I know what's desired here is a map or visual representation of concepts, but I'm struggling with the new tools that have been suggested, so I decided to revert to the medium I'm more comfortable with: writing. I'm still mulling this somewhat, but appreciate your patience and welcome your feedback.
When I started this MOOC, I did not necessarily have a theoretical or philosophical foundation for learning analytics in mind. To be honest, I saw it as mainly an extension of business analytics or intelligence, which academic institutions seemed content to adopt. Given critique's about higher ed's cost, impact and importance, analytics seems like a logical approach to address issues of accountability, as well as the return on investment of technologies that purport to do so.
However, George's overview of LA's history based on social network analysis, pedagogy and technology has provided a useful theoretical framework, and affirmed some hunches I've long suspected myself: we live in an obsessive, status posting & checking culture. In Manuel Castell's Network Society, it's clear how we're literally wired for connection, perhaps now, more so than ever. It even seems inevitable that our technology would allow (cause?) us to want to know and be known.
Amidst such an environment and appetite for connection, what does pedagogy really mean? In 1999, The Cluetrain Manifesto taught us that mass marketing is no longer a one-way interaction. Only businesses who sought out to understand and listen to their customers would flourish in the new connected society made possible by the InterWeb? So why should education be any different?
Also in 1999, Douglas Robertson proposed a model for how faculty beliefs about teaching influence their evolving practice that includes the following stages:
- Egocentrism -- focusing mainly on their role as teachers;
- Aliocentrism -- focusing mainly on the role of learners; and
- Systemocentrism -- focusing on the shared role of teachers and learners in a community.
If it occurs, Robertson identifies key characteristics of this evolution in faculty pedagogy. First, as faculty move from one stage to the next, they bring the benefits and biases of the previous stage. Second, they typically change their beliefs and practices when confronted by the limitations of a current stage, which is brought about by teaching failures. Finally, the desire for certainty and confidence either keeps faculty in a current framework or drives their progression to the next one in an effort to avoid a potentially paralyzing neutral zone: "With a familiar teaching routine that they have deemed inappropriate and with nothing to replace it, teaching becomes a struggle” (p. 279).
Similarly, faculty beliefs about teaching also influence their perceptions about what they believe various instructional technologies will allow them to do. For example, Steel (2009) analyzes case studies – all involving the central role of online discussions – that illustrate the creative tensions between how faculty conceptualize teaching and how they perceive the affordances of web-based technologies like a learning management system (LMS).
The velocity of change in the affordances offered by learning technologies presents a significant challenge as does the minimal incentives available to university teachers to use technologies effectively in their teaching practices (p. 417).
Whether faculty like it or not, when they teach online they also become webmasters. As such, they need to understand the potential affordances and limitations of web technologies as they attempt to express and implement their pedagogical preferences. Steel argues that this “reconciliation process” between pedagogical beliefs and rapidly changing technology affordances “needs to be incorporated more fully into informal teacher development approaches as well as formal programs” (p. 417).
What's been missing (but appears to be emerging more and more) is the role of the learner in learning analytics. I'm currently reading Sal Khan's One World School House (2012). In it, he argues that the personalized learning Khan Academy provides is based on the learning analytics he gradually built into it. But more importantly, it's based on a belief that students are and should be responsible for their own learning. How are we leveraging this in higher education pedagogy, either in our instruction of or in interventions with disengaged learners?
Finally, this touches on a initial concern I have with MOOCs: they seem like an old school reaffirmation of the primacy of the instructor (Robertson's egocentrism?). With most MOOCs taught by high profile teachers from equally high-profile universities, one has to wonder how they help students take responsibility for their own learning. To be sure, students are exposed to subject matter experts. But how can MOOC instructors possibly develop, grade and scale assignments that assess student mastery of course concepts? Some have turned to crowdsourcing grading, but it may be that adaptive learning also has the potential to apply and scale active learning pedagogies. I think that's what Khan Academy is doing, but as he himself would admit, adaptive learning (or personalized instruction) is built on learning analytics and the student's responsibility for learning.
If learning analytics can help inculcate, leverage or remediate students' curiosity and love of learning, then it has the potential to be truly transformative to education as we know it.