Prioritisation of the student perspective and the student voice
The use of data to shine a light into student experience and provide alternative ways of seeing student activity
Promotion and capacity building around data literacy for students
Application of Machine Learning techniques to help students identify their own learning path
Right now, I am looking at processes and data in a classroom at IT Sligo on our ‘Internet of Things’ module and will be looking at how we can present this data to students so they can make inferences about their own learning process.
My undergraduate degree was in Communications Studies at Dublin City University (DCU – then known as the National Institute of Higher Education, Dublin NIHED). There I was fortunate to meet lecturers like Luke Gibbons (Film & TV Studies), Martin Croghan (Linguistics), Des Bell (Media Studies) who opened my eyes and my mind to the way in which the media was shaping our world. We got a grounding in subjects like Sociology – where I first encountered Mead and Freud; Economics where I was introduced to Capitalism and Marxism. We had a Psychology class which featured live performances of Woodie Guthrie songs complete with accompaniment from a real fascist-killing-machine. It was quite an education and gave me a jump on media literacy and critical thinking which has profoundly shaped the way I see and interpret the world ever since.
In the following years, I was lucky to get an equally good grounding in digital literacy and the ways in which networked computing has shaped and continues to shape our world and our culture. Many of these changes are for the better. We are connected in new and rich ways. We can collaborate and work with colleagues and collaborators all over the world. We are more open to each other’s culture and we can appreciate each other’s difference better than we used to. Now, you could argue that the converse of all these statements is true also. And this exemplifies exactly the complexity that is now part of all our lives. If we can build capability around diversity and remain open to contradictions in this new world, we have huge opportunities to evolve. And yes, the converse of that is true too.
Digital literacy for me was also bound up with learning – and I was influenced by the writings of Stephen Brookfield, who in turn led me to the writings of Paulo Freire. I started reading ‘Pedagogy of the Opressed’ back in 2007, I think. And I read it slowly – savouring every page. In fact, I still hadn’t finished it last year and my promise to myself was that if I ever did finish the book, then I would have to start living it more fully.
It feels like we are at the start of another phase of technical innovation which is fundamentally shaping and molding our lives and how we exist in the world: the age of data. To thrive in the future, we will need to be data literate. We need to understand how data is being leveraged to determine our access to education, employment, finance and so many other aspects of our well-being. The power dynamics don’t seem to change much – but the medium does. It’s up to all of us to get to grips with this new one.
During these first months of my research, I am trying to get a foothold in many technical topics that are relatively new to me – or that I have not worked with for some time. I am working within a Machine Learning research group – Machine Learning being a subset of Artificial Intelligence. Learning Analytics also requires a grounding in Statistics and newer approaches and tools in this area like Python and R. So, there’s a lot to learn, and quickly. I have found some very useful resources in the form of Massive Online & Open Courses (MOOCs) on these topics.
I find it interesting that many of the so-called xMOOC (Siemens 2012) platforms were founded by scholars in the field of Artificial Intelligence and Machine Learning. For example, one of the first high-profile xMOOCs was ‘Introduction to AI’ run by Sebastian Thrun and Peter Norvig from Stanford University – which enrolled 160,000 students from around the world. This was followed by courses in ‘Machine Learning’ by Andrew Ng and ‘Introudction to Databases’ by Jennifer Widom. Out of these early initiatives emerged Udacity founded by Sebastian Thrun and Coursera founded by Daphne Koller & Andrew Ng.
Some of the courses I have looked at or enrolled include:
The way I have engaged with these courses varies – some are an opportunity to refresh on basics. Some are providing me with very practical skills which I am starting to apply to real-world data. Some are just filler content. In general, I have not interacted with other students – although I would normally value this aspect of the learning experience. I am also taking a face-to-face course at my university on Linked Data and the Semantic Web. Again, I am surprised that I actually feel less involved and interactive with other students than I would normally experience in an online learning classroom. I am wondering is this because of the architecture of the courses? Most of my previous experience of online learning has had a big component of student-to-student interaction. But these courses privilege student-to-teacher interaction. Interesting…
In October 2015, I was delighted to start a PhD with the Machine Learning & Data Mining research group at the National University of Ireland, Galway. The group sits in the IT Department and my supervisor is Dr. Michael Madden. My research interests lie at the intersection of learning and computer systems – and in particular – Learning Analytics.
Over the past 10 years we have seen the widespread deployment of Learning Management Systems (LMS) like Blackboard & Moodle across our education sectors. We are also seeing learning reach outside the individual institution with the evolution of learning on social media platforms and Massive Open Online Courses (MOOCs).
While we could argue about how effective the adoption of these systems has been, there is no doubt that they are changing how we learn and how we teach. They are generating vast quantities data and revealing new insights on how learning happens. We are seeing a powerful force emerging – but with great power comes great responsibility. The choices we make now in how we harness this data will have a profound effect on the way we see learning, the way we think about it and the way it will shape our lives in the future.
Learning spaces comprise threads of developmental connection between learners and teachers. From a teaching point-of-view, they require what Stephen Brookfield referred to as the ‘Three Rs’ of teaching: Respect, Responsiveness & Research. When we make interventions there, based on purely quantitative measures, there is a danger that we could damage these threads. We also face profound ethical issues around privacy and freedom when we consider effective use of learner and teacher data.
Despite these concerns, I am optimistic about the potential of Learning Analytics for good – in our schools, universities, CoderDojos and learning spaces. In this blog I hope to document my research into this potential.