Master's Research Project: Sugar Scramble (2019)

Ideation sessions for a potential game mechanic to communicate this scientific content was conducted with a combination of committee members, BMC students and faculty, and biology masters students.

• First, a large number of potential game mechanics were ideated live as a large group using sticky notes. Each of these game mechanics were then grouped based on similarity in idea.

• Next, we separated into two smaller groups and each took 1-2 ideas to further develop. These ideas were then presented to each other at the end of the session.

• Then, I summarized all of the generated ideas in a document and distributed it to all committee members for personal record.

• Lastly, I met with the content advisors to solicit their opinion on the ideas generated to decide on a game mechanic to further pursue. A committee meeting was held to define the visual problem, the committee members' roles, and the general scope of the project. This on-boarding meeting also covered a tentative trajectory of the design process for this project.

The principle learning goals of the game were defined in collaboration with project stakeholders. Personas were created to further explore the potential users of this game, and help inform what kind of potential in-game activities within the context of the game mechanic decided upon would be appropriate to support these learning outcomes. I solicited feedback on these deliverables from other student biomedical communicators and my faculty supervisor.

A one-sheet was created for the game outlining the game story and overall gameplay of Sugar Scramble. These documents and the user interface, items, objects, and character designs were developed iteratively with feedback from Dr. Derek Ng, the project faculty supervisor. This was done in concert with the development of prototypes

Several low fidelity paper and digital prototypes were created iteratively to test game logic, player enjoyment/engagement, and overall design. Test players included the project supervisor, other faculty members in the department of biology, and students with or without a background in biomedical communications.

From rapidly reiterating these prototypes, the game mechanic changed to more appropriately communicate the learning aims of the game while maintaining accuracy and clarity. As a committee, an executive decision was made to allow myself to design outside my own personal skillset as a developer so that the complexity of the content was fully appreciated; thus, my focus changed from creating a demo game to creating robust design documentation and all of the assets. As the prototype began to change less and less between iterations, higher fidelity prototypes were created.

According to the ATMSG, a serious game is comprised of activities: the gaming activity, learning activity, and intrinsic instruction.

The sequence of gaming events and components are then organized in a map based on the descriptions of the gaming activity in Step 1 to communicate the overall flow of a player’s experience with the game.

The sequence of gaming events and components are then organized in a map based on the descriptions of the gaming activity in Step 1 to communicate the overall flow of a player’s experience with the game.

This step is the addition to the original ATMSG which allows for quantitative analysis of the serious game given that there is back-end development for the game. Below, data points (game components) are identified and are described as to what they convey when they occur in-game. Due to the specific interest in evaluating the effectiveness of teaching the opportunities of productive negativity that serious games provide, these data points specifically relate to instances of failure (i.e. when there are gaps in a player’s knowledge, and what that gap is in particular) and assessing change in player performance.

Data points 1., 2., and 3. are instances that the player receives negative feedback due to a gap in their knowledge. 1. indicates that the player has a misconception related to what the substrates are for a particular enzyme; 2. indicates that they have a misconception related to the inter-pathway connections/relationships for that biological context; and 3. indicates the player has a misconception related to where the enzyme is located in the cell. During an evaluation, these instances of failure can be recorded and compared within the same level or between different levels (the latter is possible due to the fact that many of the levels build upon each other and share some game components/goals. Ideally, if the player is showing that they are learning, the number of these errors will decrease as the player replays a level or plays more levels.​

Productive negativity can be assessed more granularly using data points 1., 2., and 3. as described in the previous subsection on assessing failure, but it can also be assessed more broadly by recording the number of times a level is replayed until it is completed perfectly (i.e. with a 3-star score). A 1- or 2-star score indicates that the player has failed or partially failed in some way; if a player is able to take this feedback and eliminate the number of errors such that they complete the level perfectly, that is an indication of productive negativity. ​

Recording the number of times the help button is pressed can provide insight into why the player may be failing a level or making mistakes. Since the help section of the game provides instruction on how to play the game and is not related to any biochemistry content, a player frequently accessing this button may indicate that failure is a result of the game being too difficult rather than an indication of the player’s lack of knowledge. In this step of the ATMSG framework, each of the mechanics described in the game sequence in Step 2 are briefly described using “buzzwords” or short phrases. This is helpful to see if any of the gaming activities, learning activities, or intrinsic instruction conflict with each other or if any components do not actually support their respective activities.

The final step of the ATMSG is to group each activity’s set of actions, tools, and goals for each mechanic, and provide a detailed description of the implementation of the “buzzwords” listed in Step 3 (i.e. explain  what is being done in the game using what tools and to what purpose).

It was vital that the solution to each level was scientifically accurate. To check my understanding with my content supervisors, I devised a spreadsheet to denote the correct substrates and products for each enzyme, where those products are used in other parts of the metabolic cycle, and the ATP yield at each stage.

I then made diagram sketches based off this reviewed spreadsheet to visualize the solution for each level, iterate on the logic of how it would work, and ensure each solution reflected the substrates and products from the enzymes listed in the spreadsheet. These were also reviewed by my supervisors.

For each of the steps identified in the game flow diagram, a more in-depth section of documentation was created. This included indicating the purpose of each screen, how to navigate to and from these screens, and all of the user actions possible (i.e. different button states and functions). Game logic was described in words with supporting images. The proof-of-concept video I made for Sugar Scramble is supposed to be supplementary to the game descriptions here.

For each level, learning objectives, level objectives, the assets required for the solution and a description of how these interact were listed. A visual of the solution accompanies each level for reference, and character dialogue along with the dialogue's triggers are documented here. The input and output of each enzyme item was listed.

All game assets were created and listed with descriptions of their role in the game and the relevant pages in the documentation to which they pertain.