Calculus I Success - Placements, Pathways, and Contexts

Data-Driven Insights for Wyoming Community Colleges

Michael Bostick

Central Wyoming College

April 18, 2026

What Predicts Calculus Success at Wyoming Community Colleges?

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Calculus - The Gateway to STEM Degrees

  • The number of STEM jobs in Wyoming will double in the years 2021-2031. (Turbitt, 2022)

  • Average wages in WY for non-STEM occupations: $51,010 v.s STEM occupations: $75,620. (Turbitt, 2022)

  • Calculus is a gateway — and a barrier — to STEM degrees. (Redmond-Sanogo et al., 2016)

  • Half of all undergraduates attend community colleges (CCs), but are less likely to earn STEM degrees. (Bahr et al., 2023; Zhang, 2022)

  • CCs have reformed math pathways: corequisite courses, concurrent/dual enrollment, and moves toward holistic placement.

  • But we lack evidence on whether these reforms predict calculus success — especially for students who are underrepresented racially minoritized (URM). (Bahr et al., 2017; Burn et al., 2015)

Calculus Access in Wyoming

  • Students who pass calculus early in their college careers have a statistically significantly higher chance of graduating with a STEM degree. (Wang, 2015)

  • Pathways to early-college calculus in Wyoming include concurrent enrollment prerequisite, ACT Math, other Math Placement Tests.

  • Students at smaller rural schools are less likely to have access to concurrent enrollment math courses. (Gagnon et al., 2021)

  • The opportunity to reach calculus is not equally distributed before students ever set foot on a CC campus.

  • Are the traditional placements and pathways to calculus diverting students from STEM degrees? (Park & Ngo, 2021)

For Wyoming students, the path to a STEM career runs through early access to CC calculus — and many students never get the chance.

How Wyoming CCs Currently Place Students in Math

  • ACT Math is required for every Wyoming HS junior and often subsidized (Wyoming Department of Education, 2025) — defacto path of least resistance for placement.
  • ACT is sole placement tool used by every Wyoming CC (place up to Calculus 1).
  • Only 2 of 7 colleges use HS GPA or transcripts to place students into math (college algebra maximum).
Wyoming CC Mathematics Placement Practices
College HS GPA / Transcript ACT / SAT Placement Test Co-requisites
LCCC GPA Yes No MATH 1400, STAT 2070
EWC Transcript Yes Accuplacer MATH 1000, 1400
CC No Yes Tailwind No
NWCC No Yes Accuplacer MATH 1000
CWC No Yes ALEKS MATH 1000, 1400
NWC* No Yes Accuplacer, ALEKS MATH 1000, 1400
WWCC* No Yes McCann, ALEKS MATH 1000, 1400

Note. Placement information retrieved from each college’s website. *NWC and WWCC excluded from analytical sample due to missing HS GPA data. Rows highlighted in pink use HS GPA or transcript in placement.

Is the current placement/pathway system effective, or just tradition? What actually predicts calculus success in Wyoming?

Research Questions

RQ1 — Preparation, Placement, & Pathways

To what extent does HS GPA and ACT Math predict calculus success, and how are these moderated by time since high school or concurrent enrollment?

RQ2 — Student Characteristics & Learning Contexts

To what extent do demographics and prior math coursework predict success, and how are these moderated by corequisite course availability or section mean HS GPA?

Note. HS GPA = high school grade point average, ACT Math = ACT Math Score, concurrent enrollment = student took concurrent enrollment prerequisite prior to calculus, demographics = URM and biological sex, prior math coursework = the number of CC math prerequisites prior to calculus.

Wyoming CC Calculus Sections

Note. Data for this study was collected by the Wyoming Community College Commission (WCCC) from 2012 to 2023, encompassing 5448 students who enrolled in CCC. Several criteria were applied to create the analytic sample. Only students’ first CCC attempt was included, concurrent enrollment sections taught in high schools, all students in high school, and students with missing data were listwise deleted. The analysis focused on CCC sections from the 2013-2014 academic year onward, allowing summer 2012 through summer 2014 to serve as a lookback period for capturing students’ precalculus coursework history. Sections with fewer than three students were removed to allow for adequate sample sizes for multilevel modeling (Clarke, 2008). The final analytic sample consisted of 745 students in 91 course sections across 5 of Wyoming’s 7 CCs. The smallest section size was 3, and the largest was 22 students.

Data & Method

  • Wyoming CC System - 5 of 7 colleges, years 2013-2023.
  • Analytical Sample - \(n=745\) students in \(k=91\) sections (after exclusions).
  • Multilevel Logistic Regression - students (level 1) nested within course sections (level 2) (Raudenbush & Bryk, 2001).
  • Outcome Variable - Binary, Pass (A,B,C,S) or Fail (D,F,U) in Calculus I.
  • 13 Predictors - 7 student-level, 2 section-level, 2 student-level interactions, 2 cross-level interactions.

RQ1: Multilevel Logistic Regression Model

RQ1 — Preparation & Pathways

To what extent does HS GPA and ACT Math predict calculus success, and how are these moderated by time since high school or concurrent enrollment?

Model Variables
Effect Parameter Level
Intercept and Random Effect \(\gamma_{00}+u_{0j}\) --
HSGPA (gmc) \(\color{gray}{\gamma_{01}}\) Section
Coreq College \(\color{gray}{\gamma_{02}}\) Section
HSGPA (cwc) \(\mathbf{\gamma_{10}}\) Student
ACT Math (cwc) \(\mathbf{\gamma_{20}}\) Student
Years Since HS (cwc) \(\mathbf{\gamma_{30}}\) Student
Math Prerequisites (cwc) \(\color{gray}{\gamma_{40}}\) Student
Math Prerequisites (cwc) × Coreq College \(\color{gray}{\gamma_{41}}\) Cross-Level
Female \(\color{gray}{\gamma_{50}}\) Student
Concurrent Prereq (cwc) \(\mathbf{\gamma_{60}}\) Student
URM \(\color{gray}{\gamma_{70}}\) Student
URM × HSGPA (gmc) \(\color{gray}{\gamma_{71}}\) Cross-Level
HSGPA (cwc) × Years Since HS (cwc) \(\mathbf{\gamma_{80}}\) Student
Concurrent Prereq (cwc) × ACT Math (cwc) \(\mathbf{\gamma_{90}}\) Student

Note. The intercept \(\gamma_{00}\) represents the grand mean log-odds of passing, and the random effect \(u_{0j}\) captures section-specific deviations from this grand mean. \(\gamma\) coefficients represent the expected change in log-odds of passing CC calculus for a (unit) change in the variable. Level 1 = student, level 2 = section. Grand mean centered = gmc, centered within sections = cwc. Bold coefficients address RQ1.

RQ1 Result: HS GPA Beats ACT Math — But Time Erodes It

  • HS GPA was the strongest predictor of success \((\gamma_{10}=1.608, p<0.001)\).
    • A +1 SD increase in HS GPA → 87% increase in odds of passing.
  • The benefit of HS GPA fades with years out of high school \((\gamma_{80}=-0.798, p<0.001)\).
    • Students 2+ years past HS received no predicted benefit from HS GPA.
  • ACT Math was not a statistically significant predictor alone.

Wyoming CCs all use ACT Math for placement; few use HS GPA. This is backwards.

Note. Predicted probability of passing calculus across HS GPA (cwc) ± 2 SD, by years since HS (cwc) ± 2 SD. All other variables held at mean.

RQ1 Result: Concurrent Enrollment Amplifies ACT Math

  • ACT Math and concurrent enrollment prerequisite were not individually statistically significant.
  • Their interaction was \((\gamma_{90}=0.203, p<0.05)\): students who took concurrent enrollment got a boost from higher ACT scores.
    • +1 SD concurrent enrollment & +1 SD ACT Math → 44% increase in odds of passing.
  • Students with average or below-average ACT received little benefit from concurrent enrollment.

Concurrent enrollment reinforces advantages for students who were already academically stronger.

Note. Predicted probability of passing calculus across ACT Math (cwc) ± 2 SD, by concurrent enrollment prereq (cwc) ± 2 SD. All other variables held at mean.

RQ2: Multilevel Logistic Regression Model

RQ2 — Student Characteristics & Learning Contexts

To what extent do demographics and prior math coursework predict success, and how are these moderated by corequisite course availability or section mean HS GPA?

Model Variables
Effect Parameter Level
Intercept and Random Effect \(\gamma_{00}+u_{0j}\) --
HSGPA (gmc) \(\mathbf{\gamma_{01}}\) Section
Coreq College \(\mathbf{\gamma_{02}}\) Section
HSGPA (cwc) \(\color{gray}{\gamma_{10}}\) Student
ACT Math (cwc) \(\color{gray}{\gamma_{20}}\) Student
Years Since HS (cwc) \(\color{gray}{\gamma_{30}}\) Student
Math Prerequisites (cwc) \(\mathbf{\gamma_{40}}\) Student
Math Prerequisites (cwc) × Coreq College \(\mathbf{\gamma_{41}}\) Cross-Level
Female \(\mathbf{\gamma_{50}}\) Student
Concurrent Prereq (cwc) \(\color{gray}{\gamma_{60}}\) Student
URM \(\mathbf{\gamma_{70}}\) Student
URM × HSGPA (gmc) \(\mathbf{\gamma_{71}}\) Cross-Level
HSGPA (cwc) × Years Since HS (cwc) \(\color{gray}{\gamma_{80}}\) Student
Concurrent Prereq (cwc) × ACT Math (cwc) \(\color{gray}{\gamma_{90}}\) Student

Note. The intercept \(\gamma_{00}\) represents the grand mean log-odds of passing, and the random effect \(u_{0j}\) captures section-specific deviations from this grand mean. \(\gamma\) coefficients represent the expected change in log-odds of passing CC calculus for a (unit) change in the variable. Level 1 = student, level 2 = section. Grand mean centered = gmc, centered within sections = cwc. Bold coefficients address RQ2.

RQ2 Result: More Prerequisites Help — Unless Corequisites Exist

  • More math prerequisite courses was a statistically significant positive predictor \((\gamma_{40}=0.305, p<0.05)\).
    • Each additional prerequisite course → 36% increase in odds of passing.
  • Corequisite availability negated this effect \((\gamma_{41}=-0.552, p<0.01)\).
    • At colleges with corequisites available, the benefit of prerequisites was reduced by 42%.
  • Corequisite availability alone was not statistically significant.

Where corequisites exist, the long prerequisite track doesn’t build momentum.

Note. Predicted probability of passing calculus across math prerequisites (cwc) ± 2 SD, by corequisite availability. All other variables held at mean.

RQ2 Result: Section GPA Helps Everyone — Except Students who are URM

  • Section mean HS GPA was a strong positive predictor of success \((\gamma_{01}=2.912, p<0.001)\).
    • +1 SD section GPA (0.19 GPA pts) → 75% increase in odds of passing.
  • The cross-level interaction Section GPA × URM was a strong negative predictor \((\gamma_{71}=-2.734, p<0.05)\).
    • For URM, the benefit of being in a high-GPA section was reduced by 94%.
    • URM alone was not statistically significant.

Students who are URM do not share in the peer composition advantage that benefits everyone else.

Note. Predicted probability of passing calculus across section mean HS GPA (gmc) ± 2 SD, by URM status. All other variables held at mean.

Discussion & Implications

Area of Impact Key Result Policy Implication
Placement HS GPA beats ACT Math, but fades for adults 2+ years out. Transition to holistic, multiple-measure placement; stop over-relying on ACT (Hayward, 2020; Ngo et al., 2018).
Pathways Corequisite availability negates the “benefit” of long prereq sequences. Expand corequisite models; excessive prerequisites are unnecessary barriers (Schudde & Ryu, 2025).
Equity in Access Concurrent enrollment amplifies the advantage of those already prepared. CE is currently not improving equity; pathways must be intentionally designed to reach broader populations (Xu et al., 2021).
Section Equity Section GPA is a strong peer effect — but not for URM students. High-performing peers don’t close gaps without structural, inclusive support (Nomi & Allensworth, 2013; Wiles & Levesque-Bristol, 2023).

While CCs have adopted valuable reforms, tracking often persists. Inequitable placement and unexamined peer/CE structures warrant further investigation.

What Can Math Instructors and Programs Actually Do?

Result Practical Action
HS GPA beats ACT Math Implement multiple-measure placement at your college (Ngo et al., 2018), understand limitations of ACT Math score (Bahr et al., 2019)
HS GPA fades for returning students HS GPA is irrelevant 2+ years out of HS (Hayward, 2020)
Corequisites work Advocate to expand corequisite courses at your college (Schudde & Ryu, 2025)
Concurrent enrollment (CE) is not effective equitably Align HS and college expectations in CE (An & Taylor, 2019), provide other pathways to calculus for students who do not have access to CE (Taylor, 2015)
Peer effects don’t reach all students equally Build active support structures (Wiles & Levesque-Bristol, 2023) and increase classroom collaboration to benefit URM (Treisman, 1992)
Wyoming Pass rate 72% vs national pass rate 67% Let more students into Calculus to improve equitable STEM access, even if it results in slightly more failures (Tremaine et al., 2022)

Placement tools, pathway structure, and classroom support all matter, and these are things instructors and departments can influence.

Limitations & Future Directions

  • Data: Administrative records — no direct measures of placement processes, student aspirations, or instructional quality.
  • Sample: Much missing data.
  • Unmeasured factors: Students’ secondary mathematics history, socioeconomic context, geospatial access (Sonnert et al., 2016).
  • Future work: Qualitative investigation of URM student experience in calculus sections, study of corequisite implementation fidelity (Stone-Johnstone, 2023), research Wyoming dual/concurrent enrollment calculus students (Corin et al., 2020), and equity in dual/concurrent enrollment course access (Xu et al., 2021) .

Thank You

Calculus 1 Success - Placements, Pathways, and Contexts

Data-Driven Insights for Wyoming Community Colleges

Mike Bostick | mbostick@cwc.edu | Central Wyoming College

Wyoming Mathematics Articulation 2026

Eastern Wyoming College | Torrington, WY

Full model results, diagnostics, and summary statistics:

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