There is a consensus that serious games have a significant potential as a tool for instruction. However, their effectiveness in terms of learning outcomes is still understudied mainly due to the complexity involved in assessing intangible measures. A systematic approach--based on established principles and guidelines--is necessary to enhance the design of serious games, and many studies lack a rigorous assessment. An important aspect in the evaluation of serious games, like other educational tools, is user performance assessment. This is an important area of exploration because serious games are intended to evaluate the learning progress as well as the outcomes. This also emphasizes the importance of providing appropriate feedback to the player. Moreover, performance assessment enables adaptivity and personalization to meet individual needs in various aspects, such as learning styles, information provision rates, feedback, and so forth. This paper first reviews related literature regarding the educational effectiveness of serious games. It then discusses how to assess the learning impact of serious games and methods for competence and skill assessment. Finally, it suggests two major directions for future research: characterization of the player's activity and better integration of assessment in games.

https://downloads.hindawi.com/journals/ahci/2013/136864.pdf

Assessment in and of serious games: an overview

This work aims to find discriminative EEG-based features and appropriate classification methods that can categorise brainwave patterns based on their level of activity or frequency for mental state recognition useful for human-machine interaction. By using the Muse headband with four EEG sensors (TP9, AF7, AF8, TP10), we categorised three possible states such as relaxing, neutral and concentrating based on a few states of mind defined by cognitive behavioural studies. We have created a dataset with five individuals and sessions lasting one minute for each class of mental state in order to train and test different methods. Given the proposed set of features extracted from the EEG headband five signals (alpha, beta, theta, delta, gamma), we have tested a combination of different features selection algorithms and classifier models to compare their performance in terms of recognition accuracy and number of features needed. Different tests such as 10-fold cross validation were performed. Results show that only 44 features from a set of over 2100 features are necessary when used with classical classifiers such as Bayesian Networks, Support Vector Machines and Random Forests, attaining an overall accuracy over 87%.

A Study on Mental State Classification using EEG-based Brain-Machine Interface

The entertaining elements implemented in a serious game are key factors in determining whether a player will be engaged in a play-learn process and able to achieve the desired learning outcomes. Thus, optimization of subjective playing experience is a crucial part of a game design process. Flow theory can be adopted for measuring user experience and analyzing the quality of serious game designs. In addition, flow seems to have a positive influence on performance enhancement, learning and engagement. The focus of this review is especially on examining the meaning of flow in the context of serious games as well as exploring the relationship between flow and learning, factors that influence occurrence of flow and how flow is operationalized. The review revealed that there are mainly conceptual considerations about flow in serious games, but no robust empirical evidence about the meaning of flow. This is in line with other studies. We argue that research on flow should focus on the specific aspects related to the very nature of serious games that combine enjoyment and learning. Furthermore, new methods to measure flow and analyse the data need to be developed and studied.

/pdf/flow-experience-in-game-based-learning-a-systematic-3743o2f4zf.pdf

Flow experience in game based learning – a systematic literature review

Previous studies that involve measuring EEG, or electroencephalograms, have mainly been experimentally-driven projects; for instance, EEG has long been used in research to help identify and elucidate our understanding of many neuroscientific, cognitive, and clinical issues (e.g., sleep, seizures, memory). However, advances in technology have made EEG more accessible to the population. This opens up lines for EEG to provide more information about brain activity in everyday life, rather than in a laboratory setting. To take advantage of the technological advances that have allowed for this, we introduce the Brain-EE system, a method for evaluating user engaged enjoyment that uses a commercially available EEG tool (Muse). During testing, fifteen participants engaged in two tasks (playing two different video games via tablet), and their EEG data were recorded. The Brain-EE system supported much of the previous literature on enjoyment; increases in frontal theta activity strongly and reliably predicted which game each individual participant preferred. We hope to develop the Brain-EE system further in order to contribute to a wide variety of applications (e.g., usability testing, clinical or experimental applications, evaluation methods, etc.).

Brain-EE: Brain Enjoyment Evaluation using Commercial EEG Headband

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations.

A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction.

Ability of adapting to different users is considered key to maximize the impact and effectiveness of Serious Games (SGs). Advances in neurosciences are making it possible to continuously monitor the player status. This paper reports the research work on the monitoring of the player flow status with a simple (4 electrode) commercial electroencephalogram (EEG). In particular, we focus on three consequential research questions: is it possible to statistically distinguish a flow from a boredom condition? For which wavelengths? Can different levels of boredom and flow be identified? Results - even if limited because of the small size of the test, are promising and enable further research. Statistically significant differences could be observed for brainwaves under various conditions. A machine learning classifier (SVM) was successful in the 2-level distinction case, in particular with personalized training.

Exploiting Real-Time EEG Analysis for Assessing Flow in Games

Magnetic ﬁelds are responsible for a multitude of Solar phenomena, including such destructive events as solar ﬂares and coronal mass ejections, with the number of such events rising as we approach the peak of the 11-year solar cycle, in approximately 2025. High-precision spectropolarimetric observations are necessary to understand the variability of the Sun. The ﬁeld of quantitative inference of magnetic ﬁeld vectors and related solar atmospheric parameters from such observations has long been investigated. In recent years, very sophisticated codes for spectropolarimetric observations have been developed. Over the past two decades, neural networks have been shown to be a fast and accurate alternative to classic inversion technique methods. However, most of these codes can be used to obtain point estimates of the parameters, so ambiguities, the degenera-cies, and the uncertainties of each parameter remain uncovered. In this paper, we provide end-to-end inversion codes based on the simple Milne-Eddington model of the stellar atmosphere and deep neural networks to both parameter estimation and their uncertainty intervals. The proposed framework is designed in such a way that it can be expanded and adapted to other atmospheric models or combinations of them. Additional information can also be incorporated di-rectly into the model. It is demonstrated that the proposed architecture provides high accuracy of results, including a reliable uncertainty estimation, even in the multidimensional case. The models are tested using simulation and real data samples.

A. Plotnikov

Papers

Exploiting Real-Time EEG Analysis for Assessing Flow in Games

Stokes inversion techniques with neural networks: analysis of uncertainty in parameter estimation