The video game industry is becoming increasingly important due to its revenues and growing capabilities. User eXperience (UX) is an important factor which contributes to the acceptance of a video game. The UX is usually assessed at the end of the development process, and for this reason it is difficult to ensure an adequate level of interactive experience between computer game and players. Cancelation of projects or even bankruptcy of a company can be caused by bad management of UX. In this paper, we propose the game experience management (GEM), a method to evaluate, manage, measure and track the UX from early stages of computer game development. In order to compare the proposal against a method comprised by conventional approaches, teams of master degree students were formed for developing six tower defense games for teaching basic multiplication operations; surveys were conducted to compare the UX of games. In this setting, we find that games developed with GEM significantly improve UX by increasing the puppetry and consequently reducing player frustration.

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Background A learning task recurrently perceived as easy (or hard) may cause poor learning results. Gamer data such as errors, attempts, or time to finish a challenge are widely used to estimate the perceived difficulty level. In other contexts, pupillometry is widely used to measure cognitive load (mental effort); hence, this may describe the perceived task difficulty. Objective This study aims to assess the use of task-evoked pupillary responses to measure the cognitive load measure for describing the difficulty levels in a video game. In addition, it proposes an image filter to better estimate baseline pupil size and to reduce the screen luminescence effect. Methods We conducted an experiment that compares the baseline estimated from our filter against that estimated from common approaches. Then, a classifier with different pupil features was used to classify the difficulty of a data set containing information from students playing a video game for practicing math fractions. Results We observed that the proposed filter better estimates a baseline. Mauchly’s test of sphericity indicated that the assumption of sphericity had been violated (χ 2 14 =0.05; P =.001); therefore, a Greenhouse-Geisser correction was used (ε=0.47). There was a significant difference in mean pupil diameter change (MPDC) estimated from different baseline images with the scramble filter ( F 5,78 =30.965; P <.001). Moreover, according to the Wilcoxon signed rank test, pupillary response features that better describe the difficulty level were MPDC ( z =−2.15; P =.03) and peak dilation ( z =−3.58; P <.001). A random forest classifier for easy and hard levels of difficulty showed an accuracy of 75% when the gamer data were used, but the accuracy increased to 87.5% when pupillary measurements were included. Conclusions The screen luminescence effect on pupil size is reduced with a scrambled filter on the background video game image. Finally, pupillary response data can improve classifier accuracy for the perceived difficulty of levels in educational video games.

BACKGROUND Besides the current challenge, the perceived difficulty level of a learning task depends on the student's previous knowledge and skills. When a learning task is recurrently perceived as easy (or hard), it may cause poor learning results. Gamer data such as errors, attempts, or time to finish a challenge are widely used to estimate the perceived difficulty level. In other contexts, pupillometry is widely used to measure the cognitive load (mental effort); hence, this may describe the perceived task difficulty. Objective: OBJECTIVE This study aimed to assess the use of pupillary data as a cognitive load measure for describing the difficulty levels in a video game. Also, it proposes an image filter to better estimate the baseline pupil size and to reduce the screen luminescence effect. METHODS We conducted an experiment that compares the baseline estimated from our filter against that estimated from common approaches. Different pupil features were used to classify the difficulty of a dataset containing information from students playing a video game for practicing math fractions. RESULTS Results: Results showed that the proposed filter allows to better estimate a baseline, Mauchly’s Test of Sphericity indicated that the assumption of sphericity had been violated, χ2(14) =0.045389, p = .001; and therefore, a Greenhouse-Geisser correction was used, ε = 0.47, there was a significant difference against Mean Pupil Diameter Change (MPDC) estimated from different baseline images with the scramble filter, F(2.35) = 30.965, p < .001. Moreover, according to the Wilcoxon signed-rank test, pupillary features that better describe the difficulty level were MPDC (Z = -2.15, p <0.05) and Peak Dilation (Z = -3.58, p<0.00); a random forest classifier for easy- and hard-level of difficult showed an accuracy of 75% when the gamer data is used, but the accuracy increases to 87.5 % by including pupillary measurements. CONCLUSIONS The screen luminescent effect on pupil size was reduced with a scrambled filter on the background video game image. Finally, pupillary data can improve the classifier accuracy of the perceived difficulty of gamers in educational video games.