Using data and automated bots to drive decision making and change in ELT: a case study

Crispian Short: BROWNS English Language School

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.“

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

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

•A recent worldwide survey showed that out of 2,146,703,436 people, 94% were too lazy to actually read that number.

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

3. Case study: BROWNS English Language School

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

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/

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

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

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

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.

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%

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

6. Q & A

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