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Desire2Learn Analytics Oklahoma RUF
 

Desire2Learn Analytics Oklahoma RUF

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Presentation at D2L Oklahoma Regional User Forum, October 2012. Most slides created by Alfred Essa and Jeannette Brewer

Presentation at D2L Oklahoma Regional User Forum, October 2012. Most slides created by Alfred Essa and Jeannette Brewer

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  • Market demand for predictive analytics is growing very rapidly, especially in higher education Trends indicated in EduCause reportsD2L Quarterly Market Update Q1/2012Predictive models have been developed at Capella UniversityRio Salado College University of Phoenix

Desire2Learn Analytics Oklahoma RUF Desire2Learn Analytics Oklahoma RUF Presentation Transcript

  • Norman,Oklahoma
  • What is Analytics?• Analytics is the use of data, statistical and quantitative methods, and explanatory and predictive models to allow organizations and individuals to gain insights into and act on complex issues.• In colleges and universities, analytics is used to improve operational efficiency and student success. Source: Educause, Oblinger: Let’s Talk Analytics http://www.educause.edu/ero/article/lets-talk-analytics
  • What is Analytics?• The term big data is often used interchangeably with analytics, but the scientific community uses big data to describe research that uses massive amounts of data.• The use of analytics to improve administrative functions is often called business intelligence; similarly, academic analytics is used to help run the business of the higher education institution. Source: Educause, Oblinger: Let’s Talk Analytics http://www.educause.edu/ero/article/lets-talk-analytics
  • What is Analytics?• Finally, learning analytics focuses specifically on students and their learning behaviors, gathering data from course management and student information systems in order to improve student success.• Although the labels can be confusing, overall the term analytics refers to an approach that can be used to explore a broad range of questions. Source: Educause, Oblinger: Let’s Talk Analytics http://www.educause.edu/ero/article/lets-talk-analytics
  • Advanced Analytics
  • Analytics Maturity LevelsInformation Value Insight
  • Analytics: Big Data (R2) Multiple Levels of Reporting with Drill-Down Filters Extensive Data Domains Aggregates and Trends Over Time
  • Big Data: Data Sets• Enrollments. The enrollment data mart tracks user enrollments and withdrawals across one or more organizations.• Competencies. The competencies data mart tracks competencies, learning objectives, activities and rubrics by user, department, program, institution, and system.• User Logins. The user access data mart tracks the number of user logins/distinct sessions over a period of time. It is a very simple way of tracking student patterns of accessing the system.• Content and Tool Access. The module data mart tracks content access & tool usage.• Web Analytics. The web analytics data marts track internet statistics such as bandwidth usage, geographical location, and browser types.• Test and Quizzes. The quizzing data mart tracks quiz, test, and survey results, including measuring of quiz effectiveness.• Grades. The grades data mart tracks grades at student, course, department or school level, including filtering by grade ranges or date ranges.
  • Tech Data• IIS Web Analytics• Client Access (OS/Browser)• SMTP• Global/Local Traffic Manager Logs
  • Elemental LMS Data
  • Elemental LMS Data: Data Mining
  • Tool Usage: Overall vs. Pattern
  • Tool Specific Data: Content
  • Tool Specific Data: Quiz Overall
  • Tool Specific Data: Quiz Patterns
  • Grades Data: Org vs. Course
  • Institutional Effectiveness Define Outcome Standards Continuous Make Informed Design Curricula Improvement of Align Assessments Improvements Education Quality Analyze Results Report on Evidence
  • Curriculum Mapping: MechanicsInstitution Program Department Courses
  • Curriculum Mapping: Big Data
  • Learning Outcome Evaluation
  • Learner Competency Progress } View Overall Proficiencies
  • Big Data: Risk Analysis
  • Analytics Maturity LevelsInformation Value Insight
  • Analytics Optimization finding an optimal path to a desired future
  • Application Logic ExceptionalPredict Intervene Success At-Risk
  • Application Workflow Understand the Problem Interrogate Raw Data Reach a Diagnosis Intervene, Make a Referral Track the Success
  • Limitations of Current Approach• Interpretation • Not enough information for intervention• Interactivity • Unable to interrogate and make sense of the particulars• Generalizability • Same model is used for every course at every institution
  • Collective Intelligence Consensus decision making
  • Predictive Domains Multiple Semantic Units
  • Student Success System (S3)SSS is an Early Intervention System. It empower institutions with predictiveanalytics tools for improving student success, retention, completion, andgraduation rates.Highlights– Course-specific predictions of student success and risk levels– Success index that enables comparison of key success indicators– Innovative data visualizations– Case history and intervention managementAvailabilityGeneral Availability in 2013. (Pilot project starting Oct. 2012)
  • Student Success System
  • Powerful Reporting and Analysis Personalized Detailed analysis lets you drill assessment down to individual classesIntervention } }management In-depthSuccessindicators } reporting Innovative data visualizations
  • Challenges and RemediesChallenges for Institutions Student Success System RemedyInability to predict, and consequently improve student Predictive modeling identifies at-risk students basedsuccess, retention, graduation, and completion rates on engagement, performance, and profile dataLimited resources to create personalized intervention Visualizations and statistical indicators provideplans diagnostic insights to help design individualized interventionsLack of data correlating engagement with success Analyze student engagement patterns and effects on academic successInability to identify isolated students Visualize social network patterns based on discussion data, to improve social learning
  • Value to Institutions and Students• Predictive analytics provides early identification of at-risk students enabling instructors to identify and understand where issues are and create appropriate resolution plans to address the problem• Graduation and retention rates are increased when at-risk students are identified early on the process and supported throughout the term with informed counter-tactics
  • Summary – Student Success System at a Glance Institution Challenges Description of SSS • Improving Student Success • Early Intervention System driven by • Identifying academically at-risk, dis- advanced predictive analysis and data engaged or isolated students visualization to identify at-risk students • Increasing retention, completion, and and intervene to improve their retention, graduation Rates completion, graduation and success rates. Student Success System Value Ideal Customer Profile • Easily identify at-risk students, and • Institutions looking to empower understand where the issues lie • Design and implement individualized instructors with predictive analytics intervention programs to improve student success. • Improve institutional effectiveness • Increase student success
  • Norman,Oklahoma