Learning Analytics


Published on

An overview of Learning Analytics in Higher Education

Published in: Education
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • “two kinds of entity and relationships between types. In this case people are shown as green circles and papers shown as red polygons.”
  • how to visualize possibilitiesLater in the presentation, we’ll talk through some examples of how learning analytics is being used today“Society for Learning Analytics Research (SOLAR).” Web. 15 Feb. 2012. http://www.solaresearch.org/ solaresearch.org/OpenLearningAnalytics.pdfOpen framework for learning analytics visualizations
  • http://www.jenzabar.net/higher-ed-solutions/retention
  • Forsythe, R., Chacon, F. J., Spicer, D. Z., Valbuena, A, 2012, Two Case Studies of Learner Analytics in the University System of Marylandhttp://www.educause.edu/ero/article/two-case-studies-learner-analytics-university-system-maryland
  • Intervention emails, composed by the instructor and based on grades and effort in the course can be released to each risk group. grade performance improvement has been consistently demonstrated at both the course and at the departmental level.
  • From Everhart, D. presentation http://learnsmart.prod.customer.mcgraw-hill.com/about/take-a-tour/ at Educause, 2012
  • From Everhart, D. presentation http://learnsmart.prod.customer.mcgraw-hill.com/about/take-a-tour/ at Educause, 2012
  • From Everhart, D. presentation http://learnsmart.prod.customer.mcgraw-hill.com/about/take-a-tour/ at Educause, 2012
  • From Everhart, D. presentation http://learnsmart.prod.customer.mcgraw-hill.com/about/take-a-tour/ at Educause, 2012
  • Comparing activity and grades to other students in the same course. Student view is based on collaboration w/ UMBCAnalytics for Learn provides analytics features for evaluatingActivity at multiple levelsCourse designPerformance against objectivesInstitutionalProgramCourseIndividualFrom Everhart, D. presentation http://learnsmart.prod.customer.mcgraw-hill.com/about/take-a-tour/ at Educause, 2012
  • Call out “standings”– provides a social framework that influences behavior even when the others on the list are anonymous. Brings out competitive attitude in learners, gives them a context for defining strong behavior, and plays to the appeal of gaming.http://www.mhhe.com/learnsmart/tryit.htmlFrom Everhart, D. presentation http://learnsmart.prod.customer.mcgraw-hill.com/about/take-a-tour/ at Educause, 2012
  • From Everhart, D. presentation http://learnsmart.prod.customer.mcgraw-hill.com/about/take-a-tour/ at Educause, 2012
  • Everhart and Fritz in “Perceptions of Progress: Learning Analytics and Social Learning Behaviors”
  • Oblinger, D, 2012, Analytics: What We're Hearing http://www.educause.edu/ero/article/analytics-what-were-hearing
  • Learning Analytics

    1. 1. Dr Barbara NewlandPrincipal Lecturer Learning and Teaching (e-Learning) Centre for Learning and Teaching
    2. 2.  What are Learning Analytics? Current local and global developments Improving retention Enhancing student learning Discussion Summary
    3. 3.  ―refers to the interpretation of a wide range of data produced by and gathered on behalf of students in order to assess academic progress, predict future performance, and spot potential issues.‖ (Horizon, 2012) ―applies the model of analytics to the specific goal of improving learning outcomes.‖ (ELI, 2011)
    4. 4.  Increasing global interest and the 2012 Horizon report predicts that there will be widespread adoption of learning analytics within 2 to 3 years The output from learning analytics can be tailored for students, academics and institutions Often this representation is visual ie graphs, diagrams etc.
    5. 5. Adam Cooper‘swork blogblogs.cetis.ac.uk
    6. 6.  Society for Learning Analytics Research (SOLAR
    7. 7. Student use of learninganalytics tools can enablethem to view their levels ofactivity, attendance, progressand grades in comparisonwith other studentsAcademics can look at the Institutions candata to decide when to look for patternsintervene to enable better across theoutcomes both for institution andretention and achievement. within Schools or degree programmes
    8. 8.  Studentcentral ◦ Performance dashboard so academics can see who has been accessing the module and centrally to see if academics have been using their own areas BSMS503 – run stats twice per year since 2007 (Tim Vincent) ◦ Graphs are typically the most helpful ◦ ‗Hits per day graph shows clearly relative activity on the module: ◦ Huge spikes of use prior to finals exams indicating that students use the resource as a revision tool and repeatedly too. ◦ Mostly in the evenings and how many students are working in the early hours of the morning! ◦ Saturdays were the most popular for using the quizzes
    9. 9.  Methodology ◦ individual institutions and multi-institutional projects such as the Predictive Analytics Framework (PAR) ◦ analysis of big data sets of digital breadcrumbs looking for patterns ◦ ―in general it includes information about the frequency with which students access online materials or the results of assessments from student exercises and activities conducted online‖ (ELI, 2012) Findings ◦ it is easier to collect the data than to know how to use it to help students ◦ PAR project has found that similar models of learning analytics can be used in different institutions.
    10. 10.  In some universities it is being used to help to retain students through predicting those at risk of leaving
    11. 11. Jenzabar http://www.jenzabar.net/higher-ed-solutions/retention
    12. 12. Forsythe, Ret al
    13. 13.  Multi-institutional project - 16 institutions, over 1,000,000 student and 6,000,000 course level records Similar models were used in each institution System is predictive ie it sends an alert to an academic counselor that a student might not attend the following week so the counselor can contact the student.
    14. 14.  Unsurprisingly, students who do not engage achieve lower grades 5 years of data clearly correlating student VLE activity and grades found: ◦ Students earning a D or F at UMBC tend to use Blackboard on average 39 percent less than students earning higher grades. ◦ http://www.umbc.edu/blogs/oit-news/reports/
    15. 15.  Grand Canyon University VLE is designed to enable data capture on student interaction with the system Students, academics and administrators all receive different perspectives on the data according to their needs Students can see their performance relative to other students and compare their time on different learning activities (Kutty and Mueller, 2012) At Purdue and Rio Salado College – LA used to make predictions and anticipate problems Based on personalized data and predictive algorithms, system alerts trigger individualized interventions that can help students, advisors, and/or faculty tap resources to avert failure (Oblinger, 2013)
    16. 16. http://www.itap.purdue.edu/learning/tools/signals/
    17. 17. BlackboardYour courses and activity inside of Blackboard.Courses Youre Taking Students with a 3.0 or better have, on average,Course Rank Activity ? activity in this range.ENGL 393 8 DetailsIS 302 18 DetailsIS 410 2 DetailsIS 450 45 DetailsCourses Youre Teaching This represents your courses activity relative toCourse Rank Activity ? others in your discipline.ECON 102 8 / 14 DetailsECON 103 18 / 23 Details
    18. 18. Course Activity forENGL 393: Intro to Non-Rhyming Poetry This shows how active you (the black bar) are compared to other students in the course, grouped overall GPAs. Your Rank Your Stats 842 hits 8 / 32 234 sessions 34 / 62 Course Rank This shows how active this course (the black bar) is compared to others in the same discipline. Classmate Activity Detail Hover over the other students in your class to see how their activity compares to you. YOU Overall GPA of 2.0 or lower Overall GPA between 2.0 and 3.0 Overall GPA of 3.0 or higher Detail Reports Dig deep and see how your activity really compares to other students. Grade Distribution See how your grades compare to others in your class, based on their activity. Tool Usage See how your usage of various tools in Blackboard compares to others in your class, based on their activity.
    19. 19.
    20. 20.
    21. 21.  http://learnsmart.prod.customer.mcgraw- hill.com/about/take-a-tour/
    22. 22.  ―data can point learners to personalized learning pathways tailored to their needs, aspirations, abilities, and timelines.‖ ―data is actually most useful to inform thinking, questioning, planning, and next steps.‖(Oblinger, D. 2013)
    23. 23.  How can analytics be used to identify and promote effective learning behaviors? What types of alerts and dashboards for insights into analytics data are most useful, and who should be using them? What are the issues?
    24. 24.  ―Analytics requires a culture of inquiry, and inquiry creates an analytics culture.‖ ―Ask good questions; use good data.― ―Analytics is an investment‖ ―Technology makes education more personal, not less. Systems dont replace people; they empower people—both advisors and students—to make better decisions.‖(Oblinger, D, 2012)
    25. 25.  ―Data, by itself, does not improve student success. Although learning analytics offer great promise for transforming the accountability, personalization, and relevance that promise will not be fully realized until we put the power of better-informed decision making into the hands of front-line educators.‖(Wagner and Rice, 2012)
    26. 26. ―analytics should be a torch and not a hammer― Clay Shirky
    27. 27.  ELI, 2011, 7 Things You Should Know about First Generation Learning Analytics, 2011, Educause http://www.educause.edu/library/resources/7-things-you-should- know-about-first-generation-learning-analytics Forsythe, R., Chacon, F. J., Spicer, D. Z., Valbuena, A, 2012, Two Case Studies of Learner Analytics in the University System of http://www.educause.edu/ero/article/two-case-studies- learner-analytics-university-system-maryland Horizon Report, 2012, Educause Kutty, M and Mueller, B, 2012, ―Grand Canyon University: How We Are Improving Student Outcomes using Predictive Analytics.‖, Educause conference MacNeill, S. Analytics; What is Changing and Why Does it Matter? A Briefing Paper CETIS Analytics Series Vol.1, No.1 Oblinger, D, 2012, Analytics: What Were Hearing http://www.educause.edu/ero/article/analytics-what-were-hearing Oblinger, D. (2013)Analytics: Changing the Conversation, EDUCAUSE Review, vol. 48, no. 1 (January/February 2013 )Jan 28, 13 http://www.educause.edu/ero/article/analytics-changing-conversation Predictive Analytics Framework (PAR) http://wcet.wiche.edu/advance/par-framework SOLAR – Society for Learning Analytics Research http://www.solaresearch.org/ Wagner, E and Ice, P, 2012, Data Changes Everything: Delivering on the Promise of Learning Analytics in Higher Education http://www.educause.edu/ero/article/data-changes-everything-delivering-promise-learning- analytics-higher-education