Welcome!

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 you 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, you will pitch your projects to a jury of educators!

CS293 classes will be a mix of lectures followed by discussion sessions led by students during our class meetings.

Schedule

Note: tentative schedule is subject to change.
πŸ”Ž Means that the paper will be a core part of the lecture.
🌟 Means that the paper will be the focus of reading discussions.

Week Date Theme Course Material
1 Jan 6
Monday
Class Introduction
[slides]
Optional Readings:
1 Jan 8
Wednesday
Discovery & Exploration in Educational Language Data

[slides]
Required Reading: Optional Reading:
2 Jan 13
Monday
Discovery & Exploration in Educational Language Data
Parsing, Lexical Analyses
[slides]
Required Reading: Optional Reading: -
2 Jan 15
Wednesday
Centering Teachers in the Design & Development of Tools

Guest Visit by Dan Meyer
The Difference Between Great AI and Great Teaching
ASU GSV-keynote: The Difference Between Great AI and Great Teaching
Required Reading: Optional Reading:
3 Jan 20
Monday
MLK day - No class

HW1 due on Tuesday at 11:59pm
3 Jan 22
Wednesday
An Evaluation-Centered Approach to Developing LLMs for Education

Guest Lecture by Sara Wiltberger and Shubham Milind Phal from Google DeepMind
Towards Responsible Development of Generative AI for Education
Required Reading:
4 Jan 27
Monday
Discovery & Exploration in Educational Language Data
Topic Modeling, Clustering, Grounded Exploration


Project Rationale due on Tuesday at 11:59pm [slides]
Required Reading: Optional Reading:
4 Jan 29
Wednesday
Using NLP for Educational Measurement

[slides]
Required Reading: Optional Reading:
5 Feb 3
Monday
Round 1 Practice Pitches

HW2 due on Wednesday at 11:59pm
No Reading
5 Feb 5
Wednesday
Generative Language Models for Education
Simulating Students
[slides]
Required Reading:
Optional Readings:
6 Feb 10
Monday
Generative Language Models for Education
Guest Lecture by Scott Crossley
Intelligent Texts in the Classroom
Required Reading: Optional Reading:
6 Feb 12
Wednesday
Generative Language Models for Education
LLMs for Student Assessment and Feedback
Guest Lecture by Mei Tan
Required Reading for Commentary (🌟 pick one): Recommended (for Guest Lecture): Optional Reading:
7 Feb 17
Monday
Presidents' Day - No class
7 Feb 19
Wednesday
Designing NLP Tools for Empowering Teachers in the Real World

Q&A with Rakiya Brown from TeachFX
[slides]

Experimental Protocol due Wednesday at 11:59pm
Required Reading: Optional Reading:
8 Feb 24
Monday
Deploying NLP Tools To Empower Teachers
Guest Visit by Sarah Johnson

Round 2 Practice Pitches due Monday at 11:59pm
Required Reading: Optional Reading:
8 Feb 26
Wednesday
Deploying NLP Tools To Empower Teachers
Experimental Design & Evaluation
Guest visit by Mariah Olson
[slides]

HW3 due Friday at 11:59pm
Required Reading: Optional Reading:
9 March 5
Monday
Frontiers and Open Questions No reading, besides revisiting reading commentaries, past readings, discussions, homeworks
9 March 7
Wednesday
Frontiers and Open Questions No reading, besides revisiting reading commentaries, past readings, discussions, homeworks
10 March 10
Monday
Final Pitches No Reading
10 March 12
Wednesday
Final Pitches

Final paper due on Thursday, March 13 at 11:59pm
No Reading

Overview

Course Info

Office Hours

Prerequisites

Academic Accommodations

Well-Being, Stress Management, & Mental Health