Educational Technology and Its Contributions in Students’ Focus and Attention Regarding Augmented Reality Environments and the Use of Sensors

Abstract

In the course of a teaching and learning process, different cognitive skills are required for a student. Attention is an important variable to be measured, since it plays a fundamental role in the accumulation of information with the stimulus of the learner’s memory in the course of the assimilation of knowledge. The purpose of this article is to demonstrate the influence of augmented reality (AR) used as a tool for educational content in student concentration when compared with the use of traditional teaching and learning technologies. User attention was monitored through an electroencephalography sensor while performing an educational task using either an AR or a traditional interface. This presented favorable results, since it was possible to identify an increase in student attention during the interaction with the AR application, as opposed to its conventional counterpart.

Keywords

neuroeducation NeuroSky student attention augmented reality Moodle educational resources

Introduction

Our brains are made up of billions of small brain cells called neurons. When we learn something new, some of the neurons move together. A new connection will develop between them, attaching them together; the next time one of the neurons boost, the other connected neurons will also be triggered. This is how you learn to speak a new language, kick a ball around, or perform some new task. It is advisable to have high attention during learning, so that the right neurons push at the same time and connect. The strength of this new connection depends on how deep was the impression the learning experience has left. Even a deep negative impression can create a strong connection. Poor or shallow impressions lead to weak connections, which is easily overwritten and forgotten. In that case, one would have to strengthen such a connection with repetition to maintain learning (Tokuhama-Espinosa, 2008).

Neuroeducation can be an important tool to enhance learning processes because it is possible to work with information and data of the nervous system on different aspects. This branch of neuroscience helps us to understand how we learn, opening possibilities to improve educational links. It also assists in better understanding of students through data, thus making it possible to verify the most effective teaching and learning processes for them. As for the different types of classes, students are looking for more interactive and shorter classes to learn better. They claim to learn more when they attend more dynamic and less time-consuming classes at the beginning of their attention span, having the content taught in an interactive environment (Tokuhama-Espinosa, 2008). Between the processes of class preparation and assimilation of content by the student, many other processes occur and determine the aspects of learning. According to neuroeducation, these are reasoning, biological structure, stimuli and motivation, emotion, biological maturation, neuroplasticity, and continuous explosion.

From the psychological point of view, neuroeducation according to Tokuhama-Espinosa (2008) aims to explain learning behaviors. In doing so, neurologists work on this question by checking the brain physically and structurally, while psychologists investigate the mental processes underlying that brain structure.

Aranha (2012) affirms that neuroeducation is a recent area when we take into account the nomenclature used and its transdisciplinary characterization, whose objective is to unite concepts such as mind, nervous system, and education. In the process of teaching and learning, students learn through the stimuli received by sight, hearing, and other senses such as movement (Ausubel, 2003). The practices depend on the individual because the ability is unique in each brain. Learning is defined by the ability of each brain ever to change according to its previous experiences (Tokuhama-Espinosa, 2008).

Tokuhama-Espinosa began their understanding of neuroeducation in the 1970s, based on Gardner (1974), who is recognized for his theory of multiple intelligences that, already at that time, introduced several principles in neuroeducation, including the concept that there are no two brains identical to each other, due to their natures and origins. Gardner places the necessary connection between neuroscience, psychology, and education in order to be a neuroeducator.

For Mora (2013), neuroeducation is “tapping into the knowledge about how the brain works, integrating them with psychology, sociology, and medicine and trying to improve students' memory and learning processes” (pp. 25–26). In addition, Mora (2013) states that neuroeducation is born from the teachers’ own community, applying new knowledge developed in neuroscience to teaching and learning processes that, according to them, skills improve if the student is given the knowledge of the latest scientific discoveries about memory, emotion, and attention. The starting point for the reasoning behind neuroeducation, as has been mentioned, is that the brain is not a static organ, and there are critical periods in which one learning experience obtains more relevance than another (Mora, 2013). This statement rendered the idea that schools should change their education systems according to the stage in which the students are, in order to improve the specific skills that are developed during that process.

Bruner formed a group that was established as interdisciplinary human science and that generated the center of cognitive studies. The themes developed by him tend to understand mental activity and bring about changes in the nature of the human mind since the cognitive revolution (Bruner, 1986). It speaks of the need to “equip minds with skills to understand, feel and act in the world” (Bruner, 2001, p. 46).

Interactive

Using interactive teaching and learning objects helps stimulate the cognitive mechanisms responsible for student attention (Velloso, 2014). Learning occurs because stimuli mechanisms related to cognition happen, and also because of the result that the brain has when it modifies itself "to change the structure in response to the interferences" (Lent, 2015, p. 112).

According to Ladewig (2000, p. 63), “attention plays a very important role in the ability to retain relevant information because it is through it, associated with the control processes, that we store information in long-term memory.” In the process of learning, Ladewig (2000) emphasizes that attention goes through three different stages, which are defined by cognitive, associative, and autonomous. In the cognitive stage, the student understands the concepts, which causes a great overload in the mechanisms of attention; in the associative stage, the student starts to develop the knowledge, and the attention needs are minimized in relation to the previous one; Finally, in the stand-alone stage, the related ability is already developed and the attention requirements are minimal, allowing the student to redirect his attention to other focuses.

The student who is learning something new, according to Fitts and Posner (1967), passes from the first stage (Cognitive) to the second (Associative), until reaching the third and last stage (Autonomous). In the characteristics of each stage, there is an important change after the practice in the processes of attention. In the cognitive stage, subjects try to understand the objectives, stimulating the processes of attention. After this practice, they go on to the associative stage where they maintain the most stable performance being able to verify errors, while attention needs decrease significantly. After all the practice involved, they pass on to the last stage, the autonomous, already with the developed skill and with the minimum attention requirement.

According to Velloso (2014),

The cognitive sciences study attention as a set of mental processes, conscious or not. In turn, neuroscience understands attention as a particular physiological state of the brain. Teaching and learning objects, like other didactic-pedagogical materials, have their use, on the part of the subjects, strongly linked to attention. (p. 45)

Mora (2013) presents attention representatively as “a window that opens in the brain through which you learn and memorize the information that comes from the world around us” (p. 81). The possibility of a single filter indicates that all processes require attention and only one stimulus-response execution could be performed at a time (Welford, 1952), or what Broadbent (1958) defined as serial information processing.

From this perspective, this article aims to verify the impact that augmented reality (AR) resources promote to increase the level of students’ attention. To do so, this article will present a comparative analysis between the use of AR resources and a virtual learning environment (VLE), with the intention of investigating the different levels of attention observed during the interaction of the students, seeking viable alternatives to classify and identify the relation of interactive teaching and learning objects to the student’s level of attention.

Theoretical Framework

AR consists in the integration of virtual resources with real-world physical elements in which computer-generated graphical components are presented in users’ technological devices along with the real environment elements in loco. It was defined by Milgram and Kishino (1994), as an operational definition for AR, this may be the term considered to refer to any case in which an otherwise real environment is “augmented” by means of virtual objects (computer graphics). Azuma et al. (2001) corroborate that AR consists of inserting virtual objects in the real world through a computational device, so that the user interface becomes the one used in the real environment, adapted to visualize and manipulate the virtual objects placed in your space.

Many proposals that approach the use of AR have been developed with the intention of improving the process of teaching and learning. In the same way, the combination of these resources with emerging technologies aimed at the educational area has been growing, such as mobile devices. Chen and Tsai (2012) contribute in this regard, stating that although AR is not a novelty, its potential in educational applications is only now being explored. As a technology for education, Santos et al. (2014) define AR as multimedia (text, sound, images, animations, etc.) that is displayed relative to the actual environment.

In addition to involving computer graphics and computer vision, AR also integrates multimedia features, an aspect that enhances the user’s perception of the real world through the addition of virtual information (Azuma et al., 2001). According to Liarokapis and Anderson (2010), multimedia enhancement techniques are presented to improve the traditional teaching methods since, by using virtual multimedia content, students can visualize three-dimensional examples of real principles that study and interact with them naturally. In addition, Liarokapis and Anderson (2010) also emphasize that the real environment must be harmonized and synchronized with the virtual environment, both in position and in context, so that it is possible to provide an understandable and meaningful visualization to students regarding the principles to be studied.

Methodology

For the execution in the scope of this research, the activities carried out by the participants were divided into two stages. In the first stage, participants were asked to access and read a material available in a physics course hosted in the VLE Modular Object Oriented Dynamic Learning Environment (Moodle), a tool traditionally used in Federal University of Rio Grande do Sul (UFRGS) institution courses (Step 1). In the second step, participants were instructed to use the avatAR UFRGS AR application and interact with a simulation about physics and the other associated AR resources (Step 2). Regarding the platforms selected for the experiment, different levels of interaction are required from the user. On one hand, the VLE (Moodle) presents a low degree of interaction, whereas the AR paradigm presents a substantially higher degree of interaction. This research choice aims to evidentiate the different attention scales, these being directly related to the way content is exposed to users.

Demographic Data

Five participants were selected for testing with both educational technologies during a 5-minute period at each stage. These participants are undergraduate and graduate students of UFRGS in Electrical Engineering (a), Computer Engineering (b), and Computer Science (c). Two samples were discarded as the sensor stopped recording during the test procedure.

Instruments

To collect the data, the brainwave detection system Neurosky MindWave was used, along with the graphic interface of the Effective Learner (2018) application. Both will be described in the subsequent sections.

MindWave Mobile

NeuroSky’s MindWave Mobile (2018) consists of a headset that records raw electroencephalographic data through a single sensor used in contact with the participant’s dorsolateral prefrontal cortex region. By reading the brainwaves, the headset provides two personalized measurements of the participant wearing it, which correspond to attention and meditation, ranging from 0 to 100, denoting the learner’s focus level.

Starting the system, the headset displays that the connector is communicating via bluetooth with the Effective Learner application in which it is possible to follow the execution test data. At the end of the test, the user can save the data by pressing the corresponding menu button. It is important to note that the equipment can only be operated in conjunction with the application.

The study by Chen and Lin (2014) confirmed that the NeuroSky headset has sufficient validity and reliability based on the correlation between Birdwatching scores, a visual attention-based cognitive training program developed by Lumosity (Hardy, Drescher, Sarkar, Kellett, & Scanlon, 2011), and the values of attention detected by the NeuroSky headset. In addition, Rebolledo-Mendez et al. (2009) also found a positive correlation between the attention values measured by the NeuroSky headset and self-reported attention levels through a Second Life evaluation exercise. The analytical results demonstrated that the headset measured values had a satisfactory validity and reliability to identify the student’s attention in a learning activity.

The main reason for choosing this device was the easy access to data, as the device communicates with any mobile device via Bluetooth, which makes the data accessible for future analysis. The equipment provides eSense measurements for mental states ranging from 0 to 100, which are related to attention and meditation and updated once per second.

Effective Learner

To collect the data from the MindWave Mobile sensor, an application called Effective Learner was used, which consists of a graphical interface that interprets the brainwaves captured during the steps performed in the tests (Step 1 and Step 2) in numbers that represent the degree of attention applied by the participant in each interaction. The Effective Learner application provides feedback on the participant’s level of attention on a 6-point scale, from most effective to least effective. This application was chosen since it is indicated on the official website of NeuroSky, maker of MindWave Mobile.

Educational Technologies

The educational technologies involved were the Moodle VLE and the avatAR UFRGS AR application, which are discussed in the following sections.

VLE—Moodle

Moodle is considered a Virtual Environment of Teaching and Learning widely used by educational institutions. The main function of Moodle is to assist in teaching by providing support to in loco classes and also as the main means of interaction between students and teachers in distance learning.

Learning Management Systems (LMS) make interaction between teachers and students more convenient. They provide a platform on the web, and a lot of pedagogical activities can be performed on it (Jang, 2008). Moodle is an open-source LMS which has been in development since 1999. It has been widely adopted over 200 countries, having 49,000 registered sites, with the number of courses being around 3,500,000, (Moodle, 2019).

In the context of this research, the Moodle virtual environment was used to provide participants with access to the educational materials traditionally made available by the teachers of the educational institution where the tests were carried out. The educational materials that participants had access to during the implementation of Step 1 regarded basic physics. They received guidance as to read a document.

Physics AR Application

The avatAR UFRGS application is an educational technology that provides students with interaction with simulations developed in AR. In the application, students have access to various educational resources, where they can visualize micro and macroscopic physical phenomena and interact with multimedia resources such as images, videos, 3D objects, and simulations.

For the development of the avatAR UFRGS application, the Unity 3D platform was used, which allowed the construction of simulations with 3D objects and behaviors associated to these objects, via scripts. In relation to AR resources, the Vuforia platform was used, which provides the tracking of text, planar images, 3D objects, multitarget, geo-location, markerless, online, and offline recognition targets. For the choice of the Vuforia platform, a previous comparative study was carried out to show the differences between AR platforms. Through this study, it was possible to verify the potential of the Vuforia platform, mainly for the portability and to enable the tracking of numerous types of targets, providing a wide range of multimedia resources for the construction of AR applications for education (Herpich, Guarese, & Tarouco, 2017). The application described throughout this section is available for free for the Android¹ and iOS² operating systems.

In relation to the pedagogical potential of the AR application, some of the available resources for students to interact with are visual simulations, access to multimedia resources, interaction and storage of experiments in the student's inventory, as well as access to the experiments without the need to connect to the Internet. As can be seen in Figure 1, the contents are presented in stages and the user can interact with the “Level” button in order to explore each content and its simulations and steps. This feature was developed with the purpose of fragmenting the amount of information presented simultaneously on the screen, in order to respect the background knowledge of each user (always presenting initially the most basic information, moving toward more advanced ones). This progressive form of information availability on the screen occurs in conjunction with the increasing number of other pieces of media, concurrent with the proportional time required for the construction of knowledge.

Figure 1.

Application demonstration for physics teaching.

The avatAR UFRGS application was used to provide participants with the visualization and interaction of educational simulations, which enables students to change parameters and verify the changes that result from their actions. The content that participants had access to during Step 2 was about basic physics, where they were given the orientation to visualize an experiment, interacting with the simulation, and checking the associated multimedia resources.

Result Analysis

To demonstrate the influence of AR on the attention level of the participants, a comparative analysis was performed between the use of the avatAR UFRGS application and the Moodle VLE. During the tests, participants were equipped with the MindWave Mobile headset, which was used with the intention of reading the brain waves emitted by the dorsolateral prefrontal cortex of the participants in the course of interactions with the contents of physics in the application of AR (Figure 2).

Figure 2.

Participants interaction with the AR app.

From the results obtained, it was possible to observe a high level of concentration of the participants in the use of the AR application (Figure 3). When verifying the total averages between the values considered as Most effective and Least effective, it was possible to more emphatically show that the AR resources promote a significant difference in the focused attention of the participants, according to the extremes measured, in which the most effective level showed an increase of 11.41% during the use of the AR application, while the least effective level was 13.32% lower. The density of attention in the graph illustrated in the color blue presents higher levels of attention and the most effective portions of time in the obtained measurements, while the color red represents the smaller density of attention and the least effective moments. It is important to observe that the environment in which the tests were performed were manipulated as to not present distracting elements, allowing a greater focus of concentration in all cases.

Figure 3.

Attention levels of each subject during the tests.

Another aspect observed during the subjects’ interactions with the AR application consists of the multimedia resources which they interacted with. It was possible to verify that, in addition to the positive impact caused by the use of AR, other resources were important for the increase in the attention level through the insertion of virtual objects to the content traditionally visualized by the participant. Such resources were, for example, the interactive simulations in which the participant had the possibility to configure the parameters involved in the execution of the experiment, complemented with images, videos, and explanatory texts about the contents pertinent to the simulation in question.

Figure 4 represents the measurement taken with the Effective Learner application for Subject 1. The graph shows the degrees of attention and the duration of the test. The duration of the test for Subject 1 was of 10 minutes. During the first 5 minutes, the student made use of the Moodle VLE. The subject was later instructed to take 1 minute off and, during the last 5 minutes, made use of the AR application. Over the last 5 minutes, Subject 1 was found to have reached its peak of attention (shown in the color blue), which demonstrates a greater concentration level dedicated to the activities it was performing with the AR application. This situation presents indications that resources in AR can positively influence in the process of acquisition of student knowledge.

Figure 4.

Attention levels measured of a single subject.

It is also possible to observe that there was a percentage increase of the higher levels of attention of Subject 1. The authors attributed this increase to the controlled environment in which the tests were performed, as well as due to the absence of distracting elements. This percentage involves the possible recoveries and stimuli of thoughts to organize new ideas for decision-making. Distracting elements here mean noise, visual signals, and the movement of people or any objects that would cause interruptions in attention in the learning process. As mentioned earlier, these elements were eliminated from the test environment, but during class, they should also be avoided and preferably eliminated.

It should also be noted that during the tests, the index referring to the highest effectiveness (blue bar) never exceeded levels above 30%. This perspective shows that even using the AR resources, it is still necessary to continue the investigations, in order to find out which resources are able to promote an even greater increase of attention during the execution of educational activities and, consequently, also during the process of learning.

In the work, the neuropsychology of attention, Cohen, Sparling-Cohen, and O’Donnell (1993) highlighted that the word attention is an integral part of the regular and popular vocabulary. In this manner, attention must be understood as a collection of cognitive processes that produce recognizable results, as opposed to a unique process.

Attention, therefore, plays a fundamental role in the acquisition and construction of knowledge, since the processes involved for it to occur allow the subject to maintain the focus in what to learn. de Melo and Gonçalves (2009) reviewed the biological bases of attention, presenting studies with empirical evidence of the main neurophysiological hypotheses. The authors also highlight a major focus of neurophysiological research in the field of visual attention, to the detriment of other sensory channels.

Limitations

A fragility of this study is related to the low number of samples. This limitation occurred due to the low availability of the sensor used in the tests, as well as due to the small interest of the volunteers in the participation of the tests (we believe this happened because it was, although minimally, an intrusive test). Due to the limited number of participants, the duration of each test was reduced in order to minimize the exposure time of the participants to the sensor. These limitations prevented us from presenting statistically significant results. It did not, however, hamper the analysis of the evidence observed during the test sessions, which leads to believe the contribution of AR to increased attention. Thus, teachers are able to integrate digital technologies effectively when they use different platforms, which allow the representation of content in a more in-depth way, as was exposed in this work, in which the study in the AR environment was compared with a traditional LMS environment.

Conclusion

This research presented a comparative analysis between the use of educational technologies in which AR resources and the VLE Moodle were confronted with a view to identify the benefits of AR resources to increase the students’ level of attention. Teachers still find it difficult to produce interactive educational resources, because in some situations, it is necessary to have prior knowledge in programming languages and web development. Faced with this, AR applications may be a possibility, still requiring advanced knowledge, but being an attractive educational resource. Furthermore, reading is still widely used in the traditional teaching mode, while the use of AR demonstrates different representations of objects, images, colors, formats, touch, videos, audio, and simulations, thus allowing students to learn more effectively from their different senses. Some students have greater ease of learning with the use of different learning styles that can be auditory, visual, and kinesthetic. A single individual can carry only a style or a fusion of styles. For Felder and Silverman (1988), recognition of learning styles has two main applications: to guide teachers and to help them use a methodology to meet all learning styles.

As future work, it is intended to promote the use of the sensor and the AR application in the classroom, with a greater number of participants and interaction time. In addition to this expansion, investigating attention in the domains of neuroscience is another future goal, for example, investigating the behavior of the brain under the same conditions of this research evaluating other variables such as the level of stress, anxiety, and sleep quality of subjects. On the other hand, it is intended to evaluate the relation between the sensation of pleasure during learning and the student attention levels.

Also, in relation to platforms to be used in the future, it is intended to use materials with similar degrees of interaction, since the tests performed with platforms with different levels of interaction presented a satisfactory level of precision. Based on this high precision, it is likely possible to obtain a satisfactory comparison between materials with greater interactional similarity.

Subsequently, it is also intended to measure the learning style of each participant with tests already established in the literature and to perform a cross-validation of these results with the preferences observed during the measurements obtained in the tests of this study.

Finally, this research can serve as a stimulus for other initiatives to study interdisciplinary methodologies and technologies and to improve teaching practices for excellence in teaching and learning with the most significant results.

Footnotes

Acknowledgments

The authors thank Federal University of Rio Grande do Sul and Federal Institute of Rio Grande do Sul. This article is a greatly expanded version of a paper presentation to the Immersive Learning Research Network’s (iLRN) 2018 Conference ().

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

Andreia S. Bos is a PhD student at the Federal University of Rio Grande do Sul. She holds a master’s degree in Computer Science in the Graduate Program in Informatics, at the Federal University of Santa Maria (2015). She was graduated in Computing from the Federal Institute Farroupilha (2011). She has experience in the area of Computer Science and Communication, with emphasis on Information and Communication Technologies.

Fabrício Herpich is a PhD student in the Graduate Program in Informatics in Education at the Federal University of Rio Grande do Sul. He received a degree in Technology in Internet Systems (2013) and a master’s degree in Computer Science (2015). He has experience in the development of educational technologies with Virtual and Augmented Reality.

Igor Kuhn was graduated in Economic Sciences from the Federal University of Rio Grande do Sul, being also a graduate in the Special Program of Graduation in Teacher Education for Professional Education in the Federal University of Santa Maria and masters in Mathematical Modeling in the Regional University of Northwest of the State of Rio Grande do Sul (Unijuí). He has experience in the areas of Predictive Models for Engineering and Systems Identification.

Renan L. M. Guarese recently graduated in his Computer Science program at the Federal University of Rio Grande do Sul (UFRGS), with an undergraduate sandwich period at the Radford University (2015). He is currently pursuing his MSc degree at the Postgraduate Program in Computing, also at UFRGS and spent the last 3 years working both with Education Technology and Human–Computer Interaction, particularly in the field of Augmented Reality.

Liane M. R. Tarouco was graduated in physics from the Federal University of Rio Grande do Sul (1970), master in Computer Science from the Federal University of Rio Grande do Sul (1976) and PhD in Electrical Engineering/Digital System from the University of São Paulo (1990). She is currently a lecturer at the Federal University of Rio Grande do Sul. She works with the Postgraduate Program in Informatics in Education.

Milton A. Zaro has completed his graduation in physics. He holds a masters in Mining Engineering, Metallurgical, and Materials Engineering from the Federal University of Rio Grande do Sul (1977) and a PhD in Mining, Metallurgical and Materials Engineering from the Federal University of Rio Grande do Sul (1992). He received his postdoctoral degree in Mechanical Engineering from Universidade Federal de Santa Catarina, 1993. He is currently a coordinator of the lab of research in biomechanics—Instituto Brasileiro de Tecnologia do Couro, Calçado e Artefatos (IBTeC).

Michelle Pizzato received her bachelor’s degree in Chemistry from the Federal University of Rio Grande do Sul (1998), master’s degree in Science Teaching (Chemistry) at the University of São Paulo (2002), and doctor’s degree in Science at the University of Burgos (2010). She is currently a professor at the Federal Institute of Education, Science, and Technology of Rio Grande do Sul, Campus Porto Alegre.

Leandro Wives is the director of the Interdisciplinary Center for New Technologies in Education of the Federal University of Rio Grande do Sul (UFRGS) since 2017. He is an associate professor at the Institute of Informatics at UFRGS. He is a permanent advisor of Post Graduate Program in Computing and of Post Graduate Program in Computing in Education. He was graduated in Computer Science in 1997, by Universidade Católica de Pelotas. He has obtained his master’s degree (1999) and PhD (2004) in Computer Science, both by the PPGC of UFRGS.

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