Jodi Asbell-Clarke

Meet Jodi Asbell-Clarke

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CIRCL perspectives offer a window into the different worlds of various stakeholders in the cyberlearning community — what drives their work, what they need to be successful, and what they think the community should be doing. Share your perspective.

Jodi Asbell-Clarke

Jodi Asbell-Clarke is the director of the Educational Gaming Environments (EdGE) at TERC. She presented her work on Games and Ubiquitous Science Learning Environments at the 2012 Cyberlearning Summit, and her work has been featured in the 2015 Video Showcase: The Power of Implicit Game-based Learning and the 2017 Video Showcase: Research on Computational Thinking & the Game Zoombinis.

How did you get started in cyberlearning?

I’ve always liked working on something really exciting. My first job after university was at IBM as a verification analyst on the onboard Space Shuttle software during the first 25 missions of the space shuttle. Throughout my career, I’ve always been seeking that kind of “edge”.

After teaching high school at University High School, a lab school associated with the University of Illinois, I came to TERC in the early 90s and helped create curriculum and teacher professional development for Hands-On Universe and some of the early Internet-based STEM education projects. By 2005, I was applying for grants to deepen our research about online learning for science teachers, and received reviews back from NSF saying, “This is not transformative enough.” Just then, we were approached by some hollywood designers involved in the virtual world industry who wanted to build science content in the first high-definition virtual world called Blue Mars. We thought “You want transformative – we’ll give you transformative!”

At the time, virtual worlds like Second Life were at their peak (before their relative implosion in the late 2000s). Blue Mars was in beta test and had great expectations for commerce, social spaces, and education. We partnered with Blue Mars developers to build our game Martian Boneyards. It was our first big project in game-based learning and it helped us understand how digital games could be used to measure implicit science learning.

We created a virtual abandoned science center with workstations that enabled players to scan and measure artifacts they could collect in the environment. Players got a virtual PDA to explore the world and collect digital artifacts for scanning. We had placed bones over an extensive world that was revealed in phases over the 4-month game. Players spun a tale of scientific discovery, romance, and murder – all from the artifacts they found in the boneyards. We were able to analyze all their data collection, analysis, and theory building activity. We found that even though women made up only one-third of the audience, they were conducting over two-thirds of the scientific inquiry – especially in the data analysis and theory building stages. We found really cool identity formation stuff when we asked our top players what they were doing and why they were doing what they did. Top players, who were female and did not consider themselves scientifically oriented, explained that it was the game that motivated them to persevere and learn science. One top players, who was named “Doc” by the player community, said “I’m a gamer – I never give up!”. That really piqued our interest towards the power of games.

How do you think about cyberlearning and learning now?

We knew this game-based learning had potential, but as science educators we didn’t want to make a bunch more “gamers.” We wanted to leverage the time that people were spending in these environments and find ways to make it productive. The problem with doing an MMO like Blue Mars is that it was a very niche audience – not at all diverse. For our next games, we moved to mobile tablets and phones because we knew we could reach more kids. We created games that kids would choose to play and that were grounded enough in science so that the increasingly complex problem solving that happens in games could lead to implicit science learning. Can we ground those mechanics in phenomena so that during game play, kids gain implicit knowledge about science content? When people are interested in a game they try over and over to be successful at a task. Through their own choices they adopt strategies to solve problems (See The Power of Implicit Game-based Learning video.)

We developed 3 games –– Impulse, Quantum Spectre, and Ravenous –– all available on our EDGE website. One strand of our work focused on the use of educational data mining (EDM), grounded in video observations and intensive analysis and coding of behaviors that we could observe. We coded these behaviors down into features we could identify in the game data, and then work with colleague, Dr. Ryan Baker, and others to build detectors that would operate on the game data to identify behaviors consistent with game learning. We have been able use EDM methods to identify behaviors in both Impulse and Quantum Spectre that are consistent with implicit understanding of the salient science phenomena (Rowe et al., 2017).

In parallel to the EDM studies, we conducted implementation studies for each game where we had 3 conditions. In the control classes, the teacher taught the content the regular way. In a games-only condition, kids played the game, but the teachers taught the regular content. In the bridge condition, the kids were encouraged to play the game preferably outside of class, and the teacher used examples and discussion to bridge the behaviors and implicit knowledge kids were picking up with what what they were doing in class. So for example using Impulse, where you propel a ball toward a goal without crashing into other balls, a kid might say “oh, I just let the ball float, I didn’t do anything” and the teacher could seize that moment to say, “Well, let me tell you about Newton’s first law.”

We found that students did significantly better from pre- to post-test in the bridge condition. So bridging matters. And kids who displayed the behaviors we identified through EDM did better on the post-test. We concluded that what students did in the game matters, and teachers need to bridge what they are doing to make the implicit knowledge explicit. We are now using similar research methods to study the development and measurement of implicit computational thinking in the game Zoombinis, which is all about logical thinking.

What makes you wake every morning and want to work on this?

I think there is a diverse set of learners out there who’s talented is totally untapped because educators often measure the wrong things and use the wrong criteria for success. I would love to find ways to unleash the potential that each learner has, however they want to express it. I am particularly interested in students with cognitive differences. Recently I have watched learners with Autism take off when you put them in Scratch, or give them a broken computer to fix. And I think this could extrapolate into a whole bunch of other examples. Kids who have limitations in some areas may also have untapped strengths, but they are not being put into situations where their potential can grow. Our work in implicit learning and with our colleagues at Landmark College has shown me that there is so much bubbling underneath the surface of what we measure. What drives me is figuring out a way to give those learners who can contribute to our society an avenue to contribute. I want to unleash the potential that I think many different types of learners bring to our future workforce, including the people who are going to figure out my future healthcare needs and saving our planet.

A cool example I heard about recently was from a small “Autism at Work” program in the Silicon Valley. They look particularly to those with autism for quality assurance (QA). I would be the worst QA person! Because it requires total persistence and attention to detail. They found that for people with autism, it can actually be physically uncomfortable to leave a task incomplete Who more do you want for your quality assurance? They are compelled to seek every single case – which is an important Computational Thinking skill! Just think that’s a marvelous example.

What kind of help or support would you like from the community?

I look back to our early days of game-based learning funding. It was out of a one-time strand of the NSF DRK12 program called Challenge 5 – where we were encouraged to think towards the horizon. What would schools be wanting in 5-10 years down the road? And that has gotten the community to where it is today – that far-reaching thinking which has shaped and defined the cyberlearning community. With the all the uncertainty of funding, for so many reasons, I think it is on us, the Cyberlearning community to keep it going. That means sitting on review panels, helping to shape RFPs, and to keep hosting community gatherings like the Cyberlearning convenings. It is so important.

It kind of goes back to the “being on the edge” thing. The stuff that I find fun and valuable in research, and the ways that I can advance understanding, rely on people knowing that high-risk and innovation pay off. That if you don’t shoot, you don’t score.


Rowe, E., Asbell-Clarke, J., Baker, R., Eagle, M., Hicks, A., Barnes, T., Brown, R., & Edwards, T. (2017). Assessing implicit science learning in digital games. Computers in Human Behavior. DOI: 10.1016/j.chb.2017.03.043