Fall 2026

Integrated Project Estimating and Scheduling

CIVENGR 492 teaches integrated project estimating and scheduling for the construction industry.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

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Recent recorded grades — Spring 2024: 3.85 GPA, 91.3% A/AB (n=23 letter grades); Spring 2025: 3.89 GPA, 100.0% A/AB (n=23 letter grades); Spring 2026: 3.96 GPA, 100.0% A/AB (n=12 letter grades).

Grade history

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average GPA
letter grades
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AB
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Prerequisites

Course map

Junior standing or member of Engineering Guest Students

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CIVENGR 492

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Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Fall 2026

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Meeting source records

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Student experience

the class

Recent recorded grades — Spring 2024: 3.85 GPA, 91.3% A/AB (n=23 letter grades); Spring 2025: 3.89 GPA, 100.0% A/AB (n=23 letter grades); Spring 2026: 3.96 GPA, 100.0% A/AB (n=12 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Construction industry estimating and scheduling
  • Preliminary and final estimates
  • Cost and duration determinations
  • Manual and computer techniques

Skills

  • Estimating and scheduling principles
  • Quantity take offs
  • Cost and duration determinations
  • Manual and computer techniques

Grades

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

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 Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

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Student reviews

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Grade history

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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "9f2c4ff90e50f2c0c7df70fcbacfa82dcf803c4aa052d00971607d6d3173fd1f",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}