Monitoring Brain Activity of Geriatric Learners With Low‐Cost Neurophysiological Technology
ABSTRACT
Cultural stereotypes rooted in both antiquated data and misinterpretation of data have long perpetuated the belief that older adults are unable to learn new concepts because they are doomed to lose brain cells at an alarming rate during their geriatric years. However, advances in neurophysiological technologies that allow researchers to observe the functional structures of the brain, as opposed to the individual's observable behavior, provide new insights about geriatric learners' brain processes during learning. This article aims to explore the opportunities offered and challenges posed by using low‐cost electroencephalography (EEG) to monitor the brain activity of geriatric learners during learning activities. The article defines geriatric learners; discusses instructional theories and strategies for geriatric learners; and examines the potential contributions of using low‐cost EEGs to monitor the brain of geriatric learners during the learning process.
According to the U.S. Department of Health and Human Services (2014), the U.S. population of geriatric individuals totaled 44.7 million in 2013. This is an increase of 25% since 2003. Between 2012 and 2050, the United States is expected to experience considerable growth in its older population. By 2050, the population of geriatric individuals is projected to be 83.7 million, almost double the current estimated population (Ortman, Velkoff, & Hogan, 2014). These numbers demonstrate that the geriatric population in the United States is becoming a sizable group.
The Social Security, Medicare, and Health Standards (Social Security Administration, 2015) defines geriatric or old age as 65 years or older. Psychosocial theories of aging (including activity theory, disengagement theory, continuity theory), define an individual as geriatric or old in terms of physical decline (both aesthetically and with regard to health), unawareness of current social norms, and lack of ability to contribute to society (Ball, 2012). Biologically, aging is a natural process, which can be said to start at birth and goes on throughout the rest of life. “Aging is not something that happens on a certain birthday” (Ball, 2012). Instead, aging is a distinct phase within the complete life of a human being (childhood, adulthood, and old age).
As the population of geriatric individuals continues to grow, it is important to investigate the changes that people experience during the aging process (Carstensen, 2007). These include changes in the brain related to cognition and learning. Research on geriatric individuals and learning is imperative because the portion of older adults that comprise the active workforce continues to increase in the United States and worldwide (Kooij, Tims, & Kanfer, 2015). Educators should be better prepared to identify, develop, and implement instructional activities for geriatric individuals because older adults will continue to be part of educational activities, such as formal training, continuing education, or other types of learning opportunities, during their geriatric years.
This article aims to explore the opportunities offered and challenges posed by using a novel neurophysiological technique to monitor the brain activity of geriatric learners during the learning process, thereby contributing research and further discussion of the capabilities of geriatric learners. The article will define geriatric learners, discuss instructional theories and strategies for geriatric learners, and examine potential contributions of the use of low‐cost electroencephalography (EEG) measures to monitor the brain activity of geriatric learners. In this article, we recommend the low‐cost EEG as an empirical, practical assessment tool that can inform educational researchers about processes that occur in the brains of geriatric individuals during learning.
GERIATRIC LEARNERS
For purposes of this article the researchers use and define the term geriatric learners. The research literature on adult learners currently lacks a formal definition of geriatric learners. These individuals constitute a subgroup of adult learners who are formally identified based on age and continuing education. By definition, geriatric learners are age 65 or older. They may or may not have participated in higher education during the early adulthood years, traditionally considered the suitable life stage for pursuing academic goals. In addition, they are currently working to attain an undergraduate or advanced graduate degree, or are participating in an organized academic program (e.g., massive open online courses). Geriatric learners include individuals whose developmental life tasks or roles have evolved to the later stages of life or past raising children, whose employment goals strive toward retirement or a change to a completely new career for which education is necessary, or whose ultimate goal in pursuing educational opportunities may be neurologic health, personal enjoyment, a new career, or some intrinsic or extrinsic motivator.
Categorically, geriatric learners are members of the third age (Laslett, 1989). This is generally defined as the span of time between retirement and the beginning of age‐imposed physical, emotional, and cognitive limitations (Laslett, 1989; Laslett & Siegel, 1990). Coupled with adequate financial resources and good physical and psychological health, the third age offers rich possibilities for self‐fulfillment, purpose engagement, and completion (Laslett & Siegel, 1990).
Conceivably, geriatric learners could be defined medically based on their human capacity for plasticity. Adult brain plasticity is described as the capacity of neurons to adapt and change (Wilson, 2006). One of the many ways to promote growth and change in the brain is through enriched learning environments in which the learner stores new information and experiences changes in the brain's synapses (Wilson, 2006). In the absence of pathological factors, the neurological structures of aging individuals for learning and cognition remain relatively similar throughout life (Carstensen, 2007). Overall, it can be concluded then that geriatric learners are students who are 65 years or older, in the third age, experiencing neuroplasticity, and engaging in formal or informal education. This article will expand on the current theories and strategies used in teaching and learning with geriatric learners as well the potential opportunities to monitor the brain activity of geriatric learners during learning.
LEARNING THEORIES AND GERIATRIC LEARNERS
Reviewing the literature aimed at examining the successful cognition, motivation, and learning experiences for individuals that satisfy the definition of geriatric learners revealed a mixture of important theories and strategies that must be taken into consideration. These include neurologic and psychological constructs as well as instructional theories and techniques. The long‐held societal belief that older people are unable to learn has been disproved (Delahaye & Ehrich, 2008; Kanfer, 2004; Kaufman, Sauve, Renaud, & Duplaa, 2014) and replaced by the idea of aging as a process associated with changes in the way healthy geriatric individuals learn (Escolar Chua & de Guzman, 2014a, 2014b; Kanfer, 2004).
There have been numerous applications of life‐span perspectives to intellectual abilities, personality, affect, and theories of the self (Kanfer, 2004). Such research suggest that adult development is not simply a matter of decline but is better considered in terms of four distinct patterns of development: loss, growth, reorganization, and exchange (Kanfer, 2004). Despite these changes in cognitive processing, the subjective experience of normal aging is largely positive (Carstensen, 2007; Escolar Chua & de Guzman, 2014b). Aging individuals do incur a change in processing of information during the learning process, but these changes do not prevent information acquisition.
A common concern with aging, both within the context of education and in general, is mental self‐regulation. This term refers to the ability of individuals to internally manage their own thought processes and executive functions (EFs) (Schaie & Willis, 2011). EFs includes the neurologic processes of mentally organizing and manipulating information to facilitate execution of a complex, goal‐driven task which includes working memory (Buckner, 2004). EFs are contributing factors to cognitive load, which impacts the success of learning activities. As individuals age cognitively their ability to mentally self‐regulate declines, as does their working memory capacity and the speed at which they are able to process information (Carstensen, 2007; Schaie & Willis, 2011; Van Gerven, Paas, Van Merriënboer, & Schmidt, 2000). The aging mind is slower and more prone to error when processing information. Declines are especially evident on tasks that require effortful processing that relies on attention, inhibition, working memory, perspective memory, and episodic memory (Carstensen, 2007).
According to cognitive load theory, learning can be maximized when the resources of working memory are not overloaded with unnecessary information or underengaged by lack of information (Van Gerven et al., 2000). The research of Paas, Renkl, and Sweller (2004) demonstrates that the same cognitive load interventions that benefit younger learners can aid geriatric learners despite the natural declines in mental self‐regulation and working memory. Paas et al. (2004) indicates that aged learners will continue to demonstrate improved performance over time because they are able to continue building on existing schemata.
Constructivism is another learning theory that can be successfully applied to geriatric learners (Van Gerven et al., 2000). Over time people develop a large repertoire of life experience, a diverse repository of knowledge, and a vast index of skills. Constructivist learning theories recognize the learner's use of these resources as building blocks upon which to add new learning (Spigner‐Littles & Anderson, 1999). This component of constructivism is applicable to geriatric learners who have lived through decades of opportunities and presumably amassed experiences and knowledge (Spigner‐Littles & Anderson, 1999). Instruction that connects new concepts to existing ones, learner personal regulation and goal setting, and instructors as facilitators with the learner in an active role have all been found effective for geriatric learners (Spigner‐Littles & Anderson, 1999).
In addition to neurologic mental self‐regulation, cognitive load theory, and constructivism, an instructional strategy that has proven successful with geriatric learners is scaffolding. This technique is useful when learners approach a completely unknown task or a task for which they feel anxiety (Spigner‐Littles & Anderson, 1999). Scaffolding involves providing the amount of instructional support dictated by the learner's need and lessening that support as the learner nears mastery of the task. Wood, Lanuza, Baciu, MacKenzie, and Nosko (2010) observed that geriatric learners experienced success with scaffolded instruction, particularly when that instruction was used in conjunction with direct instruction and cooperative instructional methods.
Our society widely believes that geriatric learners, who received their initial education during a time when all teaching was highly structured and didactic, prefer those structured, traditional learning environments. However, geriatric learners have reported that they benefit from independent and dependent styles of instruction in both structured and informal environments (Wood et al., 2010). The literature demonstrates that geriatric learners' learning experiences benefit from a variety of techniques as well as observance of the characteristics that define them as adults.
In order to continue informing instructional theories and strategies aimed at geriatric learners, researchers must consider innovative methods such as neurophysiology that allow a better understanding of the changes that occur in the aging brain (Delahaye & Ehrich, 2008). Currently, neurophysiology technology and methods are being used to investigate and interpret cognition in a variety of learners (Reuter‐Lorenz, 2013; Romero & Scott, 2015). Interdisciplinary research on educational neuroscience can lead to innovative designs which would allow researchers the opportunity to study the effects of context and other variables of interest, and continue to update and develop new instructional theories and principles (Gorus, De Raedt, Lambert, Lemper, & Mets, 2008; Jaušcvec, 2000; McKay, Fischler, & Dunn, 2002).
NEUROIMAGING AND NEUROPHYSIOLOGICAL TOOLS IN EDUCATION RESEARCH
There are several advances in neurophysiology that provide us with the ability to collect information about what the brain does with increasing accuracy. Computerized tomography (CT) scans produce a three‐dimensional image of the tissues of the organ being scanned, thereby revealing the organ's anatomical structure (Antonenko, van Gog, & Paas, 2014). Positron emission tomography (PET) scans produce images that detail the metabolic activity, or the biochemical processes, as well as the physiological functions, or the activity stimulating the anatomic structures, in a particular organ (Collins, 2016). The drawback is that a radioactive marker is injected into the bloodstream with PET scans and may also be used with CT scans; therefore, the number of scans anyone can have is limited (Collins, 2016).
For the purposes of cognitive activity as related to educational research, another neuroimaging method, magnetic resonance imaging (MRI), provides the most applicable data (Collins, 2016). However, EEG devices are the most accessible methods used to inform research related to cognition during learning. EEG devices do not require the participant to lie in a specific position; the equipment placed on the participant is small and mobile. Low‐cost Bluetooth varieties that allow for participant mobility are available for research applications (Collins, 2016).
When using an EEG, researchers strategically place small electrodes at specific locations on the skull. These locations correspond to specific regions of the brain. Strategic placement allows researchers to capture and measure the electrical patterns happening in the brain. In modern EEGs multiple readings are taken across the brain that can be compared to build a picture of the activity taking place. Some forms of EEG even pick up particular patterns relating to a specific stimulus like a particular touch or even a single word.
An advantage of using an EEG for educational research is that the EEG detects the brain's responses to the external stimulus event. According to Kuhl and Rivera‐Gaxiola (2007), EEGs allow for online and objective measurement at specific points in time. EEGs can detect subtle fluctuations in instantaneous cognitive processes, which can help explain effects of instructional interventions (van Gog, Paas, & Sweller, 2010). Being able to associate brain responses with an instructional intervention provides educational researchers with data about the learning process which helps better understand how students absorb knowledge.
Currently, there are several solid theoretical and methodological frameworks used to reliably interpret EEG data to measure cognitive processes. These theoretical and methodological frameworks include Basar's theory of neutral oscillations (Antonenko, Paas, Grabner, & van Gog, 2010), the functional significance of alpha and theta oscillations (Antonenko et al., 2010), the event‐related synchronization/desynchronization measure (ERS/ERD) for the quantification of oscillation EEG dynamics (Antonenko et al., 2010) and the event‐related brain potential (ERP) (Andreassi, 1995). Each of these theories has a similar yet distinct manner of interpreting the data gather by the EEG in relation to brain activity and the stimuli presented to the learner (Romero, 2014).
Neurophysiological methods and techniques in education research have already generated interesting findings for the research community in the domains of language and reading (Antonenko et al., 2010), problem solving (McKay et al., 2002), cognitive load (Jaušcvec, 1997), affective processing in learning (Antonenko et al., 2010; Lamberts et al., 2000; Stipacek, Grabner, Neuper, Fink, & Neubauer, 2003), serious games and virtual reality (Rodriguez, Rey, & Alcañiz, 2013), and computer‐ and web‐based learning (Ninaus et al., 2014). The basic assumption in neuroscience research is that tasks make specific demands on the brain and these demands cause changes in the chemical and electrical neural activity. These changes result in a host of physiological responses affecting cerebral blood flow, heart rate, muscle activity, blood pressure, respiration, eye movements, electro‐termal responses, pupil size, oxygen consumption, salivation, and others.
Prior Research Related to Geriatric Learners
In research related to geriatric learners, neurophysiology technology has been recognized as providing the opportunity to assess whether or not interventions aimed at increasing cognition in the aging brain affect neural change (Romero, 2014). For the aging brain the results of neurophysiological research using functional magnetic resonance imaging (fMRI) indicated that plasticity, adaptation, and reorganization of neural circuits does occur (Reuter‐Lorenz, 2013). Neurophysiological research, with high‐tech tools, on the aging brain has allowed us a better understanding of how aging individuals process information; yet as society evolves and contexts changes we need to continue to investigate how the aging brain organizes during learning.
For neurophysiological research skeptics, investigation using MRI technology and method is merely an exercise in neuroanatomical localization (Park & McDonough, 2013). However, continued research of brain functions in the aging brain of geriatric learners with sound theoretical frameworks and adequate research design can help to characterize mental representation and identify cognitive processes that transform, store, and utilize the brain in the service of complex thought and goal‐directed behavior (Reuter‐Lorenz, 2013).
LOW‐COST EEGS FOR EDUCATIONAL RESEARCH
Most of the research on the aging brain of geriatric learners has been conducted with high‐tech neurophysiological tools and methods such as MRI and fMRI. However, novel options of neurophysiological technologies such as the low‐cost EEG technology and methodology provide a range of opportunities to enhance knowledge and understanding on how the aging brain works. The low‐cost EEG consists of a headset with attached arms which terminate in an electrode. The generic physical design of the headset automatically places the electrodes at the appropriate location on the participant's head. The headset communicates with the corresponding software via a Bluetooth connection. The software confirms the electrodes are placed properly and receiving data. This differs from the traditional EEG, which requires electrodes to be place independently by a trained technician and in which corresponding equipment is more complex and provides richer data that must be interpreted.
There are currently several manufacturers offering low‐cost EEG equipment. EMOTIV markets a 14‐channel mobile EEG headset for $800 and a 5‐channel headset for $300. Open BCI markets headsets for less than $400 which allow the user to customize their equipment by choosing a hardware board specific to their needs.
Advantages of Using Low‐Cost EEGs in Research on Geriatric Learners
This type of cost‐effective and easily operated tool opens the doors for educational researchers wanting to expand their knowledge of current theories, learning strategies, and instructional models geared towards geriatric learners and the aging brain. This new and low‐cost neurophysiology technology allows educational researchers the opportunity to make invisible thinking processes observable (Reuter‐Lorenz, 2013). The low‐cost EEG methodology can provide a simple method for recording the electrical activity of the brain without compromising the quality of data recorded (Gerě & Jaušcvec, 1999).
Unlike many neurophysiological devices which require subjects to lie in restricted positions or to ingest hazardous materials, the low‐cost EEG can noninvasively collect electrical activity from the brain of a learner (Gerě & Jaušcvec, 1999). Compared to the previously mentioned high‐tech neurophysiology methods (CT scan, PET, MRI, and fMRI), low‐cost EEGs are now affordable and easier to use. Most of these low‐cost EEGs have great portability and a good quality brain signal (Antonenko et al., 2010, 2014). Moreover, low‐cost EEG devices are highly compatible with other technologies, such as virtual reality devices (Rodriguez et al., 2013) and eye tracking. This compatibility is valuable for educational researchers that are interested in combining EEG data with other records, information, and/or numbers that help make better sense of the relationship between the stimuli presented and the data gathered.
Low‐cost EEG technologies are more user‐friendly for educational researchers compared to the high‐tech and complex neurophysiological devices used by neuroscientists (Ninaus et al., 2014; Rodriguez et al., 2013). Moreover, most low‐cost EEG devices include software that can help with data reduction and interpretation. Other neurophysiological technologies require a significant amount of technical skill to help with data reduction. The ability to use pre‐fabricated data reduction and interpretation software makes interpreting data easier for the education researchers engaged in neurophysiological research (Ingram, Estes, & Prins, 2015). Lastly, signal analysis techniques that remove irrelevant artifacts and automatize most of the data processing make low‐cost EEG technologies and methods more usable, useful, and accessible to educational researchers (Antonenko et al., 2014; Ingram et al., 2015).
Low‐cost systems have been validated and compared to high‐tech, lab‐based systems. Early comparisons found that data from low‐cost EEG systems required significant correction prior to data analysis (Antonenko et al., 2014). However, after proper alignment with the stimuli presentation the signal quality was similar between the low‐cost and laboratory EEG systems on average. Specifically, researchers found no significant differences between EEG systems in delta, theta, alpha, and beta frequency power (Ingram et al., 2015; Ries, Touryan, Vettel, McDowell, & Hairston, 2014).
Limitations of Using Low‐Cost EEGs in Research on Geriatric Learners
If careful consideration is given to the measures and research design, the low‐cost EEG is available, inexpensive, and easy to use outside of the lab for education research with various populations including children, young learners, adult learners, and geriatric learners (Ries et al., 2014). Of course, regardless of the type of tool used, caution must be exercised in drawing conclusions for learning, instruction, and performance based on neurophysiological research. It can be tempting for educational researchers to resort to reductionism and propagate neuromyths. Researchers need to avoid generalization of results. Key considerations must be given to the specific populations, context, research design, boundaries of the environment, and limitations of the neurophysiological tool.
DISCUSSION
Low‐cost EEGs have already been used in education research related to mood induction during learning (Badcock et al., 2015; Boutani & Ohsuga, 2013; Ries et al., 2014; Tan, 2012) and virtual reality (Rodriguez et al., 2013). However, research using low‐cost neurophysiological methods, and in particular low‐cost EEGs, have not been used to monitor the brain of geriatric learners. Yet given their validity, reliability, and portability, low‐cost EEGs are a great option to investigate the brains of geriatric learners in a specific context. These devices can help further investigate the changes that occur in the brain as individuals age and the critical processes that occur in the aging brain during learning and instruction.
Additionally, investigations by educational researchers about the brain of geriatric learners using low‐cost EEGs can also help dispel myths and misconceptions about geriatric individuals and their capacity to learn. One of the best support mechanisms that can be given to geriatric learners is to improve our understanding of how to best “teach an old dog new tricks.” Educational research using low‐cost neurophysiological technologies can help develop strategies to optimize cognition throughout the aging process and for evaluating the effectiveness of these strategies (Ninaus et al., 2014).
The aim of this article was to explore the opportunities offered and challenges posed by using a low‐cost EEG to monitor the brain activity of geriatric learners during the learning process. It also defined who geriatric learners are and discussed instructional theories and strategies for teaching geriatric learners. Given that the population of geriatric individuals will continue to grow in the coming years (Reuter‐Lorenz, 2013), we should recognize geriatric learners as a distinct learner population and identify teaching techniques that cater to their specific needs during the learning experience. The neurophysiological technologies explored in this article provide clarity on how the brain continues to evolve as it ages. In fact, the literature emphasizes the importance of involving aging individuals in educational programs as a means of continuing neurogenesis and neuroplasticity (Ortman et al., 2014). Research on geriatric learners needs to continue to evolve and to embrace new educational neuroscience investigations that provide a better understanding of basic brain activities during the learning process.
Conclusion
In the past, high‐tech neurophysiological technologies have been used in research to investigate the cognitive architecture and mental functions of the aging brain (Wilson, 2006). However, this type of research can be very difficult for education researchers due to the complexity of the equipment and data analysis. Now, educational neurophysiological investigations can include empirical research using easy‐to‐use, validated, reliable, portable, and affordable equipment such as low‐cost EEG devices that allow data collection and data reduction (Cabeza & Moscovitch, 2013; Mather, Cacioppo, & Kanwisher, 2013a, 2013b; Park & McDonough, 2013; White & Poldrack, 2013).
These low‐cost neurophysiological measures help further explore how educational researchers can use the low‐cost EEG as an empirically sound and functionally practical assessment tool for evaluating the cognitive processes that occur during the learning process of geriatric learners. This empirical educational neurophysiological research can serve to dismiss misconceptions about geriatric learners and avoid basing our knowledge of geriatric individuals solely on behavioral observations.
