Fuzzy sets, decision making and expert systems
Fuzzy sets, decision making and expert systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Cyberspace 2000: dealing with information overload
Communications of the ACM
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Automatic personalization based on Web usage mining
Communications of the ACM
Information retrieval on the web
ACM Computing Surveys (CSUR)
Profiling students' adaptation styles in Web-based learning
Computers & Education
Ganging up on Information Overload
Computer
Personalized Courseware Construction Based on Web Data Mining
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 2 - Volume 2
Personalized e-learning system using Item Response Theory
Computers & Education
Expert Systems with Applications: An International Journal
Searching the Web: general and scientific information access
IEEE Communications Magazine
Multi-agent Framework Support for Adaptive e-Learning
ICWL '08 Proceedings of the 7th international conference on Advances in Web Based Learning
An adjustable personalization of search and delivery of learning objects to learners
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A semantic approach to expert system for e-Assessment of credentials and competencies
Expert Systems with Applications: An International Journal
The study and design of adaptive learning system based on fuzzy set theory
Transactions on edutainment IV
Information Systems Frontiers
Personalized Learning Course Planner with E-learning DSS using user profile
Expert Systems with Applications: An International Journal
Item difficulty estimation: An auspicious collaboration between data and judgment
Computers & Education
The design and implementation of a competency-based intelligent mobile learning system
Expert Systems with Applications: An International Journal
Benchmarking data mining methods in CAT
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
A hybrid fuzzy-based personalized recommender system for telecom products/services
Information Sciences: an International Journal
An empirical study on the quantitative notion of task difficulty
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field. Previously, many researchers put effort into e-learning systems with personalized learning mechanism to aid on-line learning. However, most systems focus on using learner's behaviors, interests, and habits to provide personalized e-learning services. These systems commonly neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other. Frequently, unsuitable courseware causes learner's cognitive overload or disorientation during learning. To promote learning effectiveness, our previous study proposed a personalized e-learning system based on Item response theory (PEL-IRT), which can consider both course material difficulty and learner ability evaluated by learner's crisp feedback responses (i.e. completely understanding or not understanding answer) to provide personalized learning paths for individual learners. The PEL-IRT cannot estimate learner ability for personalized learning services according to learner's non-crisp responses (i.e. uncertain/fuzzy responses). The main problem is that learner's response is not usually belonging to completely understanding or not understanding case for the content of learned courseware. Therefore, this study developed a personalized intelligent tutoring system based on the proposed fuzzy item response theory (FIRT), which could be capable of recommending courseware with suitable difficulty levels for learners according to learner's uncertain/fuzzy feedback responses. The proposed FIRT can correctly estimate learner ability via the fuzzy inference mechanism and revise estimating function of learner ability while the learner responds to the difficulty level and comprehension percentage for the learned courseware. Moreover, a courseware modeling process developed in this study is based on a statistical technique to establish the difficulty parameters of courseware for the proposed personalized intelligent tutoring system. Experiment results indicate that applying the proposed FIRT to web-based learning can provide better learning services for individual learners than our previous study, thus helping learners to learn more effectively.