem the ITS generates, and receives immediate feedback from the ITS, which leads the student to modify his/her actions (Ferster, 2014, 2017; Laurillard, 2012) In this scenario, the teacher (and teacher intervention) is not eliminated, but rather repositioned, and how the ITS is implemented will impact to where (i.e. what point in the learner-ITS-teacher interaction cycle (Laurillard, 2012).

The important question then becomes: “HOW can/should machines (ITS) teach?” (Baker, 2016; Ferster, 2014, 2017; Roll & Wylie, 2016; Woolf, 2010). This is the question I will attempt to answer in this paper. I will pay particular attention to how ITS might solve some important problems for DE, while simultaneously challenging the idea of “distance” (historically considered to be any separation of learner and teacher) (Simonson, et al., 2012). For instance, can the separation inherent in learner-ITS-interaction actually mediate “distance” and improve immediacy and teacher presence, if the integrated technology and pedagogy afford teacher interventions that reach each learner and are tailored to their individual needs? (Buckreus, 2017; Garrison, et al., 2010; Rizzotto, 2017; Wolf & Baker, 2004). Widespread integration of ITS across learning environments may render the historic distinctions between face-to-face (f2f) and DE contexts immaterial, with “distance” utilized to enhance learning in both contexts.

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A Community of Practice (CoP) for Knowledge Translation (KT)

Video recording of my presentation based on the paper that follows below. (Sorry, the video is not the best quality, and I had a dislocated jaw at the time so my speech is a bit unclear.)


Knowledge Translation (KT) has emerged in response to significant delays in the uptake of health research evidence into clinical practice, health policy, programming and services (“evidence-to-practice”). KT encompasses processes and methods for facilitating evidence-to-practice, and has its own evidence base. However, KT training opportunities are limited, and many health-system professionals in roles facilitating evidence-to-practice lack KT competencies. As a result, many health-system initiatives developed through research, implementation, and evaluation undertaken by health-system professionals fall short in methodological rigour, and are therefore not appropriately evidence-based.

The proposed project would combine two education innovations – 1.) Community of Practice (CoP); and, 2.) Connectivist massive open online course (cMOOC) – with a complexity leadership theory (CLT) approach, to address KT training needs among health-system professionals in Alberta, towards mediating the multi-level evidence-to-practice gap. The project would address training in core KT competencies needed for facilitating evidence-to-practice, rather than addressing evidence-to-practice directly.

Figure 1. CLT Functions (f./) and complexity mechanisms (m./) mapped to KT Alberta CoP enactments (e./) (Hazy & Uhl-Bien, 2013).

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Reflections on Learner Leadership

As a student in Athabasca University’s Doctor of Education program, I have been challenged to reflect on myself as a future leader in education. This has led me to questions regarding the role of students in education leadership, within the context of global shifts that both encourage and require collaboration and lifelong learning, in which the influences of technology and globalization prominently figure (Becker et al., 2018; Betts, 2017; Buckreus & Ally, 2019; Chang, Shanahan, & Hsu, 2014; Harari, 2018; Roll & Wylie, 2016). The meaning of student has been reconceptualized as learner, embodying an autonomy that is fundamentally changing pedagogical roles and processes, and the nature of educational institutions (Becker et al., 2018; Betts, 2017; Buckreus & Ally, 2019; Chang et al., 2014; Roll & Wylie, 2016). Learner leadership[1] is a core imperative.

In this paper, I consider two dimensions of learner leadership: 1) The role of learners in shaping education agendas at meso-levels, and 2) The role of learners in shaping micro-level learning environments (Bozkurt et al., 2015; Jansen, Moosa, Niekerk, & Muller, 2014; Prinsloo, Slade, & Khalil, 2018; Zawacki-Richter, Backer, & Vogt, 2009). These dimensions embed democratic leadership and consensus leadership, respectively, situating learner leadership as a participative leadership approach (Amanchukwu, Stanley, & Ololube, 2015; Dong et al., 2018; Elwyn et al., 2017; Jansen et al., 2014; Ureña, Chiclana, Melançon, & Herrera-Viedma, 2019). I consider examples of my experiences of learner leadership, and a growing perception of a call- to-duty for my future as a leader in education and research.

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What is ‘leadership’ in the 21st century, and for 21st century education?

We live within a mix of localized and de-localized contexts, mediated by Internet communications technologies (ICTs) (Floridi, 2013, 2014). One click brings the distant near, or vice versa. Connection is constant and episodic. Existence is collective and individual, as we increasingly collaborate across personal, learning, and working contexts, while simultaneously asserting (hyper-)autonomy[1]. Historic social hierarchies are falling, and traditional models of positional leadership misalign to the realisms of onlife[2] (Floridi, 2013, 2014). What type of leadership is needed for networked (global) society, institutions, educators, and learners, in the 21st century? In this paper, I consider and contrast distributed leadership (DL) and transformational leadership (TL) as models for contemporary education, concluding DL offers “best fit” for distributed learning ecosystems in the 21st century and trends towards personalized and lifelong learning (Roll & Wylie, 2016).

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