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Learning with interactive computer graphics in the undergraduate neuroscience classroom

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Abstract

Instruction of neuroanatomy depends on graphical representation and extended self-study. As a consequence, computer-based learning environments that incorporate interactive graphics should facilitate instruction in this area. The present study evaluated such a system in the undergraduate neuroscience classroom. The system used the method of adaptive exploration, in which exploration in a high fidelity graphical environment is integrated with immediate testing and feedback in repeated cycles of learning. The results of this study were that students considered the graphical learning environment to be superior to typical classroom materials used for learning neuroanatomy. Students managed the frequency and duration of study, test, and feedback in an efficient and adaptive manner. For example, the number of tests taken before reaching a minimum test performance of 90 % correct closely approximated the values seen in more regimented experimental studies. There was a wide range of student opinion regarding the choice between a simpler and a more graphically compelling program for learning sectional anatomy. Course outcomes were predicted by individual differences in the use of the software that reflected general work habits of the students, such as the amount of time committed to testing. The results of this introduction into the classroom are highly encouraging for development of computer-based instruction in biomedical disciplines.

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References

  • Ackerman, M. J. (1995). Accessing the visible human project. D-Lib Magazine [On-line]. URL: http://www.dlib.org/dlib/october95/10ackerman.html.

  • Ambrose, S. A., Bridges, M. W., DePietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works: 7 Research-based principles for smart teaching. San Francisco: Jossey-Bass/Wiley.

    Google Scholar 

  • Barab, S. (2006). Design-based research: A methodological toolkit for the learning scientist. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 153–170). New York: Cambridge University Press.

    Google Scholar 

  • Bjork, R. A. (1999). Assessing our own competence: Heuristics and illusions. In D. Gopher & A. Koriat (Eds.), Attention and performance XVII: Cognitive regulation of performance. Interaction of theory and application (pp. 435–459). Cambridge, MA: MIT Press.

    Google Scholar 

  • Bower, G. H., Clark, M. C., Lesgold, A. M., & Winzenz, D. (1969). Hierarchical retrieval schemes in recall of categorized word lists. Journal of Verbal Learning and Verbal Behavior, 8, 323–343. doi:10.1016/S0022-5371(69)80124-6.

    Article  Google Scholar 

  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn. Washington, D.C.: National Academy Press.

    Google Scholar 

  • Brewer, D. N., Wilson, T. D., Eagleson, R., & De Ribaupierre, S. (2012). Evaluation of neuroanatomical training using a 3D visual reality model. Medicine Meets Virtual Reality, 19, 85–91. doi:10.3233/978-1-61499-022-2-85.

    Google Scholar 

  • Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. The Journal of the Learning Sciences, 2, 141–178. doi:10.1207/s15327809jls0202_2.

    Article  Google Scholar 

  • Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 380(132), 354. doi:10.1037/0033-2909.132.3.354.

    Article  Google Scholar 

  • Chariker, J. H., Naaz, F., & Pani, J. R. (2011). Computer-based learning of neuroanatomy: A longitudinal study of learning, transfer, and retention. Journal of Educational Psychology, 103(1), 19–31. doi:10.1037/a0021680.

    Article  Google Scholar 

  • Chariker, J. H., Naaz, F., & Pani, J. R. (2012). Item difficulty in the evaluation of computer-based instruction: An example from neuroanatomy. Anatomical Sciences Education, 5, 63–75. doi:10.1002/ase.1260.

    Article  Google Scholar 

  • Collins, A. (1992). Toward a design science of education research. In E. Scanlon & T. O’Shea (Eds.), New directions in educational psychology. Berlin: Springer.

    Google Scholar 

  • Collins, J. P. (2008). Modern approaches to teaching and learning anatomy. BMJ, 337, 665–667.

    Article  Google Scholar 

  • Cook, D. A. (2005). The research we still are not doing: An agenda for the study of computer-based learning. Academic Medicine, 80, 541–548. doi:10.1097/00001888-200506000-00005.

    Article  Google Scholar 

  • Cook, D. A., Erwin, P. J., & Triola, M. M. (2010). Computerized virtual patients in health professions education: A systematic review and meta-analysis. Academic Medicine, 85, 1589–1602.

    Article  Google Scholar 

  • Craik, F. I. M., & Tulving, E. (1975). Depth of processing and the retention of words in episodic memory. Journal of Experimental Psychology: General, 104(3), 268–294. doi:10.1037/0096-3445.104.3.268.

    Article  Google Scholar 

  • Felten, D. L., & Shetty, A. N. (2010). Netter’s atlas of neuroscience (2nd ed.). Philadelphia: Saunders/Elsevier.

    Google Scholar 

  • Hariri, S., Rawn, C., Srivastava, S., Youngblood, P., & Ladd, A. (2004). Evaluation of a surgical simulator for learning clinical anatomy. Medical Education, 38, 896–902. doi:10.1111/j.1365-2929.2004.01897.x.

    Article  Google Scholar 

  • Issenberg, S. B., McGaghie, W. C., Petrusa, E. R., Lee Gordon, D., & Scalese, R. J. (2005). Features and uses of high-fidelity medical simulations that lead to effective learning: A BEME systematic review. Medical Teacher, 27, 10–28.

    Article  Google Scholar 

  • Karpicke, J. D., & Roediger, H. L, 3rd. (2008). The critical importance of retrieval for learning. Science, 319, 966–968.

    Article  Google Scholar 

  • Keedy, A. W., Durack, J. C., Sandhu, P., Chen, E. M., O’Sullivan, P. S., & Breiman, R. S. (2011). Comparison of traditional methods with 3D computer models in the instruction of hepatobiliary anatomy. Anatomical Sciences Education, 4, 84–91. doi:10.1002/ase.212.

    Article  Google Scholar 

  • Koedinger, K. R., & Corbett, A. (2006). Cognitive tutors: Technology bringing learning sciences to the classroom. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 61–77). New York: Cambridge University Press.

    Google Scholar 

  • Lagemann, E. C. (2002). An elusive science: The troubling history of education research. Chicago: University of Chicago Press.

    Google Scholar 

  • Levinson, A. J., Weaver, B., Garside, S., McGinn, H., & Norman, G. R. (2007). Virtual reality and brain anatomy: A randomized trial of e-learning instructional designs. Medical Education, 41, 495–501. doi:10.1111/j.1365-2929.2006.02694.x.

    Article  Google Scholar 

  • LONI. (2013). Laboratory of neuro imaging, UCLA. [online.] URL: http://www.loni.ucla.edu/. Accessed July 15, 2013.

  • Mai, J. K., & Paxinos, G. (Eds.). (2012). The human nervous system (3rd ed.). New York: Academic Press/Elsevier.

    Google Scholar 

  • Mai, J. K., Paxinos, G., & Voss, T. (2008). Atlas of the human brain (3rd ed.). New York: Academic Press/Elsevier.

    Google Scholar 

  • Mayer, R. E., Hegarty, M., Mayer, S., & Campbell, J. (2005). When static media promote active learning: Annotated illustrations versus narrated animations in multimedia instruction. Journal of Experimental Psychology: Applied, 11, 256–265.

    Google Scholar 

  • Naaz, F., Chariker, J. H., & Pani, J. R. (2014). Computer-based learning: Graphical integration of whole and sectional neuroanatomy improves long-term retention. Cognition and Instruction, 32, 1–21. doi:10.1080/07370008.2013.857672.

  • Nolte, J., & Angevine, J. B. (2007). The human brain in photographs and diagrams (3rd ed.). Philadelphia: Mosby/Elsevier.

    Google Scholar 

  • Norman, G. R. (2009). Teaching basic science to optimize transfer. Medical Teacher, 31, 807–811.

    Article  Google Scholar 

  • Palmer, S. E. (2002). Vision science: Photons to phenomenology. Cambridge, MA: MIT Press.

    Google Scholar 

  • Pani, J. R., Chariker, J. H., Dawson, T. E., & Johnson, N. (2005). Acquiring new spatial intuitions: Learning to reason about rotations. Cognitive Psychology, 51, 285–333. doi:10.1016/j.cogpsych.2005.06.002.

    Article  Google Scholar 

  • Pani, J. R., Chariker, J. H., & Naaz, F. (2013). Computer based learning: Interleaving whole and sectional representation of neuroanatomy. Anatomical Sciences Education, 6, 11–18. doi:10.1002/ase.1297.

    Article  Google Scholar 

  • Parent, A. (1996). Carpenter’s human neuroanatomy (9th ed.). Baltimore: Williams & Wilkens/Waverly.

    Google Scholar 

  • Ratiu, P., Hillen, B., Glaser, J., & Jenkins, D. P. (2003). Visible Human 2.0: The next generation. In J. D. Westwood, H. M. Hoffman, G. T. Mogel, R. Phillips, R. A. Robb, & D. Stredney (Eds.), Medicine meets virtual reality 11—NextMed: Health horizon (pp. 275–281). Amsterdam: IOS Press.

    Google Scholar 

  • Ruiz, J. G., Cook, D. A., & Levinson, A. J. (2009). Computer animations in medical education: A critical literature review. Medical Education, 43, 838–846. doi:10.1111/j.1365-2923.2009.03429.x.

    Article  Google Scholar 

  • Saadawi, G. M., Tseytin, E., Legowski, E., Jukic, D., Castine, M., & Crowley, R. S. (2008). A natural language intelligent tutoring system for training pathologists: Implementation and evaluation. Advances in Health Sciences Education, 13, 709–722. doi:10.1007/s10459-007-9081-3.

    Article  Google Scholar 

  • Squire, L., Berg, D., Bloom, F., Du Lac, S., Ghosh, A., & Spitzer, N. (Eds.). (2008). Fundamental neuroscience (3rd ed.). New York: Academic Press/Elsevier.

    Google Scholar 

  • Standring, S. (Ed.). (2008). Gray’s anatomy: The anatomical basis of clinical practice (40th ed.). London: Churchill Livingstone/Elsevier.

    Google Scholar 

  • Surgical Planning Laboratory. (2013). The Publication Database hosted by SPL. [online]. URL: http://www.slicer.org/publications/gallery. Accessed July 15, 2013.

  • Tam, M. D. B. S., Hart, A. R., Williams, S., Heylings, D., & Leinster, S. (2009). Is learning anatomy facilitated by computer-aided learning? A review of the literature. Medical Teacher, 31, e393–e396. doi:10.1080/01421590802650092.

    Article  Google Scholar 

  • Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human Computer Studies, 47, 247–262. doi:10.1006/ijhc.2002.1017.

    Article  Google Scholar 

  • Wang, R. F., & Simons, D. J. (1999). Active and passive scene recognition across views. Cognition, 70, 191–210.

    Article  Google Scholar 

  • Woods, N. N., Brooks, L. R., & Norman, G. R. (2007). It all make sense: Biomedical knowledge, causal connections and memory in the novice diagnostician. Advances in Health Sciences Education: Theory and Practice, 12, 405–415.

    Article  Google Scholar 

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Acknowledgments

Primary support for this research came from grant R01 LM008323 from the National Library of Medicine, NIH (PI: J. Pani). Additional support was provided by grant IIS-0650138 from the National Science Foundation and Defense Intelligence Agency.

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Correspondence to John R. Pani.

Appendix: Questionnaire items not presented earlier in this paper

Appendix: Questionnaire items not presented earlier in this paper

3. In the programs that illustrated sectional anatomy, please rate the importance, in your opinion, of being able to select a structure and to use the slider to move continuously through the sections. Use a scale from 1 to 5, with 1 being “not important” and 5 being “very important”

 

Mean = 4.1, SD = 0.81

4. You learned whole anatomy first and then sectional anatomy. In the future, how should whole and sectional anatomy instruction be ordered (circle the letter in front of the statement that best characterizes your opinion)

 

Frequency

(a) Definitely start with sectional anatomy. Move to whole anatomy afterward

0

(b) Probably should start with sectional anatomy, although it may not matter

0

(c) The order would not matter

0

(d) Probably should start with whole anatomy, although it may not matter

6

(e) Definitely start with whole anatomy. Move to sectional anatomy afterward

17

5. Please rate the difficulty in moving from whole anatomy to sectional anatomy. In other words, once you know whole anatomy, how challenging is it to learn sectional anatomy with these programs? Please circle the letter in front of the statement that best characterizes your opinion:

 

Frequency

(a) Whole and sectional anatomy are independent

 

Knowing one does not help to learn the other

0

(b) Even if you know whole anatomy, sectional anatomy is still very challenging

8

(c) Even if you know whole anatomy, learning sectional anatomy is challenging

6

(d) If you know whole anatomy, learning sectional anatomy is not too bad

8

(e) If you know whole anatomy, learning sectional anatomy is easy

1

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Pani, J.R., Chariker, J.H., Naaz, F. et al. Learning with interactive computer graphics in the undergraduate neuroscience classroom. Adv in Health Sci Educ 19, 507–528 (2014). https://doi.org/10.1007/s10459-013-9483-3

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