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Descriptors:
Decision Making; Schools of Education; Data; Teacher Education Curriculum; Teacher Education Programs; Information Literacy; Information Skills; Skill Analysis; Skill Development; Management Information Systems; Data Analysis; Data Collection; Data Interpretation; Educational Needs; Curriculum Implementation; Knowledge Base for Teaching; Policy Analysis; Systems Approach; Change Strategies; Educational Change

Abstract:
Data-driven decision making has become increasingly important in education. Policymakers require educators to use data to inform practice. Although the policy emphasis is growing, what has not increased is attention to building human capacity around data use. Educators need to gain data literacy skills to inform practice. Although some professional development opportunities exist for current educators, fewer formal courses and opportunities for data literacy development in schools of education have been developed and implemented. This article explores issues around the growing need for data-driven decision making in programs in schools of education. The issues are complex and the actors needed to bring about change are multiple. A systems perspective to explore course and programmatic implementation is presented. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Research Methodology; Second Language Learning; Interdisciplinary Approach; Data Collection; Books; Language Research

Abstract:
The central goal of the field of second language acquisition (SLA) is to describe and explain how second language learners acquire the target language. In order to achieve this goal, SLA researchers work with second language data, which can take a variety of forms, including (but not limited to) such commonly used methods as naturalistic production, responses to questionnaires about motivation and attitudes, grammaticality judgments, and reaction times in online tests. Given the interdisciplinary nature of SLA, the field has drawn on the methodologies used in other fields, including linguistics, first language acquisition, psychology, sociology, and education, among others. As the number of data collection and analysis methodologies used in SLA has grown, so has the number of books describing and explaining these methodologies. In this review article, the author describes and evaluates the volume edited by Alison Mackey and Susan Gass (2012), which provides a comprehensive overview of a broad variety of different data collection and analysis methods used in SLA. She also evaluates selected chapters from the volume edited by Elma Blom and Sharon Unsworth (2010), which covers formal experimental methodologies for language acquisition in general; she evaluates only those chapters that have direct relevance for research in SLA. Finally, she briefly reviews the volumes by Zoltan Dornyei with Tatsuya Taguchi (2010, second edition) and by Kim McDonough and Pavel Trofimovich (2008), which take an in-depth look at two specific methodologies used in SLA research. All four books are evaluated with regard to their utility for courses on research methodology and design; features that make the books particularly useful for instructional purposes are pointed out. (Contains 1 note.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Biology; Foreign Countries; Science Teachers; Protocol Analysis; Semi Structured Interviews; Scientists; Science Education; Science Instruction; Visual Literacy; Grounded Theory; Data Collection

Abstract:
In the present study, we have explored an aspect of teachers' perceptions of biology diagrams. The research was performed in Turkey. The data were gathered from 50 (25 female, 25 male) teachers of primary and secondary schools and 34 (18 female, 16 male) academic staff of different universities in Turkey. Some of the participants are science specialists and the others are non-science specialists. The data were collected in 2012. A qualitative approach was adopted. The data were collected in three steps. First, biology diagrams from the internet were collected and some (12) biology diagrams were chosen by the researchers. In the second step, these selected diagrams were shown to the teachers and academic staff. In this step the participants were asked to think aloud about what they saw when they looked at the diagrams. In the third step, semi-structured interviews were carried out in order to examine further the thoughts of the participants about what they saw in these diagrams. In our study we found there were no significant differences in responses between the science and non-science specialist teachers and academic staff. We conclude that it may be helpful to train teachers in the processes of constructing and reading diagrams. (Contains 7 tables and 7 figures.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Higher Education; Institutional Evaluation; Governance; Data Analysis; Institutional Research; Professional Identity; Institutional Characteristics; Knowledge Management; Change Strategies; Educational Change; Educational Policy; Educational Development; Policy Formation; Case Studies; Data Collection; Data Processing; Educational Environment; Performance Factors; Comparative Education

Abstract:
Recent changes in European higher education have accompanied a strong desire and need by national ministries to have comparable data across institutions and a growing recognition from campus leaders that effective planning and decision-making requires reliable institutional data and analyses. This has induced changes and restructuring of duties and roles of administration, administrative staff and academic staff. In North America, internal institutional data analysis is often referred to as institutional research. We examine the roles and functions of institutional research within North America and how the changes within European higher education have created a purpose for institutional research. Specifically, we explore the topical areas of institutional assessment, data management, institutional governance, as well as the changing identity of academic professionals within European universities. Within our examination, we explore in-depth one European country's higher education system to demonstrate how history, culture and legislative changes manifest into a need for institutional research. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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At Risk Students; School Personnel; Dropout Prevention; Data Collection; Graduation; High School Students; Program Effectiveness; Interviews; Observation; Intervention; School Holding Power; Academic Persistence; Student Behavior; Teamwork; Elementary Schools; Middle Schools

Abstract:
An early warning system is an intentional process whereby school personnel collectively analyze student data to monitor students at risk of falling off track for graduation and to provide the interventions and resources to intervene. We studied the process of monitoring the early warning indicators and implementing interventions to ascertain common practices and challenges. Research questions focused on the process of collecting and reviewing student data, conducting team meetings, and designing or selecting, and following through on, student interventions. This article reports on typical practices and implementation challenges of early warning indicators meetings from 10 schools that participated in the pilot phase of the Diplomas Now School Transformation Model across the country, gathered through meeting observations and interviews. (Contains 3 tables and 1 figure.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Teaching Methods; Methods; Data Collection; Focus Groups; Secondary School Students; Disabilities; Barriers; Questionnaires; Student Attitudes; Special Needs Students; Student Needs; Males; Foreign Countries

Abstract:
Schools have a legal duty to make reasonable adjustments for disabled pupils who experience barriers to learning. Inclusive approaches to data collection ensure that the needs of all children who are struggling are not overlooked. However, it is important that the methods promote sustained reflection on the part of all children, do not inadvertently accentuate differences between pupils, and do not allow individual needs to go unrecognized. This paper examines more closely the processes involved in using Nominal Group Technique to collect the views of children with and without a disability on the difficulties experienced in school. Data were collected on the process as well as the outcomes of using this technique to examine how pupil views are transformed from the individual to the collective, a process that involves making the private, public. Contrasts are drawn with questionnaire data, another method of data collection favoured by teachers. Although more time-efficient this can produce unclear and cursory responses. The views that surface from pupils need also to be seen within the context of the ways in which schools customize the data collection process and the ways in which the format and organization of the activity impact on the responses and responsiveness of the pupils. (Contains 4 tables.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Children; Information Dissemination; Conflict; Childrens Rights; Access to Education; Educational Quality; Civil Rights; Peace; War; Data Collection; Research Methodology; Educational Opportunities

Abstract:
Poor-quality, or completely absent, data deny millions of children the right to an education. This is often the case in conflict-ridden areas. The 2011 Education for All Global Monitoring Report (UNESCO 2011b) identified four failures that are holding back progress in education and damaging millions of children's lives: failures of protection, provision, reconstruction, and peace-building. Thus, the critical lack, and the varying quality, of data on education and on human rights violations against children during and after armed conflicts amount to what can be termed the fifth failure of the international community. This article examines how currently available data, and monitoring and evaluation systems, can be used and improved to better estimate the situation of children in conflict-affected countries, in particular with respect to education. In the light of international standards for data dissemination and data quality, it highlights the need for governments and the international community to expand our current capacity to provide general information on the impact that conflict has on education, children, parents, and schools, to ensure the right to education for millions of children living in conflict-affected countries. Such an effort would include specific steps to ensure higher data quality in terms of completeness and accuracy, timeliness, serviceability, and methodological soundness. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Data Collection; Information Literacy; Classroom Environment; Data Analysis; Preservice Teachers; Inquiry; Teacher Researchers; Language Arts; English Curriculum; Secondary Education; Difficulty Level

Abstract:
Data literate educators can generate questions about learning in their classes; collect and analyze classroom data to answer them; and develop inferences and explanations. Two elements may promote data literacy: (a) breadth of classroom-based data collection and (b) depth of analysis. We examined these in inquiries conducted by 80 preservice teachers of secondary English in diverse classrooms over a 6-year period. Analyzing products, processes, and self-reports, we found inquiries evidenced a range in breadth of data collection and depth of analysis. Qualitative themes and two cases illustrate data analysis processes and challenges of pattern finding, initially crude analyses in striving for depth, and the need for data literacy mentoring. (Contains 1 table and 3 figures.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Educational Technology; Electronic Learning; Learning Processes; Learning Activities; Data Collection; Study Habits; Cognitive Style; Student Behavior; Behavioral Science Research; Social Networks; Population Groups; Incidence; Time Perspective; Time on Task; Geographic Location; Library Research

Abstract:
The migration from traditional classrooms to online learning environments is in full effect. In the midst of these changes, a new approach to learning analytics needs to be considered. Learning analytics refers to the process of collecting and studying usage data in order to make instructional decisions that will support student success. In learning analytics, "usage data" can refer to a wide range of information being produced by the observed population. The necessary tools and technology used to study learning analytics are starting to become simplified, allowing librarians to develop a better understanding of their students learning habits. Rather than analyzing college students' general learning behavior, learning analytics can provide insight into the learning styles or patterns of a specific subset of students. Within learning analytics, the "learning process is assessed more so than final learning outcomes." Therefore, learning analytics involves a redesign of assessment that traditionally focuses on outcomes. This in-process assessment draws its data from the daily learning activity of students within their social and informational networks. The author discusses three interactive components to be studied when collecting data for learning analytics: (1) timing; (2) location; and (3) population. It is important to note that there is a sense of immediacy to these components; current data are very valuable in regard to learning analytics and in-process assessment. The first step in collecting data is to investigate these components as they relate to a group of students. Ultimately, a big picture will begin to develop about the daily learning activity of students within their network of courses. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Mixed Methods Research; Internet; Information Technology; Data Collection; Case Studies; Barriers; Ethics; Research Design; Research Problems; Praxis

Abstract:
This article provides an examination of a range of mixed methods research projects that employ Internet-mediated technologies (IMT) for data collection. Using a case study approach, this article allows for the uncovering of a process by which IMT are used as a data collection medium in mixed methods praxis. Under the theoretical position of medium theory, the impact of how going online and changing the "mode" of traditional research will be explored. Advantages, drawbacks, as well as ethical issues that emerge for mixed methods using the online medium are highlighted throughout the article. Finally, implications and directions for future research using IMT are discussed. (Contains 4 figures and 2 tables.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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