Development of a Psychophysiological Artificial Neural Network to Measure Science Literacy

comprehension and modeling through cognitive attributes of subject learning. This study found that individual visual characteristics were found to be the biggest determiner for mental rotation success which indicates that as visuals become more intricate, novice learners need more support in mentally visualizing the molecules. Since, explicit instruction in diagrammatic reasoning improved student's representational competence (Miller et al., 2016), by being to explain and identify the interpretable aspects of a visual in a memorable way, when faced with that representation or similar ones, the individual would be able to transfer that previous knowledge to the new visual information. Therefore, visual science literacy could be supported in the science classroom with regular, explicit instruction into the interpretation of the visuals as per the embedded science content. One way of doing so is by using the Identify and Interpret (I2) strategy developed by non-profit Biological Sciences Curriculum Study (BSCS) which emphasizes a three step approach to analyzing visual information where students identify discrete elements of the visual, interpret what those individuals may mean separately and then caption what the full visual means by connecting the interpretations of the individual aspects of the visual (BSCS, 2012). Conclusion Through the integration of disciplines of integrative learning sciences and the combining of traditional educational research with cognitive science, this study confirms research by Lamb, Annetta, Vallett, & Sadler (2014) on computational modeling with neurocognitive data and Mnguni et al. (2016) and Stieff (2018) on the assessment of science literacy. This research adds to the literature by using a tool of artificial intelligence, in the form of an ANN, to predict behavior based on psychophysiological measurements. Before now, there has been limited research on the quantitative analysis of visual science literacy. This addresses fundamental 89

questions regarding how psychophysiological measurement can help to describe and explain behavioral outcomes (i.e. reading and writing of science texts), the basic processes that underlie unique individual human abilities, and can be used in the future to develop digital immersive educative tools to support the processes required for scientific literacy. The primary purpose of this study was to develop an ANN as a computational model for Visual Science Literacy to model the complex dynamic systems associated with this construct so that pedagogy can be developed to support its development. A secondary purpose of this study is to examine the usage of mental rotation as a cognition-based test to assess visual science literacy in a science classroom. The computational cognitive model in the form of an ANN assists in obtaining information related to the science-based curriculum and offers additional data related to student learning. The model shows good data fit and approximates human learning to the completion of scientific visual literacy tasks that provide a way to connect biological, physiological, cognitive, and behavioral data. Although critical thinking can be taught as a separate skill, it seems better established when it is related to specific areas of knowledge. Given the complexity and limited (although significant) contribution of critical thinking to scientific activities, it is not reasonable to expect that an intervention approach as suggested would prove sufficient to improve that component. Analysis of ANN weightings suggesting a hierarchical relationship in the cognitive functions underlying this form of literacy and future cognitive attribute models can insert these in order to test this view of the data channel of cognition. By improving the population’s average visual science literacy, this research supports, that individuals would be able to more accurately interpret science visual texts which would be beneficial personally and to the society as a whole. 90

References Ajwani, D., Ali, S., Katrinis, K., Li, C. H., Park, A. J., Morrison, J. P., & Schenfeld, E. (2013). Generating synthetic task graphs for simulating stream computing systems. In Journal of Parallel and Distributed Computing (Vol. 73, pp. 1362–1374). https://doi.org/10.1016/j.jpdc.2013.06.002 Akçayır, M., & Akçayır, G. (2017). Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educational Research Review, 20, 1–11. https://doi.org/10.1016/j.edurev.2016.11.002 Allen, R. J., Baddeley, A. D., & Hitch, G. J. (2017). Executive and perceptual distraction in visual working memory. Journal of Experimental Psychology: Human Perception and Performance, 43(9), 1677–1693. https://doi.org/10.1037/xhp0000413 Andreassi, J. L. (2007). Psychophysiology: Human behavior & physiological response, 5th ed. Psychophysiology: Human Behavior & Physiological Response, 5th Ed. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers. Andrich, D. (1978). Rating formulation for ordered response category. Psychometrika, 43(4), 561–573. Arbib, Michael A.& Bonaiuto, James J. (2016) From Neuron to Cognition via Computational Neuroscience (Computational Neuroscience Series) . The MIT Press. Kindle Edition. Aslin, R. N., Shukla, M., & Emberson, L. L. (2015). Hemodynamic Correlates of Cognition in Human Infants. Annual Review of Psychology, 66(1), 349–379. https://doi.org/10.1146/annurev-psych-010213-115108 Baddeley, A. (2011). Working Memory: Theories, Models, and Controversies. Annu. Rev. 91

Psychol. 2012, 63, 1–29. https://doi.org/10.1146/annurev-psych-120710-100422 Baddeley, A. D. (2017). A concept and some misconceptions. Exploring Working Memory: Selected Works of Alan Baddeley. Barbey, A. K., Koenigs, M., & Grafman, J. (2013). Dorsolateral prefrontal contributions to human working memory. cortex, 49(5), 1195-1205. Beare, J. I. (1906). Greek theories of elementary cognition from Alcmaeon to Aristotle. Clarendon Press. Belland, B. R., Walker, A. E., & Kim, N. J. (2017). A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education. Review of Educational Research, 87(6), 1042–1081. https://doi.org/10.3102/0034654317723009 Betts, G. H. (1909). The distribution and functions of mental imagery (No. 26). Teachers College, Columbia University. Bimber, O., & Raskar, R. (2005). Spatial augmented reality: Merging real and virtual worlds. Spatial Augmented Reality: Merging Real and Virtual Worlds. https://doi.org/10.1201/b10624 Birondo, N. (2001). Aristotle on Illusory Perception. Ancient Philosophy, 21(1), 57–71. doi:10.5840/ancientphil20012113 Bliss, J. P., Tidwell, P. D., & Guest, M. A. (1997). The effectiveness of virtual reality for administering spatial navigation training to firefighters. Presence: Teleoperators & Virtual Environments, 6(1), 73-86. Bond, Trevor G.; Fox, Christine M. (2015): Applying the Rasch model: Fundamental measurement in the human sciences: Psychology Press. 92

Bradley, Kelly D.; Peabody, Michael R.; Akers, Kathryn S.; Knutson, Nikki (2015): Rating Scales in Survey Research: Using the Rasch model to illustrate the middle category measurement flaw. In Survey Practice 8 (2). Brewer, W. F., & Schommer-Aikins, M. (2006). Scientists are not deficient in mental imagery: Galton revised. Review of General Psychology, 10(2), 130-146. Briggs, J. (1996). The promise of virtual reality. The Futurist, 30(5), 13. BSCS (2012a). Identify and Interpret (I2) strategy [student edition]. https://media.bscs.org/icans/Icans_I2_SE.pdf. BSCS (2012b). Identify and Interpret (I2) strategy [teacher edition]. https://media.bscs.org/icans/Icans_I2_TE.pdf. Burggren, W. W., & Mueller, C. A. (2015). Developmental critical windows and sensitive periods as three-dimensional constructs in time and space. Physiological and Biochemical Zoology, 88(2), 91-102. Burke, K. A., Greenbowe, T. J., & Hand, B. M. (2006). Implementing the science writing heuristic in the chemistry laboratory. Journal of Chemical Education, 83(7), 1032. Cabeza, R., & Moscovitch, M. (2013). Memory systems, processing modes, and components: functional neuroimaging evidence. Perspectives on Psychological Science, 8(1), 49-55. Calvo, S. C. B. T.-S. and I. T. (2001). Writing Preserves Knowledge and Memory. In N. Schlager & J. I. B.-978-0-7876-3932-7 I. B.-978-0-7876-7724-4 Lauer (Eds.) (Vol. 1, pp. 378–382). Detroit, MI: Gale. Cameron, Oliver G. (2001). Visceral Sensory Neuroscience : Interoception, Oxford University Press, Incorporated, Coburn, C. E., Hill, H. C., & Spillane, J. P. (2016). Alignment and accountability in policy 93

design and implementation: The Common Core State Standards and implementation research. Educational Researcher, 45(4), 243-251. Connolly, P. (2007). Quantitative data analysis in education: A critical introduction using SPSS. Routledge. Cooper, L. A. (1975). Mental rotation of random two-dimensional shapes. Cognitive psychology, 7(1), 20-43. Cooper, L. A., & Mumaw, R. J. (1985). Spatial aptitude. Individual differences in cognition, 2, 67-94. Courtney, S. M., Ungerleider, L. G., Keil, K., & Haxby, J. V. (1996). Object and spatial visual working memory activate separate neural systems in human cortex. Cerebral cortex, 6(1), 39-49. Cripps, B. (2017). Psychometric Testing : Critical Perspectives. John Wiley & Sons, Incorporated. Dash, D., Ferrari, P., Malik, S., & Wang, J. (2019). Overt speech retrieval from neuromagnetic signals using wavelets and artificial neural networks. 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings, 489–493. https://doi.org/10.1109/GlobalSIP.2018.8646401 Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning (pp. 233-240). DeBoer, G. (1991). A history of ideas in science education. Teachers College Press. Demarest, E. J. (2010). A Learning-centered Framework for Education Reform: What Does it Mean for National Policy?. Teachers College Press. Dempsey, J. P., Harris, K. S., Shumway, S. T., Kimball, T. G., Herrera, J. C., Dsauza, C. M., & 94

Bradshaw, S. D. (2015). Functional near infrared spectroscopy as a potential biological assessment of addiction recovery: preliminary findings. The American Journal of Drug and Alcohol Abuse, 41(2), 119–126. https://doi.org/10.3109/00952990.2014.983273 Din, F. S., & Caleo, J. (2000). Playing Computer Games Versus Better Learning. Elkins, J. (2009). Visual literacy / edited by James Elkins. New York, New York ;: Routledge. Emilsson, E. K. (1988). Plotinus on sense-perception: a philosophical study. CUP Archive. Erduran, S., & Dagher, Z. R. (2014). Reconceptualizing nature of science for science education. In Reconceptualizing the nature of science for science education (pp. 1-18). Springer, Dordrecht. Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71. https://doi.org/10.1002/piq.21143 Farah, M. J., Hammond, K. M., Levine, D. N., & Calvanio, R. (1988). Visual and spatial mental imagery: Dissociable systems of representation. Cognitive psychology, 20(4), 439-462. Farooq, H., & Jain, A. (2017). A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review. International Journal of Sensors and Sensor Networks, 5(3), 43. Fayer, S., Lacey, A., & Watson, A. (2017). Spotlight on Statistics STEM Occupations: Past, Present, And Future. Retrieved from https://www.bls.gov/spotlight/2017/science- technology-engineering-and-mathematics-stem-occupations-past-present-and- future/pdf/science-technology-engineering-and-mathematics-stem-occupations-past- present-and-future.pdf Felten, P. (2008). Resource Review: Visual Literacy. Change, 40(6), 60–63. Retrieved from 95

http://www.jstor.org/stable/40178302 Finch, H., & Edwards, J. M. (2016). Rasch Model Parameter Estimation in the Presence of a Nonnormal Latent Trait Using a Nonparametric Bayesian Approach. Educational and Psychological Measurement, 76(4), 662–684. https://doi.org/10.1177/0013164415608418 Finke, R. A. (1989). Principles of mental imagery. The MIT Press. Finn, P. (2014). Critical thinking: Knowledge and skills for evidence-based practice. Language, speech, and hearing services in schools. Fisher, M. (2020, April 8). Why Coronavirus Conspiracy Theories Flourish. And Why It Matters. - The New York Times. The New York Times. https://www.nytimes.com/2020/04/08/world/europe/coronavirus-conspiracy-theories.html Flaitz, J. (2011). Assessment for learning: US perspectives. In Assessment reform in education (pp. 33-47). Springer, Dordrecht. Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2), 3-71. Galama, T., & Hosek, J. R. (2008). U.S. competitiveness in science and technology. RAND Corp. Retrieved from https://www.rand.org/pubs/research_briefs/RB9347/index1.html Galton, F. (1880). Statistics of mental imagery. Mind, 5(19), 301-318. Ganel, T., & Goodale, M. A. (2019). Still holding after all these years: An action-perception dissociation in patient DF.(Report). Neuropsychologia, 128, 249. https://doi.org/10.1016/j.neuropsychologia.2017.09.016 García, E., & Weiss, E. (2017). Education inequalities at the school starting gate Gaps, trends, and strategies to address them Report •. Retrieved from https://files.eric.ed.gov/fulltext/ED588751.pdf 96

Glasersfeld, E. (1992). A constructivist's view of learning and teaching. Research in physics learning: Theoretical issues and empirical studies, 29-39. Goldwater, M. B., & Schalk, L. (2016). Relational categories as a bridge between cognitive and educational research. Psychological Bulletin, 142(7), 729. Goodale, M. A. (2013). Separate visual systems for perception and action: a framework for understanding cortical visual impairment. Developmental Medicine & Child Neurology, 55(SUPPL.4), 9–12. https://doi.org/10.1111/dmcn.12299 Hall, B. K. (2012). Evolutionary developmental biology. Springer Science & Business Media. Hand, B., Lawrence, C., & Yore, L. D. (1999). A writing in science framework designed to enhance science literacy. International Journal of Science Education, 21(10), 1021–1035. https://doi.org/10.1080/095006999290165 Hand, B., Cavagnetto, A., Chen, Y. C., & Park, S. (2016). Moving Past Curricula and Strategies: Language and the Development of Adaptive Pedagogy for Immersive Learning Environments. Research in Science Education. https://doi.org/10.1007/s11165-015-9499-1 Hannus, M., & Hyönä, J. (1999). Utilization of illustrations during learning of science textbook passages among low-and high-ability children. Contemporary educational psychology, 24(2), 95-123. Hegarty, M., & Waller, D. A. (2005). Individual Differences in Spatial Abilities. In P. Shah & A. Miyake (Eds.), The Cambridge Handbook of Visuospatial Thinking (pp. 121–169). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511610448.005 Henson, R. N. A., Shallice, T., & Dolan, R. J. (1999). Right prefrontal cortex and episodic memory retrieval: a functional MRI test of the monitoring hypothesis. Brain, 122(7), 97

1367–1381. https://doi.org/10.1093/brain/122.7.1367 Hitch, G. J., Hu, Y., Allen, R. J., & Baddeley, A. D. (2018). Competition for the focus of attention in visual working memory: Perceptual recency versus executive control. Annals of the New York Academy of Sciences, 1424(1), 64–75. https://doi.org/10.1111/nyas.13631 Hu, Y., Allen, R. J., Baddeley, A. D., & Hitch, G. J. (2016). Executive control of stimulus-driven and goal-directed attention in visual working memory. Attention, Perception, & Psychophysics, 78(7), 2164-2175. Huff, T., Mahabadi, N., & Tadi, P. (2019). Neuroanatomy, visual cortex. Hurd, P. D. (1958). Science Literacy: Its Meaning for American Schools, Oct . Educational Leadership . Washington, D.C : National Education Association, Dept. of Supervision and Curriculum Development . Retrieved from http://buffalo.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3PS8MwFH7oR PDkb_wxJSB40ZauabtW8CBTmbjNgxPxNJo0hYF2Y2v_f1- apu0mDD14CSWFhPZ7_fLy- vI9AGqblrHECS4NYpsHLkOm5G0WWDYPuWC4PtoW95g879zr0f690_3wBlUlvKrv X4HHPoReHqT9A_jloNiB12gC2KIRYPsrM9DfrhLH4Lm-01M Huys, Q. J. M., Maia, T. V, & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3), 404–413. https://doi.org/10.1038/nn.4238 Illman, D. L. (1994). Environmentally benign chemistry aims for processes that don't pollute. Chemical and Engineering News;(United States), 72(36). Jasińska, K. K., & Petitto, L. A. (2014). Development of neural systems for reading in the monolingual and bilingual brain: New insights from functional near infrared spectroscopy 98

neuroimaging. Developmental Neuropsychology, 39(6), 421-439. Jayawardena, A. W., Fernando, D. A. K., & Zhou, M. C. (1997). Comparison of multilayer perceptron and radial basis function networks as tools for flood forecasting. IAHS Publications-Series of Proceedings and Reports-Intern Assoc Hydrological Sciences, 239, 173-182. Jeni, L. A., Cohn, J. F., & De La Torre, F. (2013). Facing imbalanced data--recommendations for the use of performance metrics. In 2013 Humaine association conference on affective computing and intelligent interaction (pp. 245-251). IEEE. Johnson-Laird PN (1983) Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness (Harvard Univ Press, Cambridge, MA). Johnson-Laird, PN, & Byrne, RM (1991). Deduction. Lawrence Erlbaum Associates, Inc. Johnston, M. P. (2014). Secondary Data Analysis: A Method of which the Time Has Come. Qualitative and Quantitative Methods in Libraries (QQML), 3, 619–626. Kaufman, S. B. (2011). Intelligence and the cognitive unconscious. The Cambridge handbook of intelligence, 442-467. Kotu, V., & Deshpande, B. (2014). Predictive Analytics and Data Mining : Concepts and Practice with RapidMiner. Elsevier Science & Technology. http://ebookcentral.proquest.com/lib/buffalo/detail.action?docID=1875324 Kraeutner, S., Mackenzie, L., Westwood, D., Boe, S., & Kraeutner, S. (2016). Characterizing skill acquisition through motor imagery with no prior physical practice. Journal of Experimental Psychology: Human Perception and Performance, 42(2), 257–257. https://doi.org/10.1037/xhp0000148 Kulhavy, R. W., Stock, W. A., & Kealy, W. A. (1993). How geographic maps increase recall of 99

instructional text. Educational Technology Research and Development, 41(4), 47-62. Lepage, M., Bodnar, M., & Bowie, C. R. (2014). Neurocognition: Clinical and functional outcomes in schizophrenia. In Canadian Journal of Psychiatry (Vol. 59, Issue 1, pp. 5– 12). Canadian Psychiatric Association. https://doi.org/10.1177/070674371405900103 Lamb, R. (2014). Examination of allostasis and online laboratory simulations in a middle school science classroom. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2014.07.017 Lamb, R., Antonenko, P., Etopio, E., & Seccia, A. (2018). Comparison of virtual reality and hands on activities in science education via functional near infrared spectroscopy. Computers & Education, 124, 14–26. https://doi.org/https://doi.org/10.1016/j.compedu.2018.05.014 Lamb, R., Firestone, J. B., & Ardasheva, Y. (2016). A computational modeling of rapid attitude formation during surveys about immigrants and immigration. Computers in Human Behavior, 63, 179–188. https://doi.org/https://doi.org/10.1016/j.chb.2016.05.031 Lamb, R. L., Annetta, L., Vallett, D. B., & Sadler, T. D. (2014). Cognitive diagnostic like approaches using neural-network analysis of serious educational videogames. Computers & Education, 70, 92–104. https://doi.org/10.1016/j.compedu.2013.08.008 Lamb, R. L., Firestone, J. B., & Mcmanus, C. A. (2017). Examination of the Impacts of Dimensionality on the Cognitive Dynamics Associated Educational Video Game Play. https://doi.org/10.1007/s10956-015-9587-z Larsen, A. (2014). Deconstructing mental rotation. Journal of Experimental Psychology: Human Perception and Performance, 40(3), 1072–1091. https://doi.org/10.1037/a0035648 Lee, CD, & Spratley, A. (2010). The Challenges of Adolescent Literacy Reading in the 100

Disciplines Final Report from Carnegie Corporation of New York’s Council on Advancing Adolescent Literacy. Retrieved from www.carnegie.org/literacy. Liberal education outcomes: a preliminary report on student achievement in college / Association of American Colleges and Universities. (2005). Washington, D.C: Association of American Colleges and Universities. Linn, M. C, & Petersen, A. C. (1985). Emergence and characterization of sex differences in spatial ability: A meta-analysis. Child development, 1479-1498. Lipsman, N., Schwartz, M. L., Huang, Y., Lee, L., Sankar, T., Chapman, M., Hynynen, K., & Lozano, A. M. (2013). MR-guided focused ultrasound thalamotomy for essential tremor: A proof-of-concept study. The Lancet Neurology, 12(5), 462–468. https://doi.org/10.1016/S1474-4422(13)70048-6 Liu, X. (2009). Beyond science literacy: Science and the public. International Journal of Environmental and Science Education, 4(3), 301-311. Llorente, R., & Morant, M. (2014). Wearable Computers and Big Data: Interaction Paradigms for Knowledge Building in Higher Education. In Innovation and Teaching Technologies (pp. 127–137). Cham: Springer International Publishing. https://doi.org/10.1007/978-3- 319-04825-3_13 Lloret, S., Ferreres, A., Hernández, A., & Tomás, I. (2017). The exploratory factor analysis of items: guided analysis based on empirical data and software. Anales de Psicología, 33(2), 417–432. Lodge, M., McGraw, K. M., & Stroh, P. (1989). An impression-driven model of candidate evaluation. American Political Science Review, 83(2), 399-419. Lovett, A., & Forbus, K. (2013). Modeling spatial ability in mental rotation and paper-folding. In 101

Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 35, No. 35). Marks, D. F. (1973). The vividness of visual imagery questionnaire. British Journal of Psychology, 64, 17-24. Mayer, R. E. (2002). Rote versus Meaningful Learning. Theory Into Practice, 41(4), 226–232. http://www.jstor.org/stable/1477407 Mayer, R. E. (2002). Using Illustrations to Promote From Science Text. The Psychology of Science Text Comprehension, (16), 333-356. McCurdy, R. C. (1958). Toward a population literate in science. The Science Teacher, 25(7), 366-408. McKendrick, R., Parasuraman, R., & Ayaz, H. (2015). Wearable functional near infrared spectroscopy (fNIRS) and transcranial direct current stimulation (tDCS): expanding vistas for neurocognitive augmentation. Frontiers in Systems Neuroscience, 9, 27. https://doi.org/10.3389/fnsys.2015.00027 McTigue, E. M., & Flowers, A. C. (2011). Science visual literacy: Learners' perceptions and knowledge of diagrams. The Reading Teacher, 64(8), 578-589. Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons' responses and performances as scientific inquiry into score meaning. American psychologist, 50(9), 741. Michel, M., Beck, D., Block, N., Blumenfeld, H., Brown, R., Carmel, D., Carrasco, M., Chirimuuta, M., Chun, M., Cleeremans, A., Dehaene, S., Fleming, S. M., Frith, C., Haggard, P., He, B. J., Heyes, C., Goodale, M. A., Irvine, L., Kawato, M., … Yoshida, M. (2019). Opportunities and challenges for a maturing science of consciousness. In Nature Human Behaviour (Vol. 3, Issue 2, pp. 104–107). Nature Publishing Group. 102

https://doi.org/10.1038/s41562-019-0531-8 Milgram, P., & Kishino, F. (1994). Taxonomy of mixed reality visual displays. IEICE Transactions on Information and Systems. Miller, B. W., Cromley, J. G., & Newcombe, N. S. (2016). Improving diagrammatic reasoning in middle school science using conventions of diagrams instruction. Journal of Computer Assisted Learning, 32(4), 374–390. https://doi.org/10.1111/jcal.12143 Mitchell, W. (1987). Iconology : image, text, ideology / W.J.T. Mitchell. (Paperback ed.). Chicago: University of Chicago Press. Mnguni, L. E. (2014). The theoretical cognitive process of visualization for science education. In SpringerPlus. https://doi.org/10.1186/2193-1801-3-184 Mnguni, L., Schönborn, K., & Anderson, T. (2016). Assessment of visualisation skills in biochemistry students. South African Journal of Science, 112(9/10 SE-Research Article). https://doi.org/10.17159/sajs.2016/20150412. Monti, M. M., Vanhaudenhuyse, A., Coleman, M. R., Boly, M., Pickard, J. D., Tshibanda, L., Owen, A. M., & Laureys, S. (2010). Willful Modulation of Brain Activity in Disorders of Consciousness. New England Journal of Medicine, 362(7), 579–589. https://doi.org/10.1056/NEJMoa0905370 Moody, J., & Darken, C. J. (1989). Fast learning in networks of locally-tuned processing units. Neural computation, 1(2), 281-294. Moreau, D., & Conway, A. R. (2013). Cognitive enhancement: a comparative review of computerized and athletic training programs. International Review of Sport and Exercise Psychology, 6(1), 155-183. Muenchen, R. A. (2014). The popularity of data analysis software. Retrieved from 103

http://r4stats.com/articles/popularity/ Naik, A., & Samant, L. (2016). Correlation Review of Classification Algorithm Using Data Mining Tool: WEKA, Rapidminer, Tanagra, Orange and Knime. Procedia Computer Science, 85, 662–668. https://doi.org/10.1016/j.procs.2016.05.251 Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021 National Research Council. (1996). National science education standards. National Academies Press. National Research Council. (2007). Taking Science to School: Learning and Teaching Science in Grades K-8. Washington, DC: The National Academies Press. https://doi.org/10.17226/11625 Neisser, U. (2014). Cognitive psychology: Classic edition. Psychology Press. OECD iLibrary. (2016). PISA 2015 results. Opfer, J. E., Nehm, R. H., & Ha, M. (2012). Cognitive foundations for science assessment design: Knowing what students know about evolution. Journal of Research in Science Teaching, 49(6), 744–777. https://doi.org/10.1002/tea.21028 Owen, A. M., Coleman, M. R., Boly, M., Davis, M. H., Laureys, S., & Pickard, J. D. (2006). Detecting Awareness in the Vegetative State. https://doi.org/10.1126/science.1130197 Paek, Insu, and Ki Cole. (2019) Using R for Item Response Theory Model Applications, Routledge. ProQuest Ebook Central, https://ebookcentral-proquest- com.gate.lib.buffalo.edu/lib/buffalo/detail.action?docID=5896631. Paivio, A. (1986). Mental representation: Dual-coding hypothesis. Park, J., & Sandberg, I. W. (1991). Universal approximation using radial-basis-function 104

networks. Neural computation, 3(2), 246-257. Parong, J., & Mayer, R. E. (2018). Learning science in immersive virtual reality. Journal of Educational Psychology. https://doi.org/10.1037/edu0000241 Pausch, R., Proffitt, D., & Williams, G. (1997). Quantifying immersion in virtual reality. In Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1997. https://doi.org/10.1145/258734.258744 Pearson, A., Marsh, L., Hamilton, A., & Ropar, D. (2014). Spatial transformations of bodies and objects in adults with autism spectrum disorder. Journal of autism and developmental disorders, 44(9), 2277-2289. Pearson, D. G., Deeprose, C., Wallace-Hadrill, S. M., Heyes, S. B., & Holmes, E. A. (2013). Assessing mental imagery in clinical psychology: A review of imagery measures and a guiding framework. Clinical Psychology Review, 33(1), 1-23. Pearson, J., & Kosslyn, S. M. (2015). The heterogeneity of mental representation: ending the imagery debate. Proceedings of the National Academy of Sciences, 112(33), 10089- 10092. Pearson, J. (2019). The human imagination: the cognitive neuroscience of visual mental imagery. Nature Reviews Neuroscience. https://doi.org/10.1038/s41583-019-0202-9 Pike, M. F., Maior, H. A., Porcheron, M., Sharples, S. C., Wilson, M. L., Pike, M. F., … Wilson, M. L. (2014). Measuring the effect of think aloud protocols on workload using fNIRS. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI ’14 (pp. 3807–3816). New York, New York, USA: ACM Press. https://doi.org/10.1145/2556288.2556974 Portney, L. G., & Watkins, M. P. (2002). Foundations of Clinical Research: Applications to 105

Practice,. Survey of Ophthalmology, 47(6), 598. https://doi.org/10.1016/s0039- 6257(02)00362-4 Provasnik, S., Malley, L., Stephens, M., Landeros, K., Perkins, R., & Tang, J. H. (2015). Highlights From TIMSS and TIMSS Advanced 2015 Mathematics and Science Achievement of U.S. Students in Grades 4 and 8 and in Advanced Courses at the End of High School in an International Context. Retrieved from https://nces.ed.gov/pubs2017/2017002.pdf Purves, D., & Lotto, R. B. (2003). Why we see what we do: An empirical theory of vision. Sinauer Associates. R Development Core Team (2012). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Racz, F. S., Mukli, P., Nagy, Z., & Eke, A. (2017). Increased prefrontal cortex connectivity during cognitive challenge assessed by fNIRS imaging. Biomedical Optics Express, 8(8), 3842. https://doi.org/10.1364/boe.8.003842 Rajsic, J., Wilson, D., & Pratt, J. (2014). Confirmation bias in visual search. Journal of Vision, 14(10), 927. https://doi.org/10.1167/14.10.927 Ramadas, J. (2009). Visual and Spatial Modes in Science Learning. International Journal of Science Education, 31(3), 301–318. https://doi.org/10.1080/09500690802595763 Ramaley, J., Leskes, A., Frye, L., Futrell, M., Grabois, N., Grasmick, N. S., & Wilson, J. (2002). Greater expectations: A new vision for learning as a nation goes to college. Washington, DC: Association of American Colleges and Universities (AACU). Ramful, A., Lowrie, T., & Logan, T. (2017). Measurement of spatial ability: Construction and validation of the spatial reasoning instrument for middle school students. Journal of 106

Psychoeducational Assessment, 35(7), 709-727. Ramya, R. (2017). DRN Hybrid Model for Predicting Autism Using Rapidminer Tool. International Journal of Advanced Research in Computer Science, 8(8), 111–115. https://doi.org/10.26483/ijarcs.v8i8.4604 Reisberg, D., Pearson, D. G., & Kosslyn, S. M. (2003). Intuitions and introspections about imagery: The role of imagery experience in shaping an investigator's theoretical views. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition, 17(2), 147-160. Rosas, R., Nussbaum, M., Cumsille, P., Marianov, V., Correa, M., Flores, P., ... & Rodriguez, P. (2003). Beyond Nintendo: design and assessment of educational video games for first and second grade students. Computers & Education, 40(1), 71-94. Rowsell, J., McLean, C., & Hamilton, M. (2012). Visual literacy as a classroom approach. Journal of Adolescent & Adult Literacy, 55(5), 444-447. Salzman, M. C., Loftin, R. B., Dede, C., & McGlynn, D. (1996). ScienceSpace: Lessons for designing immersive virtual realities. In Conference Companion on Human Factors in Computing Systems (pp. 89-90). San Chee, Y. (2001). Virtual reality in education: Rooting learning in experience. In International Symposium on Virtual Education (pp. 43-54). Schmidhuber, J. (2015). Deep Learning in neural networks: An overview. In Neural Networks (Vol. 61, pp. 85–117). Elsevier Ltd. https://doi.org/10.1016/j.neunet.2014.09.003 Schnotz, W., Bannert, M., Seufert, T., Otero, J., León, J. A., & Graesser, A. C. (2002). Toward an integrative view of text and picture comprehension: Visualization effects on the construction of mental models BT - The psychology of science text comprehension. The 107

Psychology of Science Text Comprehension, 16, 385–416. Sengupta, P., John, M. R. S., & Sridhar, S. S. (2020). Classification of Conscious, Semi- Conscious and Minimally Conscious State for Medical Assisting System Using Brain– Computer Interface and Deep Neural Network. Journal of Medical Robotics Research. Sheehan, P. W. (1967). A shortened form of Betts' questionnaire upon mental imagery. Journal of clinical psychology, 23(3), 386-389. Shen, B. S. P. (1975). Science Literacy and the Public Understanding of Science (pp. 44–52). https://doi.org/10.1159/000398072 Shepard, R. N., & Metzler, J. (1971). Mental rotation of three-dimensional objects. Science, 171(3972), 701-703. Shivers, J., Levenson, C., & Tan, M. (2017). Visual literacy, creativity and the teaching of argument. Learning Disabilities: A Contemporary Journal, 15, 67+. Retrieved from https://link.galegroup.com/apps/doc/A493705258/AONE?u=sunybuff_main&sid=AONE &xid=03fe0e85 Sin, K., & Muthu, L. (2015). Application of Big Data in Educational Data Mining and Learning Analytics. ICTACT JOURNAL ON SOFT COMPUTING: SPECIAL ISSUE ON SOFT COMPUTING MODELS FOR BIG DATA, 4. Singhi, S. K., & Liu, H. (2006). Feature subset selection bias for classification learning. ACM International Conference Proceeding Series (Vol. 148). https://doi.org/10.1145/1143844.1143951 Smith, A. K., Ayanian, J. Z., Covinsky, K. E., Landon, B. E., McCarthy, E. P., Wee, C. C., & Steinman, M. A. (2011). Conducting high-value secondary dataset analysis: An introductory guide and resources. Journal of general internal medicine, 26(8), 920-929. 108

Snow, C. E., Dibner, K. A., National Academies of Sciences and Medicine (U.S.). Committee on Science Literacy and Public Perception of Science, E., National Academies, N. A. of E., National Academies, N. A. of S., & National Academies, N. A. of M. (2016). Science literacy: concepts, contexts, and consequences. National Academies Press. Washington, DC: National Academies Press. https://doi.org/10.17226/23595 Stafford, B. M. (2007). Echo objects: The cognitive work of images. University of Chicago Press. Stieff, M. (2011). Improving representational competence using molecular simulations embedded in inquiry activities. Journal of Research in Science Teaching. https://doi.org/10.1002/tea.20438 Stieff, M., Origenes, A., DeSutter, D., Lira, M., Banevicius, L., Tabang, D., & Cabel, G. (2018). Operational constraints on the mental rotation of STEM representations. Journal of Educational Psychology, 110(8), 1160–1174. https://doi.org/10.1037/edu0000258 Stieff, M., Scopelitis, S., Lira, M. E., & Desutter, D. (2016). Improving Representational Competence with Concrete Models. Science Education. https://doi.org/10.1002/sce.21203 Suzuki, K., Kita, Y., Oi, Y., Okumura, Y., Okuzumi, H., & Inagaki, M. (2018). Right prefrontal cortex specialization for visuospatial working memory and developmental alterations in prefrontal cortex recruitment in school-age children. https://doi.org/10.1016/j.clinph.2018.01.010 Tai, K., & Chau, T. (2009). Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface. Journal of neuroengineering and rehabilitation, 6(1), 39. 109

van Zile-Tamsen, Carol (2017): Using Rasch Analysis to Inform Rating Scale Development. In Res High Educ 58 (8), pp. 922–933. DOI: 10.1007/s11162-017-9448-0. Waley, A. (2005). Three ways of thought in ancient China. Psychology Press. Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological review, 20(2), 158. Waxenbaum, J. A., & Varacallo, M. (2019). Anatomy, Autonomic Nervous System. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/pubmed/30969667 Wijeakumar, S., Huppert, T. J., Magnotta, V. A., Buss, A. T., & Spencer, J. P. (2017). Validating an image-based fNIRS approach with fMRI and a working memory task.(Report). Neuroimage, 147, 204. https://doi.org/10.1016/j.neuroimage.2016.12.007 Witmer, B. G., Bailey, J. H., Knerr, B. W., & Parsons, K. C. (1996). Virtual spaces and real world places: transfer of route knowledge. International journal of human-computer studies, 45(4), 413-428. Wixted, John T., Phelps, Elizabeth A., & Davichi, Lila Elizabeth A. Phelps, , and Lila Davachi (2018). Stevens' handbook of experimental psychology and cognitive neuroscience, learning and memory. Retrieved from https://ebookcentral-proquest- com.gate.lib.buffalo.edu. Wixted, J. T., Goldinger, S. D., Squire, L. R., Kuhn, J. R., Papesh, M. H., Smith, K. A., Treiman, D. M., & Steinmetz, P. N. (2018). Coding of episodic memory in the human hippocampus. NEUROSCIENCE Downloaded at SUNY Buffalo, 115(5), 1093–1098. https://doi.org/10.1073/pnas.1716443115 Wright, R., Thompson, W. L., Ganis, G., Newcombe, N. S., & Kosslyn, S. M. (2008). Training generalized spatial skills. Psychonomic bulletin & review, 15(4), 763-771. Akçayır, M., & Akçayır, G. (2017). Advantages and challenges associated with augmented 110

reality for education: A systematic review of the literature. Educational Research Review, 20, 1–11. https://doi.org/10.1016/j.edurev.2016.11.002 Allen, R. J., Baddeley, A. D., & Hitch, G. J. (2017). Executive and perceptual distraction in visual working memory. Journal of Experimental Psychology: Human Perception and Performance, 43(9), 1677–1693. https://doi.org/10.1037/xhp0000413 Andreassi, J. L. (2007). Psychophysiology: Human behavior & physiological response, 5th ed. In Psychophysiology: Human behavior & physiological response, 5th ed. (pp. xxiii, 538–xxiii, 538). Lawrence Erlbaum Associates Publishers. Arbib, M. A., & Bonaiuto, J. J. (2016). From neuron to cognition via computational neuroscience. MIT Press. Aslin, R. N., Shukla, M., & Emberson, L. L. (2015). Hemodynamic Correlates of Cognition in Human Infants. Annual Review of Psychology, 66(1), 349–379. https://doi.org/10.1146/annurev-psych-010213-115108 Baddeley, A. (2011). Working Memory: Theories, Models, and Controversies. Annu. Rev. Psychol. 2012, 63, 1–29. https://doi.org/10.1146/annurev-psych-120710-100422 Baddeley, A. D. (2017). A concept and some misconceptions. Exploring Working Memory: Selected Works of Alan Baddeley. Belland, B. R., Walker, A. E., & Kim, N. J. (2017). A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education. Review of Educational Research, 87(6), 1042–1081. https://doi.org/10.3102/0034654317723009 Bond, T., & Fox, C. M. (2015). Applying the Rasch Model : Fundamental Measurement in the 111

Human Sciences, Third Edition. Routledge. http://ebookcentral.proquest.com/lib/buffalo/detail.action?docID=3569314 Cripps, B. (2017). Psychometric Testing : Critical Perspectives. John Wiley & Sons, Incorporated. Dash, D., Ferrari, P., Malik, S., & Wang, J. (2019). Overt speech retrieval from neuromagnetic signals using wavelets and artificial neural networks. 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings, 489–493. https://doi.org/10.1109/GlobalSIP.2018.8646401 Elkins, J. (Ed.). (2009). Visual literacy / edited by James Elkins. Routledge. Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71. https://doi.org/10.1002/piq.21143 Farooq, H., & Jain, A. (2017). A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review. International Journal of Sensors and Sensor Networks, 5(3), 43. Finch, H., & Edwards, J. M. (2016). Rasch Model Parameter Estimation in the Presence of a Nonnormal Latent Trait Using a Nonparametric Bayesian Approach. Educational and Psychological Measurement, 76(4), 662–684. https://doi.org/10.1177/0013164415608418 Fisher, M. (2020, April 8). Why Coronavirus Conspiracy Theories Flourish. And Why It Matters. - The New York Times. The New York Times. https://www.nytimes.com/2020/04/08/world/europe/coronavirus-conspiracy-theories.html Ganel, T., & Goodale, M. A. (2019). Still holding after all these years: An action-perception 112

dissociation in patient DF.(Report). Neuropsychologia, 128, 249. https://doi.org/10.1016/j.neuropsychologia.2017.09.016 Goodale, M. A. (2013). Separate visual systems for perception and action: a framework for understanding cortical visual impairment. Developmental Medicine & Child Neurology, 55(SUPPL.4), 9–12. https://doi.org/10.1111/dmcn.12299 Hand, B., Cavagnetto, A., Chen, Y. C., & Park, S. (2016). Moving Past Curricula and Strategies: Language and the Development of Adaptive Pedagogy for Immersive Learning Environments. Research in Science Education. https://doi.org/10.1007/s11165-015-9499-1 Hitch, G. J., Hu, Y., Allen, R. J., & Baddeley, A. D. (2018). Competition for the focus of attention in visual working memory: Perceptual recency versus executive control. Annals of the New York Academy of Sciences, 1424(1), 64–75. https://doi.org/10.1111/nyas.13631 I Can Use the Identify and Interpret (I 2 ) Strategy. (2012). Johnston, M. P. (2014). Secondary Data Analysis: A Method of which the Time Has Come. Qualitative and Quantitative Methods in Libraries (QQML), 3, 619–626. Kotu, V., & Deshpande, B. (2014). Predictive Analytics and Data Mining : Concepts and Practice with RapidMiner. Elsevier Science & Technology. http://ebookcentral.proquest.com/lib/buffalo/detail.action?docID=1875324 Lamb, R., Firestone, J. B., & Ardasheva, Y. (2016). A computational modeling of rapid attitude formation during surveys about immigrants and immigration. Computers in Human Behavior, 63, 179–188. https://doi.org/https://doi.org/10.1016/j.chb.2016.05.031 Lepage, M., Bodnar, M., & Bowie, C. R. (2014). Neurocognition: Clinical and functional outcomes in schizophrenia. In Canadian Journal of Psychiatry (Vol. 59, Issue 1, pp. 5–12). 113

Canadian Psychiatric Association. https://doi.org/10.1177/070674371405900103 Lipsman, N., Schwartz, M. L., Huang, Y., Lee, L., Sankar, T., Chapman, M., Hynynen, K., & Lozano, A. M. (2013). MR-guided focused ultrasound thalamotomy for essential tremor: A proof-of-concept study. The Lancet Neurology, 12(5), 462–468. https://doi.org/10.1016/S1474-4422(13)70048-6 Lloret, S., Ferreres, A., Hernández, A., & Tomás, I. (2017). The exploratory factor analysis of items: guided analysis based on empirical data and software. Anales de Psicología, 33(2), 417–432. Lovett, A., & Forbus, K. (2013). Modeling Spatial Ability in Mental Rotation and Paper- Folding. Mayer, R. E. (2002). Rote versus Meaningful Learning. Theory Into Practice, 41(4), 226–232. http://www.jstor.org/stable/1477407 McKendrick, R., Parasuraman, R., & Ayaz, H. (2015). Wearable functional near infrared spectroscopy (fNIRS) and transcranial direct current stimulation (tDCS): expanding vistas for neurocognitive augmentation. Frontiers in Systems Neuroscience, 9, 27. https://doi.org/10.3389/fnsys.2015.00027 Michel, M., Beck, D., Block, N., Blumenfeld, H., Brown, R., Carmel, D., Carrasco, M., Chirimuuta, M., Chun, M., Cleeremans, A., Dehaene, S., Fleming, S. M., Frith, C., Haggard, P., He, B. J., Heyes, C., Goodale, M. A., Irvine, L., Kawato, M., … Yoshida, M. (2019). Opportunities and challenges for a maturing science of consciousness. In Nature Human Behaviour (Vol. 3, Issue 2, pp. 104–107). Nature Publishing Group. https://doi.org/10.1038/s41562-019-0531-8 114

Milgram, P., & Kishino, F. (1994). Taxonomy of mixed reality visual displays. IEICE Transactions on Information and Systems. Miller, B. W., Cromley, J. G., & Newcombe, N. S. (2016). Improving diagrammatic reasoning in middle school science using conventions of diagrams instruction. Journal of Computer Assisted Learning, 32(4), 374–390. https://doi.org/10.1111/jcal.12143 Mnguni, L. E. (2014). The theoretical cognitive process of visualization for science education. In SpringerPlus. https://doi.org/10.1186/2193-1801-3-184 Mnguni, L., Schönborn, K., & Anderson, T. (2016). Assessment of visualisation skills in biochemistry students. South African Journal of Science, 112(9/10 SE-Research Article). https://doi.org/10.17159/sajs.2016/20150412 Monti, M. M., Vanhaudenhuyse, A., Coleman, M. R., Boly, M., Pickard, J. D., Tshibanda, L., Owen, A. M., & Laureys, S. (2010). Willful Modulation of Brain Activity in Disorders of Consciousness. New England Journal of Medicine, 362(7), 579–589. https://doi.org/10.1056/NEJMoa0905370 Naik, A., & Samant, L. (2016). Correlation Review of Classification Algorithm Using Data Mining Tool: WEKA, Rapidminer, Tanagra, Orange and Knime. Procedia Computer Science, 85, 662–668. https://doi.org/10.1016/j.procs.2016.05.251 Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021 Owen, A. M., Coleman, M. R., Boly, M., Davis, M. H., Laureys, S., & Pickard, J. D. (2006). Detecting Awareness in the Vegetative State. https://doi.org/10.1126/science.1130197 Parong, J., & Mayer, R. E. (2018). Learning science in immersive virtual reality. Journal of 115

Educational Psychology. https://doi.org/10.1037/edu0000241 Pausch, R., Proffitt, D., & Williams, G. (1997). Quantifying immersion in virtual reality. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1997. https://doi.org/10.1145/258734.258744 Pearson, J., & Kosslyn, S. M. (2015). The heterogeneity of mental representation: Ending the imagery debate. In Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.1504933112 Racz, F. S., Mukli, P., Nagy, Z., & Eke, A. (2017). Increased prefrontal cortex connectivity during cognitive challenge assessed by fNIRS imaging. Biomedical Optics Express, 8(8), 3842. https://doi.org/10.1364/boe.8.003842 Rajsic, J., Wilson, D., & Pratt, J. (2014). Confirmation bias in visual search. Journal of Vision, 14(10), 927. https://doi.org/10.1167/14.10.927 Ramya, R. (2017). DRN HYBRID MODEL FOR PREDICTING AUTISM USING RAPID MINER TOOL. International Journal of Advanced Research in Computer Science, 8(8), 111–115. https://doi.org/10.26483/ijarcs.v8i8.4604 Schmidhuber, J. (2015). Deep Learning in neural networks: An overview. In Neural Networks (Vol. 61, pp. 85–117). Elsevier Ltd. https://doi.org/10.1016/j.neunet.2014.09.003 Schnotz, W., Bannert, M., Seufert, T., Otero, J., León, J. A., & Graesser, A. C. (2002). Toward an integrative view of text and picture comprehension: Visualization effects on the construction of mental models BT - The psychology of science text comprehension. The Psychology of Science Text Comprehension, 16, 385–416. http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2002-02314- 116

015&site=ehost-live%5Cnpapers3://publication/uuid/77F40B55-9BA7-4059-ACAE- 3169DEF29D2C Sengupta, P., John, M. R. S., & Sridhar, S. S. (2020). Classification of Conscious, Semi- Conscious and Minimally Conscious State for Medical Assisting System Using Brain– Computer Interface and Deep Neural Network. Journal of Medical Robotics Research. Sin, K., & Muthu, L. (2015). Application of Big Data in Educational Data Mining and Learning Analytics. ICTACT JOURNAL ON SOFT COMPUTING: SPECIAL ISSUE ON SOFT COMPUTING MODELS FOR BIG DATA, 4. Snow, C. E., Dibner, K. A., National Academies of Sciences and Medicine (U.S.). Committee on Science Literacy and Public Perception of Science, E., National Academies, N. A. of E., National Academies, N. A. of S., & National Academies, N. A. of M. (2016). Science literacy: concepts, contexts, and consequences. In National Academies Press. National Academies Press. https://doi.org/10.17226/23595 Snow, C. E., Dibner, K. A., Science, C. on S. L. and P. P. of, Education, B. on S., Education, D. of B. and S. S. and, & National Academies of Sciences, E. and M. (2016). Science Literacy in Society and the World. https://www.ncbi.nlm.nih.gov/books/NBK396088/ Stieff, M., Origenes, A., DeSutter, D., Lira, M., Banevicius, L., Tabang, D., & Cabel, G. (2018). Operational constraints on the mental rotation of STEM representations. Journal of Educational Psychology, 110(8), 1160–1174. https://doi.org/10.1037/edu0000258 Waxenbaum, J. A., & Varacallo, M. (2019). Anatomy, Autonomic Nervous System. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/pubmed/30969667 Wijeakumar, S., Huppert, T. J., Magnotta, V. A., Buss, A. T., & Spencer, J. P. (2017). Validating 117

an image-based fNIRS approach with fMRI and a working memory task.(Report). Neuroimage, 147, 204. https://doi.org/10.1016/j.neuroimage.2016.12.007 Wixted, J. T., Goldinger, S. D., Squire, L. R., Kuhn, J. R., Papesh, M. H., Smith, K. A., Treiman, D. M., & Steinmetz, P. N. (2018). Coding of episodic memory in the human hippocampus. NEUROSCIENCE Downloaded at SUNY Buffalo, 115(5), 1093–1098. https://doi.org/10.1073/pnas.1716443115 Wu, T.-L., Shen, B.-K., Sun, L., Zamir, A. R., & Savarese, S. (2016). Feedback based Neural Networks Joint work with. Xu, Y., & Franconeri, S. L. (2015). Capacity for Visual Features in Mental Rotation. Psychological Science, 26(8), 1241–1251. https://doi.org/10.1177/0956797615585002 Yılmaz, H. B. (2017). On the development and measurement of spatial ability. International Electronic Journal of Elementary Education, 1(2), 83-96. Zheng, F., Sheinberg, R., Yee, M. S., Ono, M., Zheng, Y., & Hogue, C. W. (2013). Cerebral near-infrared spectroscopy (NIRS) monitoring and neurologic outcomes in adult cardiac surgery patients and neurologic outcomes: a systematic review. Anesthesia and analgesia, 116(3). Zull, J. E. (2004). The Art of Changing the Brain Neurological research supports some well- known ideas about teaching, but does it suggest new-even counterintuitive-ideas? (Vol. 62). 118

Appendix A Descriptive Statistics 119

Appendix B SPSS ANN Multilayer Perceptron Model Summary 2214536.885 Training Cross Entropy Error 4.1% Percent Incorrect Maximum Predictions number of Stopping Rule Used epochs (100) exceeded Training Time 44:34:42.72 Testing Cross Entropy Error 947243.799 4.2% Percent Incorrect Predictions Dependent Variable: Score 120

Classification Predicted Percent Sample Observed 0 1 Correct Training 0 489699 781073 94.1% 1 311055 2713816 97.0% Overall Percent 38.4% 61.6% 95.9% Testing 0 209911 333570 93.9% 1 132992 1162455 97.0% Overall Percent 38.3% 61.7% 95.8% Dependent Variable: Score 121

Appendix C Exploratory Factor Analysis in RStudio Exploratory Factor Analysis (EFA) > #install packages > library(\"psych\") > library(\"GPArotation\") > > #parallel analysis scree plot > parallel<-fa.parallel(data, fm='minres', fa='fa') > > #factor analysis with 5 factors > fivefactor <- fa(data,nfactors = 5,rotate = \"oblimin\",fm=\"minres\") > print(fivefactor) > >> > #print loadings and set cutoff to 0.3 > print(fivefactor$loadings,cutoff = 0.3) > > #factor mapping > fa.diagram(fivefactor) > 122

Appendix D Rasch Analysis in RStudio > # Load R package for Rasch analysis >library(eRm) > rm.res <- RM(raschcomplexityfulldata_trans) > > # Rasch Model estimation > rm.res > # Rasch Model estimation results > summary(rm.res) > # item parameter estimation > coef(rm.res) > > # variance covariance matrix for item parameter estimates > vcov(rm.res) > > # Confidence Intervals and the Conditional Log Liklihood > confint(rm.res, \"beta\") > > logLik(rm.res) Conditional log Lik.: -3375.432 (df=159) 123

> > # Plot Joint ICCs > plotjointICC(rm.res, xlim = c(-5, 5)) > > # Plot Person-Item Map > plotPImap(rm.res) > plotPImap(rm.res, sorted = TRUE) 124

Appendix E Correlation in RStudio View(lensfulldata_diffcor_v1) > ggscatter(lensfulldata_diffcor_v1, x = \"expertdiff\", y = \"raschdiff\", add = \"reg.line\", conf.int = TRUE, cor.coef = TRUE, cor.method = \"pearson\", xlab = \"Expert Difficulty Analysis\", ylab = \"Rasch Difficulty Analysis\") > shapiro.test(lensfulldata_diffcor_v1$expertdiff) Shapiro-Wilk normality test data: lensfulldata_diffcor_v1$expertdiff W = 0.739, p-value = 1.505e-15 > shapiro.test(lensfulldata_diffcor_v1$raschdiff) Shapiro-Wilk normality test data: lensfulldata_diffcor_v1$raschdiff W = 0.79237, p-value = 8.52e-14 # Visual inspection of expertdiff 125

> ggqqplot(lensfulldata_diffcor_v1$expertdiff,ylab = \"expertdiff\") > > # Visual inspection of raschdiff > ggqqplot(lensfulldata_diffcor_v1$raschdiff,ylab = \"raschdiff\") > > # From the plots it can be concluded that both populations come from normal distributions > > pearson.res <- cor.test(lensfulldata_diffcor_v1$expertdiff, lensfulldata_diffcor_v1$raschdiff, method = \"pearson\") > pearson.res Pearson's product-moment correlation data: lensfulldata_diffcor_v1$expertdiff and lensfulldata_diffcor_v1$raschdiff t = 5.1166, df = 158, p-value = 8.919e-07 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.2356423 0.5027649 sample estimates: cor 0.3770166 The p-value of the test is 8.919e-07, which is less than the significance level alpha = 0.05. We 126

can conclude that expertdiff and raschdiff are significantly correlated with a correlation coefficient of 0.38 and p-value of 8.919e-07. > kendall.res <- cor.test(lensfulldata_diffcor_v1$expertdiff, lensfulldata_diffcor_v1$raschdiff, method = \"kendall\") > kendall.res Kendall's rank correlation tau data: lensfulldata_diffcor_v1$expertdiff and lensfulldata_diffcor_v1$raschdiff z = 4.8563, p-value = 1.196e-06 alternative hypothesis: true tau is not equal to 0 sample estimates: tau 0.3479463 The correlation coefficient between x and y are 0.3479463 and the p-value is 1.196e-06. 127

Appendix F Equations Equation 1. Equation 2 where m is the maximum score for the item, ������j is the ability level of an individual, ������i is the difficulty level of the item and ������ki is the rating scale level of that object (Bond & Fox, 2007). 128