This paper has developed an online personalized English learning recommender system capable of providing ESL students with reading lessons that suit their different interests and therefore increase the motivation to learn. The system, using content-based analysis, collaborative filtering, and data mining techniques, analyzes real students ’ reading data and generates recommender scores, based on which to help select appropriate lessons for respective students. Its performance having been tracked over a period of one year, this recommender system has proved to be very useful in heightening ESL learners ’ motivation and interest in reading.

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Adaptive and intelligent web based education system: towards an integral architecture and framework

www.elsevier.com/locate/eswa

www.elsevier.com/locate/eswa

This case study analyzes eight expert system applications that have successfully been in industrial use for a long time. We have personally been involved in the development of these applications and are thus in a good position to analyze what is important for a successful application and what kind of mistakes can be made. Since the development of the applications started in 1986–1990 and some of them are still in use we are able to observe what has happened to those applications during their lifetime. Our key observations are related to the scope of the applications, to the trade-off between usability and automation, to the role of human experts in the use and development of expert systems, on the technical solutions used, on aspects of the operation of the expert system and on the similarities between expert systems and information systems. The key findings are expressed as 20 hypotheses for successful expert systems. The support of each application to the hypotheses is discussed.

journal homepage: www.elsevier.com/locate/eswa

journal homepage: www.elsevier.com/locate/eswa

journal homepage: www.elsevier.com/locate/eswa

journal homepage: www.elsevier.com/locate/eswa