Lincoln, Yvonna, and Egon Guba, "Postpositivism and the Naturalist Paradigm," pp. 14-46 in Yvonna Lincoln and Egon Guba, Naturalistic Inquiry . Beverly Hills, CA: Sage, 1985.*

Organizational learning II: Theory, method and practice

THE FIFTH DISCIPLINE The Art & Practice of the Learning Organization

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Mindset The New Psychology Of Success

The authors are grateful for the deliberations of our technical working group (TWG) of academic experts in educational data mining and learning analytics. These experts provided constructive guidance and comments for this issue brief. The TWG comprised Ryan S. In data mining and data analytics, tools and techniques once confined to research laboratories are being adopted by forward-looking industries to generate business intelligence for improving decision making. Higher education institutions are beginning to use analytics for improving the services they provide and for increasing student grades and retention. The U.S. Department of Education's National Education Technology Plan, as one part of its model for 21st-century learning powered by technology, envisions ways of using data from online learning systems to improve instruction. With analytics and data mining experiments in education starting to proliferate, sorting out fact from fiction and identifying research possibilities and practical applications are not easy. This issue brief is intended to help policymakers and administrators understand how analytics and data mining have been—and can be—applied for educational improvement. At present, educational data mining tends to focus on developing new tools for discovering patterns in data. These patterns are generally about the microconcepts involved in learning: one-digit multiplication, subtraction with carries, and so on. Learning analytics—at least as it is currently contrasted with data mining—focuses on applying tools and techniques at larger scales, such as in courses and at schools and postsecondary institutions. But both disciplines work with patterns and prediction: If we can discern the pattern in the data and make sense of what is happening, we can predict what should come next and take the appropriate action. Educational data mining and learning analytics are used to research and build models in several areas that can influence online learning systems. One area is user modeling, which encompasses what a learner knows, what a learner's behavior and motivation are, what the user experience is like, and how satisfied users are with online learning. At the simplest level, analytics can detect when a student in an online course is going astray and nudge him or her on to a course correction. At the most complex, they hold promise of detecting boredom from patterns of key clicks and redirecting the student's attention. Because these data are gathered in real time, there is a real possibility of continuous improvement via multiple feedback loops that operate at different time scales—immediate to the student …

/pdf/enhancing-teaching-and-learning-through-educational-data-695k7hhb05.pdf

Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief

Accountability mandates such as No Child Left Behind (NCLB) have drawn attention to the practical use of student data for school improvement. Nevertheless, schools may struggle with these mandates because student data are often stored in forms that are difficult to access, manipulate, and interpret. Such access barriers additionally preclude the use of data at the classroom level to inform and impact instruction. Fortunately, there are newly available computer technologies that allow efficient organization and access to student data. In addition to allowing easier accountability reporting, these tools allow user-friendly data access at all educational levels, meaning that teachers can use these tools to engage in the informed reflection necessary to improve classroom practice. In this article, I discuss teacher use of these systems, providing insight into the function of these tools and discussing conditions that make these tools of the most service to teachers.

http://myclass.nl.edu/tie/tie533/TeacherDataUse.pdf

Involving Teachers in Data-Driven Decision Making: Using Computer Data Systems to Support Teacher Inquiry and Reflection

Student data are gaining increased attention in education, spurred by accountability policies such as those contained in the federal No Child Left Behind legislation. Student data are useful for informing classroom practice, and user‐friendly technologies for organizing and accessing data are enabling access by all teachers. In this study, we examine the efforts of three schools to involve entire faculties in the examination of student data, supported by efficient data systems. Results indicate the importance of administrative supports in fostering such use. Data use often resulted in improved teaching practice such as collaboration, better knowledge of student needs, and efficiency of effort. These results are discussed in light of previous research.

/pdf/technology-supported-involvement-of-entire-faculties-in-1hbu8pojd9.pdf

Technology‐Supported Involvement of Entire Faculties in Examination of Student Data for Instructional Improvement

As educators face increasing pressure from federal, state, and local accountability policies to improve student achievement, the use of data has become more central to how many educators evaluate their practices and monitor students’ academic progress (Knapp et al., 2006). Despite this trend, questions about how educators should use data to make instructional decisions remain mostly unanswered. In response, this guide provides a framework for using student achievement data to support instructional decision making. These decisions include, but are not limited to, how to adapt lessons or assignments in response to students’ needs, alter classroom goals or objectives, or modify student-grouping arrangements. The guide also provides recommendations for creating the organizational and technological conditions that foster effective data use. Each recommendation describes action steps for implementation, as well as suggestions for addressing obstacles that may impedeprogress. In adopting this framework, educators will be best served by implementing the recommendations in this guide together rather than individually. Disciplines Curriculum and Instruction | Education | Educational Assessment, Evaluation, and Research | Educational Methods Author(s) Laura Hamilton, Richard Halverson, Sharnell S. Jackson, Ellen Mandinach, Jonathan A. Supovitz, Jeffrey C. Wayman, Cassandra Pickens, Emma Sama Martin, and Jennifer L. Steele This government publication is available at ScholarlyCommons: http://repository.upenn.edu/gse_pubs/279 Using Student Achievement Data to Support Instructional Decision Making NCEE 2009-4067 U.S. DEPARTMENT OF EDUCATION IES PRACTICE GUIDE WHAT WORKS CLEARINGHOUSE

https://www.readingrockets.org/sites/default/files/dddm_pg_092909.pdf

Using Student Achievement Data to Support Instructional Decision Making

In the United States, effective data use is proving to be a vexing problem. In response, scholars have recently begun viewing this as a systemic problem, believing there are actions a school district may take to make data use more efficient and tenable throughout the organization. In this article, we add to the knowledge of how school organizations can more effectively use data for educational improvement. Through the lens of the Data-Informed District, we leverage the research on organizational improvement and data use to discuss 3 specific organizational areas in which these districts may improve: establishing common understandings, professional learning for using data, and computer data systems.

/pdf/organizational-considerations-in-establishing-the-data-3qlurtary7.pdf

Organizational considerations in establishing the Data-Informed District

In The World is Flat, Thomas Friedman (2005) describes how decades pass between discoveries and the practical productivity gains from them. Two examples are illustrative: (1) the ability to generate and distribute electricity evolved in the 1890s, yet it was 30 years before electric motors produced widespread productivity increases, and (2) computer technologies began evolving in the 1940s toward the productivity gains given by our current worldwide information systems. In both cases, multiple vectors had to converge beyond the initial inventions in order to produce the vibrant U.S. economy of the twentieth century and the world economy of the twenty-first century. In the case of the ongoing productivity increases from the computer/information revolution, Friedman’s first “convergence” was of rapidly evolving computer hardware with the subsequent development of “work flow software.” This allowed businesses to move information much more rapidly. Friedman (2005, 177) notes that, while such a convergence can be valuable in isolation, “the big spurts in productivity come when a new technology is combined with new ways of doing business.” In the case of the information economy, these include the integrated use of sophisticated computers and the evolution of large, sophisticated data “warehouses” to “flatten” not only macro-level world businesses but also the work lives and productivity of an unexpectedly wide array of persons. He documents this spread of data use and subsequent increases in productivity in contexts ranging from the U.S. Army to Wal-Mart. Education has been relatively slow in coming to the rapidly evolving worlds

https://www.jstor.org/stable/10.1086/505055

Jeffrey C. Wayman

Papers

Involving Teachers in Data-Driven Decision Making: Using Computer Data Systems to Support Teacher Inquiry and Reflection

Technology‐Supported Involvement of Entire Faculties in Examination of Student Data for Instructional Improvement

Using Student Achievement Data to Support Instructional Decision Making

Organizational considerations in establishing the Data-Informed District

Data Use for School Improvement: School Practices and Research Perspectives