Using players’ Gameplay Action-Decision Profiles to prescribe training: Reducing training costs with Serious Games Analytics

EasyChair Preprint 475

Abstract

Player data from Serious Games can be used to produce Serious Games Analytics, which can in turn, be used to measure, assess, and improvement performance. These insights can also be used to support decision-making by Chief Learning Officers in 'prescribing' training – e.g., diagnosing who should receive training, how to design serious games for optimal training, and what content should be included or withheld. Data-driven training prescription can help learning organizations save money by mitigating unnecessary training sessions and identifying what kind of training fits which individual to eventually reduce training costs.

We traced player data and calculated the similarity between their course of action (COA) in situ serious games training environments compared to that of the expert’s. We found the combined metrics of Cosine similarity and Maximum Similarity Index to be useful in identifying players’ Gameplay Action-Decision (GAD) profiles. Insights to using GAD profiles as a diagnostic to measure training performance and prescribe training are offered.

Keyphrases: Expertise levels, Serious Games Analytics, cosine similarity, performance improvement, reducing training cost

@booklet{EasyChair:475,
  author    = {Christian Loh and I-Hung Li},
  title     = {Using players’ Gameplay Action-Decision Profiles to prescribe training: Reducing training costs with Serious Games Analytics},
  doi       = {10.29007/59d5},
  howpublished = {EasyChair Preprint 475},
  year      = {EasyChair, 2018}}