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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.
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    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

Open Research Online - Learning dispositions and transferable competencies: pedagogy, m... - 0 views

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    Theoretical and empirical evidence in the learning sciences substantiates the view that deep engagement in learning is a function of a complex combination of learners' identities, dispositions, values, attitudes and skills. When these are fragile, learners struggle to achieve their potential in conventional assessments, and critically, are not prepared for the novelty and complexity of the challenges they will meet in the workplace, and the many other spheres of life which require personal qualities such as resilience, critical thinking and collaboration skills. To date, the learning analytics research and development communities have not addressed how these complex concepts can be modelled and analysed, and how more traditional social science data analysis can support and be enhanced by learning analytics. We report progress in the design and implementation of learning analytics based on a research validated multidimensional construct termed "learning power". We describe, for the first time, a learning analytics infrastructure for gathering data at scale, managing stakeholder permissions, the range of analytics that it supports from real time summaries to exploratory research, and a particular visual analytic which has been shown to have demonstrable impact on learners. We conclude by summarising the ongoing research and development programme and identifying the challenges of integrating traditional social science research, with learning analytics and modelling.
George Bradford

Open Research Online - Contested Collective Intelligence: rationale, technologies, and ... - 0 views

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    We propose the concept of Contested Collective Intelligence (CCI) as a distinctive subset of the broader Collective Intelligence design space. CCI is relevant to the many organizational contexts in which it is important to work with contested knowledge, for instance, due to different intellectual traditions, competing organizational objectives, information overload or ambiguous environmental signals. The CCI challenge is to design sociotechnical infrastructures to augment such organizational capability. Since documents are often the starting points for contested discourse, and discourse markers provide a powerful cue to the presence of claims, contrasting ideas and argumentation, discourse and rhetoric provide an annotation focus in our approach to CCI. Research in sensemaking, computer-supported discourse and rhetorical text analysis motivate a conceptual framework for the combined human and machine annotation of texts with this specific focus. This conception is explored through two tools: a social-semantic web application for human annotation and knowledge mapping (Cohere), plus the discourse analysis component in a textual analysis software tool (Xerox Incremental Parser: XIP). As a step towards an integrated platform, we report a case study in which a document corpus underwent independent human and machine analysis, providing quantitative and qualitative insight into their respective contributions. A promising finding is that significant contributions were signalled by authors via explicit rhetorical moves, which both human analysts and XIP could readily identify. Since working with contested knowledge is at the heart of CCI, the evidence that automatic detection of contrasting ideas in texts is possible through rhetorical discourse analysis is progress towards the effective use of automatic discourse analysis in the CCI framework.
George Bradford

Measuring Teacher Effectiveness - DataQualityCampaign.Org - 0 views

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    Measuring Teacher Effectiveness Significant State Data Capacity is Required to Measure and Improve Teacher Effectiveness  States Increasingly Focus on Improving Teacher Effectiveness: There is significant activity at the local, state, and federal levels to  measure and improve teacher effectiveness, with an unprecedented focus on the use of student achievement as a primary indicator of  effectiveness. > 23 states require that teacher evaluations include evidence of student learning in the form of student growth and/or value-added data (NCTQ, 2011). > 17 states and DC have adopted legislation or regulations that specifically require student achievement and/or student growth to "significantly" inform or be the primary criterion in teacher evaluations(NCTQ, 2011).  States Need Significant Data Capacity to Do This Work: These policy changes have significant data implications. > The linchpin of all these efforts is that states must reliably link students and teachers in ways that capture the complex connections that  exist in schools. > If such data is to be used for high stakes decisions-such as hiring, firing, and tenure-it must be accepted as valid, reliable, and fair. > Teacher effectiveness data can be leveraged to target professional development, inform staffing assignments, tailor classroom instruction,  reflect on practice, support research, and otherwise support teachers.  Federal Policies Are Accelerating State and Local Efforts: Federal policies increasingly support states' efforts to use student  achievement data to measure teacher effectiveness. > Various competitive grant funds, including the Race to the Top grants and the Teacher Incentive Fund, require states to implement teacher  and principal evaluation systems that take student data into account.  > States applying for NCLB waivers, including the 11 that submitted requests in November 2011, must commit to implementing teacher and  principal evaluation and support systems. > P
George Bradford

Learning Dispositions and Transferable Competencies: Pedagogy, Modelling, and Learning ... - 0 views

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    Simon Buckingham Shum Ruth Deakin Crick 2012 (In review) Theoretical and empirical evidence in the learning sciences  substantiates the view that deep engagement in learning is a  function of a  combination of learners' dispositions,  values,  attitudes and skills. When these are fragile, learners struggle to  achieve their potential in conventional assessments, and critically,  are not prepared for the novelty and complexity of the challenges  they will meet in the workplace, and the many other spheres of  life which require personal qualities such as resilience, critical  thinking and collaboration skills. To date, the learning analytics  research and development communities have not addressed how  these complex concepts can be modelled and analysed. We report  progress in the design and implementation of learning analytics  based on an empirically validated  multidimensional construct  termed  "learning power". We describe a  learning analytics  infrastructure  for gathering data at scale, managing stakeholder  permissions, the range of analytics that it supports from real time  summaries to exploratory research, and a particular visual analytic which has been shown to have demonstrable impact on learners.  We conclude by  summarising the ongoing research and  development programme.
George Bradford

National Institute for Learning Outcomes Assessment - 0 views

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    "Accrediting associations have expectations that call on institutions to collect and use evidence of student learning outcomes at the programmatic and institutional to confirm and improve student learning.  This section of the NILOA website lists both regional accrediting associations and specialized or programmatic accrediting organizations along with links to those groups."
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: http://heybradfords.com/moonlight/research-resources/SemWeb_EducatorsVision 
    • 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
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    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.
George Bradford

A unified framework for multi-level analysis of distributed learning - 0 views

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    A unified framework for multi-level analysis of distributed learning Full Text: PDF Authors: Daniel Suthers University of Hawaii, Honolulu, HI Devan Rosen School of Communications, Ithaca College, Ithaca, NY Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked. Understanding distributed learning and knowledge creation requires multi-level analysis of the situated accomplishments of individuals and small groups and of how this local activity gives rise to larger phenomena in a network. We have developed an abstract transcript representation that provides a unified analytic artifact of distributed activity, and an analytic hierarchy that supports multiple levels of analysis. Log files are abstracted to directed graphs that record observed relationships (contingencies) between events, which may be interpreted as evidence of interaction and other influences between actors. Contingency graphs are further abstracted to two-mode directed graphs that record how associations between actors are mediated by digital artifacts and summarize sequential patterns of interaction. Transitive closure of these associograms creates sociograms, to which existing network analytic techniques may be applied, yielding aggregate results that can then be interpreted by reference to the other levels of analysis. We discuss how the analytic hierarchy bridges between levels of analysis and theory.
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