The paper addresses and explains some of the key questions about the use of data mining in educational technology classroom research. Two examples of use of data mining techniques, namely, association rules mining and fuzzy representations are presented, from a study conducted in Europe and another in Australia. Both of these studies examine student learning, behaviors, and experiences within computer-supported classroom activities. In the first study, the technique of association rules mining was used to understand better how learners with different cognitive types interacted with a simulation to solve a problem. Association rules mining was found to be a useful method for obtaining reliable data about learners' use of the simulation and their performance with it. The study illustrates how data mining can be used to advance educational software evaluation practices in the field of educational technology. In the second study, the technique of fuzzy representations was employed to inductively explore questionnaire data. The study provides a good example of how educational technologists can use data mining for guiding and monitoring school-based technology integration efforts. Based on the outcomes, the implications of the study are discussed in terms of the need to develop educational data mining tools that can display results, information, explanations, comments, and recommendations in meaningful ways to non-expert users in data mining. Lastly, issues related to data privacy are addressed.

. (2016). Student rules: exploring patterns of students' computer-efficacy and engagement with digital technologies in learning. Computers and Education,

A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and survivability. The survivability prediction accuracy of a MLP is improved by using identified patient cohorts as opposed to using raw historical data. Analysis of variable values in each cohort provide better insights into survivability of a particular subgroup of breast cancer patients.

No abstract

No abstract