Emerging Learning and Development Models: Part One
- 2. In this next series of 10 blog posts we are going to summarize the use of emergent learning concepts that have been triggered by the field of gamification. Gamification, as the strict use of game mechanics in non-game environments has given birth to entire new fields of training. In this series we will take you on a voyage with us as we explore emergent interactions between play and learning in adults. What is the role of play? What is play? What is eudaimonic content? How can learning be optimized for distributed systems? How does the user experience affect training? What cost efficiencies exist which limit or enhance roll out? In brief, we have spent most of this year talking about gamification and its time to take the next step. The list below includes the blog topics over the next 10 weeks.
- 3. We strongly recommend you first read all of our previous blogs to gain a background in ideas we will discuss. Those of you who have been following us can refer to previous discussions on topics such as intrinstic motivation, the neuroscience of learning and the design of training games. These posts are for advanced readers who are looking forward to the future of gamification development as a field. These reflect our priorities at ARC for Game and Train, and our systems all feature these improvements. 1. Microlearning with Intrinsic Reward Matrices 2. Coupled Completion and Fantasy Learning 3. Simulation-Game Systems 4. Behavioural Currency Systems 5. User Controlled Learning Interfaces 6. Social Network-Based Collaborative Problem Solving 7. Distributed Gameworld Knowledge Structures 8. Extrinsic Reward Behavioural Management and Cognitive Behavioural Representation 9. Intrinsic Reward Identification and Mastery-Based Progression 10. Gated Mobile Training Linked to Game Mechanic Systems We will begin with the first topic today.
- 4. Microlearning with Intrinsic Reward Matrices Microlearning has been the rage for a couple of years now and consists of presenting a short training video for a minute and then testing on it. It is used for on-demand learning and is made possible through mobile training. It can be released on the web or in person, but its true potential lies in its delivery system to meet “on demand” training needs. Earlier in our history, we relied on needs assessments in order to refine curriculum and this took a long time. Frequently that data from focus groups or written evaluation forms was then passed onto a faculty member, who then might use the data to improve teaching or not. There was significant lag between intake of data and execution of changes in training content or procedure. Microlearning permitted us to introduce new content through mobile push notifications but we had no way to figure out what content to push!
- 5. Fortunately, advances in two technologies were made in the past year: a) development of analytics derived from mobile training and b) systems for designing “just-in-time” training. When we paired these developments, we suddenly had responsive microlearning for the first time. We don’t guess at what people need to learn as they train, to determine “gaps”; we use real-time data. The development of real-time data collection came from our work in simulation-gamification of medical care which we developed with a major teaching hospital. We were able to see, moment to moment, what students were learning, when they were learning and where the gaps in their ability to use knowledge were. This last feature, assessing what is called Kirkpatrick 3 level outcomes, showed us how learners used knowledge, not what knowledge they had. This, in turn, came from my design of a complex game system for health care assessment in medicine which, after tweaking by clinicians, turned into a mobile decision making system with analytics. However, we still had a gap in our execution of “burst” learning. We knew what they had to learn but we did not know how to create new content to fill those deficiencies. This is where the work at the University of Toronto and The Michener Institute of Health Sciences both entered. They had developed a system for obtaining “just-in-time” data in medical technology training which we could import into our system for rolling out on-demand content. Having the analytics is one thing, but knowing how to do something to change them is another. This filled in the missing gap. Briefly, teams of faculty members reviewed medical technologists performance in simulations, then collated these data across user populations to determine trends in performance gaps.
- 6. The faculty teams reviewed the data quickly and then prioritized changes in training during the simulation epoch. Simulations were used regularly over a fixed period of time to permit rehearsal of skills and assessment of skill utilization. In the progression below, T-time of testing through simulation: Competencies———————>T1———————>T2———————>T3 After assessing T1 performance, a change in training content was then designed rapidly, then inserted prior to T2. Then data was analyzed post-T2 and a new cycle of responsive content design started. This was implemented at T3, and outcomes of total training matched against competencies were produced. Their data showed that using this just-in-time model, training could be modified on the run, on the basis of simulation outcome data. This work is now entering further distribution and testing over the next year and I am excited to be a part of the ongoing research.
- 7. So far, then, we have combined real time analytics with just-in-time performance measures and content adjustment to deliver microlearning content. Microlearning using video is still expensive; the production values were very high for it to be effective, so we focused on simulations using wire-frame branching algorithms. Now we were able to produce new simulations instead of video content, something which only takes a few days to complete, or even hours, depending on the gap.
- 8. Intrinsic reward matrices are algorithms we designed to award learners by attaching an earning system to the simulations, so that as they completed simulations successfully they received in-game currency. As they became faster at solving simulations, they could take on more difficult content which awarded higher in-game rewards. So we fostered intrinsic motivation by offering a leaderboard so that they could compare their mastery of simulations with other learners. This compelled players to take on more case studies and permitted us to refine the content as we went, to improve the outcomes. The use of the term “matrices” denotes a type of report card if you will, but a more complete picture, showing their progress over time, number of simulations completed, number of good choices made, ratio of earnings in-game to efficiency in case solving, confidence in case decisions and other data points. So this matrix compiles data on the individual learner showing their progress through measuring the game reward system.
- 9. We are using the game matrix not just to see “who is best”, but to see how the group, as a whole, is progressing, and to encourage them to complete more cases. Given that things like diagnosis are so inherently interesting anyhow, fostering intrinsic drive was relatively easy. By attaching microlearning/just-in-time methodologies using a game system, we were able to design a responsive learning system built on activity, rather than passive exercises such as reading. This system is like designing a microscope to capture every learning activity in the system and break it down into categories to determine how to produce burst content.
- 10. So the first of our systems, Microlearning with Intrinsic Reward Matrices (MIRM) is now something we hope you can see the value of. This started as a gamified approach to clinical care and expanded beyond a game into a combined analytic/intervention system for training.
- 11. In our next post we examine the next major system we are interested in and designing for: Coupled Completion and Fantasy Learning. Very large studies of over 250,000 gamers have shown that completion and fantasy elements are highly motivating for players over the age of 36. These elements are of great interest for female players as well. Young male players prefer combat systems and destruction! So the majority of our players, if we have a diverse work force, will thrive on adding elements of completion and fantasy into their training. This does not necessarily have to be a game…it can be digital storytelling or other narrative expositions, or even open game worlds such as Second Life adapted for education. It may simply involved social network-based learning or other methodologies. Until next week, keep on looking up!