Module 7: Future of Analytics

.title[
# Module 7: Future of Analytics
]
.subtitle[
## EME6356: Learning & Web Analytics
]
.author[
### Dr. Bret Staudt Willet
]
.date[
### July 22, 2024
]

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**View the slides:**

[bretsw.com/eme6356-su24-module7](https://bretsw.com/eme6356-su24-module7)

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- How accurate are the collected data?

- How good are the predictions of complex human behavior?

- How well does a measure represent real learning or performance?

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- Do users have the right to opt in or opt out?

- What are the risks of the collected data becoming public?

- Who owns the collected data? Who owns where the data are stored?

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- What biases exist in algorithms>?

- What factors are included or excluded in predictive modeling?

- Do users have the right to know and inspect what analyses have been conducted?

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- How anonymous are users really? How easily can de-identification be done?

- How do truth and trust exist in relationship and in tension with each other?

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- **Do no harm!** Operate in the best interests of users (e.g., students)

- **Transparency:** Be clear about the purposes for the data

- **Data literacy:** Educate users on data collection and privacy issues

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# <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="M80 160c0-35.3 28.7-64 64-64h32c35.3 0 64 28.7 64 64v3.6c0 21.8-11.1 42.1-29.4 53.8l-42.2 27.1c-25.2 16.2-40.4 44.1-40.4 74V320c0 17.7 14.3 32 32 32s32-14.3 32-32v-1.4c0-8.2 4.2-15.8 11-20.2l42.2-27.1c36.6-23.6 58.8-64.1 58.8-107.7V160c0-70.7-57.3-128-128-128H144C73.3 32 16 89.3 16 160c0 17.7 14.3 32 32 32s32-14.3 32-32zm80 320a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> <br><br> Module 6 <br> Final Thoughts?

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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:#fff;overflow:visible;position:relative;"><path d="M156.6 384.9L125.7 354c-8.5-8.5-11.5-20.8-7.7-32.2c3-8.9 7-20.5 11.8-33.8L24 288c-8.6 0-16.6-4.6-20.9-12.1s-4.2-16.7 .2-24.1l52.5-88.5c13-21.9 36.5-35.3 61.9-35.3l82.3 0c2.4-4 4.8-7.7 7.2-11.3C289.1-4.1 411.1-8.1 483.9 5.3c11.6 2.1 20.6 11.2 22.8 22.8c13.4 72.9 9.3 194.8-111.4 276.7c-3.5 2.4-7.3 4.8-11.3 7.2v82.3c0 25.4-13.4 49-35.3 61.9l-88.5 52.5c-7.4 4.4-16.6 4.5-24.1 .2s-12.1-12.2-12.1-20.9V380.8c-14.1 4.9-26.4 8.9-35.7 11.9c-11.2 3.6-23.4 .5-31.8-7.8zM384 168a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> <br><br> The Future

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class: inverse, center, middle

#  <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="M156.6 384.9L125.7 354c-8.5-8.5-11.5-20.8-7.7-32.2c3-8.9 7-20.5 11.8-33.8L24 288c-8.6 0-16.6-4.6-20.9-12.1s-4.2-16.7 .2-24.1l52.5-88.5c13-21.9 36.5-35.3 61.9-35.3l82.3 0c2.4-4 4.8-7.7 7.2-11.3C289.1-4.1 411.1-8.1 483.9 5.3c11.6 2.1 20.6 11.2 22.8 22.8c13.4 72.9 9.3 194.8-111.4 276.7c-3.5 2.4-7.3 4.8-11.3 7.2v82.3c0 25.4-13.4 49-35.3 61.9l-88.5 52.5c-7.4 4.4-16.6 4.5-24.1 .2s-12.1-12.2-12.1-20.9V380.8c-14.1 4.9-26.4 8.9-35.7 11.9c-11.2 3.6-23.4 .5-31.8-7.8zM384 168a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> <br><br> Challenge \#1: <br> **"Participant" "Rights"**

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- What rights to live and behave from observation?

- Opt-in, or opt-out?

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- Example: Traffic data

- Looking at aggregate or individual?

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- Who has access? Who gives feedback? Who benefits?

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- Who **controls** my data?

- Who **profits** from it?

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- How much initiative can be expected?

- How does the need to be purposeful fall unevenly?

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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:#fff;overflow:visible;position:relative;"><path d="M156.6 384.9L125.7 354c-8.5-8.5-11.5-20.8-7.7-32.2c3-8.9 7-20.5 11.8-33.8L24 288c-8.6 0-16.6-4.6-20.9-12.1s-4.2-16.7 .2-24.1l52.5-88.5c13-21.9 36.5-35.3 61.9-35.3l82.3 0c2.4-4 4.8-7.7 7.2-11.3C289.1-4.1 411.1-8.1 483.9 5.3c11.6 2.1 20.6 11.2 22.8 22.8c13.4 72.9 9.3 194.8-111.4 276.7c-3.5 2.4-7.3 4.8-11.3 7.2v82.3c0 25.4-13.4 49-35.3 61.9l-88.5 52.5c-7.4 4.4-16.6 4.5-24.1 .2s-12.1-12.2-12.1-20.9V380.8c-14.1 4.9-26.4 8.9-35.7 11.9c-11.2 3.6-23.4 .5-31.8-7.8zM384 168a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> <br><br> Challenge \#2: <br> **Ethical Use of Data**

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- Should we ask people for consent to observe them?

- What are their expectations for being in public?

- What is ok to collect? What forms of analysis are ethical?

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- Those who collect data should be open about when collecting or observing

- Analysis should be open as well

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- Transparency is not enough: need regulation or moderation

- Peer/community review, government oversight, etc.

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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:#fff;overflow:visible;position:relative;"><path d="M156.6 384.9L125.7 354c-8.5-8.5-11.5-20.8-7.7-32.2c3-8.9 7-20.5 11.8-33.8L24 288c-8.6 0-16.6-4.6-20.9-12.1s-4.2-16.7 .2-24.1l52.5-88.5c13-21.9 36.5-35.3 61.9-35.3l82.3 0c2.4-4 4.8-7.7 7.2-11.3C289.1-4.1 411.1-8.1 483.9 5.3c11.6 2.1 20.6 11.2 22.8 22.8c13.4 72.9 9.3 194.8-111.4 276.7c-3.5 2.4-7.3 4.8-11.3 7.2v82.3c0 25.4-13.4 49-35.3 61.9l-88.5 52.5c-7.4 4.4-16.6 4.5-24.1 .2s-12.1-12.2-12.1-20.9V380.8c-14.1 4.9-26.4 8.9-35.7 11.9c-11.2 3.6-23.4 .5-31.8-7.8zM384 168a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> <br><br> Challenge \#3: <br> **Real Impact**

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- Is "learning" simply the vocabulary on a grocery list?

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- Can't speak to motivations or answer "Why?"

- Statistical significance vs. practical significance: mean of 69% vs 76%

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- Development time, support needed, rapid innovation

- Need to view as a system

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- Feedback needs to immediate, clear, and actionable

- Give **teachers/instructors** a voice in identifying key performance indicators

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- Feedback needs to immediate, clear, and actionable (e.g., adaptive learning)

- Give **students/learners** options for filtering analyses

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- Should benefit to institutions be primary or secondary?

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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:#fff;overflow:visible;position:relative;"><path d="M156.6 384.9L125.7 354c-8.5-8.5-11.5-20.8-7.7-32.2c3-8.9 7-20.5 11.8-33.8L24 288c-8.6 0-16.6-4.6-20.9-12.1s-4.2-16.7 .2-24.1l52.5-88.5c13-21.9 36.5-35.3 61.9-35.3l82.3 0c2.4-4 4.8-7.7 7.2-11.3C289.1-4.1 411.1-8.1 483.9 5.3c11.6 2.1 20.6 11.2 22.8 22.8c13.4 72.9 9.3 194.8-111.4 276.7c-3.5 2.4-7.3 4.8-11.3 7.2v82.3c0 25.4-13.4 49-35.3 61.9l-88.5 52.5c-7.4 4.4-16.6 4.5-24.1 .2s-12.1-12.2-12.1-20.9V380.8c-14.1 4.9-26.4 8.9-35.7 11.9c-11.2 3.6-23.4 .5-31.8-7.8zM384 168a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> <br><br> Challenge \#4: <br> **Only 2 Kinds of Dances**

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What are the most important issues in educational data science in the next 5 years? (**According to ChatpGPT:** https://platform.openai.com/playground/chat)

- **Privacy and Security**: Ensuring the protection of student data against breaches and misuse.
- **Ethical Use of AI**: Implementing AI ethically in educational settings to avoid bias and ensure fairness.
- **Personalized Learning**: Developing algorithms to tailor educational experiences to individual student needs and learning styles.
- **Data-Driven Decision Making**: Enhancing the use of data analytics to inform educational policies and teaching strategies.

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What are the most important issues in educational data science in the next 5 years? (**According to ChatpGPT:** https://platform.openai.com/playground/chat)

- **Integration and Interoperability**: Improving systems so that diverse educational technologies and data sources work seamlessly together.
- **Equity and Access**: Using data to identify and address disparities in educational access and outcomes.
- **Teacher Training**: Equipping educators with the skills to interpret and leverage data effectively in their teaching practices.
- **Scalability**: Ensuring solutions can be scaled effectively to reach diverse educational environments globally.

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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:#fff;overflow:visible;position:relative;"><path d="M128 32h32c17.7 0 32 14.3 32 32V96H96V64c0-17.7 14.3-32 32-32zm64 96V448c0 17.7-14.3 32-32 32H32c-17.7 0-32-14.3-32-32V388.9c0-34.6 9.4-68.6 27.2-98.3C40.9 267.8 49.7 242.4 53 216L60.5 156c2-16 15.6-28 31.8-28H192zm227.8 0c16.1 0 29.8 12 31.8 28L459 216c3.3 26.4 12.1 51.8 25.8 74.6c17.8 29.7 27.2 63.7 27.2 98.3V448c0 17.7-14.3 32-32 32H352c-17.7 0-32-14.3-32-32V128h99.8zM320 64c0-17.7 14.3-32 32-32h32c17.7 0 32 14.3 32 32V96H320V64zm-32 64V288H224V128h64z"/></svg> Future of Analytics

<div class="caption">
<p>Midjourney AI art prompt: "futuristic African solarpunk scientists" (Nov 2022)</p>
</div>

### What *should* be the role of analytics?

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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:#fff;overflow:visible;position:relative;"><path d="M128 32h32c17.7 0 32 14.3 32 32V96H96V64c0-17.7 14.3-32 32-32zm64 96V448c0 17.7-14.3 32-32 32H32c-17.7 0-32-14.3-32-32V388.9c0-34.6 9.4-68.6 27.2-98.3C40.9 267.8 49.7 242.4 53 216L60.5 156c2-16 15.6-28 31.8-28H192zm227.8 0c16.1 0 29.8 12 31.8 28L459 216c3.3 26.4 12.1 51.8 25.8 74.6c17.8 29.7 27.2 63.7 27.2 98.3V448c0 17.7-14.3 32-32 32H352c-17.7 0-32-14.3-32-32V128h99.8zM320 64c0-17.7 14.3-32 32-32h32c17.7 0 32 14.3 32 32V96H320V64zm-32 64V288H224V128h64z"/></svg> Future of Analytics

<div class="caption">
<p>Midjourney AI art prompt: "futuristic African solarpunk scientists" (Jul 2024)</p>
</div>

### What *should* be the role of analytics?

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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:#fff;overflow:visible;position:relative;"><path d="M128 32h32c17.7 0 32 14.3 32 32V96H96V64c0-17.7 14.3-32 32-32zm64 96V448c0 17.7-14.3 32-32 32H32c-17.7 0-32-14.3-32-32V388.9c0-34.6 9.4-68.6 27.2-98.3C40.9 267.8 49.7 242.4 53 216L60.5 156c2-16 15.6-28 31.8-28H192zm227.8 0c16.1 0 29.8 12 31.8 28L459 216c3.3 26.4 12.1 51.8 25.8 74.6c17.8 29.7 27.2 63.7 27.2 98.3V448c0 17.7-14.3 32-32 32H352c-17.7 0-32-14.3-32-32V128h99.8zM320 64c0-17.7 14.3-32 32-32h32c17.7 0 32 14.3 32 32V96H320V64zm-32 64V288H224V128h64z"/></svg> <br><br> Looking ahead

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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="M128 0c17.7 0 32 14.3 32 32V64H288V32c0-17.7 14.3-32 32-32s32 14.3 32 32V64h48c26.5 0 48 21.5 48 48v48H0V112C0 85.5 21.5 64 48 64H96V32c0-17.7 14.3-32 32-32zM0 192H448V464c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V192zm80 64c-8.8 0-16 7.2-16 16v96c0 8.8 7.2 16 16 16h96c8.8 0 16-7.2 16-16V272c0-8.8-7.2-16-16-16H80z"/></svg> Semester schedule

- **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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### Analytics Assignments (60%)

- Analytics Problem Plan (100 points)
- Analytics Practice (200 points)
- **Analytics Case Presentation (200 points; group project)**
- Analytics Ethics Statement (100 points)

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### Analytics Case Presentation (200 pts) - **due end of Module 7**

- With your usual project team, further explore your topic

- Create a slide deck to share your case and present in class

- The goal is to prompt discussions in Module 8 to wrap up the semester

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<hr>

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

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

<hr>

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# Learn to Code

**https://datascienceineducation.com/**

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# <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:#fff;overflow:visible;position:relative;"><path d="M181.5 197.1l12.9 6.4c5.9 3 12.4 4.5 19.1 4.5c23.5 0 42.6-19.1 42.6-42.6V144c0-35.3-28.7-64-64-64H128c-35.3 0-64 28.7-64 64v21.4c0 23.5 19.1 42.6 42.6 42.6c6.6 0 13.1-1.5 19.1-4.5l12.9-6.4 8.4-4.2L135.1 185c-4.5-3-7.1-8-7.1-13.3V168c0-13.3 10.7-24 24-24h16c13.3 0 24 10.7 24 24v3.7c0 5.3-2.7 10.3-7.1 13.3l-11.8 7.9 8.4 4.2zm-8.6 49.4L160 240l-12.9 6.4c-12.6 6.3-26.5 9.6-40.5 9.6c-3.6 0-7.1-.2-10.6-.6v.6c0 35.3 28.7 64 64 64h64c17.7 0 32 14.3 32 32s-14.3 32-32 32H384V336 320c0-23.7 12.9-44.4 32-55.4c9.4-5.4 20.3-8.6 32-8.6V240c0-26.5 21.5-48 48-48c8.8 0 16 7.2 16 16v32 16 48c0 8.8 7.2 16 16 16s16-7.2 16-16V204.3c0-48.2-30.8-91-76.6-106.3l-8.5-2.8c-8-2.7-12.6-11.1-10.4-19.3s10.3-13.2 18.6-11.6l19.9 4C576 86.1 640 164.2 640 254.9l0 1.1h0c0 123.7-100.3 224-224 224h-1.1H256h-.6C132 480 32 380 32 256.6V256 216.8c-10.1-14.6-16-32.3-16-51.4V144l0-1.4C6.7 139.3 0 130.5 0 120c0-13.3 10.7-24 24-24h2.8C44.8 58.2 83.3 32 128 32h64c44.7 0 83.2 26.2 101.2 64H296c13.3 0 24 10.7 24 24c0 10.5-6.7 19.3-16 22.6l0 1.4v21.4c0 1.4 0 2.8-.1 4.3c12-6.2 25.7-9.6 40.1-9.6h8c17.7 0 32 14.3 32 32s-14.3 32-32 32h-8c-13.3 0-24 10.7-24 24v8h56.4c-15.2 17-24.4 39.4-24.4 64H320c-42.3 0-78.2-27.4-91-65.3c-5.1 .9-10.3 1.3-15.6 1.3c-14.1 0-27.9-3.3-40.5-9.6zM96 128a16 16 0 1 1 0 32 16 16 0 1 1 0-32zm112 16a16 16 0 1 1 32 0 16 16 0 1 1 -32 0z"/></svg> <br><br> Play in the <br> [Analytics Sandbox](https://bretsw.com/sandbox)