Connecting Levels of Analysis in Educational Neuroscience: A Review of Multi-level 1 Structure of Educational Neuroscience with Concrete Examples 2 Hyemin Han1*, Firat Soylu1*, D. Mona Anchan1 3 Educational Psychology Program, University of Alabama, Tuscaloosa, AL, United States 4 5 Corresponding Author: 6 Hyemin Han1 7 8 Email address: hyemin.han@ua.edu 9 10 *These authors contributed equally to this work. The authors' names are listed 11 alphabetically. 12 2 Abstract 13 In its origins educational neuroscience has started as an endeavor to discuss 14 implications of neuroscience studies for education. However, it is now on its way to 15 become a transdisciplinary field, incorporating findings, theoretical frameworks and 16 methodologies from education, and cognitive and brain sciences. Given the differences and 17 diversity in the originating disciplines, it has been a challenge for educational neuroscience 18 to integrate both theoretical and methodological perspective in education and neuroscience 19 in a coherent way. We present a multi-level framework for educational neuroscience, 20 which argues for integration of multiple levels of analysis, some originating in brain and 21 cognitive sciences, others in education, as a roadmap for the future of educational 22 neuroscience with concrete examples in moral education. 23 Keywords: educational neuroscience; multi-level theoretical framework; 24 neuroimaging; meta-analysis; educational intervention; computer simulation 25 Introduction 26 Educational neuroscience is a vast and emerging field that incorporates methods 27 and perspectives from brain and cognitive sciences, learning sciences, and educational 28 psychology, among others. In its origins educational neuroscience started as an initiative 29 to discuss implications of neuroscience findings for education. Going back as early as 30 1970s, these early discussions focused on if it was at all meaningful to interpret 31 neuroscience findings for education, and if so, for which specific issues and problems in 32 education neuroscience findings have implications for. 33 So far, educational neuroscience has been acting as an interdisciplinary platform 34 where two distinct fields, neuroscience and education, interact. The main theme that 35 3 characterizes the field is the interpretation of neuroscience findings for educational 36 research and practice, and increasing neuroscience literacy within the education 37 community to diminish the negative impacts of neuromyths. But as a burgeoning 38 transdisciplinary field, educational neuroscience is in the process of defining its major 39 questions, methodologies, and theoretical frameworks, in addition to forming a community 40 of scientists. As is historically typical of fields that shift from interdisciplinarity to 41 transdisciplinarity, one challenge it is facing is incorporating the diverse research 42 methodologies and paradigms from its parent fields such as education, cognitive sciences, 43 learning sciences, psychology, neuroscience, and many others in an integrated way to 44 address a unified set of research questions. This requires connecting distinct research 45 methodologies functioning at different levels of analysis and coming from different 46 theoretical orientations. 47 We argue that responding to the challenge of incorporating diverse research 48 methodologies and levels of analysis is a crucial next step for the burgeoning field of 49 educational neuroscience. Here we first discuss some of the challenges facing educational 50 neuroscience, present the levels of analysis traditionally associated with each field, and 51 discuss the need to connect these levels so that educational neuroscience can emerge as an 52 established transdisciplinary field with its own unique approach to research that 53 distinguishes it from other fields of educational and brain sciences. To exemplify how the 54 multi-level approach presented here applies to educational neuroscience, we present a 55 research project on development of moral decision making, which involves a series of 56 studies each targeting a different set of levels of analysis, from classroom interventions to 57 functional magnetic resonance imaging (fMRI) studies. We present how findings, 58 4 knowledge, and insight acquired from each of these studies address a set of central and 59 unified research questions, allowing a multi-level transdisciplinary conceptualization of 60 learning and teaching in this domain. Our expectation is that the framework and the case 61 study presented here will help with responding to concerns about viability of educational 62 neuroscience as a field. 63 Criticisms of Educational Neuroscience 64 Before discussing how to link different levels of analysis in educational 65 neuroscience, it is important to visit criticisms of educational neuroscience to pinpoint how 66 the presented approach addresses current issues in the field. Even though discussion on the 67 implications of brain science for education have been going on for decades [1,2], efforts 68 that can generally be framed under educational neuroscience (or variably mind, brain and 69 education) still invoke skepticism. Skeptics point to philosophical and methodological 70 differences, and lack of clear connections between neuroscience and education. Proponents 71 are more optimistic and point to domains where brain science findings shifted perspectives 72 and influenced teaching practice in education (e.g., reading, numerical cognition). In this 73 section we visit some of the main criticisms of educational neuroscience and discuss the 74 extent to which these criticisms were addressed. 75 Twenty years ago in an influential article Bruer [2] argued that bridging 76 neuroscience and education is a challenge, and that neuroscience findings do not really 77 have any direct and meaningful implications for education. He presented numerous 78 examples for how misled excitement about bridging neuroscience and education are 79 grounded in misinterpretation and simplification of neuroscience findings, including 80 synaptogenesis, critical periods in development, and beneficial effects of enriched 81 5 environments on synaptic growth in rats. He argued that while it is not possible to directly 82 bridge neuroscience and education, the two can be linked through mediation of cognitive 83 psychology. In this approach neuroscience findings can only be meaningful for education 84 if it goes through an interpretive filter that is cognitive psychology. Even though it has been 85 20 years since the publication of Bruer's paper, his criticisms continue to be endorsed in 86 more recent criticisms. For example Bowers [3,4] argued that it is psychological science 87 that provides a scientific grounding for education, and neuroscience rarely provides 88 insights into learning and teaching outside of psychology. In addition, he argued that 89 behavioral measures are superior to neural measures in characterizing children's learning 90 and cognitive processing; for example, when deciding whether remedial instruction should 91 target underlying deficits or instead focus on development of non-impaired compensatory 92 skills. 93 In his response to the criticisms by Bowers, Gabrieli [5] pointed out that, much like 94 cognitive or affective neuroscience, educational neuroscience is a basic science that 95 provides mechanistic accounts for functional organization of the brain. Even though 96 educational neuroscience findings do not directly prescribe strategies to use in the 97 classroom, there are numerous examples (e.g., reading, mathematics) for how educational 98 neuroscience research informs mechanisms of learning and cognition in exceptional 99 children, and provides insights on individual differences. Gabrieli presented a model where 100 applied research, involving intervention studies, mediates the communication between 101 basic research and classroom practice, where successful interventions are scaled. Gabrieli 102 presents examples for how basic research findings on dyslexia, ADHD, autism and other 103 conditions changed our understanding of the mechanisms underlying these conditions and 104 6 inspired interventions with some promising results. 105 Howard-Jones et al. [6] separately responded to Bowers' criticisms. They likened 106 the relation between neuroscience and education to how molecular biology is related to 107 drug discovery. While the basic science provides insights about "where to look," it "does 108 not prescribe what to do when you get there" (p. 7). 109 The knowledge about neural correlates of cognition, and how typical and 110 exceptional groups differ need interpretation through a pedagogical lens to develop 111 interventions guided by basic research. Only after these interventions are tested through 112 large-scale implementation studies (which are similar to clinical trials in medicine) do we 113 have the type of knowledge that is directly applicable to classrooms. In response to Bowers' 114 [4] argument that psychological level explanations are more relevant to education than 115 neuroscience, Howard-Jones et al. pointed out that these two levels do not constitute a 116 duality since the "neuroscience" in educational neuroscience is almost always a reference 117 to cognitive neuroscience. Psychological and neural explanations are in fact 118 complementary, and, like cognitive neuroscience, educational neuroscience integrates 119 these two levels. 120 The tension between the two levels of explanations, neural (or more broadly, 121 biological) and psychological (which actually includes multiple sub-levels such as 122 behavioral, cognitive, and socio-cultural) often come up in discussions about the goals and 123 the future of educational neuroscience. Howard-Jones et al. [4] describe the goal of 124 educational neuroscience as using "multiple levels of description to better understand how 125 students learn, informed by changes at both behavioral and neuronal levels that are 126 associated with such learning" (p. 6). However, critics of educational neuroscience point 127 7 to the concerning trend for biological explanations having wide appeal among educators, 128 often leading to neuromyths or simplistic and misleading interpretations of neuroscience 129 findings, some of which are used to justify curricular reform [7–9]. Even though there is 130 considerable enthusiasm in characterizing the interaction between neuroscience and 131 education as a "two-way street," suggesting a bi-directional and reciprocal interaction 132 between the two communities of researchers and practitioners [10,11], Turner [7] argues 133 that a two-way interaction does not reflect the current reality of educational neuroscience; 134 instead neuroscience plays a more dominant role and the field is still mostly occupied with 135 translating neuroscience findings for educational practice. Turner also contends that these 136 efforts are not as fruitful as it is portrayed by proponents of educational neuroscience due 137 to methodological incompatibilities (e.g., use of unauthentic and non-contextual tasks, 138 focus on group of averages instead of individual differences), and the challenges 139 educationists face in understanding neuroimaging methods, which is necessary in making 140 sense of the reported findings. 141 One pitfall of the collaboration between education and neuroscience is the 142 possibility of biological level explanations taking over the already existing level of 143 sociocultural, phenomenological, and cognitive explanations. In its journey from the 1950s 144 cognitivist era to the 21st century, educational research has moved from more reductionist, 145 post-positivist theories to post-structuralist, situated, and constructivist frameworks. While 146 doing so, educational research has developed a sensitivity towards the contextual and 147 situated nature of learning, first-person experiences (phenomenology) of the learners, and 148 individual differences in learning approaches and predispositions to learning. One of the 149 concerns with the introduction of a vast new knowledge base provided by neuroscience is 150 8 the potential of narrowing down the levels of explanations in educational theory by over-151 emphasizing the biological aspects of learning [12], which sometimes stands counter to 152 more socio-cultural approaches. The long time tensions between contextual vs. 153 decontextualized, qualitative vs. quantitative, and ungeneralizable vs. generalizable in 154 educational research [13] are re-instantiated with educational neuroscience. Part of the 155 educational research and practice community sees the introduction of neuroscience in 156 education as an invasion of biological reductionism. Thus, it is necessary to theorize about 157 how educational neuroscience will function as a multi-level enterprise; one that does not 158 only retain the levels of explanation that are deployed in neuroscience, but also finds ways 159 of incorporating the levels of explanation that is established in education. Apart from 160 theoretical differences and differences in philosophical assumptions about the nature of 161 learning in different traditions, there is also a methodological divide between neuroscience 162 and education. Educational neuroscience, being the synthesis of these two fields, needs to 163 find ways of developing theoretical frameworks that can accommodate these different 164 research methodologies. 165 On one hand, neuroscience research, apart from neuropsychological case studies, 166 seeks to construct generalizable knowledge on mechanisms of learning, cognition, and 167 affect by way of using randomized trials from random samples. On the other, educational 168 research mostly targets studying learning in context and developing better educational 169 systems. In addition to explicating generalizable principles and heuristics, this requires an 170 emphasis on understanding individual differences, the role of the environment, and the 171 wider socio-cultural and political contexts in which learning takes place. 172 Here we first explicate the need for a theoretical framework to allow linking 173 9 different levels of explanation that can be considered under educational neuroscience. We 174 present a multi-level theoretical and methodological framework for educational 175 neuroscience. The framework incorporates levels of explanation and methodologies both 176 from education and brain sciences. The purpose is to contribute to discussions on the major 177 goals of educational neuroscience as a field, discuss which approaches can provide the 178 ground for a fruitful transdisciplinary fusion of ideas and methods from relevant fields, and 179 propose a theoretical scaffold that can amalgamate the multiple levels of inquiry. To 180 exemplify how an educational neuroscience study that spans across multiple levels would 181 look like, we present a research program on moral psychology and education, involving 182 multiple studies spanning across the different levels of analysis presented. 183 Educational neuroscience is often characterized as a bridge between neuroscience 184 and education [14]. This metaphor implies that educational neuroscience is a space where 185 researchers and practitioners from two fields interact, but not a field with its own vision, 186 community of researchers, big questions, theoretical frameworks, and methodologies. 187 Alternatively, educational neuroscience can be characterized as a new field that fills the gap 188 between brain sciences and education [15]. This metaphor implies a burgeoning, 189 transdisciplinary field, in close contact with other relevant fields, but with its own big 190 questions, theories, methodologies and community of researchers. In its current state, the 191 bridge metaphor is a better characterization of educational neuroscience. However, the fast-192 paced progression of the field poses a future vision that better matches the "filling the gap" 193 metaphor. However, before this can happen, big questions for the field, theoretical 194 paradigms, and methodologies need to emerge. 195 There are two main characteristics of educational neuroscience that distinguish it 196 10 from other fields within brain science. First, the purpose of educational neuroscience is not 197 only to understand the brain mechanisms that underlie learning and cognition, but also to 198 study how learning happens in authentic contexts and to design learning environments and 199 programs based on what we know about learning. This requires incorporation of research 200 paradigms from different fields of education and brain sciences. 201 Secondly, even though the name "educational neuroscience" implies an emphasis 202 on neural-level investigations, educational neuroscience should be characterized as a 203 transdisciplinary field that incorporates multiple methodologies and levels of explanation 204 from both educational and brain science research. The main goal should not be to push for 205 neural level explanations or neuroscience methodologies as alternatives to established 206 paradigms in education. Instead, the goal is to explore how existing paradigms of 207 educational research can be complemented with paradigms in brain sciences to provide 208 more comprehensive, multi-level explanations for how learning occurs. These diverse levels 209 of explanation, i.e., socio-cultural, first-person, behavioral, cognitive, evolutionary, neural, 210 physiological, and genetic (Fig. 1), are grounded in different research traditions, some of 211 them in education, others in cognitive and brain sciences. Educational neuroscience faces 212 the challenge of theoretically connecting these levels to provide coherent multi-level 213 explanations for learning and inform educational practice and policy. One difficulty here is 214 the lack of a shared lingua across people from different fields and paradigms. There is a need for 215 a theoretical framework that is operationalized across all these levels that can act as the basis that 216 can bring together these levels. 217 Multiple Levels & Diverse Methodologies in Educational Neuroscience 218 After Marr's influential work on distinct levels of analysis for information 219 11 processing systems [16], it became common to approach cognition as a complex system 220 that has multiples levels of organization [17]. Marr introduced three levels, computational, 221 algorithmic, and implementation. The computational level describes the processes and 222 operations conducted by the system, and sub-tasks involved in each. However, it does not 223 describe how the system does these operations. The computational level is about what the 224 system does, but not about how it does it. The algorithmic level includes formal 225 representations for the processes at the computational level. This level explicates how the 226 system performs the operations described in the computational level. The implementation 227 (or physical) level involves the physical mechanism where the computation is performed, 228 whether it is biological, silicon-based, or any other form of hardware. 229 Given that approaching cognition as a computational phenomenon became 230 ubiquitous starting with the cognitive revolution in the 1950s, Marr's levels of analysis for 231 information processing systems in general, highly impacted our approach to cognition. 232 However, the human cognitive system hardly presents an ideal match for the levels 233 described in Marr's work. Marr proposed that these three levels can be analyzed 234 independently; that we don't need to understand algorithms to study computations, and 235 likewise we don't need to understand the implementation level to make sense of 236 algorithms. While the argument for independence neatly applies to computational systems 237 (i.e., the same algorithm can run on many different forms of hardware), its application to 238 human cognition and neuroscience is problematic. Churchland and Sejnowski [18] argued 239 that "the independence that Marr emphasized pertained only to the formal properties of 240 algorithms, not to how they might be discovered" (pg. 742). There is no distinct, 241 independent, and inherent algorithmic level in human cognition. The cognitive models we 242 12 develop are mathematical formalisms describing the working principles of a system. The 243 development of these models relies on studying the implementation (physical) level; 244 biological and neural systems. Churchland and Sejnowski [18] proposed a model for 245 structural levels of organization in the nervous system (from micro to macro scale), which 246 involves molecules, synapses, neurons, networks, maps, systems, and the central nervous 247 system. They argued that "it is difficult if not impossible to theorize effectively on these 248 matters [related to nature of cognition] in the absence of neurobiological constraints." (pg. 249 744) and that understanding cognition requires connecting these interrelated, non-250 independent levels. 251 Educational neuroscience, like cognitive neuroscience, seeks to understand how 252 biological mechanisms support cognition. In addition, educational neuroscience focuses on 253 how we should design learning environments based on what we know about human 254 cognition. We argue that approaching cognition as a complex system that should be studied 255 in distinct but interrelated levels is applicable to educational neuroscience as well. 256 However, given the applied and contextual nature of educational neuroscience, we propose 257 alternative levels of analysis that captures both biological and socio-cultural aspects of 258 educational neuroscience. Filling the gap between education and brain sciences, 259 educational neuroscience concerns levels of explanation and inquiry from both domains. 260 In Fig. 1, a characterization of these levels – from socio-cultural to genetic – is presented. 261 Each level of explanation feeds from a different set of fields. For example, socio-cultural 262 theories of learning abound in education, whereas neural and cognitive-level explanations 263 are inherent to cognitive neuroscience. Here we present a short description of each level, 264 proposed as part of the multi-level framework. 265 13 Socio-cultural level 266 At the sociocultural level, learning is defined as a situated activity taking place in a 267 socio-cultural context [19]. At this level, research on learning is conducted using design-268 based research [20], and a wide range of other qualitative methodologies. According to 269 situated theories, learning occurs as a result of situated activity in authentic contexts. This 270 is the most ecologically valid level of inquiry. 271 First-person level 272 The inquiries at this level concern the direct experience of learners, reported by the 273 learners themselves. It is closely related to the phenomenological tradition (e.g., [21]). This 274 is a level commonly ignored by psychological and brain sciences, unlike education, where 275 the learners' first-person experience is one of the main foci of study. Interviews, think-276 aloud activities, journals are some of the commonly used methods to study first-person 277 experience. There are also some non-mainstream approaches in brain sciences that explore 278 how first-person experience can guide neural-level investigations (e.g., 279 neurophenomenology [22, 23]). 280 Behavioral level 281 Behavioral studies focus on measuring learning and studying cognitive processes 282 through observable behavioral indicators (e.g., reaction time, accuracy). There is an 283 established tradition of behavioral science in psychology. Cognitive models are often 284 assessed based on their ability to predict and model human behavioral performance. 285 Behavioral data also accompanies and guides analysis of neuroimaging data in cognitive 286 neuroscience studies. 287 Cognitive level 288 14 Cognitive level involves study of mental processes (e.g., memory, attention, 289 perception). An important focus at this level is developing mathematical / computational 290 models of cognition and learning. Based on an information processing approach [24], 291 cognition is characterized as processing inputs (perception) to produce outputs (action), 292 instead of simply responding to stimuli (behaviorism). Cognitivism distinguishes between 293 perception and action, as well as emotion and cognition. The cognitivist paradigm is strong 294 in psychology and most cognitive neuroscience research target unfolding the neural 295 correlates of the processes at the cognitive level. 296 Neural and Physiological level 297 Perhaps, neural level explanations are the ones most emphasized in discussions 298 about educational neuroscience. With fast-paced developments in neuroimaging 299 technologies since the 1990s, neural level investigations are pioneering psychological and 300 brain sciences [25]. A wide range of methodologies is available to researchers (e.g., fMRI, 301 Electroencephalography (EEG) / Event-Related Potentials (ERP), 302 Magnetoencephalography (MEG), and functional near-infrared spectroscopy (fNIRS)). 303 One shortcoming is the lack of ecological validity of most studies conducted at the neural 304 level. Because there are a wide range of constraints limiting the tasks participants can 305 engage with, cognitive neuroscience investigations often can't use authentic tasks or take 306 place in authentic environments. This is currently a major challenge for educational 307 neuroscience. However there is a growing body of literature reporting results and new 308 methods that aim at conducting ecologically valid neural-level investigations ( see [26, 309 27]). 310 The physiological level refers to biological processes that are not considered a 311 15 direct part of the central nervous system. These include measures like heart rate, cortisol 312 level, and, electrodermal response (galvanic skin response). These measures are good 313 indirect measures of the mental and emotional states of the participants in certain task 314 conditions. They are often used in psychology and, especially in affective and social 315 neuroscience studies. Physiological measures are promising in studying student motivation 316 and affect during learning in authentic contexts. 317 Evolutionary level 318 Evolutionary explanations for human cognitive abilities often help make 319 connections among different cognitive faculties that would not be obvious otherwise. 320 Studies at this level either concern research on anthropological evidence on how human 321 cognitive abilities evolved or comparative studies with non-human animals. Evolutionary 322 psychology is an important subfield of psychology and comparative neuroscience studies 323 with non-human animals, particularly primates, support inferences about the evolution of 324 human brain and cognition. 325 Genetic level 326 Genetic level concerns how genetic markers interact with learning abilities and 327 performance. Research at this level mainly focuses on understanding cognitive and 328 behavioral disorders, how genetic dispositions affect learning and how we can develop 329 preventative or compensatory interventions [28]. 330 331 16 332 Figure 1. Levels of analysis for educational neuroscience. 333 Challenges 334 Authentic tasks 335 Most of the tasks students are engaged in in the classroom are highly complex 336 compared to the tasks traditionally used in experimental research. This makes traditional 337 experimental paradigms unsuitable for studying authentic learning processes in the 338 classroom. Experimental research requires averaging of data (both behavioral and neural) 339 collected across many trials. Experimental research also controls for the sequencing of 340 trials from different conditions to control for priming effects. Both averaging of many trials 341 of data across conditions and controlling for priming effects are difficult to do when 342 studying an authentic task (both in the lab and in the classroom). In authentic tasks the 343 sequencing of events can be a dependent factor. The learners might follow different 344 trajectories during a task. These trajectories might be informed by individual differences 345 and can be a valuable source of data. Nevertheless, the more complex and uncontrollable 346 17 nature of authentic tasks makes averaging and controlling for confounds difficult. 347 Authentic contexts 348 The major shortcoming of experimental lab studies for education is the lack of 349 ecological validity. Learning takes place in dynamic, unpredictable and complex 350 environments, such as the classroom. One aspect of this complexity is the rich social 351 interactions taking place. A second one is related to physical situatedness; diverse forms of 352 physical interactions taking place that wouldn't be possible in the lab environment. 353 Authentic contexts are not conducive to experimental research both because random 354 sampling is usually not an option (e.g., in school contexts), and neuroimaging and 355 electrophysiological methods are hard to use in authentic contexts due to high-level noise 356 induced by the dynamic environment, in addition to other practical contexts. However, 357 there have been efforts in overcoming these difficulties, where, for example, EEG [29] and 358 fNIRS [30] studies were conducted in not strictly controlled classroom contexts. 359 Relating levels 360 Relating the previously discussed levels of inquiry is a challenge. Each level comes 361 with a baggage of theoretical perspectives, research methodologies and "academic silos" 362 separating the fields that each level is grounded in. There is need for a theoretical scaffold 363 that can connect these levels. This theoretical scaffold should be able to accommodate 364 explanations on how learning takes place across each level and integrate them to provide a 365 coherent, multi-level explanation for learning and cognition. Because the levels of inquiry 366 presented originate from different fields, there are also a wide range of theoretical 367 perspectives presented. For example, the cognitive level is dominated by cognitivist 368 theories, while the first-person level is closer to phenomenological traditions. As Marr 369 18 famously observed, "Trying to understand perception by studying only neurons is like 370 trying to understand bird flight by studying only feathers: It just cannot be done" [16]. The 371 same is true for understanding bird flight through pure observational and behavioral data. 372 In the same vein, learning in authentic contexts can be fully understood only through a 373 combination of methodologies and perspectives. 374 Research Design 375 Currently research that targets combining neuroscience and education approaches 376 generally is more biased towards using neuroscience research methodologies to answer 377 some of the previously unanswered questions in education. For example imaging studies 378 on dyslexia have provided new insights on the neural mechanisms that underlie dyslexia, 379 which then informed learning interventions that help address early phonological processing 380 impairments [31]. However, implications of cognitive neuroscience studies informing 381 educational design and practice does not fully exemplify the emergence of a 382 transdisciplinary research field, connecting the aforementioned levels. Here we review 383 various research design approaches that incorporate perspectives, paradigms and research 384 methodology from education and neuroscience. These methodologies represent various 385 degrees of integration between the two fields, some of them tilting towards neuroscience, 386 others towards education, and some representing a further form of integration. The 387 methodological approaches listed below are not mutually exclusive and most studies 388 employ more than one of these approaches. 389 Types of Research Design 390 Pre-test, intervention, post-test. This form of design allows for using authentic 391 tasks in the intervention stage with only behavioral data collection, and using more 392 19 traditional neuroimaging methods during the pre/post-test stages. The data analysis focuses 393 on changes from the pre-test to post-test period as a result of the intervention. The 394 intervention can be an authentic task in the lab or a classroom activity. 395 Classroom studies. Classroom studies involve collection of different forms of data 396 using methodologies typically used in the lab. These can include, for example, EEG, eye-397 tracking, and interaction-logging. These forms of studies involve both authentic tasks and 398 authentic contexts. Multiple studies have used EEG and fNIRS during classroom sessions 399 [e.g., 30][29]. Difficulties with marking events with high level of temporal accuracy, 400 artifacts and noise due to a wide range of concurrent modes of processing and bodily 401 movement, and the impossibility of controlling the stimuli and sequencing of events in the 402 complex classroom environment are some challenges. 403 Lab studies with authentic tasks. An authentic task is characterized by natural 404 ways of interaction, where the sequencing of events is not pre-determined and one where 405 the interactions afford a continuous experience, not interrupted by constraints typical to 406 classical experimental designs (e.g., inter-trial intervals, short task trials targeting a single 407 form cognitive processing). In this type of research design the primary goal is to overcome 408 the lack of ecological validity in more traditional designs by using authentic tasks. 409 Given the constraints inherent to the neuroimaging methods [32,33] neuroimaging 410 studies often do not use authentic tasks. One exception to this is neuroimaging research on 411 video games [26] and methodological heuristics acquired from this body of research can 412 be implemented in other research using authentic tasks. Previous neuroimaging research 413 on video games has explored a wide range of phenomena including cognitive workload / 414 mental effort, engagement / arousal, attention, spatial processing, emotion and motivation, 415 20 as well as agency and perspective-taking [26,34]. 416 Individual differences 417 Higher interest in individual differences has previously been listed as one of the 418 qualities that distinguishes educational research from brain and cognitive sciences research 419 [35]. For educational studies, understanding how individual differences affect learning 420 experience and performance is of primary importance. In brain and cognitive sciences, the 421 primary goal is usually to explore large patterns that characterize a sample, and individual 422 differences, when investigated, are usually of secondary importance. 423 In an ideal world we would be able to conduct both ecologically valid and 424 reproducible studies and develop learning theories encompassing all of the levels of 425 analysis. In a less ideal world, our investigations and theories incorporate at least a large 426 subset of these levels. However, most research explicitly focus on how learning occurs at 427 one given level. One reason for this is the methodological difficulty of collecting and 428 analyzing data at each level to develop a theory that relates all these levels. For example, 429 ERP research requires collecting many trials of data for the same condition to reliably study 430 the effect of a manipulation on a specific component [36]. In addition, EEG/ERP data 431 collection requires subjects to be relatively steady, and even limit the most natural actions 432 like eye-blinking, or head movements. These constraints make it hard to design authentic 433 tasks, which would improve ecological validity. In addition, the lab environment is 434 artificial and does not provide an authentic socio-cultural context. As mentioned before, 435 there are attempts to overcome these challenges by using authentic tasks and using mobile 436 neuroimaging devices to collect data in authentic environments, like classrooms. [37–39]. 437 There are also some efforts in using participants' reported first-person experience as a 438 21 guide, while analyzing behavioral and neural data [23,40]. These are promising efforts that 439 are yet to mature and perhaps will become mainstream research methodologies in the 440 future. 441 Both, the authenticity of the socio-cultural context as well as learners' first-person 442 experiences, are typically highly prioritized in educational research. In brain sciences, 443 notions like reproducibility of empirical investigations, reliability and validity, and power 444 of statistical results are important. These priorities reflect different epistemological 445 assumptions and methodological constraints. Educational neuroscience is in need of 446 finding a meeting ground that can accommodate these differences, even when some 447 compromises are made. In the current state of things educational neuroscience sometimes 448 acts as a platform, where brain scientists share what they know about the brain and 449 cognition with educators and discuss implications. This was previously called the "one-450 way model". The desirable mode of interaction is one where there is a two-way 451 communication [7,11]. The benefits of a multi-level approach extend beyond the scientific 452 merits of investigating a phenomenon. It can also make findings about learning and 453 cognition more accessible to application-based fields and stakeholders without 454 compromising the science behind it. 455 The multi-level perspective empowers educators and acknowledges the fact that 456 educational neuroscience is not a colonization of the educational landscape by knowledge 457 and methodologies from neuroscience and other mediating disciplines, but rather various 458 fields coming together to yield to the emergence of a new field, situated in between, where 459 perspectives, methodologies and levels of explanation from each originating field is valued 460 and used. To facilitate our understanding on how the multi-level aspects of educational 461 22 neuroscience can contribute to the improvement in education in the reality, we review 462 previous studies that have attempted to connect the different levels and methodologies. In 463 particular, we focus on a case in the field of moral education as a concrete example. The 464 reviewed studies include previous meta-analyses and fMRI studies related to moral 465 functioning, intervention studies inspired by the findings from the aforementioned 466 neuroimaging studies, and computer simulations to model policy-level activities based on 467 small-scale findings. We review these studies in order to exemplify how the multi-level 468 approach can be implemented in educational neuroscience. 469 Utilizing Neuroscientific Methods in Educational Contexts 470 In this section, we reviewed how the proposed conceptual framework for 471 educational neuroscience can be implemented with a concrete example in moral education. 472 We decided to delve into the case of moral education, because the application of 473 neuroscientific methods would be particularly beneficial for moral education among 474 various fields in education. Because studies in moral psychology and moral education have 475 focused on one of the most philosophically and conceptually sophisticated nature of human 476 psychology, that is, morality, it would be significantly more susceptible to social 477 desirability bias compared to other domains of human psychology. For instance, people 478 might pretend to become a morally better person when they are participating in survey or 479 observation studies examining moral development. As a result, moral psychologists and 480 educators have tried to develop more sophisticated surveys and tests to minimize the 481 possibility of such social desirability bias [41]. Given this, neuroscientific methods can 482 potentially contribute to the expansion of our knowledge regarding how human morality is 483 functioning with biological evidence by providing us more directly research methods that 484 23 are less susceptible to the social desirability bias [42–44]. In order to see how 485 neuroscientific studies can contribute to moral education in practice, as the first step in this 486 process, we consider two specific methods, i.e., meta-analysis and fMRI methods, which 487 can illuminate psychological processes involved in moral functioning, as components in 488 the research program of educational neuroscience. 489 First, a meta-analysis of previous neuroimaging studies can identify which 490 psychological processes are commonly involved in order to target psychological 491 functioning that will be influenced by educational interventions. Clearly identifying such 492 psychological processes and mechanisms is essential for designing effective interventions 493 [45]. A meta-analysis of neuroimaging studies is a feasible option for identifying such 494 psychological processes while also providing us with a direct and statistically-valid way to 495 examine internal neural-level psychological processes. A meta-analysis can also address 496 several issues associated with traditional neuroimaging methods, such as the lack of 497 statistical power originating from relatively small sample sizes, idiosyncrasies in 498 experimental designs [46–48] and possibility of erroneous reverse inference in 499 interpretation [49]. In case of the present example of moral education, a meta-analysis of 500 previously conducted neuroimaging studies can identify common activation foci of interest 501 in moral functioning. For the meta-analysis of previous neuroimaging studies, the 502 activation likelihood estimation (ALE) implemented by Ginger ALE is one of the most 503 valid and feasible analysis methods [50,51]. While systematic or qualitative review 504 methods are also possible in a meta-analysis, ALE is a quantitative method that provides 505 us with empirical evidence pertaining to psychological processes of interest with statistical 506 validity. 507 24 Previous meta-analyses of neuroimaging studies focusing on moral functioning 508 using Ginger ALE have demonstrated common activation foci associated with moral 509 psychological processes [52–55]. However, the research questions and hypotheses of these 510 meta-analyses were not based on theories of moral development and moral education, so 511 their developmental, psychological, and educational implications for educational 512 neuroscientific studies are limited. A recent meta-analysis, however, designed its analytic 513 framework and hypotheses [56] based on the Neo-Kohlbergian perspective, a mainstream 514 moral psychological theory that has been applied in moral educational programs in diverse 515 domains, such as professional ethics programs [57,58]. This study reported that brain 516 regions associated with self-related processes, particularly autobiographical self and self-517 evaluation – the default mode network (DMN) and cortical midline structures (CMS) 518 including the medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) – 519 were commonly activated across diverse morality-related task conditions (see Figure 2). 520 Given these results, selfhood might be commonly engaged in moral functioning. fMRI and 521 intervention experiments can be guided by these findings; they may focus on self-related 522 psychological processes while setting their research questions and experimental designs. 523 25 524 Figure 2. Common activation foci of moral functioning, including the MPFC and 525 PCC, found by the meta-analysis. 526 Second, we can conduct an fMRI experiment that is designed to examine the neural 527 correlates of psychological processes of interest, e.g., moral motivation, based on the 528 findings from meta-analyses. Such an fMRI experiment can show us more specified neural-529 level processes and mechanisms of interest by employing customized experimental 530 designs, while meta-analyses are only able to show us the neural correlates of such 531 processes and mechanisms in general. In the case of moral psychology, previous fMRI 532 experiments have demonstrated the neural correlates of moral functioning by employing 533 diverse experimental paradigms [59,60]. These studies have shown that various brain 534 regions associated with cognitive [61,62], affective [63–65], motivational [66,67], and self-535 related processes [60,68] were activated in moral task conditions. 536 Particularly informative is a recent fMRI experiment with a set of hypotheses based 537 on findings from the previous meta-analysis that showed significance of self-related 538 processes in moral functioning [69]. Although several previous fMRI studies have 539 26 demonstrated the activation of self-related regions [60,68], they were mainly interested in 540 identifying activation foci themselves, but not how self-related psychological processes 541 moderated moral functioning at the neural level. Instead, the recently conducted fMRI 542 experiment investigated how brain regions associated with selfhood, the DMN and CMN, 543 moderated activity in other brain regions associated with moral emotion and motivation, 544 such as the insula, while solving moral problems by utilizing the psychophysiological 545 interaction analysis [70] and Granger causality analysis methods [71]. Figure 3 546 demonstrates the results of these analyses. As hypothesized, the analysis indicated that 547 neural activity in regions associated with selfhood in the DMN and CMS, particularly the 548 MPFC and PCC, significantly moderated activity in moral emotion and moral motivation-549 related regions, as well as the insula which has been known to assist brain regions in the 550 generation of appropriate behavioral responses to salient stimuli [72]. Consequently, this 551 fMRI experiment was able to support hypotheses based upon previously published 552 neuroimaging studies of morality and their meta-analyses, and identify psychological 553 processes that will be targeted by intervention experiments, i.e., self-related psychological 554 processes. 555 27 556 Figure 3. Brain regions moderated by the MPFC and PCC, including the insula, in 557 moral task conditions. Left: regions moderated by the MPFC. Right: regions moderated by 558 the PCC. 559 Psychological Intervention Methods Founded by Neuroscientific Studies 560 Before applying findings from the neuroimaging studies to moral education in 561 practice, we have to test whether the prototype of educational programs targeting the 562 psychological processes are identified by neuroimaging studies. It can be tested by 563 conducting relatively small-scale psychological intervention experiments. Such 564 intervention experiments can be an interface between neuroscience and education in 565 practice by providing evidence for a certain intervention that is designed based on findings 566 from neuroscientific studies. As a concrete example in the field of moral education, 567 educational interventions utilizing the stories of moral exemplars are considered hereafter. 568 Interventions based on psychology, particularly social and educational psychology, 569 have improved students' academic achievement and social adjustment in diverse 570 educational settings [73–77]. Thus, such psychological intervention methods can provide 571 28 useful insights about how to design more effective moral education programs. Basically, 572 psychological interventions are designed to tweak psychological processes that are 573 fundamentally associated with a targeted developmental outcome [45]. Hence, it would be 574 necessary to design educational interventions based on findings from psychological 575 experiments successfully identifying which psychological processes are correlated with 576 educational and development outcomes that will be targeted by the interventions. 577 In traditional moral education, the stories of moral exemplars have been widely 578 utilized in educational settings. Moral educators and parents have presented the stories of 579 moral exemplars, who did morally great behaviors, in order to promote children's moral 580 motivation by encouraging them to emulate the presented moral behaviors [78,79]. The 581 presentation of moral exemplars can promote motivation to engage in moral behavior 582 through vicarious social learning [80], moral elevation [81,82], and upward social 583 comparison [83,84]. However, the mere presentation of moral exemplars can backfire 584 when social and moral psychological mechanisms are not carefully considered. 585 Particularly, when extreme moral exemplars, such as historic moral figures (e.g., Mother 586 Teresa) that have usually been introduced in moral education textbooks, are presented, 587 students might feel negative emotional responses, such as extreme envy and resentment, 588 and tend not to emulate presented moral behaviors [85,86]. During the presentation of such 589 extreme exemplars, students might think that the presented moral behaviors are not 590 emulatable given their ability, and might activate the self-defense mechanism protecting 591 their selfhood by isolating them from moral values to deal with the negative emotional 592 responses [85,87,88]. Thus, it is necessary to carefully examine psychological processes 593 associated with interventions in order to make the interventions more effective while 594 29 minimizing possible negative outcomes. 595 As a concrete example, we reviewed an intervention study consisting of two moral 596 educational intervention experiments. These two psychological intervention experiments 597 used the stories of moral exemplars and tested which type of exemplary stories better 598 promoted motivation to engage in moral activity [89]. In order to determine which 599 psychological processes were targeted and tweaked during intervention experiments, 600 findings from aforementioned neuroimaging studies, a meta-analysis and fMRI study, were 601 reviewed. These neuroimaging studies have demonstrated that brain regions associated 602 with self-related psychological processes, particularly autobiographical memory 603 processing, were commonly involved in moral functioning in general [56], and moderated 604 moral emotion and motivation [69]. Given such findings from the previous neuroimaging 605 experiments, intervention experiments manipulated the perceived distance between 606 presented moral exemplars and participants' self-concept. 607 Two intervention experiments, one lab experiment and one classroom experiment, 608 that were founded by the neuroimaging studies were conducted. The experiments presented 609 two different types of exemplary stories: attainable and unattainable moral stories. Given 610 the significant positive interaction between self-related and moral functioning-related brain 611 regions, as the presented moral stories are perceived to be closely associated with 612 participants' self-concept, the motivating effect of the stories would become greater [90]; 613 attainable stories (e.g., stories of peer exemplars) would more strongly promote motivation 614 compared to unattainable stories (e.g., stories of historic figures), which seem distant from 615 participants. In fact, previous social psychological intervention experiments focusing on 616 non-moral motivation also reported that attainable stories better promoted motivation while 617 30 unattainable stories might backfire [91,92]. 618 A lab experiment was conducted to examine the motivating effects of different 619 types of moral stories among college students; it used engagement in voluntary service 620 activities as a proxy for moral motivation [89]. A total of 54 college students participated 621 in this experiment. Their preand post-test voluntary service engagement were measured. 622 The participants were randomly assigned to one of these three groups: attainable, 623 unattainable, and control groups. On the one hand, attainable group members were 624 presented with the stories of youth exemplars who participated in a reasonable amount of 625 service activities (≤ 2 hours per week). On the other hand, participants in the unattainable 626 group were presented with the exemplary stories of extreme service engagement (≥ 10 627 hours per week). The control group was presented with non-moral stories, such as general 628 sports news reports. After presenting attainable or unattainable moral stories to the 629 participants, their post-test voluntary service engagement was surveyed once again eight 630 week later to examine change in engagement. Findings demonstrated that participants 631 assigned to the attainable group showed significantly greater increase in the service 632 engagement compared to other groups (see Figure 4). 633 634 Figure 4. Changes in engagement rate in each condition in the lab experiment. Left: 635 engagement rate quantified in hours. Right: engagement rate quantified in percentage. 636 In addition, a classroom intervention experiment tested the same hypothesis among 637 31 107 8th graders [89]. This classroom-level experiment was performed to apply the lab-level 638 intervention to more realistic educational settings. Similar to the previous lab experiment, 639 the participants were assigned to one of these two groups: peer exemplar and historic figure 640 groups. On the one hand, the peer exemplar group was asked to present and discuss moral 641 virtues and behaviors done by peer exemplars, such as friends, teachers, and family 642 members, that deemed to be attainable. On the other hand, participants assigned to the 643 historic figure group were requested to talk about moral virtues and behaviors of historic 644 moral exemplars, such as Mother Teresa and Martin Luther King, that seemed to be 645 extraordinary and unattainable to them. Interventions were conducted for once a week for 646 an hour during eight weeks. Participants' service engagement was measured before the 647 beginning of the intervention period and twelve weeks after the pre-test survey. 648 Participants' answers were quantified on a one to five scale ("1. None"- "5. More than 649 once per week"). Survey results demonstrated that the positive change in service 650 engagement in the peer exemplar group was significantly greater compared to the historic 651 figure group (see Figure 5). 652 653 Figure 5. Changes in service engagement in each condition in the classroom 654 32 experiment. 655 The findings from these two experiments supported the hypothesis that was 656 founded by the previous neuroimaging studies. Attainable exemplars better promoted 657 moral motivation compared to unattainable exemplars. These findings are coherent with 658 the neuroimaging experiments that showed the moderating effect of self-related 659 psychological processes on moral emotion and moral motivation. Consequently, we shall 660 conclude that our conceptual framework pertaining to how to utilize neuroscience in 661 educational practice has been supported by the presented example case, moral educational 662 interventions based on neuroimaging studies of moral functioning. 663 Applying Evolutionary Modeling and Computer Simulation to Inform 664 Educators and Policy Makers 665 Although we have demonstrated that it would be possible to design more effective 666 educational interventions based on findings from neuroimaging studies, how to apply such 667 educational interventions at the large scale, such as at the school or district level, is still 668 unclear. Because findings from the aforementioned intervention experiments, lab and 669 classroom experiments, might only be valid at a relatively small scale (lab or classroom 670 level), these findings cannot be generalized without any further investigations. Because 671 even a brief educational intervention might produce long-term developmental outcomes 672 among students [73,93], we should carefully consider how to properly predict long-term, 673 large-scale outcomes of interventions based on available evidence, such as, evidence from 674 relatively small-scale intervention experiments. However, due to the lack of time and 675 resource, it is difficult to conduct multiple long-term, large-scale experiments in real 676 educational settings to examine such outcomes in reality [94,95]. 677 33 Computer simulation methods can address this limitation by enabling researchers 678 and educators to perform these predictions accurately, and thereby, provide basic 679 information regarding how to scale-up designed interventions. Particularly, simulation 680 methods based on evolutionary modeling [96] and deep learning [97] might be feasible 681 methodologies to conduct such predictions. As a part of the conceptual framework of 682 educational neuroscience, these methodologies could also be included because even though 683 they originated from parallel fields such as evolutionary biology, artificial intelligence, and 684 artificial neural network modeling, they can contribute to interfacing neuroscience, 685 education, and all other mediating disciplines in practice. 686 First, evolutionary modeling using the Evolutionary Causal Matrices (ECM) can 687 predict the future status of a certain system consisting of different types of individuals [98]. 688 The ECM predict the future status at t0+n from the status change between t0 and t0+1 with 689 iterative calculations; with n iterations, the predicted status at t0+n can be calculated [96]. 690 In the case of the moral educational intervention, we can set the t0 status as the pre-test 691 voluntary service engagement and t0+1 as the post-test engagement. By performing 692 iterative calculations, we can compare the effectiveness of interventions according to their 693 types and application frequencies. Due to the limitations of time and resource in 694 educational intervention research, the majority of simulations might be performed for 695 relatively short-term predictions; however, the theoretical framework of the simulation 696 method can be applied in relatively long-term longitudinal predictions as well. 697 For this simulation, findings from the aforementioned intervention experiments are 698 revisited. In order to predict developmental outcomes of the moral exemplar-applied 699 interventions, ECM were created using preand post-test service engagement data, and 700 34 iterative simulation processes were performed with the created ECM [99,100]. As 701 presented in Table 1, ECM for simulations were created by comparing the ratio of 702 participants who engaged in service activities at the preand post-test periods in each 703 experimental condition. They demonstrate the transitions between statuses (engaging vs. 704 not engaging) across two timepoints; for instance, participants were more likely to start or 705 continue to engage in service activities in the attainable condition compared to the 706 unattainable condition. Based on these ECM, long-term outcomes of the interventions were 707 simulated through iterative learning processes with different intervention types and 708 frequencies. As presented in Figure 6, the attainable exemplar-applied intervention can 709 better promote engagement. Its effect size declines as the frequency of application gets 710 lower (see Figure 7). Thus, the intervention should be performed at least once per every 711 10.5 months to produce a large effect. We remark that the ECM-based prediction is useful 712 at predicting future outcome sequences based on a simple stochastic model with a relatively 713 small number of estimated parameters. 714 Engaging (t) Not engaging (t) Attainable condition Engaging (t+1) .90 (ECM [1,1,1]) .44 (ECM [1,1,2]) Not engaging (t+1) .10 (ECM [1,2,1]) .56 (ECM [1,2,2]) Unattainable condition Engaging (t+1) .64 (ECM [2,1,1]) .12 (ECM [2,1,2]) Not engaging (t+1) .36 (ECM [2,2,1]) .88 (ECM [2,2,2]) Without any intervention (control condition) Engaging (t+1) .71 (ECM [3,1,1]) .28 (ECM [3,1,2]) Not engaging (t+1) .29 (ECM [3,2,1]) .72 (ECM [3,2,2]) Table 1. Created ECM for different types of interventions 715 35 716 Figure 6. Change in the mean ratio of engagement with different intervention frequencies 717 across different conditions.718 719 Figure 7. The estimate effect size of the attainable exemplar-applied intervention per 720 different intervention frequency. The red line indicates a threshold for a large effect size 721 (partial h2 = .14, see [101]). 722 36 When a large enough amount of training data is on hand, one can apply machine 723 learning algorithms in order to develop a data-driven prediction model. Furthermore, we 724 might have to employ the machine learning method when multiple covariates, such as 725 demographical variables, are required to be considered, because the ECM only allow us to 726 predict outcomes solely based on one independent variable. Among various machine 727 learning algorithms, artificial neural networks with many layers, or simply "deep learning", 728 is currently the most popular due to its outstanding performance in many classical 729 applications such as image classification, object recognition, speech recognition, etc. The 730 deep architecture of deep learning corresponds to a hierarchy of features, factors, or 731 concepts, where higher-level concepts are defined from lower-level ones, and the same 732 lower-level concepts can help define many higher-level concepts (Deng & Yu, 2014, p. 733 200). 734 In our example, the deep learning method was applied to the moral education 735 intervention data. Using Google's TensorFlow [102], a two-layered convolutional network, 736 for predicting intervention outcomes, was trained (see Figure 8) [103]. The prediction 737 network takes pre-test variables (i.e., service engagement, gender, intervention type, 738 emotional responses to intervention activity, intention to engage in service) as inputs, and 739 predicts the post-test outcome (i.e., whether or not to engage in service at the post-test). An 740 iterative training algorithm (called the stochastic gradient method) was used during 741 simulation. Findings reported that the prediction performance was maximized after about 742 4,000 iterations of the training algorithm. 1 The best prediction model with the 743 1 Note that the prediction performance decreases after a certain number of 37 convolutional network clearly outperformed simple logistic regression: while the accuracy 744 of logistic regression was 75.47%, that of the best convolutional network reached 85.16% 745 (see Table 2). Given these results, the deep learning method can enable researchers, 746 educators, and policy makers to simulate and prototype large-scale intervention 747 experiments or applications, particularly when multiple independent variables and 748 covariates should be considered in a prediction. 749 750 Figure 8. Illustrative example of a deep learning neural network 751 Iteration # TensorFlow simulation accuracy (%) Logistic regression accuracy (%) 500 79.82% 75.47% 1000 82.62% 2000 83.83% 4000 85.16% iterations. This is called overfitting, which happens when a prediction model starts capturing in the model noise of the data, losing predictability. 38 8000 70.30% Table 2. Accuracy of TensorFlow simulation across different iterative learning 752 conditions. Colored cells indicate the best accuracy outcome. 753 Aforementioned computational methodologies, the ECM and deep learning, might 754 be feasible and accurate ways to predict long-term, large-scale outcomes of educational 755 interventions based on relatively small-scale data, e.g., labor classroom-level intervention 756 experiment data. Findings from computer simulations might provide useful information 757 regarding how to employ developed interventions and establish educational policies and 758 procedures in diverse educational settings. Hence, these computational approaches can 759 constitute a fundamental part in the conceptual framework of educational neuroscience that 760 bridges the gap between neuroscience and education in practice. It is worth noting that 761 integrating the different levels of analysis for a solution is constrained by the existing 762 breadth of literature. Therefore, it may not be possible to incorporate evidence from all 763 levels of analysis as seen in this moral education example (Fig. 9). 764 765 39 Figure 9. Application of the different levels of analysis within educational neuroscience in 766 the context of the moral education example. 767 Conclusions 768 As an emerging transdisciplinary area of research, educational neuroscience is 769 facing challenges in formulating theoretical frameworks that can link and integrate 770 perspectives, findings, and research methods from neuroscience, education, and other 771 mediating disciplines. Here we first proposed a theoretical framework that integrated 772 levels of analysis from various fields including education and neuroscience; then we 773 discussed how educational neuroscience can examine learning and cognition across these 774 levels, and provide new insights that could not be possible without crossing or integrating 775 these levels. In the second part of the paper we presented a research program in moral 776 psychology and ethics education as a case study for how educational neuroscience 777 research can integrate findings and methods across multiple levels to address a set of 778 shared, core research questions. We argue that educational neuroscience differs from 779 cognitive neuroscience in that it concerns how learning takes places in authentic 780 educational contexts; in addition to understanding the mechanisms of learning, it also 781 strives to develop interventions and find evidence-based solutions to educational 782 problems. This requires development of research methodologies that can allow the study 783 of learning and cognition with authentic tasks and in authentic contexts. When 784 methodologies from various fields are integrated, this convergence can counter 785 challenges by operating quickly and generating frequent data points to inform large-scale 786 practice and policy decisions. 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