Abstract
This paper presents a neural network-based intelligent data analysis for knowledge clustering in an undergraduate nursing course. A MCQ (Multiple Choice Question) test was performed to evaluate medical-surgical nursing knowledge in a second-year course. A total of 23 pattern groups were created from the answers of 208 students. Data collected were used to provide customized feedback which guide students towards a greater understanding of particular concepts. The pattern groupings can be integrated with an on-line (MCQ) system for training purposes.
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References
Fernández-Alemán, J.L., Palmer-Brown, D., Jayne, C.: Effects of response-driven feedback in computer science learning. IEEE Trans. Education 54(3), 501–508 (2011)
Chen, C.M., Hsieh, Y.L., Hsu, S.H.: Mining learner profile utilizing association rule for web-based learning diagnosis. Expert Systems with Applications 33, 6–22 (2007)
Verdú, E., Verdú, M.J., Regueras, L.M., de Castro, J.P., García, R.: A genetic fuzzy expert system for automatic question classification in a competitive learning environment. Expert Systems with Applications 39, 7471–7478 (2012)
Aparicio, F., Buenaga, M.D., Rubio, M., Hernando, A.: An intelligent information access system assisting a case based learning methodology evaluated in higher education with medical students. Computers & Education 58, 1282–1295 (2012)
Zafra, A., Romero, C., Ventura, S.: Multiple instance learning for classifying students in learning management systems. Expert Systems with Applications 38, 15020–15031 (2011)
Epstein, M.L., Lazarus, A.D., Calvano, T.B., Matthews, K.A., Hendel, R.A., Epstein, B.B., Brosvic, G.M.: Immediate feedback assessment technique promotes learning and corrects inaccurate first responses. The Psychological Record 52, 187–201 (2002)
Higgins, E., Tatham, L.: Exploring the potential of multiple choice questions in assessment. Learn. and Teach. in Act. 2 (2003)
Kuechler, W.L., Simkin, M.G.: How well do multiple choice tests evaluate student understanding in computer programming classes? J. Inf. Syst. Educ. 14, 389–399 (2003)
Kreig, R.G., Uyar, B.: Student performance in business and economics statistics: Does exam structure matter? J. Econ. and Finance 25, 229–240 (2001)
Pamphlett, R., Farnill, D.: Effect of anxiety on performance in multiple choice examination. Med. Educ. 29, 297–302 (1995)
Wesolowsky, G.O.: Detecting excessive similarity in answers on multiple choice exams. J. Appl. Stat. 27, 909–921 (2000)
Paxton, M.: A linguistic perspective on multiple choice questioning assessment and evaluation. Assessment & Evaluation in High. Educ. 25, 109–119 (2000)
Nicol, D.: E-assessment by design: using multiple-choice tests to good effect. J. Further and High. Educ. 31, 53–64 (2007)
Masters, J.C., Hulsmeyer, B.S., Pike, M.E., Leichty, K., Miller, M.T., Verst, A.L.: Assessment of multiple-choice questions in selected test banks accompanying text books used in nursing education. J. Nurs. Educ. 40, 25–32 (2001)
Su, W.M., Osisek, P.J., Montgomery, C., Pellar, S.: Designing multiple-choice test items at higher cognitive levels. Nurse Educator 34, 223–227 (2009)
Morrison, S., Free, K.W.: Writing multiple-choice test items that promote and measure critical thinking. The Journal of Nursing Education 40, 17–24 (2001)
Holsgrove, G., Elzubeir, M.: Imprecise terms in UK medical multiple-choice questions: what examiners think they mean. Med. Educ. 32, 343–350 (1998)
Brady, A.M.: Assessment of learning with multiple-choice questions. Nurse Education in Practice 5, 238–242 (2005)
Quinn, F.: The Principles and Practice of Nurse Education, 4th edn. Stanley Thorne Ltd., Cheltenham (2000)
Farley, J.K.: The multiple-choice test: writing the questions. Nurse Educ. 14, 10–12, 39 (1989)
Holsgrove, G.J.: Guide to postgraduate exams: multiple-choice questions. British Journal of Hospital Medicine 48, 757–761 (1992)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16, 645–678 (2005)
Kohonen, T.: Self-Organisation and Asssociative Memory, 3rd edn. Series in Information Sciences. Springer, Heilderberg (1989)
Carpenter, G.A., Grossberg, S.: Adaptive Resonance Theory. In: The Handbook of Brain Theory and Neural Networks, 2nd edn. MIT Press, Cambridge (2003)
Lee, S.W., Palmer-Brown, D., Roadknight, C.M.: Performance-guided neural network for rapidly self-organising active network management. Neurocomputing 61, 5–20 (2004)
Palmer-Brown, D., Draganova, C., Lee, S.W.: Snap-drift neural network for selecting student feedback. In: IJCNN, pp. 391–398 (2009)
Brady, A.M.: Assessment of learning with multiple-choice questions. Nurse Education in Practice 5 (2005)
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Fernández-Alemán, J.L., Jayne, C., García, A.B.S., Carrillo-de-Gea, J.M., Toval Alvarez, A. (2012). Knowledge Clustering Using a Neural Network in a Course on Medical-Surgical Nursing. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_39
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DOI: https://doi.org/10.1007/978-3-642-32909-8_39
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