Background:

Professionals in higher education, educational technology, and the Institute of Electrical and Electronics Engineers (IEEE), have recently begun an effort to advance the field of Learning Engineering. While the profession of learning engineering might be new to some, the idea itself has a long history dating back to the Nobel winning economist and cognitive psychologist Herbert Simon.

In 1967 Simon wrote “The learning engineers would have several responsibilities. The most important is that, working in collaboration with members of the faculty…they design and redesign learning experiences in particular disciplines.”

Fast forward to April 2016 when the argument for the field of learning engineering was taken up in the Online Education Policy Report at MIT that was authored by Karen Willcox and Sanjay Sarma. In the report, which I highly recommend everyone read, the work of learning engineers is described as “integrating their knowledge of a discipline with broad understanding of advanced principles from across the fields of education” to “create new learning experiences from scratch and to integrate new technologies and approaches into existing experiences”. Further, the report argues for “expanded use of learning engineers and greater support for this emerging profession” as one potential catalyst for transforming teaching and learning in higher education.

It should be noted that around this same time many other organizations were working on advancing the application of learning sciences and human computer interaction through the creation of programs like Carnegie Mellon University’s (CMU) Masters of Educational Technology and Applied Learning Science (METALS) program. Within the last year the program has officially taken on the goal of training “graduate students to become learning engineers and LX (learning experience) designers)” (https://metals.hcii.cmu.edu/).

More recently, Chris Dede, John Richards, Bror Saxberg, and others (including the previously mentioned Sanjay Sarma) published the book Learning Engineering for Online Education: Theoretical Contexts and Design-Based Examples. The book pushes for an approach to the work of learning engineers that leverages design-based work grounded in the use of data, especially the types of complex big data that has become associated with online learning platforms.

(Disclaimer: I am aware that others have been doing thinking and writing about the field of learning engineering and I do not want readers to think this is an exhaustive account of how the field has/is developing! I am simply attempting to provide some background. That said, I would be thrilled to hear from others about the work they have done in this area. Please email me at kesslera@mit.edu with your thoughts.)

My Developing Ideas:

Right around the time that Willcox and Sarma were writing their report on Learning Engineering at MIT, I was a graduate student at the University of Pittsburgh. I was working with my mentor, Jennifer Cartier, and fellow graduate student Danielle Ross (Danielle is now at Northern Arizona University) when we started using the term instructional engineers to describe the preservice science teachers we were teaching and training. The term, which was partially inspired by the work of Simon, was born out of a need to better describe a mindset we hoped to develop in our preservice teachers. At the center of this mindset was the belief the teachers have the professional authority to creatively design and implement educational experiences and that this work mirrored that of the complex problems engineers are often tasked with solving.

I spent a lot of time thinking about the metaphor of teachers as instructional engineers and included some of that thinking in my dissertation when I wrote “Acting as an instructional engineer requires teachers to engage in work that is very different from the traditional ideas associated with teaching…thinking about the role of teacher and instruction in this way has interesting consequence for how we as a filed think about teachers and teaching.” I was attempting to make an argument for why and how teachers, and science teachers specifically, needed to move beyond the work of simply implementing curricular materials as they were initially constructed and instead adapt or build on those materials to engineer learning experiences for students that encouraged deeper engagement with the science content.

New Setting and New Perspective:

So, what does my previous thinking about instructional engineering have to do with learning engineering? About a year ago I transitioned to my current position and began working with the amazingly accomplished group within Open Learning. As part of my onboarding I read the Willcox and Sarma piece and realized that one of the main differences in my current work would be dealing with issues of scale and context. In my initial thinking about instructional engineers I had been focused on the work of a single person, the teacher, and the context in which they were situated, their classroom. I still believe much of my thinking about instructional engineering was important (I will probably have to write a different blog post on that). However, what I had failed to realize at the time was that other teaching contexts (i.e. online teaching and learning) might include larger and more distributed groups of learners. This structure can present different types of challenges associated with designing learning experiences, collecting evidence of learning, and iteratively improving instruction. Thus, enter the field of learning engineering.

One example of learning engineers comes from within the Office of Open Learning at MIT. In a partnership between Open Learning and academic departments throughout the university are a set of digital learning scientists and digital learning fellows.  Collectively these talented people make up the Digital Learning Lab (DLL). Each of the DLL members holds a PhD or equivalence in the content area of their academic discipline. This structure was a way to operationalize the vision for changing higher education laid out by Willcox and Sarma. One of the most interesting parts of this work for me is the fact that the members of the DLL community are tasked with developing, designing, implementing, and iteratively improving instruction that is facilitated through the use of the MITx platform, a local instance of the open EdX platform that is housed and run within Open Learning. This focus on using a single platform, which itself can be partially edited and developed by members of the DLL community, to transform instruction has resulted in a number of residential innovations. The Digital Learning Lab make up a unique community of practice and can, in my mind, act as an exemplar for how learning engineering teams can exist in higher education. If you are interested in some of their work I encourage you to check out their webpage, send me an email, or attend the ICICLE Conference on Learning Engineering at George Mason University on May 20-23. I will have the pleasure of discussing the work I have been doing with this group and MIT’s approach to learning engineering more broadly.

IEEE ICICLE:

With a number of prominent institutions and thought leaders bringing the work of learning engineering to the forefront, the time seems right for some decisions to be made about what constitutes the work of learning engineers and how they are trained. The ICICLE conference is a place where steps on establishing those decisions will take place.

I am personally excited that the conference will be an opportunity to share the work of the Design for Learning SIG. Having been a part of the SIG the last couple of months, I look forward to sharing the ideas we have been developing on the competencies and dispositions of learning engineers. I look forward to learning more about how other contexts (mainly those in the educational technology industry) envision the work of learning engineers, and I am excited to discuss what all of these ideas mean for how we focus on supporting the learning of a diverse and ever-changing student population.

 

Note: Thanks to Sheryl Barnes for her feedback on this post.

The research team aimed to develop and implement a single course intervention that would drive teachers toward encouraging student engagement through or within games that moved beyond testing memorization of content. To achieve this, the phase 2 intervention needed three key affordances:

1. The intervention needed to allow them to all have a shared experience and common language in order to share ideas with one another and recognize the value of games for collaborative learning. It seemed necessary to provide an intervention that would appeal to all teachers, no matter what grade or subject they teach.
2. The intervention needed to allow them to consider how collaborative learning with games could be integrated in their own lesson plans. Often educators have a hard time imagining what instruction they have not themselves experienced might look like, so experiential learning activities are often used in teacher training (Kolb, 2014). The intervention was intended to be a model of what collaboration could look like beyond the typical answer a kahoot question and talk with a partner type of instructional model demonstrated in many of the Phase 1 lesson plans.
3. The activity needed to provide an opportunity to reflect on why collaboration within the context of the game might be important for developing a shared understanding of content for students. Further, the intervention needed to allow for the teachers to begin developing a way of thinking about the connection between the parts of the game that supported interactions, parts of the games that did not afford purposeful interactions and the work the teachers would need to do in order to foster collaboration towards supporting students learning of specific content

The phase 2 results showed one statistical change from phase 1. This was a 31.5% gain in the use of technology that went beyond simple memorization tasks. This suggest that teachers in the phase 2 group included the use of games-based technologies that allowed their students to engage with the content of the lesson in ways that went beyond simple recall or memorization processes. This was a primary goal of the second intervention, and a statistically significant result suggest the instructional intervention achieved at least some of the research teams’ vision for transforming teachers work.

Further phase 2 results also included a number of important gains, that while not statistically significant, indicated movement in the positive direction for the desired outcomes including a 18.8% gain in teachers using the game-based technology for communication of collaboration, a 17.8% gain in teachers planning for collaboration that is facilitated through the technology and a 19.6% gain in teachers explicitly building in scaffolds for collaboration during the lesson.

While these positive gains suggest movement towards teachers using game-based collaboration instructional approaches that were better aligned with course goals, the results also suggest room for further improvement. While the statically significant improvement on code 4b is positive, nearly half of the teachers were still using technology for simple memorization or review based tasks. This high percentage, combined with limited to no movement in teacher planning for preplanning collaboration or considering collaboration as part of their SMART goal formation, suggest that the impact of the intervention was limited in the scope of overall teachers’ ability to implement collaborative learning experiences using game-based technologies.