Theories Behind Gamification

Chapter 3 of The Gamification of Learning and Instruction describes different theories used for gamifications. (Kapp, 2012, p. 51)

We are designing a gamified walking app. Not all the theories will be as useful. Let’s take a look at some and how we could apply them.

The ARCS model

ARCS stands for Attention, Relevance, Confidence and Satisfaction. This model focuses on designing instruction, and is therefore particularly useful to us.

The app needs to get, and hold the users attention over time. The app needs to instruct the user to do tasks that are relevant to their goals. The app needs to give the user confidence in what they do. And the app needs to give the user satisfaction along the way.

ARCS model – Attention

Curiosity

We want to take advantage of the user’s curiosity to pull them into the app’s action and motivate them to get out and move.

Variety and chance

The user will experience variety and chance in experiencing a development of opportunities via level-ups by going and finding resources along the way or in resource towers. When resources are retrieved from towers, they can select one of three bonus chests that give them a “random” (at least a seemingly random) amount of resources.

Antici…pation

The user’s curiosity is stimulated when they can look at the overview of possible towers that can be unlocked and built. The same applies to upgrades on these buildings and the home base that the user creates.

Guide

A virtual guide that initially gives the user a set of tasks. Later, the guide keeps track of progress, statistics, etc. with the user. This guide should appeal to the user and therefor they get to choose on from a gallery of characters.

Conflict

What can be considered the conflict to be solved is the very problem this app will solve for the user: getting out of the couch and moving more.

ARCS model – Relevance

Goal orientation

The user sets his own goals. The app will then guide the user in how the goals will be implemented. The user is challenged to race by characters that the user can meet at buildings. They push the user to travel slightly faster than they otherwise would.

Motives

The goal is to match the user’s motives. The user inputs what they want to do (Go long walks, short walks, jogging, cycling, etc), but the app also calibrates by what the user actually does. So if the user says they are interested in walking, but mostly goes jogging, we can assume that is what they are most interested in.

Show result of effort

We will show the results by giving the user an overview of statistics related to what they have achieved and their use of the app. The app can show estimated calorie burn and other things. Each session will be saved and can be reviewed by the user later.

The bet can also be displayed by visually displaying upgrades and status of constructed towers and records on the map in the app.

ARCS model – Confidence

Learning curve

The first week of the app will analyze the form of the user and then provide a custom plan to the user. Progression eventually becomes easier to calculate.

Different quests / quests can occur on the different towers that the user has posted to give the user unique opportunities to complete their goals.

Dynamic difficulty

The app can record how the user does it according to quest completion and challenges. It will use this information to try to balance the challenges in such a way that the user gets an affordable challenge.

ARCS model – Satisfaction

Positive encouragement

The guide character says encouraging phrases like “Good job”, “You did good today”, “Nice pace!”  when the user comes home from a session. (Intrinsic motivation)

Every trip the user does with the app will result in small rewards when the user returns to the home base, in addition to what they gather at buildings. (Extrinsic motivation)

If weight loss is a goal the user has put in, then the virtual guide periodically boasts about calories burned. The virtual guide can tell you how much you’ve improved, for example, “You are jogging 4 minutes less between tower 3 and tower 4 than you did a month ago,” and so on.

Consistent Measurement of Success

Data is stored for each trip. After each trip, the user gets an overview of the trip; map, time, speed, calories burned. If the same route has passed earlier, the app can tell if the day’s walk was faster than average.

Virtual characters sometimes show up on posts and challenge the user. Challenges come in some different forms. The user will most often meet those matching the users interests. If the user is interested in jogging further, they will be challenge to jog to a certain distance away. Challenges will become progressively harder with completion. Maybe the challengers level up to match your skills.

Lepper’s instructional design principles for intrinsic motivation

Control

The user sets their own goals at the start of the app. Here the user has control over which days, how often etc.

Operant conditioning

“a concept used in many games to keep players engaged for long periods of time, a variable ratio reinforcement schedule.” The app can use unpredictable intervals on rewards to keep interest over time.

Social learning theory

People are influenced by others, including virtual characters. Therefore, we will have a character in the app that acts as a guide for the user. The user chooses who will be their guide based on a small character gallery. The characters have different looks and to a small extent different personalities, so that the user can find someone they like. The guide must be a mentor and role model. They can give advice and encouraging messages. If the mentor says that it went a long walk and that it felt good, the user will be socially influenced to think in those paths themselves.

Flow

To allow the user to find a flow in the walk itself, the app must not be a distraction. The user should not need to look at their phone while they are outside. It should be enough to check your route before and after the walk. Exceptions are when you create a new building, and when you start a challenge.

Sources

Kapp, K. M. (2012). The Gamification of Learning and Instruction. John Wiley & Sons Inc.

:: Written by Anette Rana ::