Universal approximation using radial-basis-function networks
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Induction of ripple-down rules applied to modeling large databases
Journal of Intelligent Information Systems
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
ACM Computing Surveys (CSUR)
The analysis of a simple k-means clustering algorithm
Proceedings of the sixteenth annual symposium on Computational geometry
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
Using data mining as a strategy for assessing asynchronous discussion forums
Computers & Education
Data mining in course management systems: Moodle case study and tutorial
Computers & Education
Factors influencing university drop out rates
Computers & Education
On Mining and Social Role Discovery in Internet Forums
SOCINFO '09 Proceedings of the 2009 International Workshop on Social Informatics
Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review
Computers & Education - Methodological issue in researching CSCL
Using text mining and sentiment analysis for online forums hotspot detection and forecast
Decision Support Systems
Data Mining for Social Network Data
Data Mining for Social Network Data
Social Network Data Analytics
SNAPP: a bird's-eye view of temporal participant interaction
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Identifying HotSpots in Lung Cancer Data Using Association Rule Mining
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
The multilayer perceptron as an approximation to a Bayes optimal discriminant function
IEEE Transactions on Neural Networks
Modelling learning & performance: a social networks perspective
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
Predicting learner's project performance with dialogue features in online q&a discussions
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Clustering for improving educational process mining
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Using learning analytics to identify successful learners in a blended learning course
International Journal of Technology Enhanced Learning
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On-line discussion forums constitute communities of people learning from each other, which not only inform the students about their peers' doubts and problems but can also inform instructors about their students' knowledge of the course contents. In fact, nowadays there is increasing interest in the use of discussion forums as an indicator of student performance. In this respect, this paper proposes the use of different data mining approaches for improving prediction of students' final performance starting from participation indicators in both quantitative, qualitative and social network forums. Our objective is to determine how the selection of instances and attributes, the use of different classification algorithms and the date when data is gathered affect the accuracy and comprehensibility of the prediction. A new Moodle's module for gathering forum indicators was developed and different executions were carried out using real data from 114 university students during a first-year course in computer science. A representative set of traditional classification algorithms have been used and compared versus classification via clustering algorithms for predicting whether students will pass or fail the course on the basis of data about their forum usage. The results obtained indicate the suitability of performing both a final prediction at the end of the course and an early prediction before the end of the course; of applying clustering plus class association rules mining instead of traditional classification for obtaining highly interpretable student performance models; and of using a subset of attributes instead of all available attributes, and not all forum messages but only students' messages with content related to the subject of the course for improving classification accuracy.