Spring 2027

Digital Technologies for Animal Monitoring

Introduces sensor technology and AI applications for livestock and companion animal monitoring, veterinary medicine, and farm management.

offering recorded3 credits
Recorded instructors · Spring 2027 Joao Reboucas Dorea

Summary

1 / 3

Recent recorded grades — Spring 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades); Spring 2025: 3.78 GPA, 88.0% A/AB (n=25 letter grades); Spring 2026: 3.77 GPA, 88.5% A/AB (n=26 letter grades).

What you’ll be able to do

  • Explain what precision livestock is and why it is needed

  • Demonstrate familiarity with data science and artificial intelligence principles as applied to agricultural systems and veterinary medicine

  • Describe the current sensor sensing technologies used in livestock and companion animals

  • Explain principles and applications of sensor technology applied to animals

  • Identify artificial intelligence applications in veterinary medicine

  • Evaluate the major ethical concerns associated with Artificial Intelligence for agriculture

Source: UW Guide 1 UW Guide 2

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 112, 114, 171, or placement into MATH 221) or graduate/professional standing

    • take one
    • graduate/professional standing
    take one
ANSCI/BSE 344

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Any of

Professors

Spring 2027

Recent recorded grades — Spring 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades); Spring 2025: 3.78 GPA, 88.0% A/AB (n=25 letter grades); Spring 2026: 3.77 GPA, 88.5% A/AB (n=26 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

JOAO REBOUCAS DOREA is recorded teaching in Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 290
LEC 001Classroom Instruction0 / 300

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Recent recorded grades — Spring 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades); Spring 2025: 3.78 GPA, 88.0% A/AB (n=25 letter grades); Spring 2026: 3.77 GPA, 88.5% A/AB (n=26 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Sensor technology for livestock and companion animals.
  • Artificial Intelligence applications in veterinary medicine and agriculture.
  • Precision livestock farming concepts.
  • Ethical considerations of AI in agriculture.

Skills

  • Explain principles and applications of sensor technology applied to animals.
  • Demonstrate familiarity with data science and AI principles in agricultural and veterinary contexts.
  • Explain the concept and necessity of precision livestock farming.
  • Evaluate ethical concerns associated with AI in agriculture.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
ANSCI/BSE 344 · Spring 2027

Digital Technologies for Animal Monitoring

Recorded 2026-10-14
ANSCI/BSE 344 · Spring 2027

Digital Technologies for Animal Monitoring

Recorded 2026-10-14
Raw records 
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    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:112:026454",
    "course_id": "ANSCI/BSE 344",
    "course_uid": "course_1d4d8555d02038c83b5b8e4b",
    "term_id": "1274",
    "source_course_id": "026454",
    "source_subject_id": "112",
    "title": "Digital Technologies for Animal Monitoring",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  },
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:604:026454",
    "course_id": "ANSCI/BSE 344",
    "course_uid": "course_1d4d8555d02038c83b5b8e4b",
    "term_id": "1274",
    "source_course_id": "026454",
    "source_subject_id": "604",
    "title": "Digital Technologies for Animal Monitoring",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "6016b52faa7e9ae89e749d0a6d976e091b94691234f749513162030849312927",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}