Language is central to educational interactions; analyzing it can serve as a way to discover, measure, and facilitate the use of high-leverage teaching practices. Automating linguistic analysis via natural language processing (NLP) can enable tools that support educators. Such tools can provide educators with feedback on their classroom instruction, help them craft lesson plans, facilitate formative assessment, scale their support for students, among many other applications. This course offers the opportunity to understand the possibilities and the limitations of using NLP to support instruction. The course will cover topics including principles of computational social science research, ethics, bias and fairness in using NLP for education, translating expert measurement into automated scoring, large language models for education, among others. The course is centered on engaging with relevant papers and working towards a final project. The final project will allow students to dive deeper into a research question in this area, approach it from a critical social scientific lens and learn and apply NLP methods to address the question. At the end of the course, students will pitch their projects to a jury of educators.
✨ We thank Hanna Crowe, Nicole Elenz-Martin, Sergio Estrada, Taylor Pacheco, Kavitha Satya-Mohandoss, Jesus Rojas for serving as teacher reviewers and providing feedback on student project pitches. ✨
CS293 classes will be a mix of lectures followed by discussion sessions led by students during our class meetings.
Note: tentative schedule is subject to change.
🔎 Means that the paper will be a core part of the lecture.
🌟 Means that the paper will be the focus of reading discussions.
All homework assignments will be hosted on this GitHub repository.
Week | Date | Theme | Course Material |
---|---|---|---|
1 | Sep 27 Wednesday |
Class Introduction [slides] |
Optional Readings:
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2 | Oct 2 Monday |
Discovery & Exploration in Educational Language Data Parsing, Lexical Analyses [slides] |
Required Reading:
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2 | Oct 4 Wednesday |
Discovery & Exploration in Educational Language Data Topic Modeling, Clustering [slides] |
Required Reading:
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3 | Oct 9 Monday |
Guest Lecture by Michael Madaio Bias & Fairness in AI for Education [slides] HW1 due on Tuesday at 11:59pm Download the homework here. |
Required Reading:
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3 | Oct 11 Wednesday |
Using NLP for Educational Measurement Grounded Discovery, Data Annotation [slides] |
Required Reading:
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4 | Oct 16 Monday |
Using NLP for Educational Measurement Model Development [slides] Project Rationale due on Tuesday at 11:59pm |
Required Reading:
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4 | Oct 18 Wednesday |
Using NLP for Educational Measurement Measure Validation [slides] |
Required Reading:
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5 | Oct 23 Monday |
Generative Language Models for Education Case Study: Teacher Feedback [slides] HW2 due on Tueday at 11:59pm Download the homework here. |
Required Reading:
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5 | Oct 25 Wednesday |
Guest Lecture by Katie Keith Causal Effect Estimation with Text Data [slides] |
Required Reading:
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6 | Oct 30 Monday |
Practice Pitches | No Reading |
6 | Nov 1 Wednesday |
Practice Pitches | No Reading |
7 | Nov 6 Monday |
Project Work Session! Readings: Using LLMs for Student Assessment and Feedback Experimental Protocol due on Tuesday at 11:59pm |
Optional Reading:
|
7 | Nov 8 Wednesday |
Designing NLP Tools for Empowering Teachers in the Real World Q&A with Rakiya Brown from TeachFX |
Required Reading:
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8 | Nov 13 Monday |
Deploying NLP Tools To Empower Teachers Experimental Design & Evaluation [slides] HW3 due on Monday at 11:59pm Download the homework here. |
Required Reading:
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8 | Nov 15 Wednesday |
Round 2 Practice Pitches | No reading |
Nov 20 & Nov 22 | Thanksgiving Break | ||
9 | Nov 27 Monday |
Guest Lecture by Diane Litman eRevise |
Required Reading:
|
9 | Nov 29 Wednesday |
Frontiers and Open Questions | Required Reading:
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10 | Dec 4 Wednesday |
Final Pitches | No Reading |
10 | Dec 6 Wednesday |
Final Pitches
Final paper due on Tuesday, Dec 12 at 11:59pm |
No Reading |
From Stanford's Office of Accessible Education:Students who may need an academic accommodation
based on the impact of a disability must initiate the
request with the Office of Accessible Education (OAE).
Professional staff will evaluate the request with required
documentation, recommend reasonable accommodations, and
prepare an Accommodation Letter for faculty dated in the
current quarter in which the request is being made.
Students should contact the OAE as soon as possible since
timely notice is needed to coordinate accommodations. The
OAE is located at 563 Salvatierra Walk (phone: 723-1066,
URL:
http://oae.stanford.edu).
If you need an academic accommodation based on a disability, you should initiate the request with the OAE before the end of the second week of classes.
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