Handbook of Educational Data Mining by Romero C. et al. (Eds.)

By Romero C. et al. (Eds.)

Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy and Ryan S.J.d. Baker, «Handbook of academic information Mining» . guide of academic facts Mining (EDM) offers an intensive evaluate of the present nation of information during this region. the 1st a part of the e-book comprises 9 surveys and tutorials at the central facts mining suggestions which have been utilized in schooling. the second one half provides a collection of 25 case experiences that provide a wealthy assessment of the issues that EDM has addressed.

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An example is a study by Nachmias and Segev [3] in which interactions with 117 courseware Web sites were analyzed. A number of studies have extended this analysis to consider the times of access, showing patterns of student behavior over time periods. For example, studies by Cockburn and McKenzie [15], Hwang and Li [16], and Nilikant and Mitovic [17]. A number of studies have calculated the duration of access to Web sites or resources to give a different picture of student use of learning resources.

Thanks to our visual perceptual abilities, we have seen how graphical representations may be very useful to quickly discover patterns, regularities, and trends on data and provide a useful overview of the whole dataset. We described some systems where visualizations have been used in learning environments to represent user models, discussions, and tracking data. It is recognized that data mining algorithms alone are not enough, as well as it is recognized that it is infeasible to consider graphics displays alone an effective solution to the analysis of complex and large data sets.

39 References........................................................................................................................................ 1╇ Introduction With the enormous growth in the use of Web-based learning environments, there is a need for feedback and evaluation methods that are suitable for these environments. Traditional methods used to monitor and assess learning behavior are not necessarily effective or appropriate for an electronic learning environment. However, Web technologies have enabled a new way of collecting data about learning behavior.

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