Educational Analytics

Analytics: The Widening Divide By David Kiron, Rebecca Shockley, Nina Kruschwitz, Glenn Finch and Dr. Michael Haydock November 7, 2011 How companies are achieving competitive advantage through analytics IN THIS SECOND JOINT MIT Sloan Management Review and IBM Institute for Business Value study, we see a growing divide between those companies that, on one side, see the value of business analytics and are transforming themselves to take advantage of these newfound opportunities, and, on the other, that have yet to embrace them. Using insights gathered from more than 4,500 managers and executives, Analytics: The Widening Divide identifies three key competencies that enable organizations to build competitive advantage using analytics. Further, the study identifies two distinct paths that organizations travel while gaining analytic sophistication, and provides recommendations to accelerate organizations on their own paths to analytic transformation.

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

Learning and Knowledge Analytics Analyzing what can be connected

"Abstract The authors compared the underlying student response patterns to an end-of-course rating instrument for large student samples in online, blended and face-to-face courses. For each modality, the solution produced a single factor that accounted for approximately 70% of the variance. The correlations among the factors across the class formats showed that they were identical. The authors concluded that course modality does not impact the dimensionality by which students evaluate their course experiences. The inability to verify multiple dimensions for student evaluation of instruction implies that the boundaries of a typical course are beginning to dissipate. As a result, the authors concluded that end-of-course evaluations now involve a much more complex network of interactions. Highlights ► The study models student satisfaction in the online, blended, and face-to-face course modalities. ► The course models vary technology involvement. ► Image analysis produced single dimension solutions. ► The solutions were identical across modalities. Keywords: Student rating of instruction; online learning; blended learning; factor analysis; student agency"

Abstract Few traditions in higher education evoke more controversy, ambivalence, criticism, and, at the same time, support than student evaluation of instruction (SEI). Ostensibly, results from these end-of-course survey instruments serve two main functions: they provide instructors with formative input for improving their teaching, and they serve as the basis for summative profiles of professors' effectiveness through the eyes of their students. In the academy, instructor evaluations also can play out in the high-stakes environments of tenure, promotion, and merit salary increases, making this information particularly important to the professional lives of faculty members. At the research level, the volume of the literature for student ratings impresses even the most casual observer with well over 2,000 studies referenced in the Education Resources Information Center (ERIC) alone (Centra, 2003) and an untold number of additional studies published in educational, psychological, psychometric, and discipline-related journals. There have been numerous attempts at summarizing this work (Algozzine et al., 2004; Gump, 2007; Marsh & Roche, 1997; Pounder, 2007; Wachtel, 1998). Student ratings gained such notoriety that in November 1997 the American Psychologist devoted an entire issue to the topic (Greenwald, 1997). The issue included student ratings articles focusing on stability and reliability, validity, dimensionality, usefulness for improving teaching and learning, and sensitivity to biasing factors, such as the Dr. Fox phenomenon that describes eliciting high student ratings with strategies that reflect little or no relationship to effective teaching practice (Ware & Williams, 1975; Williams & Ware, 1976, 1977).

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Listing visualizations of data. IBM Research and the IBM Cognos experiment.

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.

Creating an effective infographic requires both artistic sense and a clear vision of what to tell the audience. The following are some cool infographics we have collected. Some are colorful, some are simple, but all are informative and visually pleasing. Not only do they provide information in a format that is easy to understand, but they are also artistic creations in their own right.

One of my areas of interest that has grown over the last couple years has been data visualization. I'm a visually-oriented learner, and I look forward to seeing any techniques, illustrations, or technologies that: 1) Allow people to assimilate information as fast as possible. 2) Deepen understanding of knowledge by visually illustrating data in new and interesting ways. There is nothing like having an intellectual epiphony after looking at a picture for a few seconds (pictures can definitely be worth a thousand words). 3) Present information in an aesthetically pleasing way. Or, in extreme examples, inspire a sense of awe!

Data presentation can be beautiful, elegant and descriptive. There is a variety of conventional ways to visualize data - tables, histograms, pie charts and bar graphs are being used every day, in every project and on every possible occasion. However, to convey a message to your readers effectively, sometimes you need more than just a simple pie chart of your results. In fact, there are much better, profound, creative and absolutely fascinating ways to visualize data. Many of them might become ubiquitous in the next few years.

50 MOST STUNNING EXAMPLES OF DATA VISUALIZATION AND INFOGRAPHICS Posted by Richie on Thursday, April 15, 2010 "A picture is worth a thousand words", if I had a penny for every time I heard that!! There is so much data in the world today that it has become impossible for us to analyze them with patience. Data as we perceive it, need not be boring, bland and cumbersome to remember. To make complex things seem simple, is Creativity and using pictures to represent data has been an age old method to analyze data in a fun way. From navigating the web in an entirely new dimension to understanding how the human brain works; from peeking into how Google has evolved to analyzing the inner working of the geeky mind, Infographics has completely changed the way we view content and visualize data.

Information graphics or infographics are graphic visual representations of information, data or knowledge. These graphics present complex information quickly and clearly,[1] such as in signs, maps, journalism, technical writing, and education. With an information graphic, computer scientists, mathematicians, and statisticians develop and communicate concepts using a single symbol to process information.

Learning Analytics: Dream, Nightmare, or Fairydust? From today's keynote at Ascilite 2011, here's the podcast plus the slides. I am grateful to Gary, Renee and everyone else at Ascilite for their understanding and flexibility, since after months of planning this trip, unfortunately I could not be there in person after my father passed away last weekend. For those of you who like to download and watch offline: podcast [Hi-Res version: 93.3Mb] + slides [PPTX/PDF] For detailed descriptions of work presented here, see other posts tagged learning analytics and the references below.

REFRAME REFRAME is a university-wide project reconceptualising QUT's evaluation of learning and teaching. REFRAME is fundamentally reconsidering QUT's overall approach to evaluating learning and teaching. Our aim is to develop a sophisticated risk-based system to gather, analyse and respond to data along with a broader set of user-centered resources. The objective is to provide individuals and teams with the tools, support and reporting they need to meaningfully reflect upon, review and improve teaching, student learning and the curriculum. The approach will be informed by feedback from the university community, practices in other institutions and the literature, and will, as far as possible, be 'future-proofed' through awareness of emergent evaluation trends and tools. Central to REFRAME is the consideration of the purpose of evaluation and the features that a future approach should consider.

New students are more likely to drop out of online colleges if they take full courseloads than if they enroll part time, according to findings from a research project that is challenging conventional wisdom about student success. But perhaps more important than that potentially game-changing nugget, researchers said, is how the project has chipped away at skepticism in higher education about the power of "big data." Researchers have created a database that measures 33 variables for the online coursework of 640,000 students - a whopping 3 million course-level records. While the work is far from complete, the variables help track student performance and retention across a broad range of demographic factors. The data can show what works at a specific type of institution, and what doesn't. That sort of predictive analytics has long been embraced by corporations, but not so much by the academy. The ongoing data-mining effort, which was kicked off last year with a $1 million grant from the Bill and Melinda Gates Foundation, is being led by WCET, the WICHE Cooperative for Educational Technologies.

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.

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.