Launched in 2008, the Strategic Data Project, housed at the Center for Education Policy Research at Harvard University, seeks to bridge the divide between educational research and practice in order to transform the use of data in education to improve student achievement. Through the project, the authors build robust research partnerships with…

The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The…

aimswebPlus® is an assessment, data management, and reporting system that provides national and local performance and growth norms for the screening and progress monitoring of math and reading skills for all students in kindergarten through 8th grade. aimswebPlus uses two types of measures: (1) "curriculum-based measures" (CBMs)--brief,…

Web-based survey tools such as Survey Monkey can be used in many ways to increase the efficiency and effectiveness of Extension professionals. This article describes how Survey Monkey has been used at the state and county levels to collect community and internal staff information for the purposes of program planning, administration, evaluation and…

Considerations and tools for developing mobile applications for Extension include evaluating the topic, purpose, and audience. Different computing platforms may be used, and apps designed as modified Web pages or implicitly programmed for a particular platform. User privacy is another important consideration, especially for data collection apps.…

A simple, low-cost infrared LED speedometer is described that can be fitted to a skateboard, longboard or even a bicycle to measure speed. Notes on building, setting up and calibration are given. When used with a low-cost data logger, continuous measurements of speed can be made while out and about. The device forms an interesting science club…

This dissertation examines two related questions. "How do mixed membership models work?" and "Can mixed membership be used to model how students use multiple strategies to solve problems?". Mixed membership models have been used in thousands of applications from text and image processing to genetic microarray analysis. Yet…

This paper proposes to the use of a meta-learning approach for automatic parameter tuning of a well-known decision tree algorithm by using past information about algorithm executions. Fourteen educational datasets were analysed using various combinations of parameter values to examine the effects of the parameter values on accuracy classification.…

The interaction behaviours of successful, high-achieving learners when using a Learning Management System (LMS) are different than the behaviours of learners who are having more difficulty mastering the course material. This paper explores the idea that conventional Learning Management Systems can exploit data mining techniques to predict the…

In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…

As momentum grows for tracking the role of individual educators in student performance, school districts across the country are implementing projects that involve linking teachers to their students. Programs that link teachers to student outcomes require a verification process for student-teacher linkages. Linkage verification improves accuracy by…

This systematic literature review of current scholarship on business intelligence, individual decision-making behavior, strategy as practice, and strategic planning offers a roadmap for firms seeking to increase their competitive advantage through data driven decision-making. Planning, deciding, and using information is a single phenomenon where…

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

Data centric applications, an important effort of software development in large organizations, have been mostly adopting a software methodology, such as a waterfall or Rational Unified Process, as the framework for its development. These methodologies could work on structural, procedural, or object oriented based applications, but fails to capture…

One of the common tasks of inferential statistics is to compare two data sets. Long before formal statistical procedures, however, students can be encouraged to make comparisons between data sets and therefore build up intuitive statistical reasoning. Such tasks also give meaning to the data collection students may do. This study describes the…