After introducing the concept of a token economy for gamified learning, several categories of methods of tracking student participation in a token economy for gamified education are created, described and differentiated. The categories include physical tokens, public available token records, manual electronic tracking, automated electronic tracking, and gamification-specific software. Each of these methods are reviewed in terms of cost, learning curve, ease of use, privacy, security and impact on student engagement and learning. Based on literature reviewed, it is found that in terms of cost, learning curve and ease of use, methods that do not involve customized software are cheaper and simpler than the methods that do. In terms of student engagement and learning, all of these categories reported mixed to positive effects with the exception of automated electronic tracking. The impact of a token economy on motivation and the “fit” of the tracking method within the curriculum-specific implementation of gamification are also discussed. Further research into tracking methods is recommended, including differentiating the tracking method with other factors controlled.
Keywords: gameful, gamification, learning analytics, learning management system, motivation, participation, token economy, virtual currency
As the concept of gamification has emerged and become a trend in education, educators have needed to find means to track “scores” for students based on the criteria that is desired to be reinforced. Generically, if these points are utilized toward some kind of reward, this is referred to as a “token economy” (Boniecki & Moore, 2003), or sometimes “virtual currency” (Dicheva, Dichev, Agre, & Angelova, 2015), where “points” or other in-class currency is used for some kind of benefit. As this has popularized, educators have utilized different means to track student progress in a token economy, and these means vary in ways that affect their usability and effectiveness from both educators’ and students’ points-of-view.
Rather than focusing on explanations and justifications for the idea of gamified learning, this chapter will examine different methods that can be used for tracking student participation in a token economy, weighing the benefits and drawbacks of these different approaches, finally discussing how the tracking method used also relates to the curriculum.
Though the term “gamification” was coined in 2008 (Walz & Deterding, 2015), many of the ideas associated with gamification are considerably older; offering a child some form of privilege for a job well done is not new. The term “token economy” originates from Ayllon and Azrin in the 1960s where it was used to reinforce positive behaviors in psychiatric hospitals (Atthowe & Krasner, 1968).
The most common game-related elements in gamified courses are points, badges, and leaderboards where some other elements commonly considered part of gamified learning include avatars, teams, progress bars, quests, re-trying and narrative (Cheong, Filippou, & Cheong, 2014; Dicheva, Dichev, Agre, & Angelova, 2015; King-Spezzo, 2014; Yuan Hung, 2017). Some authors who write about gamified educational practice such as King-Spezzo (2014) and Webb (2016) discuss point systems in their courses without mentioning virtual currency or token economy, but generally these individuals are still implementing a token economy, for example by having the points translate into extra credit in a course. Implementing points, leaderboards and progress bars requires some kind of tracking of student engagement and/or progress, and that is where the token economy applies.
Across various studies and meta-analyses, a handful of tracking method categories emerged as commonplace. Seemingly, the most common method for tracking a token economy is a physical token. In addition to this, there are publicly available token records (such as tally marks on a wall). More high-tech systems, such as electronic tracking methods that can be either manually updated by the instructor or automated by some means, have also been discussed (Dicheva, Dichev, Agre, & Angelova, 2015; Fotaris, Mastoras, Leinfellner, & Rosunally, 2016). Lastly, there is also the possibility that customized software is used for the specific purpose of facilitating a gamified course that tracks the token economy, as discussed by Dicheva, Dichev, Agre, & Angelova (2015), Su & Cheng (2015) and Kingsley & Grabner-Hagen (2015). For each category of tracking systems, factors affecting adoption and effectiveness will be discussed, including cost, initial learning curve, ease of use, privacy/security, and impact on student learning and engagement.
It may seem unusual that the method used to track participation in a gamified learning environment could actually have impact on learning and engagement, but motivation theories such as the ARCS model (David L., 2014) show the connection. Besides the benefits in terms of attention that any tracking method could provide, making the relevance clear to the learner by providing them an immediate token based on their action could assist with motivation. The method used being transparent to the student – and possibly publicly available to the rest of a class – may affect both a student’s confidence and satisfaction, which are also factors in motivation according to the ARCS model (David L., 2014).
Physical tokens represent an object given to the student as they fulfill the conditions you wish to reward. Boniecki & Moore (2003) and Webb (2016) utilized poker chips whereas Chapman (2016) and Nelson (2010) used printed cards. In some cases, the tokens are distributed during a class and collected at the end of the class – sometimes with the student’s name on them – so that they can be recorded.
The cost of physical tokens is generally low, with Myles, Moran, Ormsbee, & Downing (1992) suggesting the poker chips can be bought at a dollar store. The learning curve and ease of use for distributing and collecting physical tokens are trivial; Boniecki & Moore (2003) suggest giving a token immediately following participation, and the students exchange this at the end of class for credit in the course.
Privacy and security must be considered with physical tokens. Myles, Moran, Ormsbee, & Downing (1992) suggest caution in making sure that the tokens are not easy to acquire or reproduce if the educator is concerned about the system being abused, and also suggest considering before implementation “whether tokens and reinforcers may be traded or sold by students, and under what restrictions these practices might occur” (p. 167). Chapman (2016) mentions having embraced the possibility that students would share their stock cards.
In terms of impact on learning and engagement, all literature reviewed reported positive results. Utilizing physical tokens, Nelson (2010) noted significant improvements in participation, particularly among extroverted students, and reported a mixed but mostly positive effect with regards to student achievement. Boniecki & Moore (2003) wrote that students were more than twice as likely to answer questions and ask questions of their own. Chapman (2016) polled students on whether they preferred physical tokens as opposed to electronic tracking, with a slight majority (58.33%) indicating they preferred physical tokens. Chapman’s (2016) work included a quote from a student who said “I believe the stock provided something tangible to show how serious you were taking the course as well” (pp. 24).
Publicly Available Token Records
Widely available records of points are mentioned by Myles, Moran, Ormsbee, & Downing (1992) and Caton & Greenhill (2014). This may refer to stickers, checkmarks or tallies publicly displayed on a wall or display board. This tracking mechanism has a lot in common with physical tokens, where cost, learning curve, and ease of use are all very minor factors. There is also little difference with regards to learning and engagement, where Caton & Greenhill (2014) observed improved attendance and reported higher energy and enthusiasm compared to a group with no token economy.
In terms of privacy and security, there is a significant difference between physical token distribution and a publicly available record: with a publicly available record, there is no privacy and students are always aware of their performance relative to others. If considering the use of a leaderboard regardless, this is a non-issue. In terms of the actual security, although the record is publicly available, it can be put out-of-reach (Myles, Moran, Ormsbee, & Downing, 1992) and easily observed by all students, which may mitigate tampering.
Manual Electronic Tracking
Manual electronic tracking may refer to the use of a spreadsheet software or the capabilities of a learning management system (LMS) (Dicheva, Dichev, Agre, & Angelova, 2015; Fotaris, Mastoras, Leinfellner, & Rosunally, 2016) to track the token economy. In terms of cost, these are generally low- or no-cost items considering the ubiquity of LMS usage among modern education systems and the availability of free web-based spreadsheet software such as G Suite (https://gsuite.google.com/).
The learning curve and ease of use vary based on the experience of the educator and students; for an educator who has never used spreadsheet formulas or written HTML this could be daunting. There is some concern with regards to privacy and security, where an LMS page available to an entire class has many of the same privacy concerns as publicly available token records. An instructor using online tools, including G Suite, should be mindful of where student data is being stored and their institutional policies on data privacy. Security is generally a non-issue as long as care is taken to back up the records; a student quote provided by Chapman (2016) even points out a potential security benefit over physical tokens: “The stock certificates could be improved by keeping track of them online. How it stands right now [with physical tokens] is that if you misplace your stock there is no way to get it back” (pp. 24).
Automated Electronic Tracking
Automated electronic tracking refers to custom-built scripts and tools added to an LMS for the purpose of gamification, and the use of advanced data analytics, ideas which have been discussed by Dicheva, Dichev, Agre, & Angelova (2015) and Yuan Hong (2017). It is very hard to generalize this option; if facilitated by an LMS vendor or purchased externally, the cost would be drastically higher. If developed internally or by the educator, the cost may stay low but the learning curve, ease or use and/or time to implement would be much higher.
Regarding the impact on learning and engagement, Yuan Hung (2017) has actually suggested that automated gamification based on learning analytics may be easily manipulated by students and data generated by analytics does not correspond with student learning.
Finally, there is the case of gamification-specific software tools. As with automated tracking, these will vary greatly with regards to cost, learning curve, ease of use and privacy/security depending on who is developing them. As an example, 3D GameLab (formerly 3dgamelab.com, now Rezzly at https://www.rezzly.com/) was described by Kingsley & Grabner-Hagen (2015) as having a low subscription cost, being very easy to use, and having a strong positive impact on the student experience.
Does the System “Fit” with the Curriculum?
Finally, something more challenging to generalize is the “fit” of the tracking system to the curriculum and the learning environment. In discussing elements of gamified education, many authors have discussed the role of narrative and theming (Chapman, 2016; Fotaris, Mastoras, Leinfellner, & Rosunally, 2016; Kingsley & Grabner-Hagen, 2015), such that the curriculum theme is a part of the nature of the implementation of gamification. Chapman (2016), for example, discusses a business programming course where the students are creating applications for a fictitious company, and the nature of the token economy has students earning stock in the company. This system would be ill-suited for a typical elementary class, just as a token economy of quests, guilds and gold coins would likely be ill-suited for a post-secondary programming course.
Dicheva, Dichev, Agre, & Angelova (2015) and Yuan Hung (2017) caution arbitrary adoption of theming, including renaming existing elements – like assignments becoming quests, and grades becoming experience points – without giving consideration to the curriculum. Adopting fantasy or sci-fi theming reduces the learners’ abilities to connect their content to real applications (Yuan Hung, 2017) and runs the risk of excluding students who have no interest in fantasy or sci-fi subject matter (Chapman, 2016), but incorporating more fantastical themes in relevant language or fine arts courses may not carry the same risk and may help to engross students in the content.
Consideration should be given to the “fit” of the tracking system with the curriculum and theming. If the elements of the gamified learning experience are intended to be interconnected and immersive, one must consider whether the token economy’s implementation makes sense in the scope of the students’ learning.
Conclusions and Future Recommendations
Though this chapter offers categories for tracking the implementation of a token economy, it is not a comprehensive, systematic review of these categories. The tracking of a token economy does not seem to be a thoroughly researched topic with regards to gamified education; more often, these studies and publications are emphasizing the benefits and drawbacks of implementation of gamified learning in general.
Based on the literature reviewed, it can be argued that options that do not involve the development of custom software will be less expensive and easier to use than those that do. In terms of student engagement, all of these tracking methods were found to be positive in terms of engagement and/or achievement with the exception of basing the token economy on automated learning analytics.
In terms of developing future research, a larger scale review would carry benefit. There would be value in future research studies based around utilizing varying token economy tracking methods with otherwise controlled circumstances; this could produce more definitive results to assist educators with decisions about their implementation of a token economy.
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