Big Data Education Insights
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This document discusses how analyzing big data can provide valuable insights for education. It explains that big data is characterized by the 3 Vs: volume, velocity, and variety. Analyzing student data can provide insights into trends, transparency, and actionable information to improve areas like grades, outcomes, and personalized learning. It also discusses challenges in higher education like student retention and time to degree completion that big data analytics may help address. Examples of analytics applications that can help institutions understand students, instructors, programs and provide real-time dashboards and predictive modeling are presented.
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Big Data Education Insights
- 1. You have data We provide Insights™ Big Data in Education: Theory and Practice Michael Moore, MSCIS Sr. Advisory Consultant – Analytics
- 2. Big Data – Big Deal What is so important about Big Data? • Valuable insight about how we consume, behave and interact – Every click, tap, tweet, send or swipe – Digital breadcrumbs • Opportunity for personalization • Big Data has Big Value – Organizations, institutions, etc. can mine (use/extract) that data
- 3. Big Data – Big Deal • What qualifies as Big Data? • It is identified by the “3 Vs” Velocity • How fast data is changing Volume • How much data there is Variety • How many different kinds/sources Gartner - 2012
- 4. Big Data IS A Big Deal Why is Analyzing Big Data Important? • Identify Trends • Transparency and accessibility • Connections • Seem arbitrary or incongruous • Actionable Information • Decision Making
- 5. Big Data – Big Deal Scouting Football • “Moneyball” movie • Evaluate skills relevant to position – Forward, Mid-fielder, Goalkeeper • Predictive team selection
- 6. Big Data in Education So what does this mean for Education? Meaningful Insight • • • • Students Instructors Programs Institution Real-time Data • Dashboards • Statistical Analysis • Machine Learning • Data Modeling • Data Mining Transforming Education • • • • Grades Outcomes Evidence Life-long Learning Personalized Learning
- 7. Big Data – US Higher Ed Problem Student Retention •First year attrition rates exceed 25% •Some states reach 40% •Only 1 in 2 students ever complete a degree Efficacy of Higher Ed Degree Completion Time to Degree Source: Complete College America Time Is the Enemy - Summary http://completecollege.org/docs/Time_Is_the_Enemy_Summary.pdf •75% students are nontraditional •40% students are not academically prepared •40% are part time •60% FT students complete 4 yr Bachelor’s within 8 yrs •24% PT students complete 4 yr Bachelor’s within 8 yrs •20% take more courses than needed
- 9. ILP - Analytics Capability and Maturity Model Stage Four Insight and Information Value What do you want to happen for you?? Stage Three Stage One Stage Two What do I want to happen? Advanced Adaptive What will happen? What has happened? What is happening? Data Reporting Access Advanced Predictive Predictive Risk Modeling Forecasting Strategic Optimization D2L Integrated Learning and Advanced Analytics Platform
- 10. InsightsTM Assessment Reports Module
- 11. InsightsTM Achievement Reports Module
- 12. Desire2Learn Degree CompassTM “Our primary motivation for deploying Degree Compass was to respond to the unique success and retention needs of our complex student population.” Dr. Tristan Denley | Austin Peay State University | Higher Ed, Tennessee, US
- 13. InsightsTM Student Success System Module “The Risk Quadrant and Sociogram give us an incredibly different viewpoint on how our students our trending – much more discretely that we can do in our own minds.” Rick Tanski | Academy Online High School | K-12, Colorado, US
- 14. Adaptive Learning • Knowillage LeaP • Adaptive learning engine • Personalized learning experience What if textbooks could learn . . . from you?
- 15. Analytics Driving Student Success Degree Compass™ Before student is even in the course Student Success While student is in the course
- 16. Text Analytics • Semantic learning • Data mining analysis • Examples: – Survey responses – Customer feedback – Journals and publications – Discussion forums – Email threads
- 17. Text Analytics
- 18. Michael Moore, MSCIS Sr. Advisory Consultant - Analytics Direct 888.772.0325 x6604 Twitter: @MikeMooreD2L Michael.Moore@Desire2Learn.com Let the dataset change your mindset. Desire2Learn, Campus Life, CaptureCast, Desire2Learn Binder, myDesire2Learn, Insert Stuff, Insert Stuff Framework, Instructional Design Wizard, and the molecule logo are trademarks of Desire2Learn Incorporated. Subtitle The Desire2Learn family of companies includes Desire2Learn Incorporated, D2L Ltd., Desire2Learn Australia Pty Ltd, Desire2Learn UK Ltd, Desire2Learn Singapore Pte. Ltd. and D2L Brasil Soluções de Tecnologia para Educação Ltda. www.Desire2Learn.com
Notes de l'éditeur
- Digital persona - Digital signature, digital fingerprint, digital character, digital self, digital footprintIt’s all about behavior – shopping behavior purchasing behavior surfing behavior learning behavior
- Gartner – 2012 3 V’s of Big Data Velocity – how fast it is changing Volume – how much of it there is Variety – how many different kinds/sources there are
- Predictive Modeling – predicting behaviorIn essence, big data is about liberating data that is large in volume, broad in variety and high in velocity from multiple sources in order to create efficiencies, develop new products and be more competitive. Gartner – 2012 3 V’s of Big Data Velocity – how fast it is changing Volume – how much of it there is Variety – how many different kinds/sources there are
- The Moneyball Effect – using predictive analytics to rank and select up and coming playersEuropean Football is using the same approach that the Oakland A’s did – use predictive analytics to look at ALL the statistics that players generate, not just the hits or runs earned (baseball) but also other more telling stats. When Oakland A’s manager Billy Beane had little money or choice in how he could build out his roster, he looked to predictive analytics and the volumes of data about baseball players (both in the big leagues as well as the farm teams) to make informed selections that didn’t just had immediate impact (so-called star players) but also those players that the analysis told him had the best longevity and improvement capabilities – in essence, those players he should be investing in because they would deliver the best player ROI.For European football – league teams are using the same approach. Bloomberg Sports has delivered the first comprehensive tool to rate and rank football players across the federations. The comprehensive list covers the entire 2012-2013 season, highlighting the performances of some of the world’s most popular players, along with some of the sport’s rising stars. Lionel Messi of FC Barcelona topped the list with a score of 91.25, edging out Cristiano Ronaldo of Real Madrid (91.16). Franck Ribery of Champions League winner Bayern Munich came in third with a rating of (89.27). Ribery was one of five Bayern players to make the top 50 list, along with Bastian Schweinsteiger (14th), Thomas Muller (19th), Toni Kroos (22nd), Dante (31st) and Philipp Lahm (38th). Juventus had four players on the list (led by Andrea Pirlo, 5th at 88.62), while Champions League runner-up Borussia Dortmund (led by Marco Reus, 4rd overall at 89.10), Barcelona (led by Messi), Manchester United (led by Robin van Persie 6th at 88.14) and Napoli (led by MarekHamsik 8th at 87.01) also placed three players on the list.The BSports ranking provides the most scientifically-based analysis that evaluates players on skills relevant to their respective positions. For example, defenders on this list are graded heavily but not exclusively, by their performance with regard to tackling, intercepting, and clearing the ball. At the same time, the attackers on the list are judged on their performance when shooting, passing , and scoring among other attributes. The scores also take into account the performance of individuals relative to others at their position.
- You can mention here all the different types of enterprise data: Finance, Resources, etc.As an instructor, wouldn’t you like to be able to re-tool or adjust your teaching methodology while a course is in-flight? Wouldn’t students like to know if they are on-track to fail? What about identifying isolated students in real-time and guiding them on a path to success? What about tapping into new learning techniques like the flipped classroom? Increasing use of non-traditional tools like Social Media to build out your program and course content and drive a dynamic education exchange? What about using technology to bring you back to why you wanted to become a teacher or professor in the first place?These are just some of the possibilities that Big Data holds for education.
- So why do we care about tracking, measuring and monitoring education data?Stats from Complete College AmericaGetting students into HE has never been easierGI Bill (1944), Civil Rights Act (1964), Higher Education Act (1965, 2009)Over $150B in student federal financial aid a yearKeepingstudents in HE has never been harderIncreased enrollment ≠ increased degree attainmentUS dropped in world rank from 1st to 16th for young adults holding degrees Despite value of college degree less than 30% of Americans have a college degree (27%2009 US Census Bureau)
- How did we do that? Where did we start?First generation LMS was first step way back in 1999 – Stage OneFirst generation analytics technology added - Stage TwoSecond generation analytics technology with deeper/richer learning data curation (optimization of Stage Two tools and technology) – Stage ThreeSecond generation predictive and personalized/adaptive learning analytics with the Learner in absolute control of their destiny. Institutions are beacons for learners and drive focus and guidance with predictive and adaptive tools and technology. Institutions who employ these types of tools and technology will attract the largest student population and deliver the most skilled and capable graduates into the field. – Stage FourStage Four – I made this blue like Stage One as this will become the new normal or new baseline for learning in the 21st century. It will be the benchmark/baseline for all learning tools and technology moving forward. See next slides for more details on what is possible for learning as we move towards the end of the second decade of the 21st century. D2L is building the foundation (ie APIs, analytics/predictive analytics, adaptive, gaming, etc.) for Stage 4 entrance and expansion.Data access – just dataReporting/OLAP - what happenedForecasting – why did it happenPredictive modeling – what will happenOptimization (real-time predictive analysis)– what is the best that could happenSense and respond. Predict and Act.Marketing blurb:Already using the learning environment data available to report on key learning outcomes, student engagement and enrolment metrics as well as student grades data, Desire2Learn’s vision for big data in education was to move the institution from traditional activity reporting functions to a big data-driven framework with learning and academic analytics functionality at its core. By developing partnerships with key industry leaders in enterprise analytics applications, the Desire2Learn Analytics portfolio for education would be transformational.Understanding that big data concepts were new to education, Desire2Learn Analytics was specifically packaged into bundled offerings not only to suit different institutional reporting needs but also to address different institutional strategies around big data. Further, by developing a strategic roadmap to include predictive modules in their offering, Desire2Learn’s Analytics portfolio would offer an unprecedented suite of products with the capability to tap into the vast amounts of big data available in education today. Desire2Learn Analytics Portfolio delivers a multi-tiered analytics solution that offers customers a path forward to manage the analytics initiatives that are critical to their institutional effectiveness. Whether those analytics initiatives focus onincreasing operational efficiency, optimizing learning outcomes or creating the conditions for learner success, Desire2Learn’s Analytics Portfolio delivers two analytics solutions that are integral to your institution's strategic process improvement efforts.
- All about aggregating - Performance Analytics Total = % who answered correctlyUpper 27% = the percentage of ppl in the top quartile who answered the question correctlyDiscrimination index = shows discrimination between upper and lower quartile performers (generally the higher the better)If there is a negative, you prob want to remove that qu`estion from the examReliability coefficient:Only works for tests that are designed to test a coherent body of knowledge (so questions are correlated)High reliability indicates that the test is a reliable/good gauge of the student’s knowledge in a specific area (b/c noticeable patterns (correlation) will occur in student marks on the questions)B/w 0 and 1 -> >75% is very reliableHigher is goodPoint biserialCorrelation coeffecientCorrelation b/w getting the question correct and doing well on the overall examHigher is betterNegative is problematicResponse frequenciesGood at indicating effectiveness of distractor questions
- All about aggregating outcomes across levels within the org
- Course Recommendations Global centrality Major centrality Grade predictionGrade PredictionsPredictsgrades to within 0.6 of a letter grade on averageCorrectly distinguishes whethera student will earn an ABC or DF 92% of the time
- Mapping this to D2L’s Solution SetDegree Compass™ - what are your predictors for success in the programStudent Success System - at what points should we be concernedThese two together are a powerful analog for the onboardingTalk about overarching predictive analytics strategy here.Degree Compass and Student Success are the killer one-two punch. They are the first steps in D2L’s predictive analytics product strategyDegree Compass helps you make the right choice on day one. Student Success helps you in-course, through the day-to-day needs DC intervenes at an even earlier stage in the learning pathway. DC provides predictive intelligence and guidance before you even make the course choice.
- imagine how it would be almost impossible for someone to summarize what they read in hundreds of medical research papers. Now imagine reading millions and millions of research papers and then summarizing the most important information for a particular patient in less than 10 seconds. This sheds light on both the and the machines can make interconnections humans would have a hard time making because they can remember everything and make connections across different sources in very large collections of documents. In fact they can beat groups of humans who split up the work if the collection is very large. scalability and value of text analytics. Text mining is a way of using computers to read through text and associate the terms and phrases into common areas. Businesses use text mining to help identify the types of subjects and topics people are sharing with each other via their computer. Like people do when they are good listeners; with text mining companies identify, ideas, opinions, things people like or do not like. Then we can interact based on their interests.
- imagine how it would be almost impossible for someone to summarize what they read in hundreds of medical research papers. Now imagine reading millions and millions of research papers and then summarizing the most important information for a particular patient in less than 10 seconds. This sheds light on both the and the machines can make interconnections humans would have a hard time making because they can remember everything and make connections across different sources in very large collections of documents. In fact they can beat groups of humans who split up the work if the collection is very large. scalability and value of text analytics. Text mining is a way of using computers to read through text and associate the terms and phrases into common areas. Businesses use text mining to help identify the types of subjects and topics people are sharing with each other via their computer. Like people do when they are good listeners; with text mining companies identify, ideas, opinions, things people like or do not like. Then we can interact based on their interests.