ISLT 7384: Balancing Scoring, Rewards, and Achievements

This reflection aligns with the Evaluator attribute of the WSSG (Why So Serious Games) Academy. 

In Level 4 of ISLT 7384, we’re designing a digital game prototype using game. In the Balancing Achievements activity, we explored aligning evidence of learning (assessment) across scoring and rewards in a digital game.

Balancing Achievements: Exploration Through Gameplay

We started the exercise by playing a classic (and super nostalgic!) DOS game: Oregon Trail (1974). In addition to unlocking memories of the hours playing the game (Image 1), the gameplay allowed us to explore the scoring, rewards, and achievements elements of a digital game.

First we looked at mechanics. Some of the Oregon Trail mechanics I noted include:

• Choose date to leave for Oregon: The player must choose a date to leave that’s neither too early or late as it will cause them to not reach Oregon on time.
• Hunt for food: When food rations are low, the player must hunt for animals to supplement their rations.
• Cross a river: The player must choose to ford, cross, or wait to cross a river. Each option has different consequence (broken parts, losing items, wasting valuable time, etc)..

We used a spreadsheet to consider where the mechanics fall on Bloom’s taxonomy—and whether they fit into lower- or higher-order thinking. The exercise then asked us to consider the scoring and reward (Table 1). I sometimes overthought whether a mechanic tied to scoring or reward, but it helped to see how and when they might complement each other.

After completing the spreadsheet using the Oregon Trail gameplay, we then used the same method for our digital game design for learning prototype. In my game, CC/Creative (tentative title), the player controls a sprite which must catch the correct Creative Commons (CC) license for the level. Correct catches increase the score and bumping into the incorrect answer (and perhaps some sort of distraction element) reduces the score. 

The game’s learning goal is:

• After playing CC/Creative, learners will determine the best Creative Common (CC) license for the publishing situation within two attempts.

As I worked through the exercise, I quickly realized that while my game design mechanics are simple (perhaps too simple?!), the learning outcome definitely fits into higher-level thinking (Table 2). I’m now wondering if a platformer game makes sense with this type of learning goal as well as how to assess whether a simple mechanic helps the player reach the game’s learning outcome (Table 2).

Overall, the steps which worked well included completing the spreadsheet first for Oregon Trail and then for my own game idea. It allowed me to model the previous work while thinking about how to address my own design. However, this blog post perhaps fits the best step as it allowed me to consider the metacognition necessary for the design process.

Final Thoughts

After I worked through this exercise, I realized I needed to refine my design choices to improve my digital game for learning design. So much of the design comes from actually putting the time into the process as well as iterating toward our vision for a solution or product. While this exercise brought up more questions than I anticipated, it certainly helped me consider how to align the mechanics and learning outcome to the scoring, reward, and achievement elements of a digital game.

References

Oregon trail [Video game]. (1974). MECC.