13-­‐05-­‐06 Learning Analy2cs and Learner Profiling for Early Alert and Help Provision Jim Greer, Director University Learning Centre Brad Wuetherick, Program Director Gwenna Moss Centre University of Saskatchewan 2013 Learning Analy2cs A plethora of data about learners + Tools to analyse, cluster, model and predict Deeper personalized informa2on about learners Which can be used for many purposes (beneficial and poten2ally harmful) 1 13-­‐05-­‐06 Uses of Learning Analy2cs •  Guide and inform course and program design •  Improve quality and accuracy of assessments •  Improve quality of communica2on between learners, teachers, and advisors. •  Provide personalized support and early alerts •  Improve administra2ve data for strategic enrolment management How people are using LA and data •  Profiling students –  Individual risk factors (aXri2on, academic failure, subject-­‐specific deficiencies) –  Individual capabili2es (targeted recruitment for graduate work, poten2al leaders, scholars needing enrichment) –  Resource recommenda2ons (based on interests or needs) –  Group characteris2cs (class demographics, program demographics, university demographics) 2 13-­‐05-­‐06 How people are using LA and data •  Tracking student development, achievement and behaviours –  Personalized adap2ve learning support –  Adap2ve assessment –  Adap2ve learning resource recommenda2ons –  Personalized messaging and targeXed communica2on How people are using LA and data •  Adjus2ng instruc2onal strategies and course designs •  Evalua2on of programs based on measured learning outcomes •  Longitudinal studies of reten2on and effects of administra2ve ac2ons 3 13-­‐05-­‐06 Some UofS projects •  Entry census –  Gathering richer data about incoming students (richer demographics such as first-­‐in-­‐family, funding sources, living arrangements, feelings of connectedness, approaches to learning, career goals and plans, etc.) –  Merge with ins2tu2onal (admissions) data –  Richer profiling •  Builds on Large Classes Survey run 2009-­‐12 Some UofS projects •  Resource recommender system –  Learning and help resources in University Learning Centre •  Web resources •  Instruc2onal videos •  Workshops •  Personal consulta2ons –  “Amazon-­‐style”, “Nealix-­‐style” recommenda2ons •  Based on ra2ngs by “like students” 4 13-­‐05-­‐06 Some UofS projects •  Adap2ve individualized learning systems –  McGraw Hill, Pearson, and other publishers are rolling out personalized study aids, adap2ve tutors, adap2ve quizzes – being used in selec2ve courses now. –  Online assessment and self-­‐assessment tools for diagnosis and placement in courses –  Intelligent tutoring systems are finding their way out of research labs and into classrooms Planned projects at UofS •  Early alert system pilot –  Combining risk profiles with ac2vity tracking in courses •  Use of learning management system •  Clicker data •  Assignment grades –  Provide a dashboard display to student and prof –  Encourage earlier interven2on –  Poten2ally involve academic advisors 5 13-­‐05-­‐06 Planned projects at UofS •  Concept inventory score tracking –  Longitudinal assessment of learning gains in discipline-­‐specific knowledge –  Modelled on “Force concept inventory” in Physics –  Can be used to iden2fy problem points in courses in a program Privacy and ethical use of data •  Profiling can be discriminatory and prejudicial •  Knowledge of student risk factors can result in instructor bias (even if uninten2onal) •  Students have a right to keep personal informa2on private •  Importance of having privacy officer and student advocate involved •  Different access for counsellors, advisors, teachers, administrators 6