{"id":678,"date":"2013-11-05T08:55:49","date_gmt":"2013-11-05T12:55:49","guid":{"rendered":"http:\/\/blog.uvm.edu\/ctl-projects\/?p=678"},"modified":"2013-11-05T08:55:49","modified_gmt":"2013-11-05T12:55:49","slug":"educause-2013-notes","status":"publish","type":"post","link":"https:\/\/blog.uvm.edu\/waw\/2013\/11\/05\/educause-2013-notes\/","title":{"rendered":"EDUCAUSE 2013 Notes"},"content":{"rendered":"

Educause day 1<\/h1>\n
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Opening ceremony, lots of lights and sparkle like the Emmys. \u00a0Keynote speaker Sir Ken Robinson. Leading a culture of innovation.\u00a0We see the world through cultural filters. A a result, trying to predict the effect of IT on culture is impossible until it changes culture. Examples:TV, iPhone, phonograph. \u00a0Unlike animals we are constantly evolving culturally. Driven by technology. Thus we need to think differently about education. Education still locked into old cultural norms. Old model of college- job set for life is over. Thus so is k-12 standardized testing get into college model. IT the solution?<\/div>\n
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“OAAI: Deploying an Open Ecosystem for Learner Analytics” Josh Baron, Marist College.\u00a0The Open Academic Analytics Initiative (OAAI), an NGLC grant recipient, has developed a predictive model for learner analytics using open-source tools, which they are releasing under an open-source license. They\u00a0share project outcomes along with research into effective OER-based intervention strategies and other critical learner analytics scaling factors.\u00a0Student attitude data (sat, GRE) other demographics, \u00a0Sakai lms data as input. Tools: Pentaho Business intelligence \u00a0oaai predictive model. Predictive modeling markup language. Instructor received academic alert report 3 times during \u00a0semester. Each with a confidence score. Lms data normalized by class averages. \u00a0Most powerful predictor is GPA followed by grade book. Number of logins not so much. Contrast to Chico below. Intervention strategies: Awareness note to students; suggest online academic support environment or Learner-facilitator interaction. Both interventions scored same, but better than control. So just simple awareness of risk is enough. But students who received intervention more likely to withdraw, too. Slides at\u00a0OAAI+EDUCAUSE+Presentation+DRAFT+v1.pptx 3 MB, Powerpoint Slides Uploaded on 10\/17\/2013<\/a>\u00a0also see solarresearch.org.<\/div>\n
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“Leveraging Learning Analytics to Build a Culture of Evidence-Based Decision Making” Mesa community college. Speakers :\u00a0Philip Adams,\u00a0Director of Client Services<\/div>\n
Blackboard Inc. ;\u00a0Craig Jacobsen<\/a>\u00a0Residential Faculty\u00a0Mesa Community College ;James Mabry<\/a>\u00a0Vice President of Academic Affairs\u00a0Mesa Community College\u00a0Shouan Pan<\/a>President<\/div>\n
Community College ;\u00a0Sasan Poureetezadi<\/a>\u00a0Vice President, Information Technology\u00a0Mesa Community College<\/div>\n
See ECAR readiness index http:\/\/www.educause.edu\/library\/resources\/2012-ecar-study-analytics-higher-education<\/div>\n
Buy in from bottom up and\u00a0Top down, great support from president. Finally brought In blackboard analytics (The full Learn+SIS version) to pull in all the data. Many levels of access to protect privacy, role based. Bb analytics different from analytics for Learn. Faculty can look at data too.<\/div>\n<\/div>\n
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Advances in Devices, Cloud Services, and Data Analytics: High-Impact \u00a0Opportunities for Education\u00a0Anoop Gupta<\/div>\n
Distinguished Scientist<\/div>\n
Microsoft Corporation ,<\/div>\n
We are witnessing rapid advances and innovation in \u00a0devices, complemented by tremendous innovation in cloud services and \u00a0 analytics, which open up new models for scale learning such as MOOCs. New \u00a0models and pedagogies for in-classroom learning are transforming the nature\u00a0of textbooks. Novel communication and collaboration services, always-on \u00a0\u00a0feedback loops, and big data can offer deep insights into student learning, provide personalized pathways for students, and improve retention. This talk \u00a0 will share a perspective on these ongoing developments and touch on key \u00a0 products, services, and research efforts that can empower the higher \u00a0education community.\u00a0Lectures are a terrible way to teach. Students learn <30% of what they don't already know. Almost any type of tech works better, and devices generate numbers that can transform via analytics. Technology adoption phases 1) reproduce analog 2) value added versions. 3) missed it. Moocs gaining acceptance. MOOCs evolving for industrial learning. Next step MEC Massively Empowered Classrooms. Bodies of course materials customized for local delivery. Future: empowered local faculty; modular content with clear rights. Powerful authoring and compositing tools and mashups. Non siload analytics, social tools to keep student engaged, useful credentials<\/div>\n
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Do Clicks Count to Increase Student Achievement? Learner Analytics on a Large-Enrollment Hybrid Course\u00a0Kathy Fernandes<\/a>,\u00a0Director, Learning Design and Technologies ,California State University:\u00a0John Whitmer<\/a>,\u00a0Program Manager, Academic Technology and Analytics ,California State University<\/div>\n
Summary of Results 455 KB, PDF Uploaded on 10\/16\/2013<\/a><\/div>\n
Presentation slides from Educause 2013 … 3 MB, Powerpoint Slides Uploaded on 10\/16\/2013<\/a><\/div>\n
Univ California Chico case study research project. Mining data from SIS and LMS (bb). Used excel, tableau, stata, spss. Looked at one large(373 students) course, biggest raw usage of lms entire campus. 10% increase in mastery! But 7 to 11% increase in DWF. Tinyurl.com\/clicksedu13. Early questions what data do we have have and how good is it. lMS usage was 4 times better than sis data in predicting final grade. Lms explained d18% student characteristics 4%. \u00a0Student demographic data may be too course \u00a0grained.<\/div>\n
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Educause day 2<\/h1>\n
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Speeding Up on Curves\u00a0Bradley Wheeler Vice President for IT and CIO\u00a0<\/a>Indiana University\u00a0WINNER: 2013 EDUCAUSE Leadership Award.\u00a0<\/i>Speeding Up On Curves 8 MB, Powerpoint Slides Uploaded on 10\/18\/2013<\/a><\/p>\n
At least 3 Curves in the it road:<\/div>\n
1.Public to Private\u00a0Good\u00a0\u2026(price\/cost pressures).\u00a0Higher Education in Increasingly a\u00a0Private\u00a0Good.\u00a0Colleges and Universities\u00a0Must<\/span>\u00a0Substantially Change our Cost Structure<\/div>\n
2.Digital Favors Scale \u2026(rise of substitutes?)\u00a01. $$$\u00a0Residential Education (flipped classroom) \u00a02. $$ Online Courses \/ Degrees \u00a03. $ Massive Online Courses (MOCs) \u00a04. FREE Massive Open Online Courses (MOOCs)<\/div>\n
Campus strategies Independence, dependence and interdependence. Scale of digital leads to more options for price conscious students. Upper level courses subsidized by large enrollment freshmen courses.<\/div>\n
3.Campus CIO Influence \u2026(who\u2019s driving?) He is very worried.<\/div>\n
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Improving Student Success: Using Groundbreaking Analytics and Fast Data to Improve Student Retention\u00a0Vince Kellen<\/a>,\u00a0Senior Vice Provost for Academic Planning, Analytics & Technologies University of Kentucky\u00a0Using Groundbreaking Analytics and Fast Data … 7 MB, Powerpoint Slides Uploaded on 10\/24\/2013<\/a><\/div>\n
Take a lecture video, convert speech to text, index keywords to time line, add assessment tool, measure mastery. \u00a0First steps over the past year<\/div>\n
\u2022Mobile micro-surveys: Learning from the learner<\/div>\n
\u2022Student enrollment, retention, demographics, performance, K-Score,\u00a0facilities utilization, instructor workload and more<\/div>\n
\u2022High speed, in-memory analytics architectural differences<\/div>\n
\u2022Open data and organizational\u00a0considerations<\/div>\n
Coming down the road?<\/div>\n
\u2022Micro-segmentation tool to enhance user and IT productivity, develop personalized mobile student interaction\/intervention<\/div>\n
\u2022Models for learner\u00a0technographics, psychographics, in addition to behaviors, performance, background<\/div>\n
\u2022Advanced way-finding for streaming content like lecture capture<\/div>\n
\u2022Content metadata extraction and\u00a0learner\u00a0knowledge\u00a0discovery<\/div>\n
\u2022Real-time measures of concept engagement and mastery<\/div>\n
\u2022Real-time learner recommendations and support engine<\/div>\n
\u2022Use graphing algorithms to perform more sophisticated degree audit what ifs<\/div>\n
UK mobile micro survey helps collect data. Enrollment retention and graduation student performance academic career productivity measured as classroom utilization . Thief micro survey app is 97 percent iOS. Collect stats on devices, push alerts, club participation. Tied into blackboard to receive academic warning alerts. Thinking about calendar alarm alerts. Goo to class wake up. MOOKs let’s take aiming course and make it bigger. Current products aimed at big enrollment, not so much for upper level oddball courses. Adaptive learning and metadata \u00a0system Learns keywords and map to course topics. Common theme use big data to identify either at risk or even concept level difficulties and apply some automatic intervention. Major institutional commitment to aggregator data silos into open database and hire 3 ph.d statisticians. Raise skill set in colleges and units. Totally revs up IR department. Open data to whatever tools end \u00a0units want sas spss r.<\/div>\n
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This years theme is connected connections. To that end, presenting “Higher Education Is a Massively Multiplayer Game,\u201d Jane\u00a0McGonigal,\u00a0 game designer\u00a0and author who advocates the use of mobile and digital technology to channel positive attitudes and collaboration in a real world context.\u00a0She has taught game design and\u00a0game studies<\/a>\u00a0at the\u00a0San Francisco Art Institute<\/a>\u00a0and the University of California, Berkeley, and currently serves as the Director of Game Research & Development at\u00a0Institute for the Future<\/a>\u00a0[5]<\/a><\/sup>\u00a0and Chief Creative Officer at SuperBetter Labs.[6]<\/a><\/sup><\/div>\n
In the best-designed games, our human experience is perfectly optimized: we have important work to do, we’re surrounded by potential collaborators, and we learn quickly and in a low-risk environment. When we’re playing a good online game, we get constant useful feedback, we turbocharge the neurochemistry that makes challenge fun, and we feel an insatiable curiosity about the world around us. None of this is by accident. Game developers have spent three decades figuring out how to make us happier and more collaborative, how to make learning more fun and social, and how to satisfy our hunger for meaning and success. All of these game-world insights can be applied directly to reinvent higher education as we know it.\u00a0Games taken seriously as future of education. Over 1 billion people play at least one hour a day. Average player on \u201cCall of Duty” spends a month of full time work per year playing the game. 81% of US workers show up unmotivated to work. Similar for schools. So they find engagement in games. 10 positive emotions felt by gamers: joy relief love surprise pride curiosity excitement awe\/wonder contentment creativity.\u00a0massively multiplayer thumb wrestling whole hall ~5000 people stood up and played. 3 positive emotions for every one negative breeds a sense of contentment resilience \u00a0and motivation we wish occurred during learning. The opposite of \u00a0play is not work it is depression. Playing games light up caudate hippocampus thalamus. Examples foldit http:\/\/fold.it\/portal\/ protein folding gamers better than computers. Evoke a crash course in solving world problems http:\/\/www.urgentevoke.com . Find the future the game. http:\/\/exhibitions.nypl.org\/100\/digital_fun\/play_the_game<\/div>\n
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Sherpa: Combine Predictive Analytics with a Recommendation Engine to Improve Student Success\u00a0Robert Bramucci<\/a>\u00a0Vice Chancellor, Technology & Learning Services<\/div>\n
South Orange County Community College District\u00a0http:\/\/www.slideshare.net\/jpgaston\/sherpa-overview-12854534<\/div>\n
Recommendations systems, like. Amazon or Netflix personalized unlike most academic systems, \u00a0Sherpa looks at student profile ando recommends coursesservices information tasks. Triggers generated nudges via portal email txt. Allows student feedback to measure effectiveness. Nudge is personal timely relevant and actionable, not a campus wide broadcast. \u00a0 Predictive analytics. Early warnings, recommendations. Only 15% of their students use LMS so not a rich source. Most systems rely on regression. Computation heavy, \u00a0data must match assumptions they took a machine intelligence. Worked with guy from Marist. Called in experts. Three math models. And a sea of missing data. Divide data into training and predictive of past to test before predicting future<\/div>\n
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Adaptive learning back on the rise. Subset of personalized learning.<\/div>\n
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Educause day 3<\/h1>\n
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From IT to Academic Affairs: Getting Started with Learning Analytics<\/div>\n
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Dionne N. Curbeam<\/a><\/div>\n
Director of Instructional Technology & Training<\/div>\n
Prasad Doddanna<\/a><\/div>\n<\/div>\n
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Director, Information Systems<\/div>\n
Sadie R. Gregory<\/a><\/div>\n<\/div>\n
Provost and Vice President for Academic Affairs<\/div>\n
Coppin State University<\/div>\n
In the past, Coppin State University has successfully implemented many of the \u00a0 Blackboard Analytics modules. In spring 2012, Coppin started the Analytics \u00a0for Learn implementation process, with a launch date of fall 2012. This \u00a0 presentation will share the details of our implementation effort, including \u00a0strategy, feedback from faculty and students, and lessons learned. Got support from CIO and faculty champions buy in from faculty senate. Identify a few pilot courses. Training and webinars. \u00a0Analytics Day, snacks faculty analytics session and training for deans. Reports for provosts deans and chairs dashboard features 10 standard reports, built with share point. Deans and chairs can see Across courses And departments, can drill down. \u00a0Faculty see just their courses scatter plot course accesses against grades — does blackboard make a difference. Activity and \u00a0grade may show at risk and achievers. Students can chart their own progress. \u00a0Blackboard grade center side is critical. As so is grading consistently. Regular faculty use an timely responses \u00a0to results. Course instruction method inconsistencies can skew results<\/div>\n<\/div>\n
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Using Analytics to Assess the Impact of LMS Course Redesign Training\u00a0John Fritz<\/a>\u00a0AVP, Instructional Technology & New Media University of Maryland, Baltimore County<\/div>\n
Although we’ve long had an interest in learning analytics at UMBC, we’ve struggled to scale up development of our proprietary solutions. This session will show how we are using Blackboard Analytics for Learn (BA4L) to explore the LMS in much finer detail and assess the impact of faculty course redesign training. 95% students 75% faculty 60% courses. Only about 40 \u00a0ba4l clients. Program offers 2500$ stipend. For training to redesign f2f for hybrid or online , \u00a0teach, and evaluate. Folder depth ratio average items per folder divided by average folder depth. Too high or too low adversely affects use ability. \u00a0Time consuming to build a cohort on bl4l. Content, tools, and assessment major report areas.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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