FairFilter.edited.docx

Running Head: FAIR FILTER 1

FAIR FILTER 2

Fair Filter

Student’s name

Institutional affiliation

I consider fairness in the filter as a situation whereby people who like various games are evaluated with how much they play and the level of the addiction to the game. The filter is fair when it provides a platform for coaching to individuals who view the game as a platform to improve their personal skills for self-satisfaction or to enhance their skills in the competition. The filter is effective when it considers the question to enhance skills for the addict, less addictive people, and the beginner in a specific game. A fair filter considers all people with different abilities in the gaming industry. A fair filter aims at improving the skills of various games based on the strengths and weaknesses of an individual.

The best metric to consider fairness in the filter, particularly involving games, is the In-Game Metrics. The metrics are essential since it monitors and manages monetization, retention, and engagement (Kiili, Moeller & Ninaus, 2018). People with a high level of engagement, for instance, in MOBA (Multiplayer Online Batter Arena), take a lot of time to enhance their skills to remain competitive. Thus the level of engagement indicates the level of retention of players in a particular game. Furthermore, most of the games are costly in the current world since they require coaches and investments in technology. Thus, the metrics are essential in assigning people games based on their ability to afford them.

References

Kiili, K., Moeller, K., & Ninaus, M. (2018). Evaluating the effectiveness of a game-based rational number training-In-game metrics as learning indicators. Computers & Education120, 13-28.