Data Mining Applications in Computer-Supported Collaborative Learning

Data Mining Applications in Computer-Supported Collaborative Learning

Rosanna Costaguta (Universidad Nacional de Santiago del Estero (UNSE), Argentina)
DOI: 10.4018/978-1-4666-7377-9.ch014
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Our own data mining techniques allow us to discover non-explicit knowledge from a large amount of data. Currently, Computer-Supported Collaborative Learning systems generate a wealth of data, derived from the stored interactions and product of collaborative work of students and teachers. Manual processing of these interactions is both costly and tedious, and practically impossible to do in real time. Because of this, there are now trends of research that attempt to achieve automatic processing using data-mining techniques. This chapter describes the phases and tasks involved in the entire process of knowledge discovery and also presents some research applying data mining to process the contributions of students and teachers in collaborative-learning environments.
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The Knowledge Discovery Process

Constant progress in computer science and associated technologies, together with the growing expansion of their use in various aspects of life has turned the storage of large amounts of data into a frequent situation. Manually scan this accumulation of data in real-time could be tedious and expensive, and usually a humanly impossible task to carry out.

Key Terms in this Chapter

Knowledge Discovery in Databases: Is a process of discovering useful knowledge from a collection of data. This widely used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior knowledge on data sets and interpreting accurate solutions from the observed results.

Machine Learning: Is a subfield of computer science and artificial intelligence that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. Machine learning is employed in a range of computing tasks where designing and programming explicit rule-based algorithms is infeasible for a variety of reasons.

Computer Supported Collaborative Learning: Is an emerging branch of the learning sciences concerned with studying how people can learn together with the help of computers. In others words, it is a pedagogical approach wherein learning takes place via social interaction using a computer or through the Internet, for example. This kind of learning is characterized by the sharing and construction of knowledge among participants using technology as their primary means of communication or as a common resource.

Software Engineering: Is the study and application of engineering to the design, development, and maintenance of software. In short, the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software.

Data Mining: Is a process of analyzing data from different perspectives and summarizing it into useful information. The information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

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