Adaptive eLearning systems capitalise on individual learning characteristics when delivering content to maximise the effectiveness of instruction based on the assumptions that different learning outcomes require different skills or abilities, and that individuals differ in their abilities to process information, construct meaning from it, and apply the information in new situations (Jonassen & Grabowski, 2012; Shute & Towle, 2003). This entails constructing a learning experience that purposely adjusts to various conditions over a period of time, requiring the ability to interact with and manipulate data on the learning design, the users and the system, and its contents, with the intention of increasing pre-defined success criteria, such as the effectiveness of eLearning or user involvement and satisfaction (Van Rosmalen, Vogten, Van Es, Passier, Poelmans & Koper, 2006).
A number of different approaches to adaptive eLearning have been attempted based upon a range of theoretical models which can be considered as follows (Froschl, 2005; Mödritscher, Garcia-Barrios & Gütl, 2004; García-Barrios, Mödritscher & Gütl, 2005):
- Macro-adaptive approaches adapt instruction by allowing students to select components such as learning objective, levels of detail, delivery system, etc., based on their learning goals, general abilities, and achievement levels in the curriculum structure.
- Aptitude-treatment interaction approaches utilise different types of instruction or media to provide a range of instructional environments suited for optimal learning for groups of individuals with particular aptitude patterns.
- Micro-adaptive approaches diagnose the student’s specific learning needs during instruction and provide instructional prescriptions in accordance with those needs.
- Constructivistic-collaborative approaches considers adaptation by means of new learning paradigms, motivational aspects, collaborative tasks, context dependent learning etc., focussing on how knowledge is constructed through experience in specific knowledge domains according to constructivist learning theory, with additional emphasis on collaborative learning technologies.
In this way, adaptive eLearning systems can be considered as the modern embodiment of Aptitude-Treatment Interaction research, which explores interactions between individual aptitudes, attributes, or traits and different instructional methods (Jonassen & Grabowski, 2012; Shute & Towle, 2003). Enhancing learning and performance is thus a function of adapting instruction and content in accordance with the pedagogical preference and cognitive ability of each individual learner within an adaptive eLearning system (Maycock 2010).
Adaptive eLearning systems can thus be considered in terms of a series of separate components which function together to adapt the structure and presentation of instructional content according to the individual learning profile of the user (Maycock 2010; Shute & Towle, 2003). While implementations can vary from system to system, a key feature of any adaptive eLearning system is a user model, which captures the important aspects of a learner for the purpose of individualising instruction (Brusilovsky & Millán, 2007; Shute & Towle, 2003).
Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovski, A. Kobsa, & W. Nejdl (Eds.) The adaptive web: Methods and strategies of web personalisation (pp. 3-53). Heidelberg, Berlin: Springer-Verlag.
Froschl, C. (2005). User modeling and user profiling in adaptive e-learning systems (Unpublished master’s thesis). Graz University of Technology, Austria.
García-Barrios, V. M., Mödritscher, F., & Gütl, C. (2005). Personalisation versus adaptation? A user-centred model approach and its application. In Proceedings of the International Conference on Knowledge Management (I-KNOW) (pp. 120-127).
Jonassen, D. H., & Grabowski, B. L. (2012). Handbook of individual differences, learning, and instruction. New York, NY: Routledge.
Maycock, K. (2010). A framework for adaptive e-learning (Unpublished doctoral dissertation). National University of Ireland, Ireland.
Mödritscher, F., Garcia-Barrios, V. M., & Gütl, C. (2004). The past, the present and the future of adaptive e-learning. Proceedings of ICL 2004.
Shute, V., & Towle, B. (2003). Adaptive e-learning. Educational Psychologist, 38(2), 105-114.
Van Rosmalen, P., Vogten, H., Van Es, R., Passier, H., Poelmans, P., & Koper, R. (2006). Authoring a full life cycle model in standards-based, adaptive e-learning. Educational Technology & Society, 9(1), 72-83.