init UKCOTS materials

docs/202306-scalable-formative-assessment.Rmd → docs/202406-scalable-formative-assessment.Rmd

@@ -1,9 +1,9 @@
 ---
-title: "Progress toward NLP-assisted formative assessment feedback"
+title: "Research evaluating NLP tools designed to assist instructors with formative assessment for students in large-enrollment STEM education classes"
 author: |
   | Matthew Beckman
   | Penn State University
-date: "June 23, 2023"
+date: "June 14, 2024"
 
 
 output:
@@ -32,7 +32,6 @@ library(tidyverse)
 ![(QR Code) https://forms.gle/hpW72fMYE1SsB19JA](QR-GForm.png){width=20%}
 
 
-<!-- Discuss Michael Bulmer influence while folks complete this -->
 
 # Responses to our survey?
 

index.html

@@ -10,9 +10,9 @@
 
 
 
-<meta name="date" content="2023-06-23" />
+<meta name="date" content="2024-06-14" />
 
-<title>Statistics and Data Science Education: Connecting Disciplines</title>
+<title>UK Conference on Teaching Statistics 2024</title>
 
 <script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
 // be compatible with the behavior of Pandoc < 2.8).
@@ -668,62 +668,57 @@
 
 
 
-<h1 class="title toc-ignore">Statistics and Data Science Education:
-Connecting Disciplines</h1>
-<h3 class="subtitle">Statistical Society of Australia &amp; Queensland
-University of Technology</h3>
+<h1 class="title toc-ignore">UK Conference on Teaching Statistics
+2024</h1>
+<h3 class="subtitle">Royal Statistical Society</h3>
 <h4 class="author"><div class="line-block">Matthew Beckman<br />
 Penn State University</div></h4>
-<h4 class="date">June 23, 2023</h4>
+<h4 class="date">June 14, 2024</h4>
 
 </div>
 
 
-<div id="progress-toward-nlp-assisted-formative-assessment-feedback" class="section level2">
-<h2>Progress toward NLP-assisted formative assessment feedback</h2>
-<p><em>Abstract.</em> This talk seeks to articulate the benefit of
-free-response tasks and timely formative assessment feedback, a roadmap
-for developing human-in-the-loop natural language processing (NLP)
-assisted feedback, and results from a pilot study establishing proof of
-principle. If we are to pursue Statistics and Data Science Education
-across disciplines, we will surely encounter both opportunity and
-necessity to develop scalable solutions for pedagogical best practices.
-Research suggests “write-to-learn” tasks improve learning outcomes, yet
+<div id="research-evaluating-nlp-tools-designed-to-assist-instructors-with-formative-assessment-for-students-in-large-enrollment-stem-education-classes" class="section level2">
+<h2>Research evaluating NLP tools designed to assist instructors with
+formative assessment for students in large-enrollment STEM education
+classes</h2>
+<p><em>Abstract.</em> The project described here seeks to articulate the
+benefit of free-response tasks and timely formative assessment feedback
+and progress toward developing human-in-the-loop natural language
+processing (NLP) assisted feedback at scale. Research suggests
+“write-to-learn” tasks improve learning outcomes, yet
 constructed-response methods of formative assessment become unwieldy
-when class sizes grow large. In the pilot study, several short-answer
-tasks completed by nearly 2000 introductory tertiary statistics students
-were evaluated by human raters and an NLP algorithm. After briefly
-describing the tasks, the student contexts, the algorithm and the
-raters, this talk discusses the results which indicate substantial
-inter-rater agreement and group consensus. With compelling rater
-agreement, the study then examines a preliminary cluster analysis of
-response text as a mechanism for scalable formative assessment. The talk
-will conclude with recent developments building upon this pilot, as well
-as implications for teaching and future research.</p>
+when class sizes grow large. If we are to pursue Statistics and Data
+Science Education across disciplines, we will surely encounter both
+opportunity and necessity to develop scalable solutions for pedagogical
+best practices. In a pilot study, several shortanswer tasks completed by
+nearly 2000 introductory tertiary statistics students were evaluated by
+human raters and an NLP algorithm. The talk will conclude with recent
+developments building upon this pilot, as well as implications for
+teaching and future research.</p>
 </div>
 <div id="resources" class="section level2">
 <h2>Resources</h2>
 <ul>
-<li><a href="docs/202306-scalable-formative-assessment.pdf">Slides
+<li><a href="docs/202406-scalable-formative-assessment.pdf">Slides
 (PDF)</a></li>
 <li><a href="docs/ICOTS-Paper.pdf">Short Paper (PDF)</a>: Lloyd, S. E.,
 Beckman, M., Pearl, D., Passonneau, R., Li, Z., &amp; Wang, Z. (2022).
 Foundations for AI-Assisted Formative Assessment Feedback for
 Short-Answer Tasks in Large-Enrollment Classes. In <em>Proceedings of
 the eleventh international conference on teaching statistics</em>.
 Rosario, Argentina.</li>
-<li><a href="https://www.statsoc.org.au/event-5309146">Symposium Website
-(link)</a></li>
+<li><a href="https://www.ukcots.org/">UKCOTS Website (link)</a></li>
 </ul>
 </div>
 <div id="contact" class="section level2">
 <h2>Contact</h2>
 <p>Matthew Beckman<br />
-Associate Research Professor<br />
-Department of Statistics</p>
-<p>office: 421C Thomas Building<br />
-email: mdb268 [at] psu [dot] edu<br />
-webpage: <a href="https://mdbeckman.github.io/" class="uri">https://mdbeckman.github.io/</a></p>
+Assoc Research Professor | Penn State University<br />
+Incoming Director | CAUSE</p>
+<p>email: mdb268 [at] psu [dot] edu<br />
+personal webpage: <a href="https://mdbeckman.github.io/" class="uri">https://mdbeckman.github.io/</a><br />
+CAUSE webpage: <a href="https://www.causeweb.org" class="uri">https://www.causeweb.org</a></p>
 </div>
 
 

index.md

@@ -1,35 +1,34 @@
 ---
-title: "Statistics and Data Science Education: Connecting Disciplines"
-subtitle: "Statistical Society of Australia & Queensland University of Technology"
+title: "UK Conference on Teaching Statistics 2024"
+subtitle: "Royal Statistical Society"
 author: |
   | Matthew Beckman
   | Penn State University
-date: June 23, 2023
+date: June 14, 2024
 output: 
   html_document: 
     css: stylesheet.css
 ---
 
-## Progress toward NLP-assisted formative assessment feedback
+## Research evaluating NLP tools designed to assist instructors with formative assessment for students in large-enrollment STEM education classes
 
-*Abstract.* This talk seeks to articulate the benefit of free-response tasks and timely formative assessment feedback, a roadmap for developing human-in-the-loop natural language processing (NLP) assisted feedback, and results from a pilot study establishing proof of principle. If we are to pursue Statistics and Data Science Education across disciplines, we will surely encounter both opportunity and necessity to develop scalable solutions for pedagogical best practices. Research suggests "write-to-learn" tasks improve learning outcomes, yet constructed-response methods of formative assessment become unwieldy when class sizes grow large. In the pilot study, several short-answer tasks completed by nearly 2000 introductory tertiary statistics students were evaluated by human raters and an NLP algorithm. After briefly describing the tasks, the student contexts, the algorithm and the raters, this talk discusses the results which indicate substantial inter-rater agreement and group consensus. With compelling rater agreement, the study then examines a preliminary cluster analysis of response text as a mechanism for scalable formative assessment. The talk will conclude with recent developments building upon this pilot, as well as implications for teaching and future research.
+*Abstract.* The project described here seeks to articulate the benefit of free-response tasks and timely formative assessment feedback and progress toward developing human-in-the-loop natural language processing (NLP) assisted feedback at scale. Research suggests "write-to-learn" tasks improve learning outcomes, yet constructed-response methods of formative assessment become unwieldy when class sizes grow large. If we are to pursue Statistics and Data Science Education across disciplines, we will surely encounter both opportunity and necessity to develop scalable solutions for pedagogical best practices. In a pilot study, several shortanswer tasks completed by nearly 2000 introductory tertiary statistics students were evaluated by human raters and an NLP algorithm. The talk will conclude with recent developments building upon this pilot, as well as implications for teaching and future research. 
 
 
 ## Resources
 
-- [Slides (PDF)](docs/202306-scalable-formative-assessment.pdf)
+- [Slides (PDF)](docs/202406-scalable-formative-assessment.pdf)
 - [Short Paper (PDF)](docs/ICOTS-Paper.pdf): Lloyd, S. E., Beckman, M., Pearl, D., Passonneau, R., Li, Z., & Wang, Z. (2022). Foundations for AI-Assisted Formative Assessment Feedback for Short-Answer Tasks in Large-Enrollment Classes. In *Proceedings of the eleventh international conference on teaching statistics*. Rosario, Argentina.
-- [Symposium Website (link)](https://www.statsoc.org.au/event-5309146)
+- [UKCOTS Website (link)](https://www.ukcots.org/)
 
 
 
 ## Contact
 
 Matthew Beckman  
-Associate Research Professor  
-Department of Statistics  
+Assoc Research Professor | Penn State University  
+Incoming Director | CAUSE  
 
-office: 421C Thomas Building  
 email: mdb268 [at] psu [dot] edu  
-webpage: <https://mdbeckman.github.io/>
-
+personal webpage: <https://mdbeckman.github.io/>  
+CAUSE webpage: <https://www.causeweb.org>