Big Data and Learning Analytic inEducation: Research and PracticeMunassir AlhamamiETEC647ESpring, 2013
Overview• Big data and Learning analytics was featured inthe NMC Horizon Report:2010, 2011, 2012, 2013, Higher Education Edition• Big data is a collection of data sets so large andcomplex that it becomes difficult to process usingon hand database management tools ortraditional data processing applications. Thechallenges includecapture, curation, storage, search, sharing, transfer, analysis, and visualization. Wiki
Overview• The term owes its beginnings to data miningefforts in the commercial sector that usedanalysis of consumer activities to identifyconsumer trends. Learning analytics is anemergent field of research that aspires to usedata analysis to inform decisions made onevery tier of the educational system.
Big Data for education in research• Making general statements: huge samples of population• U.S. Department of Education: Office of EducationalTechnology 2012.• 1=User Knowledge Modeling:• What content does a student know (e.g., specific skills andconcepts or procedural knowledge and higher orderthinking skills)• 2=User Behavior Modeling:• What do patterns of student behavior mean for theirlearning? Are students motivated?• 3= User Experience Modeling• Are users satisfied with their experience?
Big Data for education in research• 4= User Profiling:• What groups do users cluster into?• 5= Domain Modeling:• What is the correct level at which to divide topics intomodules and how should these modules besequenced?• 6= Learning component analysis and instructionalprinciple analysis:• Which components are effective at promotinglearning? What learning principles work well? Howeffective are whole curricula?
Big Data for education in research• 7= Trend analysis: What changes over timeand how?• 8= Adaptation and Personalization: What nextactions can be suggested for the user? Howshould the user experience be changed for thenext user? How can the user experience bealtered, most often in real time?
Big Data for education in Practice• Learning analytics leverages student-related data tobuild better pedagogies, target at-risk studentpopulations, and to assess whether programsdesigned to improve retention have been effective andshould be sustained — important outcomes foradministrators, policy makers, and legislators.• Learning analytics envision being able to tailor learningto students’ personal needs and interests — relying ondata to make carefully calculated adjustments andsuggestions to keep learners motivated as they masterconcepts or encounter stumbling blocks.
Examples• 1= One of the earlier applications of learninganalytics by a university was PurdueUniversity’s Signals project, which waslaunched in 2007. Project Signals incorporatesdata from student informationsystems, course management systems, andcourse grade books to generate risk levels sothat at-risk students can be targeted foroutreach.
Examples• 2= Efforts to use student data to personalizeeducation have been made by SaddlebackCommunity College in Orange County with theirService-Oriented Higher EducationRecommendation Personalization Assistant, orSHERPA, system. This software compiles detailedprofiles of each student, recording informationabout work schedules, experiences withprofessors, and other personal information,throughout their time at the university
Examples• 3= At Austin Peay State University inTennessee, university advisors use the DegreeCompass, software that employs predictiveanalytic techniques, to help students decidewhich courses they will need to complete theirdegree along with courses in which they arelikely to be successful (go.nmc.org/apsu). Withthese insights, advisors and counselors hopeto illuminate a student’s best learning path
Examples• 4= In late 2012, CourseSmart, a digital textbookprovider with five partners in the textbookpublishing industry, announced the launch of itsanalytics package, CourseSmart Analytics, whichclosely tracks a student’s activity as they interactwith online texts, and interprets that data forprofessors, providing them with an engagementscore for a particular text. At this level, professorscan use the results of CourseSmart Analytics toassess student efforts, as well as their owndecisions in the selection of effective andengaging texts.
Examples• 5= The Glass Classroomgo.nmc.org/gclass .Santa Monica College’s Glass Classroominitiative strives to enhance student andteacher performance through the collectionand analysis of large amounts of data. Usingreal-time feedback, adaptive coursewareadjusts based on an individual’s performancein the classroom in order to meet educationalobjectives.
Examples• 6= Stanford University’s Multimodal LearningAnalytics go.nmc.org/multimo In partnershipwith the AT&T Foundation, LemannFoundation, and National ScienceFoundation, Stanford is exploring new ways toassess project-based learning activitiesthrough students’ gestures, words, and otherexpressions.
Applications• 1= Reading. Kno, an e-textbookcompany, launched the “Kno Me” tool, whichprovides students with insights into theirstudy habits and behaviors while using e-textbooks. Students can also better pacethemselves by looking at data that showsthem how much time has been spent workingthrough specific texts, and where they are inrelation to their goals: go.nmc.org/kno.
Applications• 3= Writing and Composition. In writingintensive courses, Mobius Social LearningInformation Platform is used at University ofNorth Carolina Greensboro to facilitateanonymous peer-to-peer feedback andgrading. When students submit an essay, it isautomatically distributed to the rest of theirrandomly chosen peer group, and analgorithm turns their feedback into statisticsand performance reports: go.nmc.org/mob.
Applications• jPoll at Griffith University go.nmc.org/jpoll• jPoll is an enterprise-wide tool developed byGriffith University in Australia, directed atcapturing, maintaining, and engaging studentsin a range of interactive teaching situations.Originally developed as a replacement forclicker-type technologies, jPoll is helpingeducators identify problem areas for studentsvia learning analytics.
Conclusion• Will Big Data Transform How We Live, Work,and Think??• Read the book:• Big Data: A Revolution That Will TransformHow We Live, Work, and Think by KennethCukier and Viktor Mayer-Schonberger
References• Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching andLearning Through Educational Data Mining and Learning Analytics: An IssueBrief. U.S. Department of Education Report (October).http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf• Johnson, L., Smith, R., Willis, H., Levine, A., and Haywood, K., (2011). The2011 Horizon Report. Austin, Texas: The New Media Consortium. Retrievedfrom http://www.nmc.org/pdf/2011-Horizon-Report.pdf• Johnson, L., Adams, S., and Cummins, M. (2012). The NMC Horizon Report:2012 Higher Education Edition. Austin, Texas: The New Media Consortium.Retrieved from http://nmc.org/pdf/2012-horizon-report-HE.pdf• Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., &Ludgate, H. (2013). NMC Horizon Report: 2013 Higher Education Edition.Austin, Texas: The New Media Consortium. pp. 24-27. Retrievedfromhttp://www.nmc.org/pdf/2013-horizon-report-HE.pdf