Assessment of Learners’ Motivation during Interactions with Serious Games: A Study of Some Motivational Strategies in Food-Force

This study investigated motivational strategies and the assessment of learners’ motivation during serious gameplay. Identifying and intelligently assessing the effects that these strategies may have on learners are particularly relevant for educational computer-based systems. We proposed, therefore, the use of physiological sensors, namely, heart rate, skin conductance, and electroencephalogram (EEG), as well as a theoretical model of motivation (Keller’s ARCS model) to evaluate six motivational strategies selected from a serious game called Food-Force. Results from nonparametric tests and logistic regressions supported the hypothesis that physiological patterns and their evolution are suitable tools to directly and reliably assess the effects of selected strategies on learners’ motivation. They showed that specific EEG “attention ratio” was a significant predictor of learners’ motivation and could relevantly evaluate motivational strategies, especially those associated with the Attention and Confidence categories of the ARCS model of motivation. Serious games and intelligent systems can greatly benefit from using these results to enhance and adapt their interventions

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

A game player expertise level classification system using electroencephalography (EEG)

The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users' attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG) is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of 88%, when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications

Towards the Identification of Players’ Profiles Using Game’s Data Analysis Based on Regression Model and Clustering

Personalization of serious games is an important factor for motivating and engaging players. It requires the identification of players’ profiles through the analysis of large volume of data including game data. This research study aims at identifying relevant data from an online serious game and the appropriate data mining methods for deduction of players’ profiles. Multiple linear regression is applied to analyze the influence of player’s characteristics on his performance. Moreover, clustering technique is used, in particular K-means, to extract players’ clusters and to identify their common characteristics. The regression models showed that the number of access to the game, completed quests and advantages used contribute significantly to the scores and the gaming duration, while the clustering revealed three forms of players’ participation: beginner, intermediate and advanced; who interact with the game according to their experiences

Adicción a los Videojuegos: ¿Qué podemos aprender desde la perspectiva de la neurociencia de la comunicación?

In recent years, video game addiction has received considerable empirical attention. Unfortunately, this research is stymied by inconsistencies in both conceptual and operational definitions of video game addiction. Moreover, the use of several video game addiction scales makes it difficult to estimate the prevalence and potential effects of video game addiction. While game genre is often treated as a predictor of video game addiction, existing measures often downplay the structural and social characteristics of video games that may contribute to behavioral outcomes such as increased playing time and addiction. In an effort to provide the clarity necessary to overcome these issues, we review research on video game addiction with a focus on the largely ignored unique characteristics of video games that are crucial for a more complete conceptualization of video game addiction. With this review in mind, we offer a conceptual framework for the integration of video game addiction within the broader context of behavioral addictions. Finally, we consider the neurological foundation of addiction and suggest opportunities for media neuroscientists to increase understanding and prediction of video game addiction and explore how game content features interact with reward systems in the brain.En los últimos años, la adicción a los videojuegos ha recibido una atención empírica considerable. Desafortunadamente, la investigación en este campo se encuentra obstaculizada por inconsistencias en definiciones conceptuales y operacionales de la adicción a los videojuegos. Por otro lado, el uso de varias escalas de adicción a los videojuegos dificulta la estimación de la prevalencia y los efectos potenciales de la adicción a los videojuegos. Mientras el género del juego es considerado frecuentemente como un predictor de la adicción a los videojuegos, las medidas existentes restan importancia a las características sociales y estructurales de los videojuegos que pueden contribuir a resultados conductuales tales como un incremento en el tiempo de juego y adicción. En un esfuerzo por proveer de claridad necesaria para subsanar estos problemas, se realiza una revisión sobre investigaciones relacionadas a la adicción a los videojuegos con un enfoque en las características de los videojuegos extensamente ignoradas y que son cruciales para una conceptualización más completa acerca de la adicción a los videojuegos. Con esta revisión, ofrecemos un marco conceptual para la integración de la adicción a los videojuegos dentro un contexto más amplio como el de las adicciones conductuales. Finalmente, consideramos la base neurológica de la adicción y sugerimos oportunidades para los neurocientíficos de la comunicación con el objetivo de incrementar la comprensión y predicción de la adicción a los videojuegos y explorar cómo los elementos de un juego interactúan con los sistemas de recompensa en el cerebro

Multimodal Motivation Modelling and Computing towards Motivationally Intelligent ELearning Systems

Persistent motivation to engage in e-learning systems is essential for users’ learning performance. Learners' motivation is traditionally assessed using subjective, self-reported data which is time-consuming and interruptive to their learning process. To address this issue, this paper proposes a novel framework for multimodal assessment of learners’ motivation in e-learning environments to inform intelligent e-learning systems that can facilitate dynamic, context-aware, and personalized services or interventions to maintain learners’ motivation during use. The applicability of the framework was evaluated in an empirical study in which we combined eye tracking and electroencephalogram (EEG) sensors to produce a multimodal dataset. The dataset was then processed and used to develop a machine learning classifier for motivation assessment by predicting the levels of a range of motivational factors, which represented the multiple dimensions of motivation. We investigated the performance of the machine learning classifier and the most and least accurately predicted motivational factors. We also assessed the contribution of different EEG and eye gaze features to motivation assessment. Our study has revealed valuable insights for the role played by brain activities and eye movements on predicting the levels of different motivational factors. Initial results using logistic regression classifier have achieved significant predictive power for all the motivational factors studied, with accuracy of between 68.1% and 92.8%. The present work has demonstrated the applicability of the proposed framework for multimodal motivation assessment which will inspire future research towards motivationally intelligent e-learning systems

The Role of Mindfulness, Mind Wandering, Attentional Control, and Maladaptive Personality Traits in Problematic Gaming Behavior

Objectives Problematic gaming has become a phenomenon of growing clinical relevance due to its negative impact on life and mental health outcomes. Much research has been carried out on its complex aetiology, and some studies have suggested that dispositional mindfulness, mind wandering, attentional control, and maladaptive personality traits may play some role, but they have never been included in the same prediction model. This study used Gaussian graphical models and Bayesian networks to investigate the pattern of association of these constructs and of background and gaming-related variables with problematic gaming in a sample of adult gamers. Method Participants (n=506) were administered an online survey comprising a questionnaire on background and gaming-related variables and the Gaming Disorder Test, the Five Facet Mindfulness Questionnaire-15, the Mind WanderingSpontaneous and Deliberate scales, the Attention Control-Distraction and Shifting scales, and the Personality Inventory for DSM-5-Brief Form. Results Gaussian graphical models showed that problematic gaming was directly associated with Acting with Awareness, Disinhibition, Psychoticism, playing more than 30 hr a week, ability level, and playing strategy games. Bayesian networks indicated that the occurrence of high levels of problematic gaming directly depended on the presence of low scores on Acting with Awareness. Conclusions The results suggest that one key feature of problematic gamers can be a high level of spontaneous thinking, either in the form of mind wandering or in the lack of Acting with Awareness, while maladaptive personality traits and attentional control seem to play a less central role

Détection et amélioration de l'état cognitif de l'apprenant

Cette thèse vise à détecter et améliorer l’état cognitif de l’apprenant. Cet état est défini par la capacité d’acquérir de nouvelles connaissances et de les stocker dans la mémoire. Nous nous sommes essentiellement intéressés à améliorer le raisonnement des apprenants, et ceci dans trois environnements : environnement purement cognitif Logique, jeu sérieux LewiSpace et jeu sérieux intelligent Inertia. La détection de cet état se fait essentiellement par des mesures physiologiques (en particulier les électroencéphalogrammes) afin d’avoir une idée sur les interactions des apprenants et l’évolution de leurs états mentaux. L’amélioration des performances des apprenants et de leur raisonnement est une clé pour la réussite de l’apprentissage. Dans une première partie, nous présentons l’implémentation de l’environnement cognitif logique. Nous décrivons des statistiques faites sur cet environnement. Nous avons collecté durant une étude expérimentale les données sur l’engagement, la charge cognitive et la distraction. Ces trois mesures se sont montrées efficaces pour la classification et la prédiction des performances des apprenants. Dans une deuxième partie, nous décrivons le jeu Lewispace pour l’apprentissage des diagrammes de Lewis. Nous avons mené une étude expérimentale et collecté les données des électroencéphalogrammes, des émotions et des traceurs de regard. Nous avons montré qu’il est possible de prédire le besoin d’aide dans cet environnement grâce à ces mesures physiologiques et des algorithmes d’apprentissage machine. Dans une troisième partie, nous clôturons la thèse en présentant des stratégies d’aide intégrées dans un jeu virtuel Inertia (jeu de physique). Cette dernière s’adapte selon deux mesures extraites des électroencéphalogrammes (l’engagement et la frustration). Nous avons montré que ce jeu permet d’augmenter le taux de réussite dans ses missions, la performance globale et par conséquent améliorer l’état cognitif de l’apprenant.This thesis aims at detecting and enhancing the cognitive state of a learner. This state is measured by the ability to acquire new knowledge and store it in memory. Focusing on three types of environments to enhance reasoning: environment Logic, serious game LewiSpace and intelligent serious game Inertia. Physiological measures (in particular the electroencephalograms) have been taken in order to measure learners’ engagement and mental states. Improving learners’ reasoning is key for successful learning process. In a first part, we present the implementation of logic environment. We present statistics on this environment, with data collected during an experimental study. Three types of data: engagement, workload and distraction, these measures were effective and can predict and classify learner’s performance. In a second part, we describe the LewiSpace game, aimed at teaching Lewis diagrams. We conducted an experimental study and collected data from electroencephalograms, emotions and eye-tracking software. Combined with machine learning algorithms, it is possible to anticipate a learner’s need for help using these data. In a third part, we finish by presenting some assistance strategies in a virtual reality game called Inertia (to teach Physics). The latter adapts according to two measures extracted from electroencephalograms (frustration and engagement). Based on our study, we were able to enhance the learner’s success rate on game missions, by improving its cognitive state