class: left, bottom, title-slide .title[ # Module 7: Future of Analytics ] .subtitle[ ## EME6356: Learning & Web Analytics ] .author[ ### Dr. Bret Staudt Willet ] .date[ ### July 24, 2023 ] --- class: inverse, center, middle #
**View the slides:** [bretsw.com/eme6356-su23-module7](https://bretsw.com/eme6356-su23-module7) --- class: inverse, center, middle #
<br><br> Looking Back at Module 6 --- #
Ethics of Measurement <img src="img/data.jpg" width="480px" style="display: block; margin: auto;" /> -- - 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? --- #
Ethics of Collection <img src="img/surveillance.jpg" width="480px" style="display: block; margin: auto;" /> -- - 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? --- #
Ethics of Analysis <img src="img/tools.jpg" width="480px" style="display: block; margin: auto;" /> -- - 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? --- #
Ethics of Reporting <img src="img/amplify.jpg" width="480px" style="display: block; margin: auto;" /> -- - 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? --- #
Recommended Solutions <img src="img/relax.jpg" width="480px" style="display: block; margin: auto;" /> -- - **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 --- class: inverse, center, middle #
<br><br> Module 6 <br> Final Thoughts? --- class: inverse, center, middle #
<br><br> The Future --- class: inverse, center, middle #
<br><br> Challenge \#1: <br> **"Participant" "Rights"** --- #
Privacy <img src="img/surveillance.jpg" width="600px" style="display: block; margin: auto;" /> -- - What rights to live and behave from observation? -- - Opt-in, or opt-out? --- #
Openness and Societal Benefit <img src="img/traffic.jpg" width="600px" style="display: block; margin: auto;" /> -- - Example: Traffic data -- - Looking at aggregate or individual? --- #
Data Accessibility <img src="img/open.jpg" width="600px" style="display: block; margin: auto;" /> -- - Who has access? Who gives feedback? Who benefits? --- #
Ownership <img src="img/sale.jpg" width="600px" style="display: block; margin: auto;" /> -- - Who **controls** my data? -- - Who **profits** from it? --- #
Agency <img src="img/step.jpg" width="420px" style="display: block; margin: auto;" /> -- - How much initiative can be expected? -- - How does the need to be purposeful fall unevenly? --- class: inverse, center, middle #
<br><br> Challenge \#2: <br> **Ethical Use of Data** --- #
Consent <img src="img/park.jpg" width="600px" style="display: block; margin: auto;" /> -- - 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? --- #
Transparency <img src="img/park.jpg" width="600px" style="display: block; margin: auto;" /> -- - Those who collect data should be open about when collecting or observing -- - Analysis should be open as well --- #
Regulation <img src="img/park.jpg" width="600px" style="display: block; margin: auto;" /> -- - Transparency is not enough: need regulation or moderation -- - Peer/community review, government oversight, etc. --- class: inverse, center, middle #
<br><br> Challenge \#3: <br> **Real Impact** --- #
Learning and Performance <img src="img/grocery-list.jpg" width="600px" style="display: block; margin: auto;" /> -- - Is "learning" simply the vocabulary on a grocery list? --- #
Evidence for Conclusions <img src="img/crack.jpg" width="600px" style="display: block; margin: auto;" /> -- - Can't speak to motivations or answer "Why?" -- - Statistical significance vs. practical significance: mean of 69% vs 76% --- #
Challenges in Deployment <img src="img/spill.jpg" width="600px" style="display: block; margin: auto;" /> -- - Development time, support needed, rapid innovation -- - Need to view as a system --- #
Teaching/Training <img src="img/help.jpg" width="600px" style="display: block; margin: auto;" /> -- - Feedback needs to immediate, clear, and actionable -- - Give **teachers/instructors** a voice in identifying key performance indicators --- #
Feedback and Performance <img src="img/choose.jpg" width="600px" style="display: block; margin: auto;" /> -- - Feedback needs to immediate, clear, and actionable (e.g., adaptive learning) -- - Give **students/learners** options for filtering analyses --- #
Impact on Institutions <img src="img/oxford.jpg" width="720px" style="display: block; margin: auto;" /> -- - Should benefit to institutions be primary or secondary? --- class: inverse, center, middle #
<br><br> Challenge \#4: <br> **Only 2 Kinds of Dances** --- #
The Humans Are Dead? <p align="center"> <iframe width="720" height="480" src="https://www.youtube.com/embed/hUnibKe_o18" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe> </p> --- #
Robots Replacing Teachers? <img src="img/robot.jpg" width="100%" style="display: block; margin: auto;" /> --- #
AI Giving an Extra Nudge <img src="img/bike-machine.jpg" width="100%" style="display: block; margin: auto;" /> --- #
AI Insight What are the most important issues in educational data science in the next 5 years? <img src="img/robot.jpg" width="420px" style="display: block; margin: auto;" /> **OpenAI:** (https://platform.openai.com/) - Understanding and leveraging data to improve student outcomes - Developing predictive analytics to support personalized learning - Enhancing data-driven decision making - Leveraging technology to improve learning experiences - Facilitating the use of data in the classroom --- #
AI Insight What are the most important issues in educational data science in the next 5 years? <img src="img/robot.jpg" width="420px" style="display: block; margin: auto;" /> **Bard:** (https://bard.google.com/) - Data privacy and security - Bias and fairness - Ethics - Infrastructure - Collaboration - Communication --- class: inverse, center, top #
Future of Analytics <img src="img/7-future-analytics1.png" width="450px" style="display: block; margin: auto;" /> <div class="caption"> <p>Midjourney AI art prompt: "futuristic African solarpunk scientists" (March 2023)</p> </div> -- ### What *should* be the role of analytics? --- class: inverse, center, top #
Future of Analytics <img src="img/7-future-analytics2.png" width="450px" style="display: block; margin: auto;" /> <div class="caption"> <p>Midjourney AI art prompt: "futuristic African solarpunk scientists" (July 2023)</p> </div> ### What *should* be the role of analytics? --- class: inverse, center, top #
Future of Analytics <img src="img/7-teaching-iteration1.png" width="450px" style="display: block; margin: auto;" /> <div class="caption"> <p>Midjourney AI art prompt: "teaching iteration. pen and ink drawing." (March 2023)</p> </div> ### What *should* be the role of analytics? --- class: inverse, center, top #
Future of Analytics <img src="img/7-teaching-iteration2.png" width="450px" style="display: block; margin: auto;" /> <div class="caption"> <p>Midjourney AI art prompt: "teaching iteration. pen and ink drawing." (July 2023)</p> </div> ### What *should* be the role of analytics? --- class: inverse, center, middle #
<br><br> Looking ahead --- #
Semester schedule <img src="img/across-time.jpg" width="720px" style="display: block; margin: auto;" /> - **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 --- #
Major Assignments <img src="img/build.jpg" width="560px" style="display: block; margin: auto;" /> ### Analytics Assignments (60%) - Analytics Problem Plan (100 points) - Analytics Practice (200 points) - **Analytics Case Presentation (200 points; group project)** - Analytics Ethics Statement (100 points) --- #
Major Assignments <img src="img/build.jpg" width="300px" style="display: block; margin: auto;" /> ### Analytics Case Presentation (200 pts) - **due end of Module 7** -- - Form a group -- - Select a case study topic: - Research case study - Tool case study - Real-world setting case study -- - Explore your case topic -- - Create a slide deck to share your case and present in class. --- class: inverse, center, middle #
<br><br> Questions <hr> **What questions can I answer for you now?** **How can I support you this week?** <hr>
[bret.staudtwillet@fsu.edu](mailto:bret.staudtwillet@fsu.edu) |
[bretsw.com](https://bretsw.com) |
[GitHub](https://github.com/bretsw/) --- class: inverse, center, middle # Learn to Code <img src="img/dsieur.jpg" width="320px" style="display: block; margin: auto;" /> **https://datascienceineducation.com/** --- class: inverse, center, middle #
<br><br> Play in the <br> [Analytics Sandbox](https://bretsw.com/sandbox)
[GitHub repository for code and data](https://github.com/bretsw/sandbox)
[Datasets for practice](https://bretsw.com/post/datasets/)