Predicting who will drop out of nursing courses: A machine learning exercise

Nurse Education Today

Volumn 28, Issue 4, 2008, Pages 469-475

  Moseley, Laurence G ^a   Mead, Donna M ^a  

^a UNIVERSITY OF GLAMORGAN   (United Kingdom)

Author keywords

Data mining; Machine learning; Prediction; Student drop outs

Indexed keywords

ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CHI SQUARE DISTRIBUTION; DECISION TREE; EPIDEMIOLOGY; FORECASTING; HUMAN; NURSING EDUCATION; NURSING STUDENT; PREDICTION AND FORECASTING; PSYCHOLOGICAL ASPECT; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS; STUDENT; TIME; UNITED KINGDOM; VALIDATION STUDY;

ALGORITHMS; ANALYSIS OF VARIANCE; ARTIFICIAL INTELLIGENCE; CAUSALITY; CHI-SQUARE DISTRIBUTION; DECISION TREES; EDUCATION, NURSING, BACCALAUREATE; FORECASTING; HUMANS; LOGISTIC MODELS; NEURAL NETWORKS (COMPUTER); NURSING EDUCATION RESEARCH; PREDICTIVE VALUE OF TESTS; SENSITIVITY AND SPECIFICITY; STUDENT DROPOUTS; STUDENTS, NURSING; TIME FACTORS; WALES;

EID: 41949129128     PISSN: 02606917     EISSN: 15322793     Source Type: Journal    
DOI: 10.1016/j.nedt.2007.07.012     Document Type: Article

References (32)

1. Alexander J.E., and Brophy G.H. A five-year study of graduates' performance on NCLEX-RN. Journal of Nursing Education 36 9 (1997) 443-445
2. Auditor General for Wales, 2001. Educating and Training the future health professional workforce for England. Presented to the National Assembly, 1 March 2001.
3. Baron J. Thinking and Deciding. third ed. (2000), Cambridge, UP
4. Boudreaux, R.W., 2004. Predicting the success of vocational nursing students. Ed D Dissertation University of Houston.
5. Comptroller and Auditor General, 2001. Educating and Training the future health professional workforce for England. Report by the Comptroller and Auditor General HC 277, 1 March 2001.
6. Dawes R.M. The robust beauty of improper linear models in decision-making. American Psychologist 34 (1979) 571-582
7. Dawes R.M., and Corrigan B. Linear models in decision-making. Psychological Bulletin 81 (1974) 95-106
8. Dawes R.M., Faust D., and Meehl P.E. Clinical versus actuarial prediction. Science 243 (1989) 1668-1674
9. Deary I.J., Watson R., and Hogston R. A longitudinal cohort study of burnout and attrition in nursing students. Journal of Advanced Nursing 43 1 (2003) 71-81
10. Gigerenzer G. Reckoning with Risk (2002), Penguin Books, UK
11. Gilovich, T., Griffin, D., Kahneman, D., 2002. Cambridge University Press, New York.
12. Glossop C. Student nurse attrition from pre-registration courses: investigating methodological issues. Nurse Education Today 21 3 (2001) 170-180
13. Glossop C. Student nurse attrition: use of an exit-interview procedure to determine student's leaving reasons. Nurse Education Today 22 5 (2002) 375-396
14. Grove W.E., and Meehl P.E. Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: the clinical-statistical controversy. Psychology, Public Policy and Law 2 (1996) 293-323
15. Grove W.M., Zald D.H., Lebow B.S., Snitz C.E., and Nelson C. Clinical versus Mechanical prediction: a meta-analysis. Psychological Assessment 12 (2000) 19-30
16. Guilbault R.L., Bryant F.B., Howard J., Brockway E., and Posavac J. A meta-analysis of research on hindsight bias. Basic and Applied Social Psychology 26 2-3 (2004) 103-117
17. Haughton D., Deichmann J., Eshghi A., Sayek S., Teebagy N., and Topi H. A review of software packages for data mining. American Statistician 57 4 (2003) 290-309
18. Hayes, C., 2005. Identification of cognitive and non-cognitive predictive variables related to attrition in baccalaureate nursing education programs in Mississippi. Ed D dissertation. Delta State University.
19. Jay J.C.-S., and Cynthia F.W. The hindsight bias: a meta-analysis. Organizational Behavior and Human Decision Processes 48 1 (1991) 147-168
20. Johnes G., and McNabb R. Never give up on the good times: student attrition in the UK. Oxford Bulletin of Economics and Statistics 66 1 (2004) 23-47
21. Jones J.K. The role of data mining technology in the identification of signals of possible adverse drug reactions: value and limitations. Current Therapeutic Research 62 9 (2001) 664-672
22. Kahneman D., Slovic P., and Tversky A. Judgement Under Uncertainty: Heuristics and Biases (1982), Cambridge University Press Part IV
23. Last L., and Fullbrook P. Why do student nurses leave? Suggestions from a Delphi study. Nurse Education Today 23 6 (2003) 449 pp. 39-58
24. Marchese M.C. Clinical versus actuarial prediction: a review of the literature. Perceptual and Motor Skills 75 2 (1992) 583-594
25. Meehl P.E. Clinical Versus Statistical Prediction (1954), University of Minnesota Press, Minneapolis
26. Meyer G.J., Finn S.E., Eyde L.D., Kay G.G., Moreland K.L., Dies R.R., Eisman E.J., Kubiszyn T.W., and Reed G.M. Psychological testing and psychological assessment - A review of evidence and issues. American Psychologist 56 2 (2001) 128-165
27. Moseley L.G. Is Thinking Just Too Difficult? Inaugural Lecture (1999), University of Glamorgan
28. Myers D.G. Intuition: its powers and perils (2004), Yale UP, New Haven
29. Nursing Standard, 20, 23, Feb 15 2006, p 5.
30. Sawyer J. Measurement and prediction clinical and statistical. Psychological Bulletin 66 3 (1966) 178-200
31. Tetlock P. Expert Political Judgment: How Good Is It? How Can We Know? (2005), Princeton UP, Cited in New Yorker December 5
32. Wharrad H.J., Chapple M., and Price N. Predictors of academic success on a Bachelor of Nursing course. Nurse Education Today 23 4 (2003) 246-254