Transcript
Page 1: Learning Analytics: Today, Tomorrow, and When We Get Flying Cars #psuweb Conference 2015

Learning Analytics: Today, Tomorrow, and When We Get Flying Cars

Megan Bowe@meganbowe

Page 2: Learning Analytics: Today, Tomorrow, and When We Get Flying Cars #psuweb Conference 2015

Learning Analytics, the definition(s)

1. Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.

2. Learning Analytics use techniques from information science, sociology, psychology, statistics, machine learning, and data mining used to analyze data collected during education administration and services, teaching and learning to create applications that directly influence educational practice.

1. https://en.wikipedia.org/wiki/Learning_analytics 2. https://sites.google.com/a/umail.iu.edu/iuncc/whatis

Page 3: Learning Analytics: Today, Tomorrow, and When We Get Flying Cars #psuweb Conference 2015

Educational Data Mining

Developing methods and using techniques from statistics, machine learning, and data mining to analyze data collected during teaching and learning (e.g., from a courseware platform) to test learning theories and inform educational practice

https://sites.google.com/a/umail.iu.edu/iuncc/whatis

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So. Many. Words.

• Data mining is digging through data sets to clean up and find patterns

• Analytics take those patterns into reality to find meaning and give insights

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Great! How do we do this?

My students use X and I want to

know Y

We’ve been using this LMS and SIS for

decades, there must be something to learn in

there The most business critical issue is student

retention, tell me who is at risk of dropping

outDefine a goal, then…

Page 6: Learning Analytics: Today, Tomorrow, and When We Get Flying Cars #psuweb Conference 2015

Uh, thanks Internet. This is totally clear now…

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Better.by Timothy Hartfield http://timothyharfield.com/blog/2014/09/11/learning-analytics-what-is-it-why-do-it-and-how/

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Define your data sources

Activity 1 Activity 3 Activity 5

Learning experience designActivity 2 Activity 4 Activity 6

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Get all of the data together

Data Warehouse? Data Mart?

Database? Learning Record Store?

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Dig around, mine if you must.

• What activity creates what data?

• Is there an ideal path through the activities?

• What data or sequences are missing or unexpected?

• What clean-up work needs to be done?

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Design ways to show others your insights! How do people use pdfs?

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Design ways to show others your insights! How do people use pdfs?

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Today, most of us have an LMS (like it or not)

• Grades

• Scores

• Duration

• Logins

• Page/Course access

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Tomorrow, we get more holistic views

LMS Flashcard app Youtube

Class Attendance

Report!

eBook Degree Progress

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Standards and standard units of measurement

The Experience API and matching equivalent data

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Past Present Future

InformationWhat happened?

(Reporting)

What is happening now?

(Alerts)

What will happen?

(Extrapolation)

Insight

How and why did it happen?

(Modeling and experiment

design)

What’s the next best action?

(Recommendations)

What could happen?

(Prediction, optimization, simulation)

Davenport et al “Analytics at Work”

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…and the flying cars.

(it gets creepy)

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–Tony Shan

“All in all, Fast Data, Actionable Data, Relevant Data, and Smart Data (FARS) are well poised

today to replace Big Data for the new paradigm.”

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“Big Data is Really Dead”

• Fast Data - processing a large amount of data in real time to find immediate insights and patterns

• Actionable Data - a combination of predictive analytics and hypotheses to make recommendations and use feedback to make decisions

• Relevant Data - relationships in data help to identify patterns that seem unrelated on the surface

• Smart Data - using meaning and algorithms to make predictions and support decision making

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Megan Bowe [email protected]

@meganbowe

Image: nolnet/Flickr


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