Visualization of navigation patterns on a Web site using model-based clustering
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Generation of Graphical Representations of Student Tracking Data in Course Management Systems
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage
Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Efficient MATLAB Computations with Sparse and Factored Tensors
SIAM Journal on Scientific Computing
Identifying clusters of user behavior in intranet search engine log files
Journal of the American Society for Information Science and Technology
Tensor Decompositions and Applications
SIAM Review
TNeT: Tensor-Based Neighborhood Discovery in Traffic Networks
ICDEW '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering Workshops
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 | 0.00 |
Digital systems underlie a wide range of teaching and learning activities in higher education today. The scope and reach of digital systems now increasingly extend to activities as they occur even inside lecture halls, classrooms, and informal study areas. Learning management systems, interactive student response systems, lecture capture systems, and digitally controlled smart classrooms are examples of technology trends that bring along with them an unprecedented amount of instrumentation quietly collecting lots of data about teacher and learner activities in and across these various spaces. In snapshots, these usage streams offer data that can be helpful for understanding and supporting a particular service. If combined across time and location, the varied data sources potentially open windows onto even more interesting activity patterns and relations. These mosaics, however, can be somewhat difficult to analyze due to the dimensionality of the combined data. Matrix techniques can ease the difficulties of exploring and discovering user activity patterns in such situations. This paper surveys commonly implemented matrix techniques that can be used to enable data mining of user activity information when temporal and spatial data sets are mixed and matched from varied sources.