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

Full Abstract

INTRODUCTION:
The concepts of causation and prediction are different, and have different implications for practice. This distinction is applied here to studies of the problem of student attrition (although it is more widely applicable).

BACKGROUND:
Studies of attrition from nursing courses have tended to concentrate on causation, trying, largely unsuccessfully, to elicit what causes drop out. However, the problem may more fruitfully be cast in terms of predicting who is likely to drop out.

METHODS:
One powerful method for attempting to make predictions is rule induction. This paper reports the use of the Answer Tree package from SPSS for that purpose.

DATA:
The main data set consisted of 3978 records on 528 nursing students, split into a training set and a test set. The source was standard university student records.

RESULTS:
The method obtained 84% sensitivity, 70% specificity, and 94% accuracy on previously unseen cases.

DISCUSSION:
The method requires large amounts of high quality data. When such data are available, rule induction offers a way to reduce attrition. It would be desirable to compare its results with those of predictions made by tutors using more informal conventional methods.

Learn Faster Today Improve your study skills

Author information

Author/s: Moseley, Laurence G (LG); Mead, Donna M (DM);

Affiliation: HESAS, Glyntaff Campus, University of Glamorgan, Portypridd CF37 1DL, UK. LGMoseley@btinternet.com

Journal and publication information

Publication Type: Journal Article; Validation Studies

Journal: Nurse education today (Nurse Educ Today), published in Scotland. (Language: eng)

Reference: 2008-May; vol 28 (issue 4) : pp 469-75

Dates: Created 2008/04/16; Completed 2008/07/11;

PMID: 17920163, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

External Links for this article (including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.

MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Logistic Models
Neural Networks (Computer)
Nursing Education Research
Predictive Value of Tests
Sensitivity and Specificity
Student Dropouts - psychology; statistics & numerical data
Students, Nursing - psychology; statistics & numerical data
Time Factors
Wales

These are the highest related articles currently in the database:

See 100+ related articles.