How Does Stem Learning Affect Brain

INTRODUCTION

With the transition from lecture-based to active-learning formats, science, technology, engineering, and mathematics (Stem) undergraduate classrooms are becoming more social (Linton et al., 2014; Eddy et al., 2015). Yet social interactions and their impact on student learning, especially at the undergraduate level, are vastly underexplored. Bailiwick-based education researchers typically collect information from large groups of students, but the emphasis is on how pedagogy affects individual students (Grunspan et al., 2014). There is relatively a small (but growing) number of subject-based education enquiry (DBER) studies that focus on social interactions. These studies suggest that social interactions can assistance explain shifts in students' self-efficacy (Dou et al., 2016), persistence in introductory courses (Zwolak et al., 2017), and academic performance (Theobald et al., 2017; Vargas et al., 2018).

Similarly, in that location is very limited neuroscience research on social interactions. In fact, the report of existent-earth social exchanges has been dubbed the "night matter of social neuroscience" (Schilbach et al., 2013). The lack of studies investigating these phenomena is most likely due to the high cost and fragility of traditional cerebral neuroscience methods, limiting the vast bulk of inquiry on the human being brain to studies in which one participant at a time performs a task in a highly constrained environment (east.g., inside a brain scanner). In the by few years, researchers take begun to approach the neural basis of social interactions by comparing the brain responses of multiple individuals during a variety of tasks (Hasson et al., 2012; Babiloni and Astolfi, 2014; Wheatley et al., 2019). In pioneering research, Hasson and colleagues (2004) used functional magnetic resonance imaging (fMRI) to demonstrate that the brains of different people who spotter the same moving picture show increasingly similar action patterns over fourth dimension (a phenomenon called "brain-to-brain synchrony"). Other research has constitute that successful advice is associated with like brain activity between a speaker and a listener: the more like the listener'southward brain activeness with that of the storyteller, the better the listener remembers a story (Stephens et al., 2010). Yet, participants in these studies were non tested simultaneously (i.e., there was no social interaction). The few truly interactive studies that exist demonstrate a relationship betwixt social factors and encephalon-to-brain synchrony. For example, face-to-face dialogues take been shown to induce more encephalon synchrony compared with back-to-back dialogues or monologues (Jiang et al., 2012). It has also been demonstrated that conversational leaders show more synchronous brain activity with "followers" than followers do among one some other (Jiang et al., 2015).

In this Essay, I volition discuss how social interactions in Stem classrooms can be studied from a neuroscience perspective. Though this line of research is yet at an early stage, I volition fence that it holds great potential for cross-disciplinary inquiry in biology education. I will first describe how portable technologies can be used to mensurate the encephalon activity of groups of students in classrooms and review several recent studies that used this approach. These studies propose that students exhibit synchronized brain activeness patterns, and the extent of brain synchrony reflects students' engagement, social closeness, and learning outcomes. I will then hash out what might drive brain synchrony between students in classrooms and conclude with suggestions for future research.

PORTABLE Encephalon TECHNOLOGIES IN STEM EDUCATION RESEARCH

This Essay focuses on electroencephalography (EEG), because it is currently the only brain-measuring engineering science that allows the recording of students' and teachers' brain activity in classrooms at just a fraction of the price of laboratory-based neuroscience equipment. Other neuroimaging techniques, such as fMRI, positron emission tomography, and magnetoencephalography, require stationary encephalon scanners that toll millions of dollars. Another neuroimaging method that is gaining popularity is functional almost-infrared spectroscopy (fNIRS), which uses virtually-infrared low-cal to measure brain activation. Although wireless fNIRS devices are now becoming available, almost all fNIRS inquiry to date has been conducted in laboratory settings (Cui et al., 2011).

What does EEG mensurate? EEG measures the brain's electrical activity from electrodes placed on the scalp (typically 32–256 electrodes). The EEG bespeak is thought to reflect the summation of postsynaptic potentials across thousands of neurons (Biasiucci et al., 2019; Brienza and Mecarelli, 2019). The EEG point is primarily generated by cortical pyramidal neurons. Due to their anatomical geometry, when these neurons are activated in synchrony, their electric signals calculate and propagate to the scalp. Thus, the EEG signal varies according to the synchronized or desynchronized activity of large populations of neurons. Biasiucci et al. (2019) propose the following illustration: EEG is similar to measuring the roar of a crowd from outside a stadium. It cannot identify individual conversations, merely it can detect changes in the overall action of the oversupply (for a recent primer on EEG, see Biasiucci et al., 2019).

Whereas other neuroimaging tools, such as fMRI, tin can find encephalon activations within a range of a few millimeters, with EEG, it is mathematically very difficult to infer where in the brain the EEG signal originates. Another limitation of EEG is that the signal is often contaminated past other sorts of physiological electrical activeness (e.g., ocular and other muscular activity) and environmental dissonance (e.g., reckoner screens). This is especially a business concern when using EEG in classrooms, and therefore, EEG information should be closely examined and artifacts should be identified and removed (run across Dikker et al., 2017). On the other hand, EEG is very useful in determining when a sure brain response is happening, making EEG a rich source of data when it comes to disentangling dissimilar stages of data processing (Luck, 2014).

Most EEG inquiry is still being conducted in laboratory settings, but with recent developments in low-cost, portable, wireless, and dry (i.e., gel-free) EEG technology, researchers can now conduct neuroscience investigations outside the laboratory in ecologically valid environments, such as classrooms. Biological science education researchers might be especially interested in smartphone-based EEG systems that combine an off-the-shelf EEG device with a smartphone or a tablet (Debener et al., 2012; Stopczynski et al., 2014; Poulsen et al., 2017) likewise every bit EEG systems that offer less obtrusive electrode configurations (Debener et al., 2015). Withal, i should note that portable EEG devices take fewer electrodes, which might restrict information assay and the types of research questions that can be answered. Also, data acquired with portable EEG devices are more than susceptible to artifacts, such as caput motion and center movement artifacts, resulting in a college percent of information exclusion. Nonetheless, it has been demonstrated that portable EEG devices tin can yield comparable information to laboratory-grade systems (at least with tasks that are known to generate robust EEG effects; Badcock et al., 2013; Ries et al., 2014; Grummett et al., 2015).

BRAIN-TO-BRAIN SYNCHRONY IN CLASSROOMS

Several recent studies have used portable EEG methods to collect brain data from groups of students in simulated and existent-earth classrooms. Poulsen et al. (2017) measured the brain activity of a group of 12 immature adults in a classroom setting while they were presented with brusk video clips. The videos elicited synchronized brain activity patterns across participants compared with randomly scene-scrambled versions of the aforementioned videos. While this study helped validate the method of recording EEG in a classroom, its educational relevance is limited due to the blazon of materials that were used (segments of popular movies) and the fact that the participants were non an organic grouping of students.

In another recent study, Dikker et al. (2017) recorded EEG activeness from a group of 12 students in a biology high school classroom. Students' brain action was recorded throughout various classroom activities, such as lectures, instructional videos, and group discussions (Figure i). The extent to which encephalon action was synchronized across students was institute to predict self-reported educatee engagement: Students who reported existence more than engaged exhibited higher brain synchrony with their peers. Further, brain-to-encephalon synchrony between pairs of students reflected how close they felt toward each other: pairs of students who demonstrated higher brain synchrony likewise reported higher social closeness (Dikker et al., 2017).

FIGURE 1. — Effigy i. Portable EEG recordings in a classroom (from Dikker *et al.*, 2017). Inset: An case of a portable EEG device (EMOTIV EPOC+).

In some other study, EEG activity was recorded not just from the students, but also from their teacher, which is a challenging task due to the sensitivity of EEG to caput movement and speech-related artifacts. By instructing the teacher to be mindful of their head move and sufficiently preprocessing the data, the authors were able to measure the encephalon synchrony between students and their teacher. Student-to-teacher encephalon synchrony was significantly correlated with students' self-reported engagement: Students who were more engaged showed higher brain synchrony with the instructor. Further, students who reported feeling closer to the instructor exhibited more student-to-instructor encephalon synchrony (Bevilacqua et al., 2019).

Taken together, these two studies suggest that the social dynamics amidst students and their teacher are, to some extent, reflected in their brain-to-brain synchrony. This is intriguing, because students in these studies were not prompted to recollect most their social relationships while EEG information were beingness collected. This finding is consistent with recent fMRI inquiry, which reported that neural responses to naturalistic movies were highly similar amid friends, with similarity of neural responses decreasing as social altitude increased (Parkinson et al., 2018).

So far, I have focused on pupil engagement and social dynamics—but how does brain-to-brain synchrony relate to learning outcomes? Bevilacqua et al. (2019) did non find a meaning association between brain synchrony and students' retention retentivity, but two other studies indicate otherwise (Cohen et al., 2018; Davidesco et al., 2019). Cohen et al. (2018) demonstrated that encephalon-to-brain synchrony betwixt students who watched science-related instructional videos predicted their performance in a retention examination. In another study, brain activity was meantime measured from iv not–science major students and their instructor in a false classroom throughout a sequence of iv mini-lectures in biology and chemical science. Students' knowledge was measured a week before, immediately later, and a calendar week post-obit the EEG session. Both student-to-student and student-to-teacher brain synchrony significantly predicted students' memory retention a week afterward the lesson took place. Interestingly, moment-to-moment variations in brain synchrony throughout the lecture indicated what specific information students retained: Encephalon synchrony was higher for exam questions for which students demonstrated learning (i.e., answered incorrectly in the pretest and correctly in the posttest) compared with test questions for which students' answers remained unchanged (Davidesco et al., 2019).

WHAT MIGHT GIVE RISE TO Encephalon-TO-BRAIN SYNCHRONY DURING CLASSROOM INTERACTIONS?

While the underlying mechanisms of brain-to-brain synchrony are not well understood, Dikker et al. (2017) proposed that shared attending plays a crucial role. At the nearly basic level, brain-to-brain synchrony is driven by the fact that all students were exposed to the same stimulus (e.g., the instructor'due south voice). When confronted with an external stimulus, brain action becomes temporally aligned to the rhythm of the input, a phenomenon called "stimulus entrainment" (Lakatos et al., 2008). In a classroom, because all students have similar sensory-motor systems designed to auscultate the world, their brains all become entrained to the instructor'south voice or to whatsoever other shared stimuli, thus syncing with one another.

Critically, stimulus entrainment but provides a fractional explanation to encephalon synchrony in classrooms. Our brains act as a selective filter of the external world (Enns and Lleras, 2008; Berkes et al., 2011); thus, sharing the aforementioned audiovisual input does non guarantee that we all "see" the globe the same mode. For example, instructors may trigger completely different responses in each of their students' brains depending on students' attention. Attending has a critical office in the learning process, as it controls the flow of incoming information by suppressing irrelevant information while enhancing sensitivity to task-relevant data (Kanwisher and Wojciulik, 2000).

Many studies have demonstrated that attention can modulate how the brain processes information (e.g., Davidesco et al., 2013). For example, in a cocktail party–like scenario, when participants are confronted with two speakers and asked to direct attention to only i of them, brain activity in loftier-club auditory regions tracks only the attended speaker's voice (Mesgarani and Chang, 2012; Golumbic et al., 2013). Similarly, as students pay close attending to a lecture, their brains go entrained to the lecturer's voice, and thus their brain activeness aligns with the brain activity of the instructor and other students. In contrast, every bit students lose interest, their brains go less entrained to the lecturer (and perhaps more entrained to other stimuli), resulting in decreased similarity in neural responses to the teacher and other students (Dikker et al., 2017).

Brain SYNCHRONY AS A REFLECTION OF INTERPERSONAL COORDINATION

During social interactions, not only do people's brains become synchronized, simply likewise their behaviors become aligned (Cornejo et al., 2017). For example, during conversations, people tend to imitate each other'due south choices of speech sounds, grammatical forms, and words, a process known every bit "interactive alignment" (Garrod and Pickering, 2004). Interpersonal coordination also occurs nonverbally: Interlocutors tend to synchronize their facial expressions, manual gestures, and noncommunicative postures. This typically happens spontaneously, unconsciously, and chop-chop (within a few seconds after the onset of an interaction; Louwerse et al., 2012).

Fifty-fifty though instruction and learning are highly social processes, with students and teachers interacting constantly with one another, in that location is very trivial research on interpersonal coordination in classrooms. In one of the few studies that addressed this upshot, Bernieri (1988) measured the movement synchrony and behavioral matching in video recordings of high school students. The students were videotaped in pairs as they attempted to teach each other a prepare of imaginary words. Ratings of movement synchrony and behavioral matching were higher in genuine interaction video clips compared with control video clips. Farther, the degree of movement and behavioral synchrony were positively correlated with rapport ratings between students. In another more contempo study, bilingual undergraduate and graduate students engaged in teaching-like tasks, where one person was required to transmit information that was unknown to their partner in order to accomplish a shared goal. An independent group of native English speakers rated the degree of alignment between interlocutors. Interactive alignment in both linguistic (eastward.g., word stress placement in multisyllabic words) and nonverbal behaviors was establish to be significantly college at the terminate of the conversation compared with the beginning, suggesting that interpersonal coordination increases over time (Trofimovich and Kennedy, 2014; Trofimovich et al., 2014).

Interpersonal coordination relies on our ability to anticipate both our own linguistic and behavioral actions and those of others (Ramnani and Miall, 2004; Sebanz et al., 2006; Konvalinka et al., 2010; Sänger et al., 2011; Romero et al., 2012; Pickering and Garrod, 2013). For case, using fMRI, Stephens et al. (2010) reported that neural responses in the frontal cortex of the listener's encephalon preceded the responses in the speaker'due south brain. These anticipatory responses suggest that listeners are actively generating predictions of what the speaker is about to say. Interestingly, the extent of encephalon areas where the listeners' activity preceded the speaker'southward activeness was found to be the best predictor of listeners' comprehension of the story to which they listened (Stephens et al., 2010). Similarly, Davidesco et al. (2019) demonstrated that in frontal and central EEG locations, student-to-teacher encephalon synchrony best predicted learning outcomes when the students' brain responses preceded the teachers' brain responses. Nevertheless, the role of prior cognition in this process is not articulate. It is possible that students who knew more about the topic before the lecture were better able to generate predictions well-nigh what the instructor was about to say, and that immune them to learn more than efficiently.

CONCLUSIONS AND Adjacent STEPS

Social interactions in Stem classrooms every bit well as how they are reflected in students' encephalon activity seem to exist promising directions for interdisciplinary research in biology education. Social interactions are often overlooked in DBER, despite having a directly impact on students' academic performance (Linton et al., 2014; Theobald et al., 2017; Vargas et al., 2018). Similarly, social interactions are understudied in human being neuroscience, where about inquiry is conducted on private participants in controlled laboratory environments.

This emerging line of research provides an opportunity for cross-disciplinary collaborations betwixt subject-based education researchers and cognitive and social neuroscientists (Davidesco and Milne, 2019; Mestre et al., 2018). The connection between neuroscience and education has been described in the past equally "a bridge too far" (Bruer, 1997). Since then, there has been growing interest and fence regarding the relevance of cognitive neuroscience to education research and practice (Ansari and Coch, 2006; Goswami, 2006; Sigman et al., 2014; Bowers, 2016). Bridging neuroscience and education is challenging, because these disciplines have very different goals and research traditions. Neuroscientists typically adopt a reductionist approach and report cerebral functions in isolation. Educational researchers, on the other hand, focus on the learner every bit a whole and how the learner is embedded in a context, such as a classroom. A common concern is that traditional cognitive neuroscience methods (eastward.1000., fMRI) are conducted in artificial laboratory environments and therefore cannot provide useful data to understand real-world learning. From a more pragmatic standpoint, the concern is that neuroscience methods are simply likewise expensive to apply to educational enquiry (Varma et al., 2008).

This Essay highlighted recent developments in portable and wearable encephalon technologies, such as portable EEG, which now allow researchers to apply neuroscience methods in classroom-based research at just a fraction of the toll (Dikker et al., 2017; Bevilacqua et al., 2019). These methods can complement other measures used in DBER, such as achievement tests, self-reports, and think-aloud interviews, and can deepen our understanding of the learning process in several important ways. First, neuroscience methods provide continuous information without interfering with naturally occurring learning activities. Using these methods, we can ameliorate explore the cognitive processing that is taking place during learning, rather than just before and after learning (Mayer, 2017). Further, these methods tin potentially measure out implicit processes that learners are unaware of or unable to report accurately, such equally lapses of attention (Dahlstrom-Hakki et al., 2019). Finally, neuroscientific information tin be used to explore individual differences that mediate learning and predict how students would benefit from different pedagogies (Gabrieli, 2016; Mayer, 2017).

Encephalon-to-brain synchrony is an instance of what can exist measured in neuroscience research in Stalk classrooms, but this miracle is non unique to Stalk. In fact, information technology is not unique to classrooms at all, but likely occurs in whatever blazon of social interaction (Hasson et al., 2012). But in the example of classroom learning, brain-to-brain synchrony can be an informative measure of student engagement, social dynamics, and learning outcomes (Dikker et al., 2017; Cohen et al., 2018; Bevilacqua et al., 2019; Davidesco et al., 2019).

Still, in that location are several inherent limitations associated with classroom-based EEG research. Outset, EEG is a correlative method, where neural activity is associated with behavioral measures (e.g., exam scores). Thus, EEG cannot assess causal relationships. Second, the low spatial resolution of EEG limits the data bachelor to researchers. Questions about what specific encephalon regions are involved in classroom learning cannot currently exist addressed with EEG. Tertiary, EEG recordings are sensitive to muscle-related artifacts acquired by head motion, eye movements, and speech. For this reason, classroom-based EEG research to date has focused primarily on passive forms of learning, such equally listening to lectures and watching instructional videos, where student movement and oral communication are limited (Dikker et al., 2017; Bevilacqua et al., 2019). However, now that this method has been successfully deployed in classrooms, future research can start exploring more interactive forms of learning, such as small-group work.

Contempo calls to enhance active learning in Stem education have emphasized group work based on the understanding that collaborative groups tin enhance learning, improve students' attitudes toward scientific discipline, and reinforce their social identity as scientists (Springer et al., 1999; Tanner et al., 2003; National Research Council, 2012; President's Council of Advisors on Science and Technology, 2012). All the same, groups tin can be dysfunctional, and we do not know enough about how to all-time use group learning in Stem education, especially at the college level (Theobald et al., 2017). Future cross-disciplinary enquiry teams can collect both EEG and audio-video information from small groups of students during collaborative learning tasks. Whereas previous work has typically measured brain-to-brain synchrony throughout the unabridged elapsing of an activity, the high temporal resolution of EEG allows researchers to examine how brain synchrony (as well as other EEG measures) unfolds over fourth dimension. For example, researchers can identify moments of high and low synchrony in the EEG data and examine the corresponding audio and video recordings to identify whatever recurring patterns. This inquiry might generate new insights into what makes grouping learning constructive.

In summary, I believe that neuroscience research in classrooms provides an exciting opportunity for both field of study-based education researchers and cognitive and social neuroscientists. From a DBER perspective, this enquiry volition hopefully yield a deeper, more mechanistic understanding of Stem learning by illuminating what is happening in students' brains during the learning process and what factors mediate learning. From a neuroscience perspective, this inquiry provides a unique opportunity to study the brain mechanisms that back up learning in real-world classroom environments.

ACKNOWLEDGMENTS

This work has been supported by NSF grant no. 1661016. The illustration in Effigy one is reprinted from Current Biological science, 27(ix), Dikker et al., "Brain-to-brain synchrony tracks real-world dynamic grouping interactions in the classroom," pages 1375–1380, Copyright (2017), with permission from Elsevier.

REFERENCES

Ansari, D., & Coch, D. (2006). Bridges over troubled waters: Education and cognitive neuroscience. Trends in Cerebral Sciences , 10(four), 146–151. Medline, Google Scholar
Babiloni, F., & Astolfi, L. (2014). 2014). Social neuroscience and hyperscanning techniques: Past, present and futurity. Neuroscience & Biobehavioral Reviews , 44, 76–93. Medline, Google Scholar
Badcock, N. A., Mousikou, P., Mahajan, Y., De Lissa, P., Thie, J., & McArthur, Thou. (2013). Validation of the Emotiv EPOC® EEG gaming organisation for measuring enquiry quality auditory ERPs. PeerJ , ane, e38. Medline, Google Scholar
Berkes, P., Orbán, Grand., Lengyel, M., & Fiser, J. (2011). Spontaneous cortical activeness reveals hallmarks of an optimal internal model of the environment. Scientific discipline , 331(6013), 83–87. Medline, Google Scholar
Bernieri, F. J. (1988). Coordinated move and rapport in teacher-educatee interactions. Journal of Nonverbal Behavior , 12(2), 120–138. Google Scholar
Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, K., … & Dikker, Southward. (2019). Brain-to-encephalon synchrony and learning outcomes vary by student-instructor dynamics: Evidence from a existent-world classroom electroencephalography study. Journal of Cognitive Neuroscience , 31(3), 401–411. Medline, Google Scholar
Biasiucci, A., Franceschiello, B., & Murray, M. M. (2019). Electroencephalography. Current Biology , 29(3), R80–R85. Medline, Google Scholar
Bowers, J. Southward. (2016). The practical and principled problems with educational neuroscience. Psychological Review , 123(v), 600–612. Medline, Google Scholar
Brienza, M., & Mecarelli, O. (2019) Neurophysiological basis of EEG. In: Mecarelli, O. (Ed.), Clinical electroencephalography . Cham, Switzerland: Springer. Google Scholar
Bruer, J. T. (1997). Education and the brain: A span too far. Educational Researcher , 26(eight), 4–sixteen. Google Scholar
Cohen, Southward. South., Madsen, J., Touchan, G., Robles, D., Lima, S. F. A., Henin, Due south., & Parra, L. C. (2018). Neural engagement with online educational videos predicts learning performance for private students. Neurobiology of Learning and Memory , 155, sixty–64. Medline, Google Scholar
Cornejo, C., Cuadros, Z., Morales, R., & Paredes, J. (2017). Interpersonal coordination: Methods, achievements, and challenges. Frontiers in Psychology , 8, 1685. Medline, Google Scholar
Cui, X., Bray, Due south., Bryant, D. G., Glover, Grand. H., & Reiss, A. Fifty. (2011). A quantitative comparing of NIRS and fMRI across multiple cognitive tasks. Neuroimage , 54(4), 2808–2821. Medline, Google Scholar
Dahlstrom-Hakki, I., Asbell-Clarke, J., & Rowe, E. (2019). Showing is knowing: The potential and challenges of using neurocognitive measures of implicit learning in the classroom. Heed, Encephalon, and Education , 13(1), 30–forty. Google Scholar
Davidesco, I., Harel, G., Ramot, Yard., Kramer, U., Kipervasser, S., Andelman, F., … & Malach, R. (2013). Spatial and object-based attention modulates broadband high-frequency responses across the homo visual cortical bureaucracy. Periodical of Neuroscience , 33(iii), 1228–1240. Medline, Google Scholar
Davidesco, I., Laurent, E., Valk, H., W, T., Dikker, S., Milne, C., & Poeppel, D. (2019). Encephalon-to-brain synchrony between students and teachers predicts learning outcomes. Retrieved from bioRxiv 644047. doi: ten.1101/644047 Google Scholar
Davidesco, I., & Milne, C. (2019). Implementing cognitive science and subject area-based educational activity enquiry in the undergraduate scientific discipline classroom. CBE—Life Sciences Instruction , 18(3), es4. Link, Google Scholar
Debener, S., Emkes, R., De Vos, Grand., & Bleichner, M. (2015). Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Scientific Reports , 5, 16743. Medline, Google Scholar
Debener, S., Minow, F., Emkes, R., Gandras, K., & De Vos, M. (2012). How about taking a low-cost, pocket-size, and wireless EEG for a walk? Psychophysiology , 49(xi), 1617–1621. Medline, Google Scholar
Dikker, S., Wan, L., Davidesco, I., Kaggen, 50., Oostrik, M., McClintock, J., … & Poeppel, D. (2017). Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Electric current Biology , 27(9), 1375–1380. Medline, Google Scholar
Dou, R., Brewe, E., Zwolak, J. P., Potvin, G., Williams, East. A., & Kramer, L. H. (2016). Beyond performance metrics: Examining a decrease in students' physics self-efficacy through a social networks lens. Physical Review Physics Education Inquiry , 12(2), 020124. Google Scholar
Eddy, Southward. L., Brownell, S. E., Thummaphan, P., Lan, K.-C., & Wenderoth, K. P. (2015). Caution, pupil feel may vary: Social identities impact a student'southward feel in peer discussions. CBE—Life Sciences Education , 14(4), ar45. Link, Google Scholar
Enns, J. T., & Lleras, A. (2008). What's next? New evidence for prediction in human vision. Trends in Cerebral Sciences , 12(ix), 327–333. Medline, Google Scholar
Gabrieli, J. D. (2016). The promise of educational neuroscience: Comment on Bowers (2016). Psychological Review , 123(5), 613–619. Medline, Google Scholar
Garrod, Due south., & Pickering, Thousand. J. (2004). Why is chat and then like shooting fish in a barrel? Trends in Cerebral Sciences , 8(1), viii–11. Medline, Google Scholar
Golumbic, Due east. Chiliad. Z., Ding, Due north., Bickel, South., Lakatos, P., Schevon, C. A., McKhann, G. M., … & Simon, J. Z. (2013). Mechanisms underlying selective neuronal tracking of attended speech at a "cocktail party." Neuron , 77(5), 980–991. Medline, Google Scholar
Goswami, U. (2006). Neuroscience and didactics: From research to exercise? Nature Reviews Neuroscience , 7(v), 406–413. Medline, Google Scholar
Grummett, T., Leibbrandt, R., Lewis, T., DeLosAngeles, D., Powers, D., Willoughby, J., … & Fitzgibbon, South. (2015). Measurement of neural signals from inexpensive, wireless and dry EEG systems. Physiological Measurement , 36(7), 1469–1484. Medline, Google Scholar
Grunspan, D. Z., Wiggins, B. Fifty., & Goodreau, S. Grand. (2014). Understanding classrooms through social network analysis: A primer for social network analysis in education research. CBE—Life Sciences Education , 13(2), 167–178. Link, Google Scholar
Hasson, U., Ghazanfar, A., Galantucci, B., Garrod, S., & Keysers, C. (2012). Brain-to-brain coupling: A mechanism for creating and sharing a social earth. Trends in Cognitive Sciences , 16(ii), 114–121. Medline, Google Scholar
Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject synchronization of cortical action during natural vision. Science , 303(5664), 1634–1640. Medline, Google Scholar
Jiang, J., Chen, C., Dai, B., Shi, K., Ding, G., Liu, L., & Lu, C. (2015). Leader emergence through interpersonal neural synchronization. Proceedings of the National Academy of Sciences USA , 112(fourteen), 4274–4279. Medline, Google Scholar
Jiang, J., Dai, B., Peng, D., Zhu, C., Liu, 50., & Lu, C. (2012). Neural synchronization during confront-to-confront communication. Journal of Neuroscience , 32(45), 16064–16069. Medline, Google Scholar
Kanwisher, N., & Wojciulik, Due east. (2000). Visual attention: Insights from brain imaging. Nature Reviews Neuroscience , 1(2), 91–100. Medline, Google Scholar
Konvalinka, I., Vuust, P., Roepstorff, A., & Frith, C. D. (2010). Follow yous, follow me: Continuous common prediction and adaptation in joint borer. Quarterly Periodical of Experimental Psychology , 63(11), 2220–2230. Medline, Google Scholar
Lakatos, P., Karmos, One thousand., Mehta, A. D., Ulbert, I., & Schroeder, C. Eastward. (2008). Entrainment of neuronal oscillations as a mechanism of attentional selection. Science , 320(5872), 110–113. Medline, Google Scholar
Linton, D. L., Farmer, J. Thousand., & Peterson, E. (2014). Is peer interaction necessary for optimal active learning? CBE—Life Sciences Pedagogy , thirteen(2), 243–252. Link, Google Scholar
Louwerse, Chiliad. M., Dale, R., Bard, E. G., & Jeuniaux, P. (2012). Behavior matching in multimodal communication is synchronized. Cerebral Science , 36(8), 1404–1426. Medline, Google Scholar
Luck, Southward. J. (2014). An introduction to the event-related potential technique . Cambridge, MA: MIT Press. Google Scholar
Mayer, R. E. (2017). How can encephalon research inform academic learning and instruction? Educational Psychology Review , 29(four), 835–846. Google Scholar
Mesgarani, Due north., & Chang, E. F. (2012). Selective cortical representation of attended speaker in multi-talker speech perception. Nature , 485(7397), 233–236. Medline, Google Scholar
Mestre, J. P., Cheville, A., & Herman, Thousand. 50. (2018). Promoting DBER-cognitive psychology collaborations in Stalk didactics. Journal of Engineering Educational activity , 107(1), v–10. Google Scholar
National Enquiry Council (2012). Discipline-based pedagogy inquiry . Washington, DC: National Academies Press. Google Scholar
Parkinson, C., Kleinbaum, A. Thousand., & Wheatley, T. (2018). Similar neural responses predict friendship. Nature Communications , ix(ane), 332. Medline, Google Scholar
Pickering, M. J., & Garrod, Due south. (2013). An integrated theory of linguistic communication production and comprehension. Behavioral and Brain Sciences , 36(04), 329–347. Medline, Google Scholar
Poulsen, A. T., Kamronn, S., Dmochowski, J., Parra, L. C., & Hansen, 50. G. (2017). EEG in the classroom: Synchronised neural recordings during video presentation. Scientific Reports , seven, 43916. Medline, Google Scholar
President's Quango of Advisors on Scientific discipline and Technology. (2012). Appoint to excel: Producing one one thousand thousand additional college graduates with degrees in science, technology, engineering, and mathematics . Washington, DC: U.S. Government Function of Science and Technology. Google Scholar
Ramnani, Due north., & Miall, R. C. (2004). A system in the human brain for predicting the actions of others. Nature Neuroscience , seven(ane), 85–90. Medline, Google Scholar
Ries, A. J., Touryan, J., Vettel, J., McDowell, G., & Hairston, Due west. D. (2014). A comparison of electroencephalography signals acquired from conventional and mobile systems. Journal of Neuroscience and Neuroengineering , 3(1), 10–20. Google Scholar
Romero, Five., Coey, C., Schmidt, R. C., & Richardson, M. J. (2012). Movement coordination or motility interference: Visual tracking and spontaneous coordination modulate rhythmic movement interference. PLoS ONE , 7(9), e44761. Medline, Google Scholar
Sänger, J., Lindenberger, U., & Müller, V. (2011). Interactive brains, social minds. Communicative & Integrative Biological science , iv(6), 655–663. Medline, Google Scholar
Schilbach, L., Timmermans, B., Reddy, Five., Costall, A., Bente, G., Schlicht, T., & Vogeley, K. (2013). Toward a 2nd-person neuroscience. Behavioral and Brain Sciences , 36(four), 393–414. Medline, Google Scholar
Sebanz, N., Bekkering, H., & Knoblich, G. (2006). Joint activeness: Bodies and minds moving together. Trends in Cerebral Sciences , 10(2), seventy–76. Medline, Google Scholar
Sigman, Yard., Peña, M., Goldin, A. P., & Ribeiro, Southward. (2014). Neuroscience and pedagogy: Prime fourth dimension to build the bridge. Nature Neuroscience , 17(4), 497–502. Medline, Google Scholar
Springer, L., Stanne, Chiliad. E., & Donovan, S. South. (1999). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: A meta-analysis. Review of Educational Research , 69(one), 21–51. Google Scholar
Stephens, G. J., Silbert, L. J., & Hasson, U. (2010). Speaker–listener neural coupling underlies successful communication. Proceedings of the National Academy of Sciences USA , 107(32), 14425. Medline, Google Scholar
Stopczynski, A., Stahlhut, C., Larsen, J. E., Petersen, M. K., & Hansen, L. K. (2014). The smartphone brain scanner: A portable real-time neuroimaging system. PLoS ONE , 9(2), e86733. Medline, Google Scholar
Tanner, Thou., Chatman, Fifty. S., & Allen, D. (2003). Approaches to jail cell biology teaching: Cooperative learning in the scientific discipline classroom—beyond students working in groups. Cell Biology Education , 2(1), 1–5. Link, Google Scholar
Theobald, E. J., Eddy, South. L., Grunspan, D. Z., Wiggins, B. Fifty., & Crowe, A. J. (2017). Student perception of group dynamics predicts individual performance: Comfort and equity matter. PLoS I , 12(7), e0181336. Medline, Google Scholar
Trofimovich, P., & Kennedy, South. (2014). Interactive alignment between bilingual interlocutors: Evidence from two information-commutation tasks. Bilingualism: Linguistic communication and Noesis , 17(four), 822–836. Google Scholar
Trofimovich, P., McDonough, G., & Foote, J. A. (2014). Interactive alignment of multisyllabic stress patterns in a 2nd language classroom. TESOL Quarterly , 48(iv), 815–832. Google Scholar
Vargas, D. L., Bridgeman, A. One thousand., Schmidt, D. R., Kohl, P. B., Wilcox, B. R., & Carr, L. D. (2018). Correlation betwixt educatee collaboration network centrality and academic operation. Physical Review Physics Education Research , 14(2), 020112. Google Scholar
Varma, South., McCandliss, B. D., & Schwartz, D. L. (2008). Scientific and pragmatic challenges for bridging education and neuroscience. Educational Researcher , 37(3), 140–152. Google Scholar
Wheatley, T., Boncz, A., Toni, I., & Stolk, A. (2019). Beyond the isolated brain: The promise and challenge of interacting minds. Neuron , 103(2), 186–188. Medline, Google Scholar
Zwolak, J. P., Dou, R., Williams, E. A., & Brewe, East. (2017). Students' network integration equally a predictor of persistence in introductory physics courses. Concrete Review Physics Education Enquiry , xiii(ane), 010113. Google Scholar