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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On the Nature of Reality and How We Come to Know

My ontological and epistemological beliefs were shaped during my undergraduate studies in the applied philosophy discipline of bioethics. Bioethics involves the articulation of ethical tenets – autonomy, justice, beneficence, and non-maleficence – through various decision-making frameworks aligned to specific case contexts[1]. I hold autonomy paramount, from which the other three tenets derive. Thus, I consider the individual to be the primary moral good from which rights extend, with the individual embedded in a social web that includes intimate and broader collective relationships, for which moral good derives from both the collective and the individual.

Though my beliefs do not extend from or align with those of Saint Thomas Aquinas in his Summa Theologiae[2], I do share Aquinas’ perspective of dualism regarding free will versus determinism[3] (i.e. that both may simultaneously exist). My belief in dualism shapes my perspectives regarding reality and how we come to know.

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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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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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