Inference of hidden structures in complex physical systems by multi-scale clustering

We survey the application of a relatively new branch of statistical physics--"community detection"-- to data mining. In particular, we focus on the diagnosis of materials and automated image segmentation. Community detection describes the quest of partitioning a complex system involving many elements into optimally decoupled subsets or communities of such elements. We review a multiresolution variant which is used to ascertain structures at different spatial and temporal scales. Significant patterns are obtained by examining the correlations between different independent solvers. Similar to other combinatorial optimization problems in the NP complexity class, community detection exhibits several phases. Typically, illuminating orders are revealed by choosing parameters that lead to extremal information theory correlations.Comment: 25 pages, 16 Figures; a review of earlier work

Design Variables of Attraction in Quest-Based Learning

Critics of the American education system point to student boredom, lack of personalized and relevant instruction, and a deficit of 21st century skills as challenges to producing productive citizens of a modern, digital society (Barab et al., 2009; Eccles & Wingfield, 2002; Ketelhut, 2007; U.S. Department of Education Office of Educational Technology, 2010). Digital learning, including game-based approaches, offers opportunities to bring about meaningful, engaging, individualized learning (Barab & Dede, 2007; Gee, 2005; Squire, 2003). Quest-based learning is an instructional design theory of game-based learning that focuses on student activity choice within the curriculum, which offers promising pedagogical possibilities in the area. This study expands upon current research of video game characteristics and variables of attractiveness in learner choice. Identifying these attractive characteristics in game-based educational design can increase engagement (Barab et al., 2009), educational effectiveness (Sullivan & Mateas, 2009), and impact instructional design decisions. Quests were coded and tagged to identify features and attributes. An educational quest taxonomy was developed building on Merrill’s Knowled ge Object (Redeker, 2003; Wiley, 2000) classification and expanded to include current digital tools and thinking. Electronically collected decision data from a quest-based learning management system was analyzed using descriptive statistical analysis and data mining techniques. Educational quests were differentiated by a number of data points and identified as more or less attractive using an initial interest score and a completion score. User rating was also considered for descriptive purposes. Data mining and text mining highlighted the specific characteristics of attractive quests including clusters of characteristics identified as most attractive as well as their significance. Suggestions for future attractive quest-based learning design are suggested. (Keywords: Quests, quest-based learning, game-based learning, 3D GameLab, play styles, learner preferences, rewards, badges, gamification, MMORPGs, virtual environments, informal learning.

Implementasi Algoritma QUEST pada Data Mining untuk Penentuan Seleksi Undangan Saringan Masuk Perguruan Tinggi (Studi Kasus Kampus Institut Teknologi Telkom)

ABSTRAKSI: Penyeleksian calon mahasiswa mempengaruhi kualitas suatu perguruan tinggi. Oleh sebab itu penyeleksian mahasiswa sangat penting bagi perguruan tinggi. Ada berbagai cara digunakan untuk menyeleksi mahasiswa yang akan masuk ke suatu perguruan tinggi. Bisa dengan jalur tes tulis, ada juga dengan Jalur Penelusuran Potensi Akademik Nasional (JPPAN). Tulisan ini akan memfokuskan mengenai penerimaan mahasiswa dengan JPPAN. Penyeleksian mahasiswa secara manual akan memakan waktu yang lama dan cendrung bias. Oleh sebab itu perlu metode yang bisa untuk menyeleksi secara otomatis dan tidak bias dengan cara belajar dari data yang sudah ada sebelumnya. Salah satu metode yang bisa dipakai untuk penyeleksian mahasiswa adalah QUEST (Quick Unbiased Efficient Statistical Trees). QUEST merupakan metode pohon klasifikasi yang bisa menggunakan pemangkasan (pruning) pada saat membangun pohon. QUEST mampu menyeleksi calon mahasiswa dengan waktu yang cepat dengan akurasi yang bagus.Kata Kunci : data mining, clasification, QUEST, decision tree, knowledge discoveryABSTRACT: Student admission can influence quality of university. So student admission is very important for a university. There are so many ways to select student for admission. It can be done by writing test or Investigation of Nasiotional Academic Potential (JPPAN). This paper will focus on admission by JPPAN. Student admission that do manually can be longer and prone to bias. That is why, we need system do student admission manually and unbiased that can learn from big data that we already have. One of methods that can be used for student admission is QUEST (Quick Unbiased Efficient Statistical Trees). QUEST is classification tree yang can do pruning as building the tree. QUEST can select student quickly and accurately.Keyword: data mining, clasification, QUEST, decision tree, knowledge discover

Experiences in Mining Educational Data to Analyze Teacher's Performance: A Case Study with High Educational Teachers

Educational Data Mining (EDM) is a new paradigm aiming to mine and extract knowledge necessary to optimize the effectiveness of teaching process. With normal educational system work it’s often unlikely to accomplish fine system optimizing due to large amount of data being collected and tangled throughout the system. EDM resolves this problem by its capability to mine and explore these raw data and as a consequence of extracting knowledge. This paper describes several experiments on real educational data wherein the effectiveness of Data Mining is explained in migration the educational data into knowledge. The experiments goal at first to identify important factors of teacher behaviors influencing student satisfaction. In addition to presenting experiences gained through the experiments, the paper aims to provide practical guidance of Data Mining solutions in a real application

Grids and the Virtual Observatory

We consider several projects from astronomy that benefit from the Grid paradigm and associated technology, many of which involve either massive datasets or the federation of multiple datasets. We cover image computation (mosaicking, multi-wavelength images, and synoptic surveys); database computation (representation through XML, data mining, and visualization); and semantic interoperability (publishing, ontologies, directories, and service descriptions)

Some Pattern Recognition Challenges in Data-Intensive Astronomy

We review some of the recent developments and challenges posed by the data analysis in modern digital sky surveys, which are representative of the information-rich astronomy in the context of Virtual Observatory. Illustrative examples include the problems of an automated star-galaxy classification in complex and heterogeneous panoramic imaging data sets, and an automated, iterative, dynamical classification of transient events detected in synoptic sky surveys. These problems offer good opportunities for productive collaborations between astronomers and applied computer scientists and statisticians, and are representative of the kind of challenges now present in all data-intensive fields. We discuss briefly some emergent types of scalable scientific data analysis systems with a broad applicability.Comment: 8 pages, compressed pdf file, figures downgraded in quality in order to match the arXiv size limi

Grist: Grid-based Data Mining for Astronomy

The Grist project is developing a grid-technology based system as a research environment for astronomy with massive and complex datasets. This knowledge extraction system will consist of a library of distributed grid services controlled by a work ow system, compliant with standards emerging from the grid computing, web services, and virtual observatory communities. This new technology is being used to find high redshift quasars, study peculiar variable objects, search for transients in real time, and fit SDSS QSO spectra to measure black hole masses. Grist services are also a component of the "hyperatlas" project to serve high-resolution multi-wavelength imagery over the Internet. In support of these science and outreach objectives, the Grist framework will provide the enabling fabric to tie together distributed grid services in the areas of data access, federation, mining, subsetting, source extraction, image mosaicking, statistics, and visualization