Экспериментальные результаты исследования качества кластеризации разнообразных наборов данных с помощью модифицированного алгоритма хамелеон

In this work results of modified Chameleon algorithm are discussed. Hierarchical multilevel algorithms consist of several stages: building the graph, coarsening, partitioning, recovering. Exploring of clustering quality for different data sets with different combinations of algorithms on different stages of the algorithm is the main aim of the article. And also aim is improving the construction phase through the optimization algorithm of choice k in the building the graph k-nearest neighborsВ статье рассмотрены результаты работы модифицированного алгоритма Хамелеон. Иерархический многоуровневый алгоритм состоит из нескольких этапов: построение графа, огрубление, разделение и восстановление. Главной целью работы является исследование качества кластеризации различных наборов данных с помощью набора комбинаций алгоритмов на разных этапах работы алгоритма и улучшения этапа построения через оптимизацию алгоритма выбора k при построении графа k-ближайших соседей.

Data Mining in Educational Settings

Educational mining is an evolving trend in research capacity thatdeals with the growth of tools and techniques to discover hiddenpattern lying in the data in an educational context. Mining is helpingall the educational stakeholders working in different lines ofeducation. It is helping administrators to manage resources effectivelyand faculty to analyze students’ performance, their learning behavior,and to predict their future path. Problem definition, data gatheringand preparation, model building and evaluation, and knowledgedevelopment are the four steps typically involve in educationalmining

Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts

This Innovative Practice Full Paper presents an approach of using software development artifacts to gauge student behavior and the effectiveness of changes to curriculum design. There is an ongoing need to adapt university courses to changing requirements and shifts in industry. As an educator it is therefore vital to have access to methods, with which to ascertain the effects of curriculum design changes. In this paper, we present our approach of analyzing software repositories in order to gauge student behavior during project work. We evaluate this approach in a case study of a university undergraduate software development course teaching agile development methodologies. Surveys revealed positive attitudes towards the course and the change of employed development methodology from Scrum to Kanban. However, surveys were not usable to ascertain the degree to which students had adapted their workflows and whether they had done so in accordance with course goals. Therefore, we analyzed students' software repository data, which represents information that can be collected by educators to reveal insights into learning successes and detailed student behavior. We analyze the software repositories created during the last five courses, and evaluate differences in workflows between Kanban and Scrum usage

Detecting symptoms of low performance using production rules

This is an electronic version of the paper presented at the International Conference on Educational Data Mining (EDM'09), held in Cordoba (Spain) on 2009E-Learning systems offer students innovative and attractive ways of learning through augmentation or substitution of traditional lectures and exercises with online learning material. Such material can be accessed at any time from anywhere using different devices, and can be personalized according to the individual student’s needs, goals and knowledge. However, authoring and evaluation of this material remains a complex a task. While many researchers focus on the authoring support, not much has been done to facilitate the evaluation of e-Learning applications, which requires processing of the vast quantity of data generated by students. We address this problem by proposing an approach for detecting potential symptoms of low performance in e-Learning courses. It supports two main steps: generating the production rules of C4.5 algorithm and filtering the most representative rules, which could indicate low performance of students. In addition, the approach has been evaluated on the log files of student activity with two versions of a Web-based quiz system.This work has been funded by Spanish Ministry of Science and Education through the HADA project TIN2007-64718

Building and Analyzing a Corpus of Contextualized Traces Collected during a Technology Enhanced Teaching Module

International audience—Sharing and analyzing data collected within Technology Enhanced Learning environments is an interesting issue for researchers to validate their models and systems. In this paper we present a corpus we built and analyzed in order to validate our proposed " Proxy approach " as an approach for sharing and analyzing learning data corpora

Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors [discussant]

This symposium addresses how different classes of research methods, all based upon the use of log data from educational software, can facilitate the analysis of students’ learning strategies and behaviors. To this end, four multi-method programs of research are discussed, including the use of qualitative, quantitative-statistical, quantitative-modeling, and educational data mining methods. The symposium presents evidence regarding the applicability of each type of method to research questions of different grain sizes, and provides several examples of how these methods can be used in concert to facilitate our understanding of learning processes, learning strategies, and behaviors related to motivation, meta-cognition, and engagement

Построение графа связности в алгоритме кластеризации сложных объектов

The article describes the results of modifying the algorithm Chameleon. Hierarchical multi-level algorithm consists of several phases: the construction of the count, coarsening, the separation and recovery. Each phase can be used various approaches and algorithms. The main aim of the work is to study the quality of the clustering of different sets of data using a set of algorithms combinations at different stages of the algorithm and improve the stage of construction by the optimization algorithm of k choice in the graph construction of k of nearest neighborsВ статье рассмотрены результаты работы модификации алгоритма Хамелеон. Иерархический многоуровневый алгоритм состоит из нескольких фаз: построение графу, огрубление, разделение и восстановления. На каждой фазе могут быть использованы различные подходы и алгоритмы. Главной целью работы является исследование качества кластеризации различных наборов данных с помощью набора комбинаций алгоритмов на разных этапах работы алгоритма и улучшения этапа построения через оптимизации алгоритма выбора k при построении графа k ближайших соседе

Monitoring student progress using virtual appliances: a case study

The interactions that students have with each other, with the instructors, and with educational resources are valuable indicators of the effectiveness of a learning experience. The increasing use of information and communication technology allows these interactions to be recorded so that analytic or mining techniques are used to gain a deeper understanding of the learning process and propose improvements. But with the increasing variety of tools being used, monitoring student progress is becoming a challenge. The paper answers two questions. The first one is how feasible is to monitor the learning activities occurring in a student personal workspace. The second is how to use the recorded data for the prediction of student achievement in a course. To address these research questions, the paper presents the use of virtual appliances, a fully functional computer simulated over a regular one and configured with all the required tools needed in a learning experience. Students carry out activities in this environment in which a monitoring scheme has been previously configured. A case study is presented in which a comprehensive set of observations were collected. The data is shown to have significant correlation with student academic achievement thus validating the approach to be used as a prediction mechanism. Finally a prediction model is presented based on those observations with the highest correlation.Work partially funded by the Learn3 project (“Plan Nacional de IþDþI” TIN2008-05163/TSI), TELMA Project (“Plan Avanza”, TSI-020110-2009-85), and the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650).Publicad