We have a wide offering of general education courses designed to prepare you to major in Business and STEM (Science, Technology, Engineering, and Mathematics) fields.
Student Focused
Current Faculty Research
Math Happenings
Promotion from Associate Professor to Professor
Suho Oh
Promotion from Assistant Professor to Associate Professor & Tenure
Hamilton Hardison and Cody Patterson
Promotion from Associate Professor of Instruction to Professor of Instruction
Bikai Nie and Amanda Walker
Promotion from Assistant Professor of Instruction to Associate Professor of Instruction
Pritha Chakraborty, Jaroslaw Jaracz and Jackson Rebrovich
Promotion from Lecturer to Assistant Professor of Instruction
Hyun Chul Jang, Yichen Ma, Amy Lawrence-Wallquist, and Le Tran
@TXST Math
Upcoming Events
Math CATS Tutoring
–
until Sept. 24
- Location:
- DERR 238; 238
- Cost:
- Free
- Contact:
- Illona Weber
ih10@txstate.edu - Campus Sponsor:
- Department of Mathematics
Math CATS is here to assist in almost all MATH courses for free! If you're intimidated by the subject, come in and let's problem solve together. Tutors are here to help reiterate, reinforce and help you relate to the content you heard in lecture. NO APPOINTMENT NEEDED!
Click here for more information
more about event
Problem Solvers
–
until Sept. 22
- Location:
- DERR 336; 336
- Cost:
- Free
- Contact:
- Cameron Farnsworth
clf129@txstate.edu - Campus Sponsor:
- Department of Mathematics
Love a good problem? Like to solve difficult puzzles?
Join professors, graduate students and undergraduates as we tackle problems presented from several mathematical journals. An interest in higher level mathematics is all that is required to join our round table. Offer what you know, learn what you don't in a relaxed environment with some of our department's finest!
Join professors, graduate students and undergraduates as we tackle problems presented from several mathematical journals. An interest in higher level mathematics is all that is required to join our round table. Offer what you know, learn what you don't in a relaxed environment with some of our department's finest!
- Location:
- DERR 334; 334
- Cost:
- Free
- Contact:
- Vivian Healey
healey@txstate.edu - Campus Sponsor:
- Department of Mathematics
COME LEARN ABOUT MATH EDUCATION RESEARCH AND DR. LEE’S CAREER PATH
Math CATS Tutoring
–
until Sept. 25
- Location:
- DERR 238; 238
- Cost:
- Free
- Contact:
- Illona Weber
ih10@txstate.edu - Campus Sponsor:
- Department of Mathematics
Math CATS is here to assist in almost all MATH courses for free! If you're intimidated by the subject, come in and let's problem solve together. Tutors are here to help reiterate, reinforce and help you relate to the content you heard in lecture. NO APPOINTMENT NEEDED!
Click here for more information
more about event
- Location:
- Zoom
- Cost:
- Free
- Contact:
- Vera Ioudina
vi11@txstate.edu - Campus Sponsor:
- Department of Mathematics
An AI Slop Typology: Embedding Professional Norms for Responsible AI Use in Data Science Courses
Tori Ellison
University of Illinois Urbana-Champaign
Abstract: Since the widespread adoption of generative AI, data science educators have encountered new
and often recognizable patterns in student work, from subtle departures from professional
communication norms to idiosyncratic technical and conceptual errors that differ from the more familiar
mistakes associated with gaps in student understanding. These patterns, and the technical errors and
misalignments with research goals and professional norms that they can produce, can be difficult for
instructors and students alike to identify and articulate, complicating both how educators teach students
to critically evaluate their work and how educators evaluate that work when it is submitted for credit.
In this talk, I introduce an “AI Slop Typology” and accompanying course materials and policies developed
through several years of teaching data science and machine learning. The framework identifies seven
recurring patterns observed in student work and connects each to the professional norms it violates and
the potential consequences of similar work in professional practice. In doing so, the framework helps
students think critically about what responsible, professionally appropriate AI-assisted data science work
should look like.
The framework also offers instructors a potential middle ground course policy between allowing
irresponsible, unchecked AI-like work to go unpenalized and treating AI use primarily as an academic
integrity violation. Predefined, proportional penalties introduce some friction into unchecked AI-assisted
assignment completion while encouraging students to review, verify, and take responsibility for AI-
generated material before submitting it. Click here for more information
more about event
Tori Ellison
University of Illinois Urbana-Champaign
Abstract: Since the widespread adoption of generative AI, data science educators have encountered new
and often recognizable patterns in student work, from subtle departures from professional
communication norms to idiosyncratic technical and conceptual errors that differ from the more familiar
mistakes associated with gaps in student understanding. These patterns, and the technical errors and
misalignments with research goals and professional norms that they can produce, can be difficult for
instructors and students alike to identify and articulate, complicating both how educators teach students
to critically evaluate their work and how educators evaluate that work when it is submitted for credit.
In this talk, I introduce an “AI Slop Typology” and accompanying course materials and policies developed
through several years of teaching data science and machine learning. The framework identifies seven
recurring patterns observed in student work and connects each to the professional norms it violates and
the potential consequences of similar work in professional practice. In doing so, the framework helps
students think critically about what responsible, professionally appropriate AI-assisted data science work
should look like.
The framework also offers instructors a potential middle ground course policy between allowing
irresponsible, unchecked AI-like work to go unpenalized and treating AI use primarily as an academic
integrity violation. Predefined, proportional penalties introduce some friction into unchecked AI-assisted
assignment completion while encouraging students to review, verify, and take responsibility for AI-
generated material before submitting it. Click here for more information
Math CATS Tutoring
–
until Sept. 27
- Location:
- DERR 238; 238
- Cost:
- Free
- Contact:
- Illona Weber
ih10@txstate.edu - Campus Sponsor:
- Department of Mathematics
Math CATS is here to assist in almost all MATH courses for free! If you're intimidated by the subject, come in and let's problem solve together. Tutors are here to help reiterate, reinforce and help you relate to the content you heard in lecture. NO APPOINTMENT NEEDED!
Click here for more information
more about event