Adaptive learning Environment (ALE)

Adaptive learning Environment (ALE)

ALE is also referred as intelligent tutoring system. 

Support students to acquire knowledge and conduct investigation according to certain learner’s attributes:

- consider different students’ characteristics

- learning status of each student

- personal factors: learning style, preferences, learning progress, affective/emotional state, motivational aspects, knowledge levels and cognitive abilities

To provide adaptive and intelligent system capacities to teachers and students, for example, intelligent support, adaptive interfaces, personalized support (Mampadi, Chen, Ghinea, and Chen 2011; Papanikolaou, Grigoriadou, Magoulas, and Kornilakis 2002; Yang et al. 2013). 

According to Clancey (1984), conventional intelligent tutoring system typically consists of four components:

- an expert model or expert knowledge model that contains the teaching materials, 

- a student model or learner model that evaluates students’ learning status and performance, 

- an instructional model or pedagogical knowledge model that determines teaching content, educational tools and presentation methods based on the outcomes of the student model, 

- a user interface for interacting with students 

Examples of adaptation strategies for providing personalized learning in web-based systems Brusilovsky (2001): 

- adaptive presentation, which presents personalized learning materials

- adaptive navigation support, guides individual students to browse learning content based on the recommended learning paths.

Further readings to demonstrate several examples of adaptive hypermedia learning systems with experiments to confirm effectiveness, based on the two adaptation strategies: Tseng et al. (2008a,b), Gonzalez and Ingraham (1994), Papanikolaou et al. (2002), Karampiperis and Sampson (2005), Martens (2005).

- Adaptive learning systems which considers different personal factors, Kinshuk et al. 2012, Tseng et al. (2008a,b) and Yang et al. (2013a,b).

- Adaptive learning systems based on mobile, wireless communication and sensing technologies: Hwang et al. (2010), Hsieh et al. (2011)

Current challenge: applying intelligent tutoring or adaptive learning techniques to real-world learning situations.

References and further reading:

WJ Clancey, Methodology For Building An Intelligent Tutoring System, in Methods And Tactics In Cognitive Science, ed. by W Kintsch, PG Polson, JR Miller (Lawrence Erlbaum Associates, Hillsdale, NJ, 1984), pp. 51–84.

F Mampadi, SYH Chen, G Ghinea, MP Chen, Design of adaptive hypermedia learning systems: a cognitive style approach. Comput. Educ. 56(4), 1003–1011 (2011).

KA Papanikolaou, M Grigoriadou, GD Magoulas, H Kornilakis, Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment. Comput. Educ. 39, 333–360 (2002).

CC Yang, CM Hung, GJ Hwang, SS Tseng, An evaluation of the learning effecttiveness of concept map-based science book reading via mobile devices. Educ. Technol. Soc 16(3), 167–178 (2013).

TC Yang, GJ Hwang, SJH Yang, Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. Educ. Technol. Soc. 16(4), 185–200 (2013).

P Karampiperis, D Sampson, Adaptive learning resources sequencing in educational hypermedia systems. Educ. Technol. Soc 8(4), 128–147 (2005)

Kinshuk, T Lin, User exploration based adaptation in adaptive learning systems. Int. J. Inf. Syst. Educ 1(1), 22–31 (2003)

Kinshuk, NS Chen, S Graf, GJ Hwang, Adaptive Learning Systems, in Knowledge Management, Organizational Intelligence and Learning and Complexity, ed. by UNESCO-EOLSS Joint Commitee (Encyclopedia of Life Support Systems(EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Oxford,UK, 2012). 

M Specht, G Weber, S Heitmeyer, V Schöch, AST: Adaptive WWW-Courseware for Statistics, in Proceedings of Workshop “Adaptive Systems and User Modeling on the World Wide Web” at 6th International Conference on User Modeling, June 2-5, 1997, Chia Laguna, Sardinia, ed. by P Brusilovsky, J Fink, J Kay (Italy, 1997), pp. 91–95. 

JCR Tseng, HC Chu, GJ Hwang, CC Tsai, Development of an adaptive learning system with two sources of personalization information. Comput. Educ. 51(2), 776–786 (2008).

SS Tseng, JM Su, GJ Hwang, GH Hwang, CC Tsai, CJ Tsai, An object-oriented course framework for developing adaptive learning systems. Educ. Technol. Soc. 11(2), 171–191 (2008).

P Brusilovsky, Adaptive hypermedia. User Model User Adapt Interact 11, 87–110 (2001).

GJ Hwang, FR Kuo, PY Yin, KH Chuang, A heuristic algorithm for planning personalized learning paths for context-aware ubiquitous learning. Comput. Educ. 54(2), 404–415 (2010).

SW Hsieh, YR Jang, GJ Hwang, NS Chen, Effects of teaching and learning styles on students’ reflection levels for ubiquitous learning. Comput. Educ. 57(1), 1194–1201 (2011).

CC Yang, CM Hung, GJ Hwang, SS Tseng, An evaluation of the learning effectiveness of concept map-based science book reading via mobile devices. Educ. Technol. Soc 16(3), 167–178 (2013a).

TC Yang, GJ Hwang, SJH Yang, Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. Educ. Technol. Soc. 16(4), 185–200 (2013b).

A Martens, Modeling Of Adaptive Tutoring Processes, in Web-Based Intelligent e-Learning Systems: Technologies and Applications, ed. by ZM Ma, Chapter 10th edn. (Information Science Publishing, Hershey, London, 2005), pp. 193–215.

AV Gonzalez, LR Ingraham, Automated exercise progression in simulation-based training. IEEE Transactions on System, Man, Cybernetics 24(6), 863–874 (1994).