Learning Analytics

Learning Analytics: Student Learning and

RetentionDr Barbara Newland

Principal Lecturer Learning and Teaching (e-Learning)Centre for Learning and Teaching

What are Learning Analytics?

Current local and global developments

Improving retention

Enhancing student learning

Discussion

Summary

Overview

“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)

Learning Analytics

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.

Global developments

Example - bipartite sociogram

Adam Cooper’s work blogblogs.cetis.ac.uk

Society for Learning Analytics Research (SOLAR)

Reasons for learning analytics

Institutions can look for patterns across the institution and within Schools or degree programmes

Academics can look at the data to decide when to intervene to enable better outcomes both for retention and achievement.

Student use of learning analytics tools can enable them to view their levels of activity, attendance, progress and grades in comparison with other students

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

Current situation at Brighton

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.

Current research

In some universities it is being used to help to retain students through predicting those at risk of leaving

Retention

Jenzabar http://www.jenzabar.net/higher-ed-solutions/retention

Forsythe, R et al

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.

Predictive Analytics Framework (PAR)

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/

Student learning

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)

Improving Student Outcomes using Predictive Analytics

Purdue University – course signals

http://www.itap.purdue.edu/learning/tools/signals/

Accesses by Grade (SP2012)

Blackboard Analytics for Learn: Student View

McGraw-Hill LearnSmart

http://learnsmart.prod.customer.mcgraw-hill.com/about/take-a-tour/

LearnSmart video

“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)

Personalised learning pathways

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?

Discussion

“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 don't replace people; they empower people—both advisors and students—to make better decisions.”

(Oblinger, D, 2012)

Summary

“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)

Summary

“analytics should be a torch and not a hammer“

Clay Shirky

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 We're 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 Educationhttp://www.educause.edu/ero/article/data-changes-everything-delivering-promise-learning-analytics-higher-education

References