RELATION BETWEEN SEASON AND BRAIN HEMISPHERE ACTIVITY IN UNDERGRADUATE STUDENTS Boris Zakharov1, Anatoly Tkachev2 1Natural Sciences Department, LaGuardia Community College, City University of New York, 31-10 Thomson Ave., Long Island City, Queens, NY 11101, USA bzakharov@lagcc.cuny.edu 25 Lesosechnaya Street, Apt. 49, Novosibirsk, 630060, Russia metapractice@gmail.com Abstract. The mode and teaching methods in education practice remain the same at all seasons. However, it is well known that the mood of the students is changing from season to season. This change happens because of many factors that affect brain activity and result on cognitive ability of students. The study explores the seasonal variation of left and right hemisphere brain activity among undergraduate students of a big urban Community College. Left and right hemisphere activity was identified by visual observation of neurological cues during the face-to-face interview. The study shows a significant shift from predominantly left hemisphere brain activity during the Fall session toward the prevalence of right hemisphere brain activity during the Spring session. Female students demonstrate a higher stability of left and right hemispheric activities between Fall and Spring sessions, whereas male students show a meaningful difference in their hemispheric activity between seasons. Keywords: learning, Fall and Spring sessions, gender, left and right brain hemisphere, lateralization of brain activity. INTRODUCTION The study grows from a common observation, which is familiar to all educators, who know how dramatically students’ mood may vary. Some days our students do a great job in the class, along with the other days, when students cannot concentrate on the studying. The variations happen with some regularity and depend on the physiological processes that determine the state of our students’ minds and, as a result, their ability to learn. Among educators such “crazy days” are called “right brain days” (Grinder, 1991). Our study was based on a few fundamental paradigms of modern Psychology and Neuroscience. It is well known that emotions result from interaction among physiological states and cognitive factors (Schachter, Singer, 1962). The modern neuroscience recognizes the high complexity of the cognitive process that consists of multiple parallel processing of sensory input. The substantial data suggests functional specialization of different areas of the cerebral cortex, which results in modular organization of the human brain (Gazzaniga, 2000, 2011; Gazzaniga et al., 2013). Hypothesis. We hypothesize that at a different time automatically may come to action (or more precise, take the charge in learning behavior) different (left or right) cerebral hemispheres. And because their ways to process the incoming information are different, then the result of the learning process will be also different. We have found that there are some data that let us to design a comparatively simple way for the evaluation of this hypothesis. The unity of human mind and body is an axiom of the neuroscience and psychology. The idea of the body as a mirror of the mind explains the existence of the neurological cues that represent brain activity in body language. The particular eye movements are indicative of specific sensory components of thought (Buckner et al., 1987; Sharot et al., 2008). Posture, facial expression, head movements (Kingsbourne, 1972; Gazzaniga, 2000), and usage of words (Tkachev, 1988, 1999) also indicate the particular (left or right) hemisphere activity. MATHERIAL AND METHOD The study was conducted among undergraduate students who take courses in Human Anatomy and Physiology, and Vertebrate Anatomy and Physiology. The data were collected during the face-to-face interview in Fall and Spring semesters of 2011-2013. During the interview students were asked how the classroom environment affects his or her study. The answer and neurological cues (facial expression and involuntary eye movements) were observed and recorded in an individual interview protocol. 118 (36 male and 82 female) undergraduate students of the LaGuardia Community College (CUNY) were interviewed: 68 (21 male and 46 female) during the Spring sessions and 50 subjects (15 male and 35 female) during the Fall sessions were interviewed. RESULTS AND DISCUSSION Fall session Spring session Male students Female students Male students Female students Left (%) Right (%) Left (%) Right (%) Left (%) Right (%) Left (%) Right (%) 53 60 40 29 71 47 53 47 Table 1. Neurological cue of left-right brain hemispheres activity in the class between male and female undergraduate students demonstrates meaningful shift from the left brain hemispheric activity in the Fall semester to right brain activity in the Spring semester. Observation of the neurological cues of left-right brain hemisphere activity in the class among male and female undergraduate students demonstrates a meaningful shift from the brains’ left hemispheric activity in the Fall semester to brains’ right hemispheric activity in the Spring semester. 80 70 60 50 Males 40 Females 30 20 10 0 Fall Spring Fig. 1. Increase in right brain hemisphere activity during the transition from the Fall session to Spring session. Acknowledgement of seasonal dynamic of brain cortex hemispheres’ activity requires increased flexibility from educators. The male students demonstrate the most dramatic change in their “state of mind”. During the Fall semester they did very well with the traditional lecture-centered learning and usually demonstrated high analytical ability. During the Spring semester traditional lectures bore them and the best way to study for male students at that time is to do something creative. For example, Peer Instruction technique (Crouch, Mazur, 2001; Lasry et al., 2008; Mazur, Watkins, 2009; Miller et al., 2006; Nicol, Boyle, 2003; Rao, DiCarlo, 2000) will have the strongest impact on this group of students during the Spring semester. At the same time female students demonstrate stable continuous efforts toward learning. For them the combination of traditional and creative styles of learning is beneficial during the whole academic year (Hazari et al., 2007; Labudde et al., 2000; Lorenzo et al., 2006). ACKNOWLEDGMENTS This study was inspired by the LaGuardia Community College (CUNY) Carnegie Seminar for the Teaching and Learning and its facilitator Michele Piso. Authors are very grateful to the members of the seminar: Patricia Sokolski, Dennis Aguirre, Maria Entezari, Karim Sharif, Kathleen Karsten, Dionne Miller, Mangala Kothari, Dong Wook, Reem Jaafar, Rahman Zahidur, and Philip Gimber. Authors value the help of all colleagues that made this study possible. LITERATURE Amabile T.M. (1979). Effects of external evaluations on artistic creativity. Journal of Personality and Social Psychology, 37: 221-233. Benson D.E. (1994). The neurology of thinking. Oxford University Press. Buckner W. M. N.M., Reese E., Reese R. (1987). Eye movement as an indicator of sensory components in thought. Journal of Counseling Psychology 34 (3): 283-287. Charron S., Koechlin E. (2010). Divided representation of concurrent goals in the human frontal lobes. Science, 328: 360-363. Cook N.D. (2002). Tone of voice and mind: The connections between intonation, emotion, cognition and consciousness. John Benjamins North America, Philadelphia. Corballis P. (2003). Visuospatial processing and the right-hemisphere interpreter. Brain and Cognition 53(2): 171-176. Crouch C.H., Mazur E. (2001). Peer instruction: Ten years of experience and results. American Journal of Physics 69 (9): 970-977. Denenberg V. (1977). Assessing the effects of early experience. In: Methods in psychobiology 3: 127-147. Academic Press. Gazzaniga M.S., Bogen J.E., Sperry R.W. (1967). Dyspraxia following division of the cerebral commissures. Archives of Neurology 16(6): 606-612. Gazzaniga M.S., LeDoux J.E. (1978). The integrated mind. Plenum Press, New York. Gazzaniga M.S., Smylie C.S. (1983). Facial recognition and brain asymmetries: clues to underlying mechanisms. Ann. Neurology 13: 536-540. Gazzaniga M.S., Smylie C.S. (1990). Hemispheric mechanisms controlling voluntary and spontaneous facial expressions. Journal of Cognitive Neuroscience 2: 239-245. Gazzaniga M.S. (2000). Cerebral specialization and interhemispheric communication. Does the corpus callosum enable the human condition? Brain 123: 1293-1326. Gazzaniga M.S. (2011). Who’s in charge? Harper Collins Publishers, New York. Gazzaniga M.S., Ivry R.B., Mangun G.R. (2013). Cognitive neuroscience. The biology of the mind. W.W. Norton, New York – London. Green A., Nicholson N.S., Vaid J., White N., Steiner R. (1990). Hemispheric involvement is shadowing vs. interpretation: A time-sharing study of simultaneous interpreters with matched bilingual and monolingual controls. Brain and Language, 39: 107-133. Grinder M. (1991). Righting the Educational Conveyor Belt. Metamorphous Press. Portland, OR. Goldberg E., Podell K., Harner R., Lovell M. (1994). Cognitive bias, functional cortical geometry, and the frontal lobes: laterality, sex, and handedness. Journal of Cognitive Neuroscience, v. 6, N 3: 276-296. Grodzinsky Y., Amunts K. (2006). Broca’s region. Oxford University Press, New York. Hari R., Kujala M.V. (2009). Brain basis of human social interaction: From concepts to brain imaging. Physiological Review 89: 453-479. Hazari Z., Tai R., Sadler P. (2007). Gender differences in introductory university physics performance: The influence of high school physics preparation and affective factors. Science Education 91 (6): 847-876. Hirstein W., Ramachandran V.S. (1997). Capgras syndrome: A novel probe for understanding the neural representation of the identity and familiarity of persons. Proceedings of the Royal Society B: Biological Sciences 264 (1380): 437-444. Hugdahl K. (2005). Symmetry and asymmetry in the human brain. European Review 13: 119133. Kingsbourne M. (1972). Eye and head turning indicates cerebral lateralization. Science 179: 539541. Labudde P., Herzog W., Neuenschwander M.P., Violi E., Gerber C. (2000). Girls and physics: teaching and learning strategies tested by classroom interventions in grade 11. International Journal of Science Education 22 (2): 143-157. Lamb M.R., Robertson L.C., Knight R.T. (1989). Attention and interference in the processing of global and local information: effects of unilateraltemporal-parietal junction lesions. Neuropsychologia 27: 471-483. Lasry N., Mazur E., Watkins J. (2008). Peer instruction: From Harvard to community colleges. American Journal of Physics 76 (11): 1066-1069. Lennenberg E.H. (1967). Biological foundations of language. Wiley, London. Levy J., Trevarthen C., Sperry R.W. (1972). Reception of bilateral chimeric figures following hemispheric deconnexion. Brain 95: 61-78. Lorenzo M., Crouch C.H., Mazur E. (2006). Reducing the gender gap in the physics classroom. American Journal of Physics 74 (2): 118-122. Mazur E., Watkins J. (2009). Just-in-Time Teaching and Peer Instruction. In: Just in Time Teaching Across the Disciplines. Stylus Publishing, Sterling, VA. Meaney M., Diorio J., Francis D., Widdowson J., Laplante P., Caldji C., Sharma S., Seckl J., Plotsky P. (1996). Early environmental regulation of forebrain glucocorticoid receptor gene expression: Implications for adrenocortical responses to stress. Developmental Neuroscience 18: 49-72. Metcalfe J., Funnell M., Gazzaniga M.S. (1995). Right-hemisphere memory superiority: studies of a split-brain patient. Psychological Science 6(3): 157-164. Miller R.L., Santana-Vega E., Terrell M. (2006). Can good questions and peer discussion improve calculus instruction? Primus 16 (3): 193-203. Mӧttӧnen R., Jӑrvelӑinen J., Sams M., Hari R. (2005). Viewing speech modulates activity in the left SI mouth cortex. Neuroimage 24: 731-737. Nicol D.J., Boyle J.T. (2003). Peer instruction versus class-wide discussion in the large classes: A comparison of two interaction methods in the wired classroom. Studies in Higher Education 28 (4): 457-474. Nishitani N., Avikainen S., Hari R. (2004). Abnormal imitation-related cortical activation sequences in Asperger’s syndrome. Ann. Neurol. 55: 558-562. Nishitani N., Schürmann M., Amunts K., Hari R. (2005). Broca’s region: from action to language. Physiology 20: 60-69. Pecchia L., Martin J.L., Ragozzino A., Vanzanella C., Scognamiglio A., Mirarchi L., Morgan S.P. (2013), User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner. BMC Medical Informatics and Decision Making, 13: 2-14. Phelps E.A., Gazzaniga M.S. (1992). Hemispheric differences in mnemonic processing: the effects of left hemisphere interpretation. Neuropsychologia 30: 293-297. Rao S., DiCarlo S. (2000). Peer instruction improves performance on quizzes. Advances in Physiology Education 24 (1): 51-55. Sakai K.L., Tatsuno Y., Suzuki K., Kimura H., Ichida Y. (2005). Sign and speech: amodal commonality in left hemisphere dominance for comprehension of sentences. Brain 128: 1407-1417. Schachter S., Singer J.E. (1962). Cognitive, social, and physiological determinants of emotional state. Psychological Review 69: 379-399. Sharot T., Davidson M.L., Carson M.M., Phelps E.A. (2008). Eye Movements Predict Recollective Experience. PLoS ONE 3(8): e2884. doi:10.1371/journal.pone.0002884. Singer S.F. (1994). Localization and lateralization in the delusion of substitution. Psychology 27: 168-176. Snell R.S. (1999). Clinical neuroanatomy for medical students (5th Edition). Lippincott Williams & Wilkins. Springer S., Deutsch G. (1997). Left brain, right brain. Freeman, San Francisco.