Assessment in and of serious games: an overview

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

Game based learning for 21st century transferable skills: challenges and opportunities

Bellotti, F., Bottino, R. M., Nadolski, R. J., & Fernández Manjón, B. (2012, 4-6 July). Game based learning for 21st century transferable skills: challenges and opportunities. Presentation at the Workshop Game based learning for 21st century transferable skills: challenges and opportunities, 12th IEEE International Conference on Advanced Learning Technologies (ICALT 2012), Rome, Italy: IEEE Computer Society CPS.It is broadly acknowledged that digital games offer a high potential to foster and support learning. The term “serious game” refers to games whose primary purpose is other than entertainment, and several serious games have a purpose for learning and training. Most research studies analyze the relationship among games (characteristics/genres), learning objectives, and target groups from various perspectives. Such studies investigate, for instance, which games and game mechanics are best suited for applying the learning objectives while simultaneously considering the game context and target population. This workshop will address, in particular, how digital games can contribute to contemporary knowledge society requirements towards an effective acquisition of more transferable skills (i.e. those abilities that support learning in task performance across multiple disciplines and subject areas, thus enhancing sustainable learning). Examples of transferable skills: collaboration, critical thinking, creative thinking, problem solving, reasoning abilities, learning to learn or decision making. This workshop will explore new opportunities offered by (digital) serious games in meeting these new demands. Two complementary perspectives are considered in this workshop: (1) how can games foster formal and informal learning and (2) how can their design, development and deployment contributes towards this learning purpose. The first perspective refers to the fact that learning processes cannot be understood by merely looking at the specific characteristics of the ICT-based tools used to promote learning, but it is necessary instead to consider the complete context in which games are deployed (including goals, tools, tasks, and culture). Educational researchers become increasingly aware of this integrated perspective. In fact, it is needed to address the interplay between the game technology and the educational practice: that is, the activities that can be accomplished thanks to technology mediation for achieving the agreed learning goals. The second perspective refers to the methods, techniques and tools that are applied in the design and the development of pedagogically sound games. In particular, this perspective aims to focus on methods and tools that can support effective user assessment in game based learning. Breakthroughs in this area can be made by advancing the effectiveness and efficiency of issues including, but not limited to (a) user feedback mechanisms, (b) user data gathering and management, (c) sensor data fusion and integration, (d) data analysis methods, and (e) easy-to -use user interfaces. We regard the interplay of these two perspectives (i.e., the use and design of games for education) crucial for the future of game based learning and this workshop intends to stimulate a fruitful dialogue between them. We invite authors to submit original research work that contributes to new developments in the area of game based learning for 21st century transferable skills including devices, hardware/software tools, design and development methodologies, educational applications, evaluation and assessment studies or case studies of exemplary use.This project is partially funded under the European Community Seventh Framework Programme (FP7/2007 2013), Grant Agreement nr. 258169

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

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

Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators' dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed

Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators’ dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed

The brain in flow: a systematic review on the neural basis of the flow state

Background: Flow state is a subjective experience that people report when task performance is experienced as automatic, intrinsically rewarding, optimal and effortless. While this intriguing phenomenon is the subject of a plethora of behavioural studies, only recently researchers have started to look at its neural correlates. Here, we aim to systematically and critically review the existing literature on the neural correlates of the flow state. Methods: Three electronic databases (Web of Science, Scopus and PsycINFO) were searched to acquire information on eligible articles in July, 2021, and updated in March, 2022. Studies that measured or manipulated flow state (through questionnaires or employing experimental paradigms) and recorded associated brain activity with electroencephalography (EEG), functional magnetic resonance (fMRI) or functional near-infrared spectroscopy (fNIRS) or manipulated brain activity with transcranial direct current stimulation (tDCS) were selected. We used the Cochrane Collaboration Risk of Bias 2 (RoB 2) tool to assess the methodological quality of eligible records. Results: In total, 25 studies were included, which involved 471 participants. In general, the studies that experimentally addressed flow state and its neural dynamics seem to converge on the key role of structures linked to attention, executive function and reward systems, giving to the anterior brain areas (e.g., the DLPC, MPFC, IFG) a crucial role in the experience of flow. However, the dynamics of these brain regions during flow state are inconsistent across studies. Discussion: In light of the results, we conclude that the current available evidence is sparse and inconclusive, which limits any theoretical debate. We also outline major limitations of this literature (the small number of studies, the high heterogeneity across them and their important methodological constraints) and highlight several aspects regarding experimental design and flow measurements that may provide useful avenues for future studies on this topic.Spanish Government 20CO1/012863Ministry of Science and Innovation, Spain (MICINN) Spanish Government PID2019-105635GBI00Junta de Andalucia DOC_0022

TOWARDS STEADY-STATE VISUALLY EVOKED POTENTIALS BRAIN-COMPUTER INTERFACES FOR VIRTUAL REALITY ENVIRONMENTS EXPLICIT AND IMPLICIT INTERACTION

In the last two decades, Brain-Computer Interfaces (BCIs) have been investigated mainly for the purpose of implementing assistive technologies able to provide new channels for communication and control for people with severe disabilities. Nevertheless, more recently, thanks to technical and scientific advances in the different research fields involved, BCIs are gaining greater attention also for their adoption by healthy users, as new interaction devices. This thesis is dedicated to to the latter goal and in particular will deal with BCIs based on the Steady State Visual Evoked Potential (SSVEP), which in previous works demonstrated to be one of the most flexible and reliable approaches. SSVEP based BCIs could find applications in different contexts, but one which is particularly interesting for healthy users, is their adoption as new interaction devices for Virtual Reality (VR) environments and Computer Games. Although being investigated since several years, BCIs still poses several limitations in terms of speed, reliability and usability with respect to ordinary interaction devices. Despite of this, they may provide additional, more direct and intuitive, explicit interaction modalities, as well as implicit interaction modalities otherwise impossible with ordinary devices. This thesis, after a comprehensive review of the different research fields being the basis of a BCI exploiting the SSVEP modality, present a state-of-the-art open source implementation using a mix of pre-existing and custom software tools. The proposed implementation, mainly aimed to the interaction with VR environments and Computer Games, has then been used to perform several experiments which are hereby described as well. Initially performed experiments aim to stress the validity of the provided implementation, as well as to show its usability with a commodity bio-signal acquisition device, orders of magnitude less expensive than commonly used ones, representing a step forward in the direction of practical BCIs for end users applications. The proposed implementation, thanks to its flexibility, is used also to perform novel experiments aimed to investigate the exploitation of stereoscopic displays to overcome a known limitation of ordinary displays in the context of SSVEP based BCIs. Eventually, novel experiments are presented investigating the use of the SSVEP modality to provide also implicit interaction. In this context, a first proof of concept Passive BCI based on the SSVEP response is presented and demonstrated to provide information exploitable for prospective applications

Exploiting physiological changes during the flow experience for assessing virtual-reality game design.

Immersive experiences are considered the principal attraction of video games. Achieving a healthy balance between the game's demands and the user's skills is a particularly challenging goal. However, it is a coveted outcome, as it gives rise to the flow experience – a mental state of deep concentration and game engagement. When this balance fractures, the player may experience considerable disinclination to continue playing, which may be a product of anxiety or boredom. Thus, being able to predict manifestations of these psychological states in video game players is essential for understanding player motivation and designing better games. To this end, we build on earlier work to evaluate flow dynamics from a physiological perspective using a custom video game. Although advancements in this area are growing, there has been little consideration given to the interpersonal characteristics that may influence the expression of the flow experience. In this thesis, two angles are introduced that remain poorly understood. First, the investigation is contextualized in the virtual reality domain, a technology that putatively amplifies affective experiences, yet is still insufficiently addressed in the flow literature. Second, a novel analysis setup is proposed, whereby the recorded physiological responses and psychometric self-ratings are combined to assess the effectiveness of our game's design in a series of experiments. The analysis workflow employed heart rate and eye blink variability, and electroencephalography (EEG) as objective assessment measures of the game's impact, and self-reports as subjective assessment measures. These inputs were submitted to a clustering method, cross-referencing the membership of the observations with self-report ratings of the players they originated from. Next, this information was used to effectively inform specialized decoders of the flow state from the physiological responses. This approach successfully enabled classifiers to operate at high accuracy rates in all our studies. Furthermore, we addressed the compression of medium-resolution EEG sensors to a minimal set required to decode flow. Overall, our findings suggest that the approaches employed in this thesis have wide applicability and potential for improving game designing practices