instructions are attached with directions and articles
Instructions- (Learning & Development in Corporations) Based on the topic/question (Learning & Development in Corporations) and institution you identified earlier this term, draft an initial plan to gather the data necessary to adequately address the question and inform decision making. While we’ll receive peer and instructor feedback next week before completing the final plan, this week’s assignment is to fully develop an initial draft. Remember, a data gathering plan is practical, actionable document which should outline the clear logistical and institutional steps necessary to collect appropriate, useful data for later analysis. Therefore, your draft plan should include at least the following points: · Specific types of data needed and a brief discussion of how each is clearly linked to your question/topic (i.e., methodological congruence). Learning & Development in Corporations · Then, for each type of data, you should address: · Source(s) for each type of data (e.g., institutional research department, student interviews, course learning assessments, state or national datasets, etc.). · Potential obstacles or steps in order to gain access to the data · Timeline for data collection · Any relevant limitations of the data type and/or source · Validity, reliability, trustworthiness, and ethical considerations of your data gathering plan. · Anticipated audience: who will ultimately make decisions based on the data you gather? · How does that role/audience influence the types of data, sources, and other considerations you should make before embarking on data collection? You will submit this draft in two places: One copy will be uploaded to the corresponding Assignment folder and one copy will be shared with your assigned review partner. Consult the module Announcements from your instructor to obtain the name and NLU email address of your partner. Please cc your instructor in your email. · Your assignment should be approximately 750 words in length (typically, three double-spaced pages), not counting cover page, reference list page, appendices, figures, or tables. · · Your assignment should include a title page and a reference list page, and be completed in Times New Roman 12-point font, double-spaced, with appropriate header, page numbers, one-inch margins, and meet all other requirements of APA Stylebook. · · Please use at least three appropriate scholarly references formatted in the most current APA format (three resources outside of the resources provided in this course). · · An abstract is not required. · · Please refer to the rubric associated with this assignment for detailed guidance about expectations and grading. Data driven decision-making in the era of accountability: Fostering faculty data cultures for learning Data driven decision-making in the era of accountability: Fostering faculty data cultures for learning Matthew T. Hora, Jana Bouwma-Gearhart, Hyoung Joon Park The Review of Higher Education, Volume 40, Number 3, Spring 2017, pp. 391-426 (Article) Published by Johns Hopkins University Press DOI: For additional information about this article Access provided by University of Wisconsin @ Madison (16 Mar 2017 04:15 GMT) https://doi.org/10.1353/rhe.2017.0013 https://muse.jhu.edu/article/650170 https://doi.org/10.1353/rhe.2017.0013 https://muse.jhu.edu/article/650170 Hora, Bouwma-GearHart, and Park / Data driven decision-making 391 The Review of Higher Education Spring 2017, Volume 40, No. 3, pp. 391–426 Copyright © 2017 Association for the Study of Higher Education All Rights Reserved (ISSN 0162–5748) Data driven decision-making in the era of accountability: Fostering faculty data cultures for learning Matthew T. Hora, Jana Bouwma-Gearhart, and Hyoung Joon Park One of the defining characteristics of current U.S. educational policy at all levels is a focus on using evidence, or data, to inform decisions about in- stitutional and educator quality, budgetary decisions, and what and how to teach students. This approach is often viewed as a corrective to the way that teachers have made decisions in the past—on the basis of less reliable infor- mation sources such as anecdote or intuition—and is seen by advocates as a core feature of successful educational reform (Mandinach, 2012). Underlying Matthew T. Hora, PhD is an Assistant Professor of Adult and Higher Education at the University of Wisconsin-Madison where he conducts research on higher education-workforce relations, organizational change, and classroom teaching in postsecondary contexts. Jana Bouwma-Gearhart, PhD is the Associate Dean of Research in the College of Educa- tion, and an Associate Professor of Science and Math Education at Oregon State University, where she focuses on issues related to STEM education, organizational change, and teacher professional development. Hyoung Joon Park is a Ph.D. student in the Department of Educational Leadership and Policy Analysis at the University of Wisconsin-Madison. His research focuses on utilization- oriented evaluation, evidence-based practices, individual and organizational learning, and STEM education. 392 The Review of higheR educaTion SPrinG 2017 the current push for data driven decision-making (hereafter DDDM) is the idea of continuous improvement, which refers to systems that are designed to continually monitor organizational processes in order to identify problems and then enact corrective measures (Bhuiyan & Baghel, 2005). In education this model has been widely adopted and is often associated with large data- sets that are analyzed with sophisticated algorithms to identify which states, districts, and schools are succeeding or failing according to federal and state accountability criteria (Darling-Hammond, 2010). Yet research on data use in K-12 settings has demonstrated that the provi- sion of data alone does not magically lead to improved teaching and learn- ing. This is because DDDM is not simply a matter of giving educators data reports, but one that involves translating these data into information and actionable knowledge that administrators and teachers can apply to current and future problems (Spillane, 2012). Imagine a principal and group of teach- ers struggling to understand precisely what voluminous amounts of student achievement data reports mean in terms of student advising, curriculum change, and classroom teaching. Each person will necessarily interpret the data through their own unique perspectives and experiences. Additionally, their situation within a particular school or institution will also influence how they interact with data, including the social networks, cultural norms, artifacts (i.e., designed objects), policies and procedures, and practices that collectively shape how people think and act within complex organizations (Coburn & Turner, 2011; Halverson, Grigg, Prichett, & Thomas, 2007). Such insights into the processes of sense-making as a situated phenomenon have led to a growing body of research on data use in K-12 contexts known as “practice-based research,” which focuses on how educators actually think, make decisions, and work in specific situations rather than on describing the effects of interventions or prescribing best practices (Coburn & Turner, 2012; Little, 2012). In seeking to understand the impacts of the environment on data practices, this line of inquiry emphasizes the cultural aspects of data use, where educators engage in routinized practices with colleagues while using shared language and tools to conduct their work (Spillane, 2012). Given documented challenges with the effective institution of DDDM in schools, particularly at the classroom level, such insights can be an important tool to improve interventions by ensuring that they are aligned with or responsive to the norms and practices of specific organizations, as opposed to a “top- down” approach that is a far less effective approach to reform (Fullan, 2010; Mandinach, 2012; Spillane, Halverson & Diamond, 2001). What does this all mean for higher education? Policymakers and post- secondary leaders are devoting considerable efforts towards introducing a “culture of evidence” to higher education that is not dissimilar to the data- based accountability movement in K-12 education (Morest, 2009). This is evident in performance-based funding (Hillman, Tandberg & Gross, 2014), Hora, Bouwma-GearHart, and Park / Data driven decision-making 393 institutional rating systems (Kelchen, 2014), and the increasing use of data mining and analytics (Lane, 2014; Picciano, 2012). At the classroom level, some argue that the use of predictive modeling can improve teaching and learning through learning analytics, which is seen as an evidence-based way to tailor instruction to student needs and to generally improve faculty1 decision-making (Baepler & Murdoch, 2010; Wright, McKay, Hershock, Miller, & Tritz, 2014). Taken together, these developments indicate that higher education has entered an accountability phase not unlike that in the K–12 sector at the beginning of the 1990s. Thus, a pressing question facing higher education is whether the les- sons learned from the DDDM movement in K-12 schools will be heeded, particularly insights gleaned from practice-based research regarding the importance of understanding local data cultures. Besides using such insights to improve the design of new initiatives, they also can shed light on an important question facing the broader field of education – are institutions utilizing technology and data systems to support compliance with account- ability pressures or to support learners? (Halverson & Shapiro, 2012). But little is known about how faculty think about and use teaching-related data as part of their regular work and the roles that postsecondary institutions play in supporting the effective use of educational data. This state of affairs is particularly problematic given the tendency for colleges and universities to not engage faculty in continuous improvement systems regarding educa- tional change. As Blaich and Wise (2011) note, the norm is to “gather data, to circulate the resulting reports among a small group of people, and then to just shelve them if nothing horrible jumps out” (p. 12). To successfully take the next step of engaging campus stakeholders—especially faculty—in productive conversations about DDDM will require in-depth knowledge of the faculty cultures for data use. In this paper we report findings from a practice-based study that examines the cultural practices of data use among 59 science and engineering faculty from three large, public research universities. In this exploratory study we documented how faculty use teaching-related data “in the wild” using in- terviews and classroom observations, which were analyzed using inductive thematic analysis, exploratory data reduction, and causal network techniques. The study was guided by the following questions: (1) What types of data and other information are used by faculty? (2) What are some defining charac- teristics regarding faculty data use? (3) Can patterns be discerned in these data practices across the study sample? and (4) What role do these cultural practices and contextual factors play in shaping individual-level practice? 1By faculty we mean all people who hold undergraduate teaching positions—whether full- or part-time, in a tenure track or not—in postsecondary institutions, with the exception of graduate teaching assistants. 394 The Review of higheR educaTion