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George Bradford

Analytics in Higher Education - Benefits, Barriers, Progress, and Recommendations (EDUC... - 0 views

    Jacqueline Bichsel - 2012

    Many colleges and universities have demonstrated that analytics can help significantly advance an institution in such strategic areas as resource allocation, student success, and finance. Higher education leaders hear about these transformations occurring at other institutions and wonder how their institutions can initiate or build upon their own analytics programs. Some question whether they have the resources, infrastructure, processes, or data for analytics. Some wonder whether their institutions are on par with other in their analytics endeavors. It is within that context that this study set out to assess the current state of analytics in higher education, outline the challenges and barriers to analytics, and provide a basis for benchmarking progress in analytics.
George Bradford

Analytics in Higher Education: Establishing a Common Language | EDUCAUSE - 0 views

    Analytics in Higher Education: Establishing a Common Language
    Title: Analytics in Higher Education: Establishing a Common Language (ID: ELI3026)
    Author(s): Angela van Barneveld (Purdue University), Kimberly Arnold (Purdue University) and John P. Campbell (Purdue University)
    Topics: Academic Analytics, Action Analytics, Analytics, Business Analytics, Decision Support Systems, Learning Analytics, Predictive Analytics, Scholarship of Teaching and Learning
    Origin: ELI White Papers, EDUCAUSE Learning Initiative (ELI) (01/24/2012)
    Type: Articles, Briefs, Papers, and Reports
bcby c

Spring Focus Session Community Ideas - Google Docs - 0 views

    educause learning initiative
    2012 online spring focus session: learning analytics
    Teaching and Learning Community's ideas
George Bradford

Learning and Knowledge Analytics - Analyzing what can be connected - 0 views

    Learning and Knowledge Analytics
    Analyzing what can be connected
George Bradford

Assessment and Analytics in Institutional Transformation (EDUCAUSE Review) | EDUCAUSE - 0 views

  • At the University of Maryland, Baltimore County (UMBC), we believe that process is an important factor in creating cultural change. We thus approach transformational initiatives by using the same scholarly rigor that we expect of any researcher. This involves (1) reviewing the literature and prior work in the area, (2) identifying critical factors and variables, (3) collecting data associated with these critical factors, (4) using rigorous statistical analysis and modeling of the question and factors, (5) developing hypotheses to influence the critical factors, and (6) collecting data based on the changes and assessing the results.
  • among predominantly white higher education institutions in the United States, UMBC has become the leading producer of African-American bachelor’s degree recipients who go on to earn Ph.D.’s in STEM fields. The program has been recognized by the National Science Foundation and the National Academies as a national model.
  • UMBC has recently begun a major effort focused on the success of transfer students in STEM majors. This effort, with pilot funding from the Bill and Melinda Gates Foundation, will look at how universities can partner with community colleges to prepare their graduates to successfully complete a bachelor’s degree in a STEM field.
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  • Too often, IT organizations try to help by providing an analytics “dashboard” designed by a vendor that doesn’t know the institution. As a result, the dashboard indicators don’t focus on those key factors most needed at the institution and quickly become window-dressing.
  • IT organizations can support assessment by showing how data in separate systems can become very useful when captured and correlated. For example, UMBC has spent considerable effort to develop a reporting system based on our learning management system (LMS) data. This effort, led from within the IT organization, has helped the institution find new insights into the way faculty and students are using the LMS and has helped us improve the services we offer. We are now working to integrate this data into our institutional data warehouse and are leveraging access to important demographic data to better assess student risk factors and develop interventions.
  • the purpose of learning analytics is “to observe and understand learning behaviors in order to enable appropriate interventions.
  • the 1st International Conference on Learning Analytics and Knowledge (LAK) was held in Banff, Alberta, Canada, in early 2011 (
  • At UMBC, we are using analytics and assessment to shine a light on students’ performance and behavior and to support teaching effectiveness. What has made the use of analytics and assessment particularly effective on our campus has been the insistence that all groups—faculty, staff, and students—take ownership of the challenge involving student performance and persistence.
    Assessment and analytics, supported by information technology, can change institutional culture and drive the transformation in student retention, graduation, and success.

    U.S. higher education has an extraordinary record of accomplishment in preparing students for leadership, in serving as a wellspring of research and creative endeavor, and in providing public service. Despite this success, colleges and universities are facing an unprecedented set of challenges. To maintain the country's global preeminence, those of us in higher education are being called on to expand the number of students we educate, increase the proportion of students in science, technology, engineering, and mathematics (STEM), and address the pervasive and long-standing underrepresentation of minorities who earn college degrees-all at a time when budgets are being reduced and questions about institutional efficiency and effectiveness are being raised.
George Bradford

Seeking Evidence of Impact: Opportunities and Needs (EDUCAUSE Review) | EDUCAUSE - 0 views

  • Conversations with CIOs and other senior IT administrators reveal a keen interest in the results of evaluation in teaching and learning to guide fiscal, policy, and strategic decision-making. Yet those same conversations reveal that this need is not being met.
  • gain a wider and shared understanding of “evidence” and “impact” in teaching and learning
  • establish a community of practice
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  • provide professional-development opportunities
  • explore successful institutional and political contexts
  • establish evidence-based practice
  • The most important reason is that in the absence of data, anecdote can become the primary basis for decision-making. Rarely does that work out very well.
  • autocatalytic evaluation process—one that builds its own synergy.
  • We live by three principles: uncollected data cannot be analyzed; the numbers are helped by a brief and coherent summary; and good graphs beat tables every time.
    • Reports and testimonies from faculty and students (57%)
    • Measures of student and faculty satisfaction (50%)
    • Measures of student mastery (learning outcomes) (41%)
    • Changes in faculty teaching practice (35%)
    • Measures of student and faculty engagement (32%)
  • The survey results also indicate a need for support in undertaking impact-evaluation projects.
    • Knowing where to begin to measure the impact of technology-based innovations in teaching and learning
    • Knowing which measurement and evaluation techniques are most appropriate
    • Knowing the most effective way to analyze evidence 
  • The challenge of persuasion is what ELI has been calling the last mile problem. There are two interrelated components to this issue: (1) influencing faculty members to improve instructional practices at the course level, and (2) providing evidence to help inform key strategic decisions at the institutional level.
  • Broadly summarized, our results reveal a disparity between the keen interest in research-based evaluation and the level of resources that are dedicated to it—prompting a grass-roots effort to support this work.
    The SEI program is working with the teaching and learning community to gather evidence of the impact of instructional innovations and current practices and to help evaluate the results.
    The calls for more accountability in higher education, the shrinking budgets that often force larger class sizes, and the pressures to increase degree-completion rates are all raising the stakes for colleges and universities today, especially with respect to the instructional enterprise. As resources shrink, teaching and learning is becoming the key point of accountability. The evaluation of instructional practice would thus seem to be an obvious response to such pressures, with institutions implementing systematic programs of evaluation in teaching and learning, especially of instructional innovations.
George Bradford

[!!!] Penetrating the Fog: Analytics in Learning and Education (EDUCAUSE Review) | EDUC... - 0 views

  • Continued growth in the amount of data creates an environment in which new or novel approaches are required to understand the patterns of value that exist within the data.
  • learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
  • Academic analytics, in contrast, is the application of business intelligence in education and emphasizes analytics at institutional, regional, and international levels.
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  • Course-level:
  • Educational data-mining
  • Intelligent curriculum
  • Adaptive content
  • the University of Maryland, Baltimore County (UMBC) Check My Activity tool, allows learners to “compare their own activity . . . against an anonymous summary of their course peers.
  • Mobile devices
  • social media monitoring tools (e.g., Radian6)
  • Analytics in education must be transformative, altering existing teaching, learning, and assessment processes, academic work, and administration.
    • George Bradford
      See Bradford - Brief vision of the semantic web as being used to support future learning: 
    • George Bradford
      See Peter Goodyear's work on the Ecology of Sustainable e-Learning in Education.
  • How “real time” should analytics be in classroom settings?
  • Adaptive learning
  • EDUCAUSE Review, vol. 46, no. 5 (September/October 2011)
  • Penetrating the Fog: Analytics in Learning and Education
    Attempts to imagine the future of education often emphasize new technologies-ubiquitous computing devices, flexible classroom designs, and innovative visual displays. But the most dramatic factor shaping the future of higher education is something that we can't actually touch or see: big data and analytics. Basing decisions on data and evidence seems stunningly obvious, and indeed, research indicates that data-driven decision-making improves organizational output and productivity.1 For many leaders in higher education, however, experience and "gut instinct" have a stronger pull.
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