Franklin Pierce University

As student needs, workforce demands, accreditation expectations and technological innovation evolve, graduate education remains responsive to the changing national and global needs. Higher education institutions are receiving more access to data and various analytical tools than ever before. Despite this, assessment requires more than merely collecting information. Accreditors and institutions expect documentation of intentional use of data in institutional decision-making, collaborative efforts, and a commitment to triangulating data with human experience. The most successful institutions maintain a student first vision and balance innovation and accountability with an emphasis on student learning success enabling a culture of continuous quality improvement.

Improving Graduate Education Outcomes

Assessment is regarded as a means for accreditation compliance and an administrative task. However, my approach is shaped by the belief that assessment, in definition, is ongoing, continuous and utilized as a method for improvement. When assessment findings are utilized as a method to strengthen teaching and learning, provide student support, and guide institutional decision-making, institutions thrive through innovative practices used to enhance the student experience in graduate programs.

Through my work with multiple healthcare and professional programs, the use of data and evidence is paramount in discovering trends, evaluating effectiveness and preemptively identifying student needs. The greatest impact occurs not when institutions merely view assessment as part of the accreditation process, but when assessment becomes embedded in programmatic and institutional culture for the sake of continuous quality improvement. Actions are derived from the data, which improves outcomes and student success.

Challenges in Assessing Student Success and Program Effectiveness

The biggest challenge in assessing student success is creating meaning from copious amounts of data regarding student outcome measures. Data exists across multiple software systems and departments, requiring manual processing hours to collect the data coupled with difficulties in developing a comprehensive image of student success and learning.

Additionally, programs and institutions utilize lagging indicators, including retention, graduation and licensure exams scores. These indicators remain vital to the assessment environment, yet they identify needed modifications after cohorts have completed curriculum. Institutions must review the leading indicators to ensure issues are addressed in the early intervention phase through targeted supports. Professional studies and graduate students often balance academics with personal responsibilities, including familial needs, financial burdens, and studies; therefore, institutions must review program effectiveness by utilizing engagement, persistence, professional competence, and overall well-being.

“Data may reveal the gaps, but the people behind the numbers reveal the true story of student learning and development.”

Assessment of program effectiveness is a complex and multifaceted process with many caveats, such as data presentation in accordance with institutional and accreditor standards. These metrics move beyond student success and outcomes requiring institutions to determine if curricula, instructional practices, assessment, and support services achieve the benchmarks and intended purposes. The measures required for program effectiveness comprise direct and indirect measurements of assessment, including stakeholder feedback, longitudinal analysis, licensure exam pass rates and scores, course outcomes, and teaching effectiveness evaluations. These measures are required to support continuous quality improvement (CQI) and evaluate key performance indicators (KPIs).

Data-driven balance with Human Side of Student Learning and Development

Remaining student centered in all aspects of data analysis is the key to maintaining the human side of student learning and development. Human judgment must be used to evaluate data. Quantitative data identifies trendlines, but qualitative data and key stakeholder experiences, including student experience, require collection.

Learning is human-centered and must be driven by individual learners and their needs to be effective. In this scenario, the data driven approach combines data with faculty knowledge, student feedback, and advising sessions and dialogue. While data may reveal the gaps and issues, the people behind the aggregate numbers reveal the holistic perspective in student learning and development.

Instead of designing student evaluations to be satisfaction-based mechanisms, evidence collection should encompass student learning experience and engagement with the faculty, course, course materials, and learning environment. The faculty evaluations must encourage reflection regarding student feedback, professional growth and continuous quality improvement in teaching and learning practices. Data-driven evidence combined with empathy ensure institutions remain student-centered in all decision-making processes.

Technology’s Influence on Assessment

Technology serves as a transformational tool in assessment and academic quality assurance practices. Through the integration of platforms and learning management systems (LMS), institutions are positioned to monitor student success and program effectiveness in real time and intervene to ensure students are equipped with tools to be successful.

This utilization creates a dynamic shift from lack of feedback-to-feedback loop closure and a shift from retrospective reporting to continuous quality improvement. This responsive use of data provides students with early intervention opportunities while also providing faculty with the opportunity to modify and adjust instructional practices when gaps are identified.

Artificial intelligence (AI) is transforming access to information, thus requiring institutions to transform how learning is assessed. With the utilization of AI, greater emphasis must be placed on critical thinking, problem-solving, communication, ethical reasoning, and applied learning. Just as assessment is better when human centered, the use of technology enhances data-driven decisions and student support services while maintaining human interactions.

Advice for Meaningful and Student-Centered Learning Experiences

Begin with students at the center of all decisions. Students are the most important stakeholders in the educational experience. Decisions, data-use, and learning experiences center on creating the best experiences for students.

Once student-centered decisions are established, intentionality is utilized in the assessment process. Every evaluation, assessment, learning experience, and course work must outline a clear objective to support student growth and development. The focus must be on evidence that influences actions and leads to improvement opportunities.

I encourage educators to break the siloes to ensure collaboration exists across institutional departments and academic programs, student services, institutional research, and leadership. Perhaps the most critical component is human connection. Despite the advancement in AI and technology, students seek mentorship, meaningful feedback, and relationships and belongingness. When institutions create an environment of innovation, empathy, and accountability, student success is inevitable as these elements create learning experiences where students thrive.