Adaptive and Intelligent Web-based Educational Systems

By Peter Brusilovsky, Christoph Peylo. International Journal of Artificial Intelligence in Education, 2003

512 citations recorded by the source corpus.

Adaptive and intelligent Web-based educational systems (AIWBES) provide an alternative to the traditional “just-put-it-on-the-Web” approach in the development of Web-based educational courseware (Brusilovsky & Miller, 2001). AIWBES attempt to be more adaptive by building a model of the goals, preferences and knowledge of each individual student and using this model throughout the interaction with the student in order to adapt to the needs of that student. They also attempt to be more intelligent by incorporating and performing some activities traditionally executed by a human teacher - such as coaching students or diagnosing their misconceptions. The first pioneer intelligent and adaptive Web-based educational systems were developed in 1995-1996 (Brusilovsky

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/ba8147f2ef4b4dc6ad85bff4c8762396ccac5f6c

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Citations in the corpus

Preparing teachers for the application of AI-powered technologies in foreign language education (2019, background): “Brusilovsky & Peylo, 2003, p. 158) list as major Intelligent Tutoring technologies the following: curriculum sequencing (providing the student with the most suitable individually planned sequence of topics and learning tasks to help find an “optimal path” through the learning material), intelligent…”
Web usage mining for predicting final marks of students that use Moodle courses (2013, background): “The use of web-based education systems or e-learning systems has grown exponentially in the last years, spurred by the fact that neither students nor teachers are bound to any speciﬁc location and that this form of computer-based education is virtually independent of a speciﬁc hardware platform [1].”
A systematic literature review of personalized learning terms (2020, background): “This allows learning to meet individual needs, interests and circumstances which can be quite diverse (Brusilovsky and Peylo, 2003; Liu and Yu, 2011).”
A Systematic Review on Educational Data Mining (2017, methodology): “Similarly, Brusilovsky & Peylo [17] provided tools that can be used to support EDM.”
Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II (2019, methodology): “Drawing from Participatory Design [36,40,56], in addition to having stakeholders evaluate what is wrong with a proposed concept (which may address other stakeholders’ needs), PSD also involves them in generating alternative designs, to address conflicts among stakeholder groups.”