While gamification of learning tasks has made huge progress and is a promising way to make learning more effective and enjoyable, many users have a strong sense that something is missing. There is still a clear difference between ‘real’ games that we play for fun and gamified tasks that we have to do. Unfortunately, some attempts at gamification, especially when applied to things we have to learn at work, feel as if colorful buttons or scoring, or a reward and punishment concept, has been added around what otherwise remains the same dull task.
In this session we will take a closer look at what constitutes a game and what about this stimulates users so that we can better understand how to carry this over to necessary learning tasks. Referring to some approaches in the philosophy of games (Hurka, Suits, Gingerich), we will examine different views on what makes a game and what gives games their value for players. Three approaches will be outlined:
1) The reward/punishment model
2) The overcoming difficulty model
3) The freedom and unnecessary tasks model
We’ll then examine common examples of necessary learning tasks in working environments to understand why it is progressively harder to capture each of these models. Next, we will explore how, in particular, the freedom model can be achieved. Here the key element of a game is the stimulation of a feeling of spontaneous freedom and control, a sensation that is deeply satisfying and helps explain why games are so enjoyable. It is also especially difficult to replicate this feeling when a user knows that they must complete a certain task. We then turn to theories of learning, starting with constructivism, which points towards ways in which a learner can feel in control of the way their own understanding is created. We will apply this theory to specific examples and sketch some dos and don’ts. Finally, we will present knowledge graph solutions as a way to encourage flexibility and individual connected understandings. You will leave this session with a far wider understanding of the requirements of gamification and a better idea of how to apply this to your own solutions.
In this session you will learn:
Navigable knowledge graphs, AI-supported question/answer methods