Empowering instructors through customizable collection and analyses of actionable information

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Presented at Learning Analytics for Curriculum and Program Quality Improvement Workshop at LAK16, Edinburgh (April 2016). The use of analytics to support learning has been increasing over the last few years. However, there is still a significant disconnect between what algorithms and technology offer and what everyday instructors need to integrate actionable items from these tools into their learning environments. In this paper we present the evolution of the Student Relationship Engagement System, a platform to support instructors to select, collect, and analyze student data. The approach provides instructors the ultimate control over the decision process to deploy various actions. The approach has two objectives: to increase instructor data literacies and competencies, and to provide a low adoption barrier to promote a data-driven pedagogical improvement culture in educational institutions. The system is currently being used in 58 courses and 14 disciplines, and reaches over 20,000 students. For videos, see https://youtu.be/rPf45SzTvuM and https://youtu.be/sJBtNTruCGk

Education

The University of Sydney Page 1
Empowering instructors
through customizable collection and analyses of actionable information
@dannydotliu
danny.liu@sydney.edu.au

The University of Sydney Page 2
Four tensions of learning analytics
Research Teaching
Top-down Bottom-up
Human Computer
Macro Micro
Data & algorithmsStudents & teachers
Massified & aggregate Contextualised
Practical & actionableTheoretical
Institutional Bespoke

The University of Sydney Page 3
The realities of learning and teaching at scale
Research Teaching
Top-down Bottom-up
Human Computer
Macro Micro
Data & algorithmsStudents & teachers
Massified & aggregate Contextualised
Practical & actionableTheoretical
Institutional Bespoke

The University of Sydney Page 4
Attendance
Interim
grades
LMS
metrics, other
3rd party
data
The Student Relationship Engagement System

The University of Sydney Page 5
Personalising connections with students
– Empowering staff
– Flexible & intuitive
– Targeted and personalised
– Multi-channel
– Benefits
– Highly customisable
– Efficient – key data in one
place, operating at scale
– Connect staff and all
students (not just at-risk)

The University of Sydney Page 6
Student outcomes
Discontinued
Failed
Passed
F
P
C
D
HD
1st year arts unit ~500 enrolment 1st year science unit ~1000 enrolment
“Many thanks Adam. Yes, things are going much better this semester. I really appreciate
how you keep in contact and keep an eye on us. It's such a big class, I don't know how you
do it.”
“Just to let you know that your emails really helped me survive last semester. I never
realised how big a change it would be from school.”

The University of Sydney Page 7
Co-evolution of the SRES
– Organic adoption by academics
– Co-evolution of capabilities
0
5000
10000
15000
20000
25000
0
10
20
30
40
50
60
70
2012 2013 2014 2015
Numberofstudents
Numberofunitsorschools
Number of units Number of schools Number of students
Pilot
EWS
introduced
EWS
integrated
New
analyses
More data
types
Data
import

The University of Sydney Page 8
Learning analytics by stealth?
– Co-evolving capabilities and competencies around data-driven
pedagogy and curriculum

The University of Sydney Page 9
Practical learning analytics
Research Teaching
Top-down Bottom-up
Human Computer
Macro Micro
Data & algorithmsStudents & teachers
Massified & aggregate Contextualised
Practical & actionableTheoretical
Institutional Bespoke

The University of Sydney Page 10
Lessons learned and next steps
– Looking back
– It was ugly but it worked
– Personalisation at scale
– Ease of use is important – does it save time?
– Attendance was a (unsurprisingly?) popular metric
– Everyone uses a customisable system differently
– Looking forward
– Trans-Tasman redevelopment effort
– Facilitate wider roll-out
– Research & evaluation of impact on students and staff

The University of Sydney Page 11
Adoption pipeline
Colvin et al. (2015) Student retention and learning analytics: A snapshot of Australian
practices and a framework for advancement. Office of Learning and Teaching, Sydney.
“First, implementers require an analytic tool or combination of tools that manage data
inputs and generate outputs in the form of actionable feedback… As these increasingly
meet the real needs of learners and educators the organisational uptake is accelerated.”

The University of Sydney Page 12
“Learning analytics is not an elixir for ineffective teaching,
nor does it reveal an ideal pedagogy; instead, it provides
data-driven tools or suggestions to help instructors make
changes that can be measured in terms of student outcomes.”
Pistilli, M. D., Willis III, J. E., & Campbell, J. P. (2014). Analytics Through an Institutional Lens:
Definition, Theory, Design, and Impact. In Learning Analytics (pp. 79-102). Springer New York.

The University of Sydney Page 13
– Adam Bridgeman
– Charlotte Taylor
– Abelardo Pardo
– Kathryn Bartimote-Aufflick
– Melanie Nguyen
– Hong Dao Nguyen
– And many staff and students
@dannydotliu
danny.liu@sydney.edu.au
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The University of Adelaide's Beacon of Enlightenment Strategic and Operational Plans outline a number of key targets for online learning. The 2013-14 Benchmarking eLearning Projects provided a baseline of the University's performance in regard to these online learning targets and a defined set of benchmarks. This presentation will provide a background to the Benchmarking eLearning Project, the data collected and how the data is leveraged in the faculty to improve the online learning experience of students. The newly established Guidelines for Minimum use of MyUni and a targeted approach to enhancing online course design in line with the targets will be discussed. Future plans to automate reporting using an expanded source of data to inform support activities and ensure the university is improving over time will be outlined. Delivered at Innovate and Educate: Teaching and Learning Conference by Blackboard. 24 -27 August 2015 in Adelaide, Australia.

Bridging the Distance: Enhancing the Success of Distance Learners

Mark Brown

The document summarizes Massey University's "Bridging the Distance" project which aims to enhance the success of its distance learners. It outlines the university's context with many distance and international students. It then describes the project's conceptual framework which takes a targeted approach to interventions focused on at-risk students. Finally, it discusses online tools and supports developed including orientation courses, early intervention systems, and customized resources to improve retention, progression and completion rates among distance learners.

SHEILA-CRLI seminar

Yi-Shan Tsai

Scaling up learning analytics

Rebecca Ferguson

Talk by Rebeca Ferguson (Open University, UK, and LACE project). The promise of learning analytics is that they will enable us to understand and optimize learning and the environments in which it takes place. The intention is to develop models, algorithms, and processes that can be widely used. In order to do this, we need to move from small-scale research within our disciplines towards large-scale implementation across our institutions. This is a tough challenge, because educational institutions are stable systems, resistant to change. To avoid failure and maximize success, implementation of learning analytics at scale requires careful consideration of the entire ‘TEL technology complex’. This complex includes the different groups of people involved, the educational beliefs and practices of those groups, the technologies they use, and the specific environments within which they operate. Providing reliable and trustworthy analytics is just one part of implementing analytics at scale. It is also important to develop a clear strategic vision, assess institutional culture critically, identify potential barriers to adoption, develop approaches that can overcome these, and put in place appropriate forms of support, training, and community building. In her keynote, Rebecca introduced tools, resources, organisations and case studies that can be used to support the deployment of learning analytics at scale

Learning Analytics In Higher Education: Struggles & Successes (Part 2)

Lambda Solutions

Sampson-Keynote@SMTE2019

Demetrios G. Sampson

Presentation (R).pptx

swathiparwathi

This document discusses how data science can transform education. It outlines benefits like informed decision making, improved student outcomes through predictive analytics, and adaptive assessments. Data-driven decision making helps with accountability, targeted interventions, and program evaluation. Applications of data science in student performance analysis include assessing progress, individualized feedback, and customizing learning paths. Personalized learning uses adaptive platforms, content recommendations, and real-time feedback. The document also covers ethical considerations and future trends like augmented reality, learning analytics, and AI-driven personalization.

2015 j. heinlein re-imagining the future of education

EADTU

1) edX is a non-profit online learning platform founded by Harvard and MIT to expand access to quality education through online courses. 2) edX's mission is to expand access to quality education, advance research, and improve on-campus education. The edX platform offers features like auto-grading, virtual labs, gamification, and social learning tools. 3) The future of education is predicted to be unbundled, accessible, global, lifelong, personalized, and blended. Trends include increasing access to education through online courses, personalized learning based on student data, and blending online and in-person education.

SHEILA workshop at EC-TEL 2018

LACE Project

The document discusses developing evidence-based institutional learning analytics policies. It describes the methodology used in the SHEILA project which included surveys and interviews with academic staff, students, senior managers and experts. The surveys and interviews aimed to understand stakeholders' views on learning analytics, adoption challenges, and expectations. Key findings included strong interest in using learning analytics to improve student and teaching performance, but also barriers around skills, culture, technology, ethics and privacy. Academic staff surveys found the highest expectations were around accurate data collection and presentation, while lowest were around obligations to act on at-risk students. Focus groups with staff further explored opportunities and concerns around learning analytics.

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