An Adaptive Course Generation Framework

An Adaptive Course Generation Framework

Frederick W. B. Li, Rynson W. H. Lau, Parthiban Dharmendran
Copyright: © 2010 |Pages: 18
DOI: 10.4018/jdet.2010070104
(Individual Articles)
No Current Special Offers


Existing adaptive e-learning methods are supported by student (user) profiling for capturing student characteristics, and course structuring for organizing learning materials according to topics and levels of difficulties. Adaptive courses are then generated by extracting materials from the course structure to match the criteria specified in the student profiles. In addition, to handle advanced student characteristics, such as learning styles, course material annotation and programming-based decision rules are typically used. However, these additives demand certain programming skills from an instructor to proceed with course construction; they may also require building multiple course structures to handle practical pedagogical needs. In this paper, the authors propose a framework based on the concept space and the concept filters to support adaptive course generation where comprehensive student characteristics are considered. The concept space is a data structure for modeling student and course characteristics, while the concept filters are modifiers to determine how the course should be delivered. Because of the “building block” nature of the concept nodes and the concept filters, the proposed framework is extensible. More importantly, the authors’ framework does not require instructors to equip with any programming skills when they construct adaptive e-learning courses.
Article Preview

A learning process is driven by “what to learn”, i.e., the scope of learning, and “how to learn”, i.e., how a student approaches such learning scope. Adaptive e-learning addresses these two questions by offering students with tailored learning materials. Existing work on adaptive e-learning tackles this problem by applying student profiles on well organized courseware. More specifically, a student profile captures the learning preferences, background knowledge/experiences and learning progress of the student. It forms the basis for filtering a pool of course materials to pick out relevant ones. For instance, InterBook (Brusilovsky et al., 1998) organizes course materials in a hierarchical structure along with indices according to the topics and level of difficulties. (Middleton et al., 1998) improves the discovery of relevant course materials by knowledge classification based on ontology and collaborative choices made by a group of users. The ontology (Studer et al., 1998) formulates the grouping and the relation among concepts. It is commonly applied to organize course materials and to form the metric for determining the user required materials. Another example can be found in (Dolog et al., 2004). The utilization of collaborative information (Balabanović et al., 1997) can enhance the accuracy of the retrieved course materials, as it complements the incompleteness or impreciseness of individual user profiles. (Freyne et al., 2007) has exploited user browsing and searching patterns to give more precise modeling on collaborative information. All of the above methods focus on addressing the “what to learn” problem.

Complete Article List

Search this Journal:
Volume 22: 1 Issue (2024)
Volume 21: 2 Issues (2023)
Volume 20: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 19: 4 Issues (2021)
Volume 18: 4 Issues (2020)
Volume 17: 4 Issues (2019)
Volume 16: 4 Issues (2018)
Volume 15: 4 Issues (2017)
Volume 14: 4 Issues (2016)
Volume 13: 4 Issues (2015)
Volume 12: 4 Issues (2014)
Volume 11: 4 Issues (2013)
Volume 10: 4 Issues (2012)
Volume 9: 4 Issues (2011)
Volume 8: 4 Issues (2010)
Volume 7: 4 Issues (2009)
Volume 6: 4 Issues (2008)
Volume 5: 4 Issues (2007)
Volume 4: 4 Issues (2006)
Volume 3: 4 Issues (2005)
Volume 2: 4 Issues (2004)
Volume 1: 4 Issues (2003)
View Complete Journal Contents Listing