Observational citizen science is an effective way to supplement the environmental datasets compiled by professional scientists. Involving volunteers in data collection has the added educational benefits of increased scientific awareness and local ownership of environmental concerns. This thesis provides an in-depth exploration of observational…

Participatory sensing is a data collection method in which communities of people collect and share data to investigate large-scale processes. These data have many features often associated with the big data paradigm: they are rich and multivariate, include non-numeric data, and are collected as determined by an algorithm rather than by traditional…

This is the decade of data analytics and big data, but not everyone agrees with the definition of big data. Some researchers see it as the future of data analysis, while others consider it as hype and foresee its demise in the near future. No matter how it is defined, big data for the time being is having its glory moment. The most important…

Learning technologies are now commonplace in education, and generate large volumes of educational data. Scholars have argued that analytics can and should be employed to optimize learning and learning environments. This article explores what is really meant by "analytics", describes the current best-known examples of institutional…

There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…

The purpose of this module is to illustrate examples of how courses in educational leadership programs can effectively and efficiently meld lessons on leadership with lessons on data collection and analysis. The rationale behind emphasizing this combination is very straightforward: America's schools need leaders who are adept with data-based…

This paper reports on a case study of a project-focused introduction to big data science course. The pedagogy of the course leveraged boundary theory, where students were positioned to be at the boundary between a client's desire to understand their data and the academic class. The results of the case study demonstrate that using live clients…

For students to be successful in STEM, they need "statistical literacy," the ability to interpret, evaluate, and communicate statistical information (Gal 2002). The science and engineering practices dimension of the "Next Generation Science Standards" ("NGSS") highlights these skills, emphasizing the importance of…

Data-driven decision making is part of our everyday lives. For example, we examine our children's immunization charts in order to indicate what vaccinations they have received, as well as what vaccinations are missing and expired. We use information, like a chart, to make decisions about what to do and what not to do. Data-driven decision making…

This article describes the process and role frontline access and public service staff play in needs assessment and evaluation of user services, specifically in understanding the voice of the customer. Information includes how the University of Arizona Libraries have incorporated daily data collection into the strategic planning process, resources…

Data collection and analysis are frequently neglected by school leaders over the course of the decision-making process. All schools gather large amounts of information about students and teachers, but most data are used to satisfy administrative requirements rather than evaluate school improvement in a systematic fashion. Apprehension regarding…

This set of activity sheets requires students to organize and display data with a box-and-whiskers graph. Students use the plot as a summary display to detect patterns and to highlight the important features of the data for purposes of comparison. (PK)

Since the first edition of "Schools and Data", the No Child Left Behind Act has swept the country, and data-based decision making is no longer an option for educators. Today's educational climate makes it imperative for all schools to collect data and use statistical analysis to help create clear goals and recognize strategies for…

An international numerical coding structure is presented for classification of data on education according to level category, educational field, and program type. Explanation of the overall system and a guide for its use is followed by a listing of levels, fields, and programs of education in coded order. The various educational program…

The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…