Borja Manero

Game Learning Analytics: Learning Analytics for Serious Games

Video games have become one of the largest entertainment industries, and their power to capture the attention of players worldwide soon prompted the idea of using games to improve education. However, these educational games, commonly referred to as serious games, face different challenges when brought into the classroom, ranging from pragmatic issues (e.g. a high development cost) to deeper educational issues, including a lack of understanding of how the students interact with the games and how the learning process actually occurs. This chapter explores the potential of data-driven approaches to improve the practical applicability of serious games. Existing work done by the entertainment and learning industries helps to build a conceptual model of the tasks required to analyze player interactions in serious games (gaming learning analytics or GLA). The chapter also describes the main ongoing initiatives to create reference GLA infrastructures and their connection to new emerging specifications from the educational technology field. Finally, it explores how this data-driven GLA will help in the development of a new generation of more effective educational games and new business models that will support their expansion. This results in additional ethical implications, which are discussed at the end of the chapter.

Serious games: A journey from research to application

This paper presents our perspective on serious games, covering relevant aspects of the current situation and what we consider are the key elements to be developed over the next few years. Serious games have undergone a long journey from niche research to application in real settings, coming closer to the generalization of games in mainstream education. We describe some of the lines of research we consider more relevant for completing this journey.

Investigating the Impact of Gaming Habits, Gender, and Age on the Effectiveness of an Educational Video Game: An Exploratory Study

This study examines the influence of players’ age, gender, and gaming preferences and habits (from now on, “gaming preferences”) on the effectiveness of a specific videogame that has been designed to increase the interest towards classical theater among teenagers. Using a validated instrument, participants were divided into four groups based on their gaming preferences: (1) Well-rounded (WR) gamers, who play all types of games often; (2) Hardcore players, who frequently tend to play first-person shooter (FPS) and sports games; (3) Casual players, who play moderately and tend to play music, social, and puzzle games; and (4) Non-gamers, who barely play videogames at all. Among all of the participants’ personal factors (age, gender, and type of player) that were measured, only gaming preferences seemed to have a significant (p<.05) positive influence on students’ interest in theater-going. Neither age nor gender seemed to affect the outcomes. Casual and Well-rounded gamers scored higher in the game than Non-gamers and Hardcore players. Due to these results, we also explored whether the gaming profile affected traditional educational approaches. Traditional education worked better than videogames only for students who do not usually play videogames. This study suggests that gaming preferences may influence the effectiveness of different educational approaches. Knowing students’ gaming preferences in advance may help educators find the best educational approach for each student.

Are Serious Games Working as Expected

This paper reviews “The foolish lady” experiment: a serious game aimed to increment youngsters’ interest towards the classical theater play. The article overviews all the phases of the project, from game design to the evaluation in schools, the results obtained and how these have helped us shape our current research in new and innovative ways, such as taking into account players gaming profiles to better adapt the learning approach to the student.

Learning Objects Definition and Use in

This paper describes how learning objects are defined and treated in , a pilot e-learning system that aims to offer a personalized learning experience geared to individual student’s needs. The term learning object (LO) has been on the educational agenda for several years now and has become the Holly Grail of content creation and aggregation in e-learning, promising smart learning environments, fantastic economies of scale and exciting learning experiences tailored to individual needs. Nevertheless, there are a number of aspects where more research is needed. First, there is a lack of conceptual clarity in the definition, standardization and use of LOs. Also, there has been limited emphasis on the need for introducing adaptable learning features within the LO construct. The objective of this paper is to contribute to the practical application of these concepts by presenting the learning objects and how the system handles personalization features to create an infrastructure for performing an individualized learning.

A methodology for assessing the effectiveness of serious games and for inferring player learning outcomes

Although serious games are proven to serve as educational tools in many educational domains, there is a lack of reliable, automated and repeatable methodologies that measure their effectiveness: what do players know after playing serious games? Do they learn from them? Previous research shows that the vast majority of serious games are assessed by using questionnaires, which is in stark contrast to current trends in the video game industry. Commercial videogame developers have been learning from their players through Game Analytics for years via non-disruptive game tracking. In this paper, we propose a methodology for assessing serious game effectiveness based on non-disruptive in-game tracking. The methodology involves a design pattern that structures the delivery of educational goals through a game. This structure also allows one to infer learning outcomes for each individual player, which, when aggregated, determine the effectiveness of a serious game. We tested the methodology by having 320 students play a serious game. The proposed methodology allowed us to infer players’ learning outcomes, to assess the game effectiveness levels and to identify issues in the game design.