Data mining: Going beyond traditional statistics

Abstract

Creating a new theory is not like destroying an old barn and erecting a skyscraper in its place. It is rather like climbing a mountain, gaining new and wider views, discovering unexpected connections between our starting point and its rich environment. But the point from which we started out still exists and can be seen, although it appears smaller and forms a tiny part of our broad view gained by the mastery of the obstacles on the adventurous way up.Albert EinsteinIf the latter half of the twentieth century was marked by transition into an information age, the twenty-first century is when the rubber meets the road. Our existing methods and approaches to understanding data, the backbone of information, are being seriously challenged, even threatened, by the evermore-unrelenting accumulation of data. As the amount of data increases exponentially, higher education is confronting the crisis of information explosion. Our students expect individualized attention in learning. More individualized and refined research is urgently needed to inform institutions' decision making and to enhance student learning.Institutional data often contain valuable information essential for more in-depth understanding of students and their college experiences. How can we effectively extract quality knowledge from these data to better understand 7 1 NEW DIRECTIONS FOR INSTITUTIONAL RESEARCH, no. 131, Fall 2006

Digital media, technologies and scholarship: Some shapes of eResearch in educational inquiry

“…On the contrary, the more pragmatic and groundedin-data logic of these methodological approaches appears to be well suited for a practical domain such as education. For example, data and text mining involves an iterative process of sniffing through large amounts of data and discovering patterns and relationships (Zhao & Luan, 2006). This exploration combines interpretative investigation with scientific data-based reasoning.…”

Methodological Choice and Design

“…For example, such methods as social network analysis, system modelling, data mining and visualisation have been used in a number of large-scale educational research projects to investigate students' profiles, learning processes and outcomes and to inform teacher and institutional decision-making (Romero & Ventura, 2006Zhao & Luan, 2006). For example, such methods as social network analysis, system modelling, data mining and visualisation have been used in a number of large-scale educational research projects to investigate students' profiles, learning processes and outcomes and to inform teacher and institutional decision-making (Romero & Ventura, 2006Zhao & Luan, 2006).…”

“…For example, data mining or knowledge discovery in databases involves an iterative process of sifting through large amounts of data and discovering patterns and relationships without a priori assumption about the existence or nature of these relationships (Zhao & Luan, 2006). For example, data mining or knowledge discovery in databases involves an iterative process of sifting through large amounts of data and discovering patterns and relationships without a priori assumption about the existence or nature of these relationships (Zhao & Luan, 2006).…”

“…Numerical representation (e.g. This convergence of information, decisions and actions demands better tools that help us to work with integrated datasets and a variety of media that often exceed direct human interpretive capacities (Voithofer, 2005;Zhao & Luan, 2006). Modularity allows media objects to be combined without losing their individual characteristics, perpetually recombined in various configurations and presented through diverse interfaces.…”

Veri Analizinde İstatistik mi Veri Madenciliği mi?

“…Aksine analiz sonucunda elde edilen bulgular incelenir ve yorumlanır. Veri madenciliği ve istatistikte bu sebeplerden ötürü hipotezin rolü farklılık gösterir [16], [24].…”