Using data and automated bots to drive decision making and ...
Transcript of Using data and automated bots to drive decision making and ...
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Using data and automated bots to drive decision making and change in ELT: a case study
Crispian Short: BROWNS English Language School
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Takeaways
1. Where are we now?
2. Where do we want to be?
3. Case study: BROWNS
English Language School
4. Useable framework &
research
5. Industry call to action
6. Q & A
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Definitions
Data
facts and statistics collected together for reference or analysis.
Clean Data Correction and/or removal of erroneous, incorrect or irrelevant data
Unclean Data Data that needs correction and/or removal of erroneous, incorrect or irrelevant data
Data Driven Decision Making(DDDM) Provost and Fawcett (2013) define data-driven decision making (DDD) as “the practice of basing decisions on the analysis of data rather than purely on intuition.“
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Where are we now?
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Overabundance of Data - Academic
End of course scores Counselling reports
Weekly test scores Pre arrival test scores
Participation marks Assignment scores
Attendance records Moderation feedback
In class work Validation feedback
Teacher feedback End of course surveys
Level check scores Graduation surveys
Behavior warnings Mid course surveys
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Overabundance of Data - Business
Class averages Current course numbers
Gross Profit Current offers
Net Profit Future offers
Net wages Conversion rates
Gross Wages Visa grant rates
Forecast student numbers Visa decline rates
Current student numbers Total tuition
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•A recent worldwide survey showed that out of 2,146,703,436 people, 94% were too lazy to actually read that number.
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2.Where do we want to be?
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Where do we want to be - Data
BITE SIZED AND EASILY DIGESTIBLE
EASY TO INTERPRET
PAINTS AN IMMEDIATE
OVERALL PICTURE
MINIMIZES OR
REMOVES THE “GUT FEELING”
DRIVES EFFECTIVE
AND EFFICIENT DECISION MAKING
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3. Case study: BROWNS English Language School
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3. Case study: BROWNS English Language School
Pain Points 1. Unclean forecast data
2. Nationality mix on an excel spreadsheet
3. Multiple number driven reports to compare FY
4. Multiple number driven reports to analyze current, past and forecasted
campus capacity
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3. Case study: BROWNS English Language School
Solution
Auto Click Typer
“a free automation program that helps you emulate mouse and keyboard buttons.”
https://auto-click-typer.software.informer.com/2.0/
Microsoft Power Business Intelligence(BI)
“Power BI is a business analytics solution that lets you visualize your data and share insights across your organization, or embed them in your app or website. Connect to hundreds of data sources and bring your data to life with live dashboards and reports” Microsoft.
https://powerbi.microsoft.com/en-us/what-is-power-bi/
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3. Case study: BROWNS English Language School
Solution
Power bi dashboard – replica of actual
• https://app.powerbi.com/groups/me/reports/04dada2e-8d7b-4976-8d57-7aa08d0badeb/ReportSectionf1005891503cc2eb3a13?ctid=ab222cd8-4c93-461b-adcd-356d96f0cf46&openReportSource=ReportInvitation
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3. Case study: BROWNS English Language School
Solution
• Saved 30 – 60 minutes per week in cleaning data
• Saved 30 minutes per week in meetings
• Decreased requests for data from Depts by 100% - numbers/capacity
• Increased clarity(same language) across departments which in turn
decreased number of requests to justify data
• Increased ability to manage up
• Told a story across, up and down the chain
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3. Case study: BROWNS English Language School
Further Automation
• Attendance Letters
• Letters of offer
• Study plans
• Class requests
• Course Change requests
• Stakeholder booking systems
Further Dashboards
• Student performance
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4. Usable Research & Framework
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Usable Research & Framework
1. Discovery phase - define the problem, develop a hypothesis, and collect and explore data.
2. Insight phase - perform the data analysis.
3. Decision & Action phase - link insights to actionable recommendations and then execution plan.
4. Outcomes phase – review the outcomes of long term objectives and solutions.
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Kaplan Higher Education Institute in Singapore –
Intervention Strategy
• https://edtech.cioadvisorapac.com/cxoinsights/using-data-dashboards-to-support-struggling-students-a-big-data-application-in-higher-education-nwid-818.html
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• Analyzed first semester core Accounting subject
• Took data from 5 different main sources
• Fed it into a dashboard
• Dashboard Illuminated at risk students(12%)
• Created a metric on when exactly to intervene
• Post intervention - At risk students dropped to 8%
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Call to Action - Creating new metrics
• Student Scorecard – What is a “good student?” incorporating attendance, academic results, satisfaction, effort and engagement (course & School).
• Dashboards illustrating individual, class, course, school and teacher performance.
• Increased automation to increase touch points(face to face contact)
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• “Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories.” – Steven Levitt, Co-author of Freakonomics
• (For DDDM to be successful) “managers hold a favourable view towards new technology adoption and prefers the ease of use for better decision-making.” Reijkumar, 2018
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6. Q & A
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Further Reading
• https://evolllution.com/technology/metrics/using-data-to-drive-decision-making/
• https://research.acer.edu.au/cgi/viewcontent.cgi?article=1004&context=aer
• https://www.coursera.org/lecture/decision-making/the-4-aspects-of-the-data-and-analytics-framework-UzQtk
• Kaplan Higher Education Institute in Singapore – Intervention Strategy
• https://edtech.cioadvisorapac.com/cxoinsights/using-data-dashboards-to-support-struggling-students-a-big-data-application-in-higher-education-nwid-818.html