How Technology and Learning Analytics Are Transforming Education
The University of Minnesota’s College of Education and Human Development (CEHD)is focused on improving the lives of children, families, and communities by forging research-driven solutions to complex problems. These solutions come from our brightest minds and from decades of real-world experience across eight departments and 25 research centers and institutes. Bodong Chen, Assistant Professor and Faculty Research in the LT Media Lab, brings us this post.
Growing up in a rural area of the Chinese province of Sichuan, my education was traditional. I had no access to computers, and classes were rooted in old-fashioned pedagogy — based around the concept of a teacher dispensing information to be written down and then memorized by students.
It was in my college years when I had the idea that, by taking advantage of modern technology, we could craft new educational models that may better prepare students for life in the 21st century. Beginning with my university education in Beijing, I started a journey that would lead me to the Institute for Knowledge Innovation and Technology at OISE/University of Toronto and, eventually, the LT Media Lab here at the University of Minnesota’s CEHD. I’ve been honored to be a part of the team here as we focus on designing and developing innovative environments to empower students to learn critical skills and help teachers infuse technology in their classrooms. As our understanding of how students learn expands — along with the rapid improvement of technology — we find ourselves at the dawn of a new era of education.
Understanding Technology Use in Education
While computers and other digital technology have become commonly used in schools since the early 1980s, levels of technology integration in education vary across countries, districts, schools and classrooms. Research on the impact of technology on learning has arrived at mixed results, leading to heated debates on the need for expansive tech plans in schools. However, maybe we have been asking the wrong question, as technology is not a magic thing that can be plugged into a classroom and transform learning. Work at the LT Media Lab, led by Dr. Cassie Scharber, has come a long way to recognize different ways learning technology is used in education: While technology has been used broadly to replace or amplify traditional ways of teaching — by focusing on convenience and efficiency — less common to use technology to transform teaching and learning. This final designation is where we believe the leadership of CEHD and LT Media Lab can make a real difference in the future of education. Transformational educational technologies don’t merely aim to make yesterday’s methods easier or more efficiently implemented, they approach education by looking beyond what we believe students can do or how teachers should teach. By studying how people learn and examining what technology can do to create environments that facilitate higher-order competencies, we need to keep challenging our very assumptions about “best practices” in education.
A Transformational Approach to Education Technology
The explosion of mobile technology, “big data” and advanced educational software is radically reshaping education as we know it. We’re at the perfect time to discover ways to move beyond the traditional paradigm of a teacher-centered classroom. Through technology we can provide learning opportunities that are more relevant, situated, flexible, collaborative and personalized, while also trying to give students the agency and responsibility that are commonly deprived from them. Here are just a few examples happening today in LT Media Lab.
Access to technology increases flexibility. The ubiquity of mobile devices allows students to learn whenever they want or wherever they are. Suddenly, a bus ride home become an opportunity to watch a MOOC (massive open online course) lecture or interact with other students through an app like Flipgrid. As a result, we’re seeing more professors moving to a “flipped classroom” model, where knowledge is learned outside of class and class time is used for collaborative, hands-on discussion and application of that knowledge.
Increasing contextual awareness. Beyond giving students unlimited access to academic resources, mobile technology can afford students new ways of engaging in learning. My colleague Dr. Aaron Doering pioneered Adventure Learning and more recently created some innovative GIS-based (geographic information systems) mobile tools to enable students to engage in adventure- and project-based initiatives informed by real-world locations. This melding of real-world exploration and mobile technology creates a far richer learning environment than simply reading a book or watching a video on a subject.
Collaborative knowledge building. No matter if it’s general social media or technology designed for students, there are so many ways to interact with other students today compared to my childhood years in China. More importantly, to fill the “ingenuity gap” in solving complex problems faced by today’s society (Thomas Homer-Dixon, 2000), we need to bring education into closer alignment with a knowledge society. A major line of my research has been devoted to engaging primary students in collaborative knowledge building, in which they work as teams to build knowledge to address real-world questions — such as “how do worms sense light?” and “what is the empty space within an atom?” Technology plays a critical role in sustaining collaborative environments for students to develop cognitive, metacognitive and emotional skills crucial for a knowledge society.
The Power of Learning Analytics
Learning analytics is a very new field of study, but one that has the potential to transform many of our common approaches to education. There are many different voices in the field, including people with backgrounds in computer science, learning sciences, educational administrators, Silicon Valley entrepreneurs, etc. This diversity means that there is no one general definition of “learning analytics” that works for everybody. (People may even disagree on whether learning analytics should be singular or plural.) To me, that is what’s exciting about this field; it brings in so many different viewpoints and encourages design thinking to apply analytics to influence learning and teaching from many different angles.
While celebrating excitements of achieving “personalized learning” — a grand challenge for education (Pea, 2014) — my personal disposition in this field is to empower rather than take away students decision-making ability. This disposition goes along with my efforts to promote higher-level agency among students and work against creating “black-box” analytics tools to prescribe learning pathways. Learning analytics shouldn’t be used in a controlling fashion. Instead, it should enable and facilitate learners’ local decision-making. Rather than prescribing learning, my vision is to have analytics describe information that is not necessarily evaluative, and instead provide information to aid evaluation; students make choices with presented information and their choices and resulting changes then feed into analytics. The relationship between analytics and learners become more collaborative and dynamic, leading towards not merely knowledge or skill development, but also cultivation of metacognitive skills, epistemic fluency and habits of mind crucial for the knowledge age.
Currently, I have two early-stage projects that are examining different aspects of the potential of learning analytics. For the first project, with supports from the Center for Writing, I am developing a tool that analyzes student writing — in the form of online knowledge-building discourse — and raises awareness of emerging concepts in student discourse. This tool attempts to provide personalized feedback to each student, recommending concepts or lines of inquiry which might be of interest and suggesting collaboration based on their conceptual engagement so far. In this way, learning analytics can empower students to make more informed decisions during their collaborative efforts.
The other project, being conducted in partnership with the X-Learning Lab at Peking University in China with funding from the international Digital Learning for Development initiative, investigates teachers in underserved regions of China who participate in professional development MOOCs offered by Peking University. I’m spearheading the learning analytics component of this project, aiming at depicting rich behavioral profiles by mining “big” MOOC data. Results will be combined with “thick” qualitative data gained from a small number of teachers. Along with the Chinese education experts at the X-Learning Lab, we’ll examine how teachers from different regions pursue these opportunities differently and the impact it has on their real-world teaching to understand whether MOOCs could become an alternative for teachers’ professional development in developing countries.
Two Guiding Principles for Using Technology in Education
With technology opening up so many new horizons in education, I’m excited for the future. What our schools and universities will look like a decade from now is ours to decide, and I’m proud that CEHD and LT Media Lab are helping lead the way. In closing, I’d like to suggest two principles that should inform every decision we make about technology in education.
Look beyond “best practices.” Don’t focus merely on what’s possible for students today or how to increase efficiency in the classroom. Reflect on your assumptions about teaching and learning, and look beyond what’s currently considered the best. Together, we can create a better education for this century.
Work as a team. All the technology, knowledge and innovation in the world won’t make a difference if we can’t work together as researchers, educators, administrators and students. Collaboration is key to introducing technology into education. I’m really excited about a brand-new collaboration opportunity with a group of science teachers in Minneapolis South High School to introduce a collaborative learning environment I’ve been working on.
Developing programs and tools that encourage teamwork and collaboration is vital and something we at CEHD are doing our best to nurture. We hope you’ll join us.
Homer-Dixon, T. (2000). The Ingenuity Gap: Facing the Economic, Environmental, and Other Challenges of an Increasingly Complex and Unpredictable Future. Vintage.
Pea, R. (2014). The Learning Analytics Workgroup: A Report on Building the Field of Learning Analytics for Personalized Learning at Scale. Retrieved from https://ed.stanford.edu/sites/default/files/law_report_complete_09-02-2014.pdf
Originally published at cehdvision2020.umn.edu on January 29, 2016.