Applying standards to systematize learning analytics in serious games

Learning Analytics is an emerging field focused on analyzing learners' interactions with educational content. One of the key open issues in learning analytics is the standardization of the data collected. This is a particularly challenging issue in serious games, which generate a diverse range of data. This paper reviews the current state of learning analytics, data standards and serious games, studying how serious games are tracking the interactions from their players and the metrics that can be distilled from them. Based on this review, we propose an interaction model that establishes a basis for applying Learning Analytics into serious games. This paper then analyzes the current standards and specifications used in the field. Finally, it presents an implementation of the model with one of the most promising specifications: Experience API (xAPI). The Experience API relies on Communities of Practice developing profiles that cover different use cases in specific domains. This paper presents the Serious Games xAPI Profile: a profile developed to align with the most common use cases in the serious games domain. The profile is applied to a case study (a demo game), which explores the technical practicalities of standardizing data acquisition in serious games. In summary, the paper presents a new interaction model to track serious games and their implementation with the xAPI specification. Literature review of serious games tracking learners in-game interactions.Develop a general model to track serious games derived from this review.Analyze current learning analytics standards to represent the tracking model.Experience API specification is selected to propose an implementation of the model.Our conclusion is Experience API is a good fit to track learning in serious games.

• Publication year

  2017

• Venue

  Comput. Stand. Interfaces

• Publication date

  2017-02-01

• Fields of study

  Computer Science, Education

• Identifiers
  DOI 10.1016/J.CSI.2016.09.014
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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