Copy the page URI to the clipboard
Wolff, Annika; Zdrahal, Zdenek; Herrmannova, Drahomira and Knoth, Petr
(2013).
DOI: https://doi.org/10.1007/978-3-319-02738-8_7
Abstract
This chapter will explore the use of predictive modeling methods for identifying students who will benefit most from tutor interventions. This is a growing area of research and is especially useful in distance learning where tutors and students do not meet face to face. The methods discussed will include decision-tree classification, support vector machine (SVM), general unary hypotheses automaton (GUHA), Bayesian networks, and linear and logistic regression. These methods have been trialed through building and testing predictive models using data from several Open University (OU) modules. The Open University offers a good test-bed for this work, as it is one of the largest distance learning institutions in Europe. The chapter will discuss how the predictive capacity of the different sources of data changes as the course progresses. It will also highlight the importance of understanding how a student’s pattern of behavior changes during the course.
Viewing alternatives
Metrics
Public Attention
Altmetrics from AltmetricNumber of Citations
Citations from DimensionsItem Actions
Export
About
- Item ORO ID
- 39271
- Item Type
- Book Section
- ISBN
- 3-319-02737-9, 978-3-319-02737-1
- ISSN
- 1860-949X
- Keywords
- predictive modeling; education; virtual learning environment; student outcome
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) - Research Group
- Centre for Research in Computing (CRC)
- Copyright Holders
- © 2014 Springer International Publishing Switzerland
- Depositing User
- Kay Dave