A generic and efficient emotion-driven approach toward personalized assessment and adaptation in serious games

Abstract

Emotions have a major role in the player-game interaction. In serious games playing contexts, real-time assessment of the player's emotional state is crucially important to enable an emotion-driven adaptation during gameplay. In addition, a personalized assessment and adaptation based on the player's characteristics remains a challenge for serious games designers.This paper presents a generic and efficient emotion-driven approach for personalized assessment and adaptation in serious games, in which two main methods and their algorithms are proposed. The first one is a method for assessing, in real time, the player's emotion taking into account the personality type and the playing style of the player.The second one is an emotion-driven personalized adaptation method based on Markov modeling of dependency between the serious game events and the change in the player's emotional state. Therefore, the proposed approach has been evaluated by playing an affective vs. non-affective version of a serious game that we have developed to illustrate the applicability of the above-mentioned methods. The overall results showed that owing to our approach, a serious game become able to enhance its adaptivity toward playing outcomes and improve its overall playability.

Learning analytics for enhancing the usability of serious games in formal education: A systematic literature review and research agenda

“…According to the selected studies, applying LA to SEGs has several objectives including: Understanding how students learn using games and modeling their behaviors (affective, social and cognitive states) during gameplay (Hou, 2012 ) (Kerr & Chung, 2012 ) (Cheng et al, 2015 ) (Minović et al, 2015 ) (Israel-Fishelson & Hershkovitz, 2020 ); Implementing teaching support to improve the process of teacher inquiry (Rodríguez-Cerezo et al, 2014 ) (Sergis & Sampson, 2017 ) (Kiili & Ketamo, 2018 ); Conducting formative and summative assessments of students’ learning based on in-game data (stealth assessment) (Serrano-Laguna et al, 2014 ) and game evaluation (Freire et al, 2016 ) (Abdellatif et al, 2018 ); Predicting learning results based on students’ interactivity (Loh & Sheng, 2015 ) (Kouraki et al, 2017 ) (Rowe et al, 2017 ) (Hernández-Lara et al, 2019 ); In-game personalization and adaptation by providing an appropriate level of challenge according to learners’ competencies level (Kiili et al, 2018 ) (Mostefai et al, 2019 ); and Validating the actual educational and game designs or finding possible improvements in game design as well as the cost efficiency of using games in education (Freire et al, 2016 ). …”

Comparing the Impact of Heart Rate-Based In-Game Adaptations in an Exergame-Based Functional High-Intensity Interval Training on Training Intensity and Experience in Healthy Young Adults

“…Knowledge from different research fields and disciplines (e.g., sport science and HCI) should be used to examine different parameters assessing physical ( Wallace et al, 2014 ; Burgess, 2017 ; Coyne et al, 2018 ; McLaren et al, 2018 ), cognitive ( Solovey et al, 2014 ; Grassmann et al, 2016 ; Oschlies-Strobel et al, 2017 ; He et al, 2019 ; Hughes et al, 2019 ; Zhou et al, 2020 ), and mental ( Schrader et al, 2017 ; Mostefai et al, 2019 ) load. Objective parameters could be defined by physiological and performance-related factors ( Vasilyev et al, 2019 ).…”

A generic classification of learner's emotion in educational serious games into positive vs. negative dimension

“…The emotion's frequency column, as shown in the In Fig. 4, citing the same emotions on both opposite sides (failure vs. success) can be explained by the dependency between the player's personality and his/her emotional states [5]. Generally, the emotional response to a stimulus differs from one person to another according to their personality types [20].…”

“…However, based on our best knowledge, there is not yet researches that discuss the issue of positive and negative emotions of the learner in SGs and their relationship with learning and playing performance. From our point of view [5], in a SG positive emotions are emotions that reinforce learning and improve performance during player-game interaction. Negative emotions are emotions that negatively affect learning and reduce performance during game play.…”