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Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining's four guiding principles- prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM's emerging role in helping to advance educational research-from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

PurposeIn this paper, the challenging and thorny issue of assessing graduate attributes (GAs) is addressed. An interdisciplinary team at The University of Alberta ---developed a formative model of assessment centered on students and instructor interaction with course content.Design/methodology/approachThe paper starts by laying the theoretical groundwork on which this novel GA assessment tool is based, that is, competency-based education, assessment theory and GA assessment. It follows with a description of the online assessment tool for GAs that was developed in the course of this project.FindingsThe online assessment tool for GAs targets three types of stakeholders: (1) students, who self-assess in terms of GAs, (2) instructors, who use the tool to define the extent to which each GA should be inculcated in their course and (3) administrators, who receive aggregate reports based on the data gathered by the system for high-level analysis and decision-making. Collected data by students and professors advance formative assessment of these transversal skills and assist administration in ensuring the GAs are addressed in academic programs. Graduate attributes assessment program (GAAP) is also a space for students to build a personal portfolio that would be beneficial to highlight their skills for potential employers.Research limitations/implicationsThis research has strong implications for the universities, since it can help institutions, academics and students achieve better results in their practices. This is done by demonstrating strong links between theory and practice. Although this tool has only been used within the university setting by students, instructors and administrators (for self-, course and teaching and program improvement), it could increase its social and practical impact by involving potential employers and increase our understanding of student employability. Moreover, because the tool collects data on a continuous basis, it lends itself to many possible applications in educational data mining,Practical implicationsThe GAAP can be used and adapted to various educational contexts. The plugin can be added to any Learning Management System (LMS), and students can have access to their data and results throughout their education.Social implicationsThe GAAP allows institutions to provide a longitudinal formative assessment of students’ graduate attributes acquisition. It provides solid and valid evidence of students’ progress in a way that would advance society and citizenship.Originality/valueTo date, the GAAP is the first online interactive platform that has been developed to longitudinally assess the acquisition of GAs during a complete academic cycle/cohort. It provides a unique space where students and instructors interact with assessment scales and with concrete data for a complete university experience profile.

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learningAddresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk studentsExplores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and studentsFeatures supplementary resources including a primer on foundational aspects of educational mining and learning analyticsData Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

Canadian institutions of higher education are major players on the international arena for educating future generations and producing leaders around the world in various fields. In the last decade, Canadian universities have seen an influx in their incoming international students, who contribute over $3.5 billion to the Canadian economy (Madgett & Bélanger 2008, p. 195). Research in Canadian post-secondary institutions is booming, especially in education (SSHRC, 2011)—for the academic year 2010-2011, of the 12 subject areas, the total SSHRC funding for projects in education, ranked fourth, exceeding $27 million. All of these variables place Canadian higher education in a leading and strategic position in several educational research fields. One can imagine the wealth of knowledge about trends in higher education that could be revealed if the large amount of data generated by Canadian universities were systematically analyzed and handled using techniques such as data mining. However, not much can be achieved from the unharnessed knowledge accumulated on a daily basis, as the advancement of data mining research that would provide the ultimate tool to learn about trends and changes in Canadian institutions is often held back by inadequate data warehousing, as well as by privacy, confidentiality, and copyright regulations. In this paper, we engage in a critical discussion/analysis of the interface between data mining research in higher education and the legal implications of such a tool.

Pour un bon nombre de commentateurs depuis les années 1960, il semble évident que la pensée de Gadamer est conservatrice. L'A. du présent article cherche à réfuter cette position et prétend de surcroît qu'elle méconnaît l'essence même de l'herméneutique philosophique. À l'appui de sa thèse, l'A. met en relief le caractère foncièrement critique de l'herméneutique philosophique et fait valoir le rôle central de la phronesis, de la raison délibérative, dans la pensée gadamérienne. À travers les thèmes du traditionalisme, de la finitude, de l'autorité et des préjugés, l'A. tente de montrer en quoi Gadamer, en dépit des apparences, se distingue fondamentalement des véritables penseurs conservateurs. For a considerable number of commentators since the sixties it appears obvious that Gadamer's thought is conservative. The A. seeks to refute this standpoint and holds in addition that to hold such a view is fail to grasp the very essence of philosophical hermeneutics. In support of his thesis the A. brings out the fundamentally critical character of philosophical hermeneutics and emphasizes the central role of phronesis, of deliberative reason, in Gadamer's thought. By taking up the topics of traditionalism, of finitude, of authority and of prejudices, the A. seeks to show how Gadamer, in spite of appearances, is to be distinguished fundamentally from genuine conservative thinkers.