Cracking the Code: What Predicts a Science Major?

Understanding STEM persistence through Expectancy-Value Theory

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Introduction
The Psychology Behind the Choice
The Groundbreaking Experiment
The Scientist's Toolkit
Why This Matters

For decades, educators have wrestled with a puzzling question: Why do some students thrive in science majors while others switch paths, despite similar talent? With STEM dropout rates nearing 40% in some fields ⁹ , the stakes are high—for students, universities, and innovation economies. At the heart of this mystery lies a powerful psychological framework: Expectancy-Value Theory (EVT).

The Psychology Behind the Choice

Developed by Jacquelynne Eccles and colleagues, EVT argues that career decisions hinge on two core beliefs ⁶ ⁹ :

Expectancy of Success

"Can I excel in this field?" - The belief in one's ability to succeed in specific tasks or domains.

Subjective Task Value

"Is this worth my effort?" - The perceived benefits and costs of engaging with a task.

Task value further splits into four dimensions:

Interest Value: Enjoyment of the subject
Utility Value: Alignment with career goals
Attainment Value: Fit with personal identity
Perceived Cost: Mental/physical effort required ⁴ ⁶

Early Formation of Beliefs

By middle school, students already link science to gender stereotypes ("Math is for boys"), shaping their self-concept ⁹ . EVT operates within an ecological model where institutional policies and peer interactions amplify or dampen motivation ¹ .

The Groundbreaking Experiment: Mapping Motivation Profiles

A pivotal 2019 study led by researchers at the University of Maryland tested EVT's predictive power in real-world STEM persistence ² .

Methodology:

Sample

600 first-semester STEM students from diverse backgrounds

Assessment

Surveys measuring competence beliefs, task values, and perceived costs

Analysis

Latent Profile Analysis (LPA) grouped students by motivation patterns

Results: Three Distinct Profiles Emerged ²

Profile	Competence Beliefs	Task Values	Perceived Costs
Moderate All	Moderate	Moderate	High
High Competence/Value	Very High	Very High	Low
High Value-Low Cost	High	High	Moderate Low

Academic Outcomes by Profile

URM Representation

Key Findings:

The "Moderate All" group showed the worst outcomes—fewer courses, lower GPAs—and contained disproportionately high numbers of underrepresented minority (URM) students ² .
Perceived cost (effort, lost opportunities) was the critical divider. Students who saw science as high-cost disengaged even with strong competence ² ⁴ .
Utility value predicted long-term persistence best. Students who linked science to future goals took 30% more STEM courses ⁹ .

The Scientist's Toolkit: Measuring Motivation

Reagent	Function	Example Use Case
Expectancy-Value Survey	Quantifies competence beliefs, interest, utility, and cost dimensions	Baseline assessment in intervention studies
Latent Profile Analysis	Identifies subgroups with shared motivation patterns	Detecting at-risk student profiles
ERP Brain Imaging	Measures neural responses to errors (e.g., Pe amplitude)	Testing unconscious competence beliefs
Ecological Observation	Tracks institutional policies impacting student workload	Evaluating departmental reform impacts

Source: ² ⁵ ¹

Why This Matters

This research isn't just theoretical. It reveals actionable strategies:

Redesign Courses

Emphasize real-world utility over weed-out exams ⁸ .

Highlight Role Models

Normalize struggle to reduce error anxiety ⁵ .

Audit Policies

Review departmental practices that inflate costs ¹ .

"Students don't leave STEM because they can't succeed. They leave when the personal costs outweigh the values" ² .

The future of science depends on making those values visible—and attainable—for every student.

For further reading, see Eccles & Wigfield (2002) in the Annual Review of Psychology ⁶ or the 2019 Journal of Science Education study on motivational profiles ² .