The Machine's Role in Educating Australians
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Open Colleges is one of Australia's oldest and largest providers of online learning. For over 100 years we have been helping people improve their lives through learning. Being an online education organisation, we depend heavily on technology to provide an engaging learning experience for students and a smarter teaching experience for educators. Having over 60,000 concurrent students, all progressing asynchronously has resulted with every student being unique and having different learning patterns and behaviours. This introduces a variety of challenges for our educators and support staff. Introducing Machine learning & Artificial intelligence into our business is the practical & efficient approach to deal with these challenges at scale. The use cases range from nurturing prospecting students in their course selection; identifying students in need to intervene proactively; Understanding behaviours to drive motivation and engagement; and analysing learning patters to improve quality of courses and services. In this presentation we’ll talk about how we have addressed these use cases through leveraging scalable machine learning services and suitable big data ecosystem. Speakers George Gemayel, Chief Technology Officer, Open Colleges Gnanaguru Sattanathan, Business Intelligence Analyst, Open Colleges
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The Machine's Role in Educating Australians
- 1. DataWorks Summit Sydney 2017 The Machine’s Role in Educating Australians
- 6. About Us George Gemayel Chief Technology Officer, Open Colleges Background in software engineering Over 10 years working with all things Data George.Gemayel@opencolleges.edu.au Gnanaguru (Guru) Sattanathan Business Intelligence Analyst, Open Colleges Platform engineering fellow 7 years of building data pipelines Guru.Sattanathan@opencolleges.edu.au
- 7. About Open Colleges Providing life changing education for over 100 years. We offer over 130 flexible online courses that allow you to fit study around your lifestyle. • Over 55,000 students annually, enrolling every minute • Asynchronous learning, submitting assessments every minute • Completely online study, any device, anywhere • Over 800,000 Australians educated in total • 450 Staff (incl. techies, marketers, support staff, faculty) • Headquarters in Surry Hills, Sydney • Students anywhere and everywhere http://www.opencolleges.edu.au
- 9. First Challenge Enrolling the right student into the right course
- 11. Course diversity
- 12. Is this course right for me ? How this course will help me succeed ? Will I be able to study this course ? Will I find a job if I take up this course ? Is it worth studying ?
- 13. Student profile Interested courses Social analytics User persona Attributes …….
- 14. How do we enroll the right student in the right course ? How do we get them talk to the right enrolment consultant ?
- 15. Start building Machine learning models And they are organic
- 16. And how do these machine learning models evolve ?
- 17. The platform Connectivity & Ingestion Store Compute Models BI
- 18. Second Challenge Providing individual personalised support, at scale
- 19. Level of Student Insights: Good Anette Certificate III in Individual Support (Disability) Been in the sector for 6 years – Ladder climber
- 20. Measure Key Digital Interactions Logged in: Total session time: Onboarding: Support Centre /FAQ: Assessment downloads: Launched Quiz: Download/print PDF: Sent a message: Clicked on Study Buddy: Sub-topics: Module overview: 4 times 2.75 hours 5 views 2 1 10 downloads 1 session - incomplete 1 1 28 views 6 views
- 21. Level of Student Insights: Great
- 22. RPL Closing the loop Proactive Communications Schedule an Intervention Suggested Services Available To Staff You don’t need to ask, I already know!
- 23. So What Is It All Worth? Engagement in First 2 days 21% Abandon Rates 34% Academic Progression within 30 days 95%
- 24. Open Colleges Data Ethos Data is not a project, its part of our DNA
- 25. Tech, Platform & People big data tech stacks *aaS Providers In house engineering
- 26. Open Colleges Data Ethos Transparent Open to all Experience-led De-centralise Close to the business. Enable self-service Real-Time On demand, to humans and machines Indiscriminate Capture everything. Use what’s valuable Strategic Vendor agnostic. Platform over projects
- 27. DataWorks Summit Sydney 2017 Thank You
Editor's Notes
- Some stats about our organisation. We have bee around for 100 years, but in the last 6, we have transformed into a digital college. We offer online course. Our key differentiator is that we are online. And we are large – one of the largest online colleges in Australia with 130 courses and 55,000 annual students.
- Over the last two years we've had to rethink what we need to know and the data we need to action Ultimately, we want to know what our students are doing. We also want to know whether what they're doing (and we're doing) is contributing to their success (or the value they get from studying with Open Colleges). The easiest way to describe it is that we're trying to understand and manage 55,000 individual cohorts each of which is free to take their own approach to one of >130 courses, and are at different stages. Meaning... No two courses are the same - Study Periods, modules, assessments, duration etc. are all variable; Our primary model is asynchronous and self-directed - so time-based assumptions don't really work There are few standard linear elements or milestones you can rely on (like assessment...) hard to get a clear sense of where a student is "up to" at a point in time, relative to where they "should" be, and what they need to do next. There are 55,000 students! each starting in their own time, going at their own pace, addressing the course they're in in the manner and sequence that works for them. So...no two people are necessarily doing the same thing at the same point or the same time... It's pretty easy to work out at an individual student level. Different story at scale – the objective here is to do this at scale. Transition by introducing second challenge first, then first challenge around lead analytics
- 1 minute Diversity of students Different types of students – Studies while commuting everyday to Studies for hobby Reiterate Async learning Everyone studies in a different way and everyone is individually supported by OC teams
- 1 minute Numerous courses. Some have a clear idea to chose what they want to study Others need to be guided through
- 2 minutes With this different kind of students and with these variety of courses We have support them in answering various questions before they choose any course And these needs to be answered by an expert in our team who specialises in that course category or Who is specilises in talking a specific group of students
- 2 minutes And on top of that, we have data/ metadata around the students through various systems. Systems includes, our websites, social media platforms, etc.
- 1 minutes So we have data around the students collected by our systems/machines. And we have data about the type of students who enrolled on different courses and the kind of enrolment consultants who supported them
- 2 minutes This is where we started making sense of our data really. Predict which student needs to talk to which enrolment consultant Based on History of enrolments from similar students ML models are organic, why ? They need to evolve by itself to serve the purpose And also they need to be reborn sometimes. Too much of standardization on ML models is a risk.
- 2 minutes We say its organic. So how do they evolve ? Especially an organization like us would need many organic models and so gardening is the most important part. How the gardening happens = Solid system & people process around the platform. Agile + Data platform process.
- 3 Minutes Solid pipeline, which supports both speed and batch layer. Storage – Structured & Unstructured. Compute – Processing power , cluster computer like Spark. BI is the most complex part, It is actually driven by the analysts
- This is Anette – she has been enrolled with us for 12 days What has her 12 been like so far …. She looks like a pretty good students But we want to know more……
- So we begin to measure interactions, particularly to measure engagement and behaviour. Get more information about logging frequency and patterns How she hovered over support articles and FAQs How she engaged with social elements of the LMS How she interacted with content. The trick here is to spend time analysing what is important, and do so incrementally.
- We know she has taken a walkthrough Read content Spent time reading discussion forums more than content. Printed the content to take offline Mailed her trainer Connected with a study buddy Measure a pattern around her logins and preferred sessions.
- Handshake = welcome email 5 times Finger = hover on support on 5 times RPL = ask trainer about RPL Message bubbles = reading discussions more than content Ctrl = copy and paste content
- Walk through this together. The results speak for themselves.
- You can’t DO data. It needs to be embedded in the business. Part of the DNA. It is not just a piece of tech, it needs to be a business capability and a strength.
- 4 minutes Importance of all three Big data – Massive, too many components *AAS Providers – Ready made API’s, Platforms, doesn’t solve all the needs and it comes with a cost We need both. In house engineering – Ability to customise big data tech, use both Tech & Readily available platforms together. *Invest in people.
- Transparent – not just provide data, but disseminating insights. Be experience led Decentralise – make data valuable. Put it close to the business. Real-time – powers your digital services. Indiscriminate – a data scientist will ask you for data one day. Storage is cheap, deal with ingestion when you need it. Strategic – right tool for the right job. And build a capability, don’t just focus on projects that deliver outdated requirements and has a shelf life of 6 months.