Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Cyberspace 2000: dealing with information overload
Communications of the ACM
A testing system for diagnosing miconceptions in DC electric circuits
Computers & Education
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Ganging up on Information Overload
Computer
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Application of Machine Learning Techniques to Web-Based Intelligent Learning Diagnosis System
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Personalized e-learning system using Item Response Theory
Computers & Education
Personalized curriculum sequencing utilizing modified item response theory for web-based instruction
Expert Systems with Applications: An International Journal
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Seeking activity: on the trail of users in open and community source frameworks
Proceedings of the 35th annual ACM SIGUCCS fall conference
Intelligent web-based learning system with personalized learning path guidance
Computers & Education
Insights and surprises from usage patterns: some benefits of data mining in academic online systems
Proceedings of the 36th annual ACM SIGUCCS fall conference: moving mountains, blazing trails
Dynamic question generation system for web-based testing using particle swarm optimization
Expert Systems with Applications: An International Journal
A framework of an agent-based personal assistant for internet users
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Data mining for adaptive learning in a TESL-based e-learning system
Expert Systems with Applications: An International Journal
Mining fuzzy specific rare itemsets for education data
Knowledge-Based Systems
Discovering discriminative test items for achievement tests
Expert Systems with Applications: An International Journal
Mining term networks from text collections for crime investigation
Expert Systems with Applications: An International Journal
Artificial Intelligence Review
Constructing concept maps for adaptive learning systems based on data mining techniques
Expert Systems with Applications: An International Journal
Data Mining User Activity in Free and Open Source Software FOSS/ Open Learning Management Systems
International Journal of Open Source Software and Processes
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 fields. Most past researches for web-based learning focused on the issues of adaptive presentation, adaptive navigation support, curriculum sequencing, and intelligent analysis of student's solutions. These systems commonly neglect to consider whether learner can understand the learning courseware and generate misconception or not. To neglect learner's learning misconception will lead to obviously reducing learning performance, thus generating learning difficult. In order to discover common learning misconceptions of learners, this study employs the association rule to mine the learner profile for diagnosing learners' common learning misconceptions during learning processes. In this paper, the association rules that occurring misconception A implies occurring misconception B can be discovered utilizing the proposed association rule learning diagnosis approach. Meanwhile, this study applies the discovered association rules of the common learning misconceptions to tune courseware structure through modifying the difficulty parameters of courseware in the courseware database so that learning pathway is appropriately tuned. Besides, this paper also presents a remedy learning approach based on the discovered common learning misconceptions to promote learning performance. Experiment results indicate that applying the proposed learning diagnosis approach can correctly discover learners' common learning misconceptions according to learner profile and help learners to learn more effectively.