دانلود رایگان مقاله انگلیسی داده کاوی در تحقیقات کلاس درس فناوری آموزشی - الزویر 2017

Abstract

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 nonexpert users in data mining. Lastly, issues related to data privacy are addressed.

General discussion and concluding remarks

In this paper, the contribution of EDM for educational technology classroom research has been examined within the context of two studies with different types of datasets and purposes. The first study, which made use of video data converted first into log-file data before mining, investigated EDM as a potential software evaluation method for improving the design of a stand-alone simulation tool to benefit learners’ needs. The second study, which made use of questionnaire data, investigated EDM as a method for providing detailed student data for informing school-based technology integration initiatives.

The first study provides a good example of how EDM can be used to advance educational software evaluation practices in the field of educational technology. The employment of association rules mining in this research study provided the authors with (a) reliable data about how learners with different cognitive types interacted with a simulation to solve a problem, and, (b) insights about how learning analytics can be designed and incorporated in the learning design of the simulation. Due to the fact that in this study the association rules mining method produced an enormous body of complicated output – something that can easily discourage educational researchers from employing data mining tools and methods in their research – the authors recommend that educational data mining tools employ alternative ways of reporting results to educational researchers.

The second study provides a good example of how educational technologists can use EDM for guiding and monitoring school-based technology-integration efforts. Taking into consideration the complexity of such efforts (Borko et al., 2009), the results of the second study showed that EDM was quite useful for examining complex interactions and relations among key factors affecting technology integration. What is more, the second study made use of questionnaire data, something uncommon for EDM methods due to the nature of this type of data as it tends to be incomplete and inconsistent.