Scaling Student “Placement” Through a Data-Driven Ecosystem - Ethan J. Tanis

Author:

Ethan J. Tanis

Grand Valley State University

Padnos College of Engineering

Submitted to satisfy competencies for the Employer Relations Academy

Scaling Student “Placement” Through a Data-Driven Ecosystem

For many colleges and universities, experiential learning is viewed as a high-impact educational practice. However, when internships or co-ops are required, securing those experiences becomes more than an educational objective. It becomes an operational challenge. Every student must find an employer, employers must have sufficient opportunities, and institutional staff must facilitate the process without unsustainable individualized intervention.

This challenge is particularly evident in the Padnos College of Engineering (PCE) at Grand Valley State University, where every engineering student completes three full-time co-op rotations before graduation. Students generally complete all three rotations with the same employer. “Placement” hereon refers to students securing their own sites. At any given time, approximately 150 to 200 students may be moving through the process with about 150 actively in a co-op semester. (GVSU Padnos College of Engineering, 2026)

The traditional approach to placement often relies heavily on individual advising, employer outreach, career fairs, and students independently searching for opportunities. While each of these strategies has value, they become difficult to scale. The more effective approach is to treat placement as an ecosystem in which students, employers, academic programs, career services, technology, and data reinforce one another. This paper argues that institutions can improve the scalability, accessibility, and effectiveness of student placement by building a data-driven ecosystem rather than relying primarily on high-touch individual intervention.

Understanding the Placement Ecosystem

A data-driven placement ecosystem is an intentional process that connects the major stages of the student-employer relationship: student intake, preparation, employer discovery, applications, placement, assessment, and future placement. Rather than viewing placement as a single transaction, the institution manages a continuous lifecycle.

This distinction matters because students and employers experience significant friction throughout the process. Students may face application fatigue, uncertainty about where to search, and difficulty identifying employers that are appropriate for their experience. Employers face similar challenges in reaching qualified students and understanding institutional processes. Meanwhile, career services and employer-relations professionals often have limited capacity to provide individualized support to every student and employer.

The goal should not be to eliminate personalized support. Instead, institutions should determine which parts of the process require high-touch intervention and which can be intentionally designed for self-service. This creates capacity for staff to focus on the situations where their expertise adds the greatest value.

This approach also aligns with the broader purpose of career readiness. The National Association of Colleges and Employers (NACE) identifies career readiness as a foundation for successful workforce entry and emphasizes that employers can use career-readiness competencies to develop talent through internships and other experiential learning opportunities. (National Association of Colleges and Employers, 2026).

Applying the Model in Practice

In PCE, the placement ecosystem begins before students actively search for a co-op. The Engineering Professionalism course provides a structured launch point, with students completing resume and interview preparation, networking, employer research, career fair preparation, and co-op expectations before recruiting begins. This shifts the model from reactive support to proactive preparation.

On the employer side, PCE used two to three years of placement data to identify employers that had previously hired engineering students. This informed a curated group of approved employers in Handshake, giving students a starting point of organizations already familiar with engineering talent and the PCE co-op model. The purpose of this approach is not simply technological convenience. It reduces search friction.

The ecosystem also treats employer engagement as a funnel. Job postings create visibility, while career fairs, networking, interviews, offers, and accepted offers represent successive stages. This helps employer-relations staff identify specific bottlenecks and provide targeted support.

The student side follows a similar approach. An intake process identifies eligibility and places students into an active placement pipeline. With profiles and resumes prepared in advance, staff can quickly connect students with employers, either directing students to relevant postings or providing employers with candidate resumes based on their recruitment needs.

Critical Analysis and Benefits

The primary benefit of this model is scalability. A system can perform tasks consistently for hundreds of students that would be difficult for one staff member to complete individually. It also creates greater transparency. Instead of relying on an employer-relations professional to personally know which students and employers are appropriate matches, students can access opportunities through the system and employers can receive groups of eligible students in a timeline and manner best suited to them based on organization makeup and workflows.

There are limitations, however. Technology does not create a successful ecosystem by itself. PCE’s experience suggests that academic buy-in and intentional process design are more important than the technology platform. A poorly designed process can simply make an inefficient system digital.

There is also a risk of overreliance on historical data. Employers that successfully hired students in the past are useful prospects, but historical success should not become a closed ecosystem that prevents new employers from entering. A strong system must use data to inform decisions while continuing to develop new employer relationships. PCE's experience provides strong evidence that reducing search friction can dramatically increase student engagement with available opportunities. The number of approved employers posting opportunities remained relatively stable over the period, ranging from 86 to 90 employers, while the number of job postings increased from 749 to 1,031, a 38% increase. More significantly, student applications increased from 682 to 1,594, a 134% increase.  This growth occurred as PCE used historical placement data and newly interested employers to curate relevant employers and opportunities directly to students through Handshake's curated collections. Rather than requiring students to independently navigate thousands of opportunities, the system gave them a trusted starting point based on where PCE students had previously been successful or employers expressed specific interest. The result was substantially greater student engagement without a corresponding increase in the number of employers in the system. While other factors may have contributed to these results, the experience demonstrates the potential of intentional curation and reduced search friction as scalable employer-relations strategies.

This distinction is important for employer relations professionals. Increasing the number of employers in a system is not necessarily the same as increasing access to opportunities. In this case, the greater opportunity came from making existing employer relationships more visible, relevant, and actionable to students.

Measuring Success

A mature placement ecosystem should measure more than the final placement rate. Institutions should evaluate the entire funnel.  The most valuable measurement strategy would connect across the entire student lifecycle. PCE’s use of an experience-management workflow already provides a foundation by connecting student documentation, employer approvals, faculty review, evaluations, and learning outcomes.

Recommendations and Future Development

Institutions seeking to build this type of ecosystem should begin with four recommendations.

First, mine historical placement data (Experiential Learning and/or First Destination Survey results) before developing an employer strategy. Past student success provides a practical starting point for identifying employers that understand the institution and have demonstrated a willingness to hire students.

Second, build intentional funnels rather than isolated engagement opportunities. Employer relations should identify the steps between initial visibility and hiring and develop interventions for employers and students who stall at each stage.

Third, design for self-service without eliminating human support. Students should be able to discover opportunities and access relevant information without waiting for an appointment, while staff remain available for complex advising, employer development, and relationship management.

Finally, centralize the placement lifecycle and use emerging technology strategically. A CRM should serve as the central hub connecting student intake, preparation, employer engagement, applications, placement, assessment, and future opportunities. Artificial intelligence could eventually help identify patterns in employer engagement, recommend opportunities, identify students who may need intervention, and reduce routine administrative work. However, technology should support a clearly defined process rather than substitute for one.

NACE’s current emphasis on technology, career and self-development, and measurable competency development reinforces the need for institutions to connect career preparation with actionable systems and outcomes (National Association of Colleges and Employers, 2026)

Conclusion

Student placement should not be viewed solely as a career services function, an employer-relations function, or a Handshake problem. It is an ecosystem challenge. PCE’s experience demonstrates how academic preparation, employer relationships, historical data, technology, and student self-service can be intentionally connected to create a more scalable placement process.

The most important lesson is that data is only valuable when it leads to action. Historical placement data can identify employers. Employer engagement data can reveal opportunities for intervention. Student data can identify who needs support. Experience data can improve future programming. When these pieces are connected, placement becomes more than a series of individual transactions. It becomes a continuously improving system.

The future of employer relations and workforce development will require professionals to move beyond managing relationships and begin designing ecosystems. The goal is not simply to do more with fewer resources. It is to build systems in which students, employers, academic programs, and career professionals can each do their part more effectively. In a required experiential-learning environment, that shift can make placement more predictable, more transparent, and more scalable.

References

GVSU Padnos College of Engineering. (2026, August). GVSU Padnos College of Engineering - Employer Co-op Resources. Retrieved from gvsu.edu/pce: https://www.gvsu.edu/pce/co-op-employer-resources-54

National Association of Colleges and Employers. (2026). The NACE competancy assessment tool. Retrieved from https://www.naceweb.org/career-readiness/competencies/the-nace-competency-assessment-tool

National Association of Colleges and Employers. (2026). What is career readiness? Retrieved from https://www.naceweb.org/career-readiness/competencies/career-readiness-defined

AI Use Statement

AI was used to assist.  AI had a transcript of an hour-long conference presentation by the author to aid with creation, organization, editing, readability, and refinement of this paper.  All concepts, analysis, recommendations, and conclusions are based on the author’s professional experience and original ideas.