All too often, students learn with the goal of preparing for a test or an exam – they cram the week before and forget most of what they learned soon after. While this strategy is usually sufficient for achieving good grades or at least a pass, it undermines the goal of learning for future tasks and challenges. Florian Schimanke, a doctoral candidate at the University of Bamberg, develops mobile learning games that promote more sustaining learning strategies. At E-Learn 2017, he won the best paper award for his work.
AACE social media intern Rima Chamout interviewed him about his research and the E-Learn conference. Florian Schimanke is a regular at AACE E-LEARN, he has been attending since 2013: “Actually,this was my fifth AACE E-Learn. A great conference each time”, he comments. As a member of the platform Academic Experts, you can connect to Florian Schimanke and his work on AACE’s social network and conference planning platform – and meet him at future AACE conferences.
Rima: Can you share your personal lessons learned from E-Learn 2017? What sums up your conference experience?
Mr. Schimanke: Well, the most important and most interesting part for me is always the discussion after my own talk. So, you present your work, and it’s very interesting how people react to it, what ideas people have about what you’ve just presented. There are a lot of interesting takeaways from that, so new ideas, new directions where you can go with your research. So, there was one guy on my talk, who pushed me into the direction of using the learning theory of constructivism for my research, which was pretty interesting and which was something that we didn’t think about up to this point, so yes, that was maybe my top takeaway from this year’s conference.
Rima: I wanted to congratulate you on winning the ‘best paper award’. What is the key part of your research findings for practitioners?
Mr. Schimanke: I think the key takeaway is that a lot of students learn in a result driven way. They are driven by passing an exam, by getting good grades, and this is something we have found out in a little survey that we did at our university. The takeaway that you can take out of that is: you won’t take that away from your students, so they will always be result driven, we call this “Bulimic Learning”. But what we can do is to provide them with some tools that can help in building a long term sustainable knowledge. When you start with sustainable learning, long-term learning, you can build up a certain knowledge that will stay in your long term memory after you pass an exam or a test. So, this is something that is really important and that is the most important goal, actually, of our research.
Rima: And how can other researchers build on your work?
Mr. Schimanke: Right now, we are developing a framework which contains two algorithms that do a repetition scheduling for the learning content that you are about to learn, and that does a content selection at a point in time when you are actually learning outside of these calculated intervals. So we are planning on releasing that framework to the public, as an open source framework, and everybody who is interested in contributing to that work or taking a look at what we are doing, and maybe try it out with their own students or even with their own learning strategy, are very welcome to help us with that. They are very welcome to use it and provide us with some data about how effective our way of learning is.
Rima: What was your reaction to winning the best paper award?
Mr. Schimanke: My own reaction was very surprised at first because I just learned about it from the conference program. So, that was a big surprise when I looked into the program and saw that we won the prize. But, yes, it’s a huge deal and we are really really honored by winning that award. It was really great and we are very happy about it.
Rima: Does this award influence your work?
Mr. Schimanke: It certainly will. Winning such an award really does change the way that people look at your work. It really changes the awareness of what you are doing in a very positive way. So, I received a lot of congratulations, a lot of cooperating requests, a lot of emails from people I didn’t know before, and who told me that what we were doing goes into the right direction and want to work with us, even improving our work. So, it really influences our work in a positive way, just because of that awareness.
Rima: What are the next steps for continuing your research?
Mr. Schimanke: We are currently in the middle of collecting some data about how effective our work is. We are working together with a game developer, who has developed a geography learning game and who is using our framework in this game to collect some data and to provide us with it. This data informs us about how our framework and our algorithms impact the way that people learn and on the success that people have when they are using that game. And this is basically the biggest step that we are just thinking about now because this work shows if everything that we have been doing up till now is really as effective as we hope it to be.
Rima: Can you explain more about what the project with the game developer will be like?
Mr. Schimanke: Right now, the framework is just for IOS apps, so just for iPads and iPhones. And the game I am talking about is actually an iPhone game which is available on the App Store for everybody. We are not advertising that it does include our framework and our algorithms because we don’t want to influence the people using it. We just want to collect data about how the game has been used and how effective our algorithms are on the learning success. So what we are planning to do, later on, is to broaden up that approach and to provide that framework also maybe for browser games or games with other platforms like Android or stuff like that.
Rima: Can you give me a little background about what interested you in writing about this topic?
Mr. Schimanke: I actually work at a small university in Germany and we can see how our students learn each and every day, and how they are very very result driven. Basically, they are learning just for passing a test and for getting good grades. And from a researcher’s and instructor’s point of view, this is what we want to avoid. So we were looking for a way to solve that problem; we were looking for a way to build a more sustainable, a long-term learning. This is when we came up with the ‘space repetition approach’ which has been there for a very long time but hasn’t been used in game-based learning up to now. So, this was something that we wanted to take a closer look at because learning games have been proven to have a very very motivating effect. We want to use that motivating effect on students and combine that with ‘space repetition effect’ that is proven to build a long term memory. That was basically the reason we started to look into that and it just became more and more interesting. We had some interesting results that we wanted to build on, and that was the reason why we dove deeper into that topic.
Rima: Do you think that your work applies also for younger children or is it only for older students?
Mr. Schimanke: I think you can use it basically at any age. You can also use it with children’s games where, for example, you have to identify certain objects on the screen or where you have to solve very very basic mathematic tasks. So this is something that you can use at any age with any group of ages, with any children, with students at universities, with students in schools. So, there is basically no limit.
Rima: Can you please explain more about what you mean by ‘rehearsal’ and how it consolidates information in the long-term memory?
Mr. Schimanke: When you have learned something in the past, and you were already able to put that into your long-term memory, you have to activate it from there every once in a while to make sure that it doesn’t vanish from your long-term memory. So you have to basically pull it out, you have to use it in your work, use it from your knowledge just to make sure that it stays there all the time, and that it doesn’t vanish. So, our ‘space repetition approach’ uses this ‘rehearsal’ because knowledge is pulled out of your long-term memory every once in a while, at relative long intervals. So, we just want to make sure that you don’t lose that information from your long-term memory by pulling it out at intervals that are just as long that you do not forget it. The longer the interval is, the harder it is to pull it out and work with it, to remember it, and this is something that has a positive impact on that rehearsal, or on building that long-term memory.
Rima: Can you give me a concrete example of how the framework works, you were talking about a formula in your study?
Mr. Schimanke: We actually use two algorithms in our framework, one is for the ‘space repetition’ calculating which calculates the intervals at which the information is presented. So, this algorithm is very closely connected to the actual learning performance. If you have some information that you are very good at remembering, it will be presented to you at longer intervals. If there is something that you are not very good at remembering, it will be presented to you at shorter intervals. And this can change all the time. So if you have information that you were good at remembering in the past, and you are not that good in the present, this interval will become shorter as well as intervals can become longer the better you are able to remember it. So, this is one part of the framework. This is done by an algorithm that is called SM2, which was developed by the researchers behind the “Super Memo” project. And we built on that by providing another algorithm which basically runs as an auxiliary algorithm that takes over the content selection when you learn or when you play the game outside the calculated intervals. This is very important because when you learn outside the calculated intervals, this may change the values that I used for the calculation. And we need to protect those values to make sure that the calculation of the intervals stays the way it has to be. So, this a combination of the SM2 algorithm and our algorithm which is called “FS” for “Follow-up Sequence” and those algorithms work very closely together to make sure that there is a content selection at the times when you learn outside the calculated intervals when you learn inside or outside the calculated intervals.
Rima: Do you have any recommendations for teachers and instructors who would like to use your method with their students?
Mr. Schimanke: I think that you always have to keep in mind that students often follow their own schedule and their own learning strategies and, certainly, we will not be able to change that. What teachers can do is to provide their students with knowledge that there are other learning strategies that may be very helpful for their long-term memory, as for example, space repetition, and how they work. So, if they know about those tools and strategies, they can use them. But using them always depends on the students themselves because most of them are result driven and often they don’t have the time to build up a long term memory beforehand. So they have to really change and adapt the way they are learning. Teachers can help by providing students with the knowledge about learning strategies and with a knowledge about tools, for those learning strategies may be a good first step.
Rima: Would you be interested in sharing the name of your app for the IOS that students are using now?
Mr. Schimanke: Yes, sure. It is called “Where is that?” (https://itunes.apple.com/app/where-is-that/id492240967?mt=8) So it is a geography e-learning game where you are presented with some tasks, like for example, finding the capital of the United States? And then you have to pinpoint that location on a map. You are rewarded points depending on how far you are off that target. Our idea is that we can improve your knowledge about those places in the world by doing a space repetition and calculations about those places you are not very good with. For example, a lot of people, from Europe, know about the capitals in Europe, but they don’t know about the capitals in Africa. Even they cannot find the correct country. So based on that, you are presented more frequently with tasks from Africa than from Europe just to build up that knowledge about that region of the world.
Rima: So now your app is basically about geography only?
Mr. Schimanke: it’s only about geography right now. It’s a game that is basically about factual knowledge which suits very well with the ‘space repetition approach’. So, doing rehearsal, doing repetitions is a very common task where you learn about factual knowledge. You can also use space repetition in other domains, but factual knowledge is actually one of our focus areas right now.
Rima: are you planning in the future to include more subject matter, as for example, language arts or history, and so on?
Mr. Schimanke: Sure. One of the main challenges that we are facing is content creation. So finding game developers and content providers for our app and games, is very hard task. Our algorithm can be used in any kind of game and learning app. So, if there are some developers out there or some researchers who are interested in this topic, feel free to contact us and we will provide you with our algorithms and framework, and you can use that in your games. we just need back some data to improve our work.
Rima: Ok. That is really great. Thank you for your time. Have a good day.
Mr. Schimanke: You are very welcome.
About The Researcher:
Florian Schimanke is an IT Systems Coordinator and a Research Assistant at University of Applied Sciences, Hameln, Germany. He also is pursuing a Doctoral degree in “Using Spaced Repetition Algorithms in Mobile Learning Games” at University of Bamberg.
Schimanke, F., Mertens, R. & Schmid, U. (2017). Spaced Repetition in Mobile Learning Games – A Cure to Bulimic Learning?. In J. Dron & S. Mishra (Eds.), Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 955-964). Vancouver, British Columbia, Canada: Association for the Advancement of Computing in Education (AACE).