Module 2: Performance Analytics

.title[
# Module 2: Performance Analytics
]
.subtitle[
## EME6356: Learning & Web Analytics
]
.author[
### Dr. Bret Staudt Willet
]
.date[
### May 22, 2023
]

---

# <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M512 32H160c-35.35 0-64 28.65-64 64v224c0 35.35 28.65 64 64 64H512c35.35 0 64-28.65 64-64V96C576 60.65 547.3 32 512 32zM528 320c0 8.822-7.178 16-16 16h-16l-109.3-160.9C383.7 170.7 378.7 168 373.3 168c-5.352 0-10.35 2.672-13.31 7.125l-62.74 94.11L274.9 238.6C271.9 234.4 267.1 232 262 232c-5.109 0-9.914 2.441-12.93 6.574L176 336H160c-8.822 0-16-7.178-16-16V96c0-8.822 7.178-16 16-16H512c8.822 0 16 7.178 16 16V320zM224 112c-17.67 0-32 14.33-32 32s14.33 32 32 32c17.68 0 32-14.33 32-32S241.7 112 224 112zM456 480H120C53.83 480 0 426.2 0 360v-240C0 106.8 10.75 96 24 96S48 106.8 48 120v240c0 39.7 32.3 72 72 72h336c13.25 0 24 10.75 24 24S469.3 480 456 480z"/></svg>

**View the slides:**

[bretsw.com/eme6356-su23-module2](https://bretsw.com/eme6356-su23-module2)

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M96 191.1H32c-17.67 0-32 14.33-32 31.1v223.1c0 17.67 14.33 31.1 32 31.1h64c17.67 0 32-14.33 32-31.1V223.1C128 206.3 113.7 191.1 96 191.1zM512 227c0-36.89-30.05-66.92-66.97-66.92h-99.86C354.7 135.1 360 113.5 360 100.8c0-33.8-26.2-68.78-70.06-68.78c-46.61 0-59.36 32.44-69.61 58.5c-31.66 80.5-60.33 66.39-60.33 93.47c0 12.84 10.36 23.99 24.02 23.99c5.256 0 10.55-1.721 14.97-5.26c76.76-61.37 57.97-122.7 90.95-122.7c16.08 0 22.06 12.75 22.06 20.79c0 7.404-7.594 39.55-25.55 71.59c-2.046 3.646-3.066 7.686-3.066 11.72c0 13.92 11.43 23.1 24 23.1h137.6C455.5 208.1 464 216.6 464 227c0 9.809-7.766 18.03-17.67 18.71c-12.66 .8593-22.36 11.4-22.36 23.94c0 15.47 11.39 15.95 11.39 28.91c0 25.37-35.03 12.34-35.03 42.15c0 11.22 6.392 13.03 6.392 22.25c0 22.66-29.77 13.76-29.77 40.64c0 4.515 1.11 5.961 1.11 9.456c0 10.45-8.516 18.95-18.97 18.95h-52.53c-25.62 0-51.02-8.466-71.5-23.81l-36.66-27.51c-4.315-3.245-9.37-4.811-14.38-4.811c-13.85 0-24.03 11.38-24.03 24.04c0 7.287 3.312 14.42 9.596 19.13l36.67 27.52C235 468.1 270.6 480 306.6 480h52.53c35.33 0 64.36-27.49 66.8-62.2c17.77-12.23 28.83-32.51 28.83-54.83c0-3.046-.2187-6.107-.6406-9.122c17.84-12.15 29.28-32.58 29.28-55.28c0-5.311-.6406-10.54-1.875-15.64C499.9 270.1 512 250.2 512 227z"/></svg> <br><br> Looking Back at Module 1

---

### The three Vs of big data (Russom, 2011, p. 6)

---

**Opportunities:**

- Help decision-making

- Treat patients

- Monitor parking

- Prepare stock in preparation of hurricanes

- Monitor students’ self-regulated learning and engagement

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M128 288V64.03c0-17.67-14.33-31.1-32-31.1H32c-17.67 0-32 14.33-32 31.1v223.1c0 17.67 14.33 31.1 32 31.1h64C113.7 320 128 305.7 128 288zM481.5 229.1c1.234-5.092 1.875-10.32 1.875-15.64c0-22.7-11.44-43.13-29.28-55.28c.4219-3.015 .6406-6.076 .6406-9.122c0-22.32-11.06-42.6-28.83-54.83c-2.438-34.71-31.47-62.2-66.8-62.2h-52.53c-35.94 0-71.55 11.87-100.3 33.41L169.6 92.93c-6.285 4.71-9.596 11.85-9.596 19.13c0 12.76 10.29 24.04 24.03 24.04c5.013 0 10.07-1.565 14.38-4.811l36.66-27.51c20.48-15.34 45.88-23.81 71.5-23.81h52.53c10.45 0 18.97 8.497 18.97 18.95c0 3.5-1.11 4.94-1.11 9.456c0 26.97 29.77 17.91 29.77 40.64c0 9.254-6.392 10.96-6.392 22.25c0 13.97 10.85 21.95 19.58 23.59c8.953 1.671 15.45 9.481 15.45 18.56c0 13.04-11.39 13.37-11.39 28.91c0 12.54 9.702 23.08 22.36 23.94C456.2 266.1 464 275.2 464 284.1c0 10.43-8.516 18.93-18.97 18.93H307.4c-12.44 0-24 10.02-24 23.1c0 4.038 1.02 8.078 3.066 11.72C304.4 371.7 312 403.8 312 411.2c0 8.044-5.984 20.79-22.06 20.79c-12.53 0-14.27-.9059-24.94-28.07c-24.75-62.91-61.74-99.9-80.98-99.9c-13.8 0-24.02 11.27-24.02 23.99c0 7.041 3.083 14.02 9.016 18.76C238.1 402 211.4 480 289.9 480C333.8 480 360 445 360 411.2c0-12.7-5.328-35.21-14.83-59.33h99.86C481.1 351.9 512 321.9 512 284.1C512 261.8 499.9 241 481.5 229.1z"/></svg> Pessimism about Analytics

**Challenges:**

- Misuse of big data, fake news, privacy issues, data security and other ethical considerations
  
--

- Surveillance issues

- Leaving digital traces and footprints in online spaces
  
--

- Analytics reports can be lengthy and difficulty to understand

- Lack of infrastructure for implementation of analytics in many contexts

---

# <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M96 96c-17.7 0-32 14.3-32 32s-14.3 32-32 32s-32-14.3-32-32C0 75 43 32 96 32h97c70.1 0 127 56.9 127 127c0 52.4-32.2 99.4-81 118.4l-63 24.5 0 18.1c0 17.7-14.3 32-32 32s-32-14.3-32-32V301.9c0-26.4 16.2-50.1 40.8-59.6l63-24.5C240 208.3 256 185 256 159c0-34.8-28.2-63-63-63H96zm48 384c-22.1 0-40-17.9-40-40s17.9-40 40-40s40 17.9 40 40s-17.9 40-40 40z"/></svg> <br><br> Module 1 <br> Final Thoughts?

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# <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M224 96c38.4 0 73.7 13.5 101.3 36.1l-32.6 32.6c-4.6 4.6-5.9 11.5-3.5 17.4s8.3 9.9 14.8 9.9H416c8.8 0 16-7.2 16-16V64c0-6.5-3.9-12.3-9.9-14.8s-12.9-1.1-17.4 3.5l-34 34C331.4 52.6 280.1 32 224 32c-10.9 0-21.5 .8-32 2.3V99.2c10.3-2.1 21-3.2 32-3.2zM100.1 154.7l32.6 32.6c4.6 4.6 11.5 5.9 17.4 3.5s9.9-8.3 9.9-14.8V64c0-8.8-7.2-16-16-16H32c-6.5 0-12.3 3.9-14.8 9.9s-1.1 12.9 3.5 17.4l34 34C20.6 148.6 0 199.9 0 256c0 10.9 .8 21.5 2.3 32H67.2c-2.1-10.3-3.2-21-3.2-32c0-38.4 13.5-73.7 36.1-101.3zM445.7 224H380.8c2.1 10.3 3.2 21 3.2 32c0 38.4-13.5 73.7-36.1 101.3l-32.6-32.6c-4.6-4.6-11.5-5.9-17.4-3.5s-9.9 8.3-9.9 14.8V448c0 8.8 7.2 16 16 16H416c6.5 0 12.3-3.9 14.8-9.9s1.1-12.9-3.5-17.4l-34-34C427.4 363.4 448 312.1 448 256c0-10.9-.8-21.5-2.3-32zM224 416c-38.4 0-73.7-13.5-101.3-36.1l32.6-32.6c4.6-4.6 5.9-11.5 3.5-17.4s-8.3-9.9-14.8-9.9H32c-8.8 0-16 7.2-16 16l0 112c0 6.5 3.9 12.3 9.9 14.8s12.9 1.1 17.4-3.5l34-34C116.6 459.4 167.9 480 224 480c10.9 0 21.5-.8 32-2.3V412.8c-10.3 2.1-21 3.2-32 3.2z"/></svg> <br><br> Recursive Fun

**Q: Who is Bret Staudt Willet?**

*Bret Staudt Willet is an American composer, producer, and multi-instrumentalist. He is best known for his work with artists like Kelly Clarkson, P!nk, Rihanna, and Justin Bieber. He has also collaborated with producers such as Dr. Luke, Max Martin, and Zedd. Willet is a three-time Grammy Award winner and has been nominated for multiple awards.*

**https://beta.openai.com/playground/**

---

**Q: What is the difference between data analytics and data science?**

*Data analytics and data science are closely related fields that use data to draw insights and solve problems. The key difference between the two is that data analytics is focused on analyzing data to draw insights and make predictions, while data science is a broader field that combines data analytics with machine learning, statistics, programming, and other areas to create solutions to complex problems.*

**https://beta.openai.com/playground/**

---

**Q: What is performance analytics?**

*Performance analytics is the use of data to measure and analyze the performance of a business or organization. It involves collecting and analyzing data from multiple sources to identify patterns and trends, and to identify areas of improvement. Performance analytics can be used to measure the success of a business or organization in meeting its goals, as well as to identify root causes of performance issues. It can also be used to help identify areas of opportunity and to develop strategies to improve performance.*

**https://beta.openai.com/playground/**
---

---

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###

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### Measure

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---

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### Measure <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Collect <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Analyze <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Report

---

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# Performance at Home

Measure <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Collect <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Analyze <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Report

**What might we measure?**

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# Performance at Home

- How has your electricity usage increased due to working from home, if at all?

- How have your sleep patterns changed since having a baby?

- How has your heart rate changed since starting a new exercise routine?

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# Performance Analytics Example

### Fitbit

**What might we measure?**

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# Performance Analytics Example

### Fitbit: Daily Steps During COVID-19

View the code [on GitHub](https://github.com/bretsw/fitbit)

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# Performance Analytics Example

### Fitbit: Resting Heart Rate During Move

View the code [on GitHub](https://github.com/bretsw/fitbit)

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# Performance Analytics Example

### Fitbit: Heart Rate Yesterday

View the code [on GitHub](https://github.com/bretsw/fitbit)

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# Performance at Work

**What might we measure?**

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# Performance at Work

- How has your productivity changed due to working from home, if at all?

- Who do you tend to seek for professional advice?

- How has the sentiment of team messages changed during COVID-19?

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# Performance Analytics Example

### GitHub User Profile: @bretsw

[View the webpage](https://github.com/bretsw)

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# Performance Analytics Example

### GitHub User Profile: @hadley

[View the webpage](https://github.com/hadley)

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# Performance Analytics Example

### GitHub: {tidytags} R Package Contributors

[View the webpage](https://github.com/bretsw/tidytags/graphs/contributors)

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# Performance Analytics Example

### GitHub: {rtweet} R Package Contributors

[View the webpage](https://github.com/ropensci/rtweet/graphs/contributors)

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# Performance Analytics Example

### GitHub: {tidytags} Development Timeline (Nov 2021)

[View the webpage](https://github.com/bretsw/tidytags/graphs/commit-activity)

Also check out [{tidytags} Code Frequency](https://github.com/bretsw/tidytags/graphs/code-frequency)

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# Performance Analytics Example

### GitHub: {tidytags} Development Timeline (Jan 2022)

[View the webpage](https://github.com/bretsw/tidytags/graphs/commit-activity)

Also check out [{tidytags} Code Frequency](https://github.com/bretsw/tidytags/graphs/code-frequency)

---

---

- **Module 1:** Introduction to Analytics
- **Module 2: Performance Analytics**
- **Module 3:** Learning Analytics
- **Module 4:** Web Analytics
- **Module 5:** Data Visualization
- **Module 6:** Ethics in Learning Analytics
- **Module 7:** Future of Analytics
- **Module 8:** Case Discussions

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- <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M306.7 325.1L162.4 380.6C142.1 388.1 123.9 369 131.4 349.6L186.9 205.3C190.1 196.8 196.8 190.1 205.3 186.9L349.6 131.4C369 123.9 388.1 142.1 380.6 162.4L325.1 306.7C321.9 315.2 315.2 321.9 306.7 325.1V325.1zM255.1 224C238.3 224 223.1 238.3 223.1 256C223.1 273.7 238.3 288 255.1 288C273.7 288 288 273.7 288 256C288 238.3 273.7 224 255.1 224V224zM512 256C512 397.4 397.4 512 256 512C114.6 512 0 397.4 0 256C0 114.6 114.6 0 256 0C397.4 0 512 114.6 512 256zM256 48C141.1 48 48 141.1 48 256C48 370.9 141.1 464 256 464C370.9 464 464 370.9 464 256C464 141.1 370.9 48 256 48z"/></svg> Explore

- <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M208 0C322.9 0 416 78.8 416 176C416 273.2 322.9 352 208 352C189.3 352 171.2 349.7 153.9 345.8C123.3 364.8 79.13 384 24.95 384C14.97 384 5.93 378.1 2.018 368.9C-1.896 359.7-.0074 349.1 6.739 341.9C7.26 341.5 29.38 317.4 45.73 285.9C17.18 255.8 0 217.6 0 176C0 78.8 93.13 0 208 0zM164.6 298.1C179.2 302.3 193.8 304 208 304C296.2 304 368 246.6 368 176C368 105.4 296.2 48 208 48C119.8 48 48 105.4 48 176C48 211.2 65.71 237.2 80.57 252.9L104.1 277.8L88.31 308.1C84.74 314.1 80.73 321.9 76.55 328.5C94.26 323.4 111.7 315.5 128.7 304.1L145.4 294.6L164.6 298.1zM441.6 128.2C552 132.4 640 209.5 640 304C640 345.6 622.8 383.8 594.3 413.9C610.6 445.4 632.7 469.5 633.3 469.9C640 477.1 641.9 487.7 637.1 496.9C634.1 506.1 625 512 615 512C560.9 512 516.7 492.8 486.1 473.8C468.8 477.7 450.7 480 432 480C350 480 279.1 439.8 245.2 381.5C262.5 379.2 279.1 375.3 294.9 369.9C322.9 407.1 373.9 432 432 432C446.2 432 460.8 430.3 475.4 426.1L494.6 422.6L511.3 432.1C528.3 443.5 545.7 451.4 563.5 456.5C559.3 449.9 555.3 442.1 551.7 436.1L535.9 405.8L559.4 380.9C574.3 365.3 592 339.2 592 304C592 237.7 528.7 183.1 447.1 176.6L448 176C448 159.5 445.8 143.5 441.6 128.2H441.6z"/></svg> Discuss

- <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M64 64C28.7 64 0 92.7 0 128V384c0 35.3 28.7 64 64 64H512c35.3 0 64-28.7 64-64V128c0-35.3-28.7-64-64-64H64zm16 64h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H80c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM64 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H80c-8.8 0-16-7.2-16-16V240zm16 80h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H80c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16zm80-176c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V144zm16 80h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V240c0-8.8 7.2-16 16-16zM160 336c0-8.8 7.2-16 16-16H400c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V336zM272 128h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H272c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM256 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H272c-8.8 0-16-7.2-16-16V240zM368 128h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H368c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM352 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H368c-8.8 0-16-7.2-16-16V240zM464 128h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H464c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM448 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H464c-8.8 0-16-7.2-16-16V240zm16 80h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H464c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16z"/></svg> Create

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### Discussion (50 points)

- Initial post on Flip due by second Friday (but post as early as possible)

- At least 3 response due on Flip by end of Module 2 (second Sunday night)

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### Analytics Problem Plan (100 points) - **due end of Module 3**

- "Write a brief report (approximately 750-1000 words) identifying and describing a real-world problem that might be addressed via analytics."

- "The emphasis of this assignment is on the conceptualization of the problem and the clear identification of a possible data source."

- "Then briefly describe how analytics will be used to solve the problem"

- "You will not need to collect actual data related to this problem, just plan for how it would be done."

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<hr>

**What questions can I answer for you now?**

**How can I support you this week?**

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