Amanda Ellis

Everyone loves a good deal. We see it in bright, bold colors when we go to the grocery store: “Buy one get one,” “Free item with purchase,” “Best value for the money,” etc. Like customers in the grocery store, students are increasingly demonstrating customer-like behavior and demanding even more “value” from higher education institutions. The consumer-like behavior shifts the common goal of higher education from mastering content knowledge to gaining practical skills that align with future employment prospects. As instructors and program directors, this places a burden on us to create courses and programs that not only provide students with the opportunity to learn biostatistics but also opportunities for workforce development.

To appeal to students, we should focus not only on the content they will learn in our courses and programs, but also on the practical skills they will gain. The Department of Biostatistics at the University of Kentucky (UK) has been redesigning courses to incorporate workforce readiness. This column discusses the ways we are helping students bridge the gap between classroom knowledge and the workplace. It focuses on techniques used in graduate-level biostatistics courses, including courses for students in the Master of Science in Biostatistics (MSBST) program and services courses for students in programs like the Master of Public Health (MPH).

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We have employed these methods in graduate-level biostatistics courses, but the strategies and lessons learned can be applied at any level.

Identifying Skills

To help students connect the course content and practical skills, we first had to identify the primary audience for each course and the careers those in the classes may seek. Students in the MPH program have different future careers from those in the MSBST program. We also recognized that the practical skills necessary for students who would be producers of data analysis differ from those who would be consumers of data analysis. Therefore, it is crucial to understand the occupational goals of the audience when identifying the skills to incorporate into the courses and help students see the value in how the course work may connect with their future careers.

One way we identified workforce-ready skills was by connecting with community partners, which can be anywhere that students might potentially seek employment after their degree programs. For example, we joined the Kentucky Department of Public Health when redesigning our introductory data analysis course for the MPH program and asked what skills the department would like new graduates to have.

Working with potential employers such as local businesses, industry associations, and educational institutions helps educators better understand the specific skills and qualifications in demand in their communities. Community partners can also provide valuable resources, such as internships, job shadowing, and mentorship opportunities, to help students gain real-world experience and make valuable connections in their fields, all of which will help students see the practical value of the content they are learning in the courses.

In addition to connecting with community partners, workforce-ready skills can also be identified by reviewing job postings and gathering student input. Examining job postings lets educators pinpoint the specific skills and qualifications employers are looking for in candidates. Instructors can then tailor the curriculum to meet the job market’s needs.

Input from students can also provide valuable insights into the skills and qualifications that they believe are necessary for success in their chosen fields.

Combining the information from community partners, job postings, and student feedback will create a comprehensive approach to identifying the skills in demand in the workforce.

The workforce skills we identified to incorporate in courses serving the MPH students included various technical and analytical abilities. In conjunction with the Kentucky Department of Public Health, we determined that MPH students should be proficient in study and survey design, interviewing techniques, and descriptive measures, along with experience in using statistical software. The ability to disseminate analyses in written communication effectively is also considered a crucial skill for MPH students.

We identified skills in the MSBST students by connecting with partners in the university that were either looking to hire or had recently hired master’s-degree—level biostatisticians. The skills to incorporate in courses for MSBST students included proficiency in statistical software, learning new methodology independently, working as a team scientist, and disseminating analyses in written communication effectively. These skills are essential for students in the MSBST programs to be successful in their future careers in public health, academia, and industry.

Incorporating Workforce Readiness Skills into the Classroom

We used various methods to incorporate workforce readiness skills into the curriculum. Techniques included authentic assessment, project-based learning, collaborative work, unstructured assignments, and student-directed content delivery. While our experience indicates that educators are familiar with most of these techniques, examples of how we applied each one to infuse workforce skills in the classroom and relevant student feedback may be of interest.

Authentic Assessment

Authentic assessment is a method of evaluating student learning that uses tasks and challenges representative of real life. This approach emphasizes practical and complex assignments that stress depth over breadth. Educators implement authentic assessments to closely mirror the types of problems and challenges that students will encounter in their careers, so by definition, authentic assessments provide a perfect way to incorporate practical skills into the classroom.

When developing authentic assessments for the courses, we took care to consider not only how the course content is applied in the real world but also how students would interact with the content in the workforce.

For example, we recognized that a master’s-level biostatistician might need to communicate linear regression output to a collaborating health scientist in everyday language. As part of our Introduction to Linear Regression course, we ask students to write a linear regression tutorial for an audience of a public health practitioner. Students work on the tutorial periodically throughout the semester as they learn the material. While writing the tutorial, they focus on written communication skills.

This assignment has the bonus that students can check their content knowledge as they work on the material, ensuring that they understand topics before moving on to the next ones.

In contrast, we recognized that someone with an MPH might need to write a report with a more rudimentary analysis, such as descriptive measures focusing on public health impact. We tailored an authentic assessment to represent what the primary audience for the course is likely to experience in the workforce. Incorporating authentic assessment methods does a better job of preparing students for the challenges they will face in their careers and ensures they have the necessary skills and knowledge to succeed.

One student’s feedback (edited for grammar) was:

I enjoyed the application part of this class (using things we learned to do analyses and putting them together in a report). I feel like this method of instruction better prepares me for the workforce. I also enjoyed learning how to use the code and then using that code in my analyses. I feel like the instruction was straightforward, which I appreciate.

Project-Based Learning

Project-based learning (PBL) is an educational approach in which students learn by actively engaging in real-world projects; it is often a type of authentic assessment. In PBL, students research and solve a complex problem, question, or challenge that is typically open-ended and ill-defined. Completing the project provides opportunities to develop essential skills such as critical thinking, problem-solving, collaboration, and communication. This type of learning lends itself to helping students develop skills they will need in the workforce.

For example, we recognized that one of the workforce development skills needed by students with an MPH degree was communicating statistical ideas—particularly descriptive measures. As part of our Introduction to Data Analysis course, we required MPH students to complete reports that involved applying biostatistics concepts to public health problems, using PBL for designing their reports.

Students created the reports throughout the semester, using actual data from the Behavioral Risk Factor Surveillance System (BRFSS) and received feedback while completing parts of the report. By the end of the semester, they produced a condensed version of an epidemiologic profile that served as a final project.

One student’s feedback (edited for grammar) was:

I enjoyed the gradual building of the final report over the semester and the consistent, periodic (detailed!) feedback. It felt like the goal was truly to teach how to build an effective epi report rather than to deliver a grade…it’s hard to phrase exactly, but I feel like I learned much more this way than if someone had given me directions and said, “You have three months; apply the material and hope for the best,” which is the approach some classes take!

Collaborative Work

When designing courses, we recognized that both students who will be producers of data analysis and those who will be consumers of data analysis should develop collaborative work skills. Working in a team allows students to leverage the strengths of each team member and provides opportunities for cross-disciplinary collaboration.

For example, a team may include biostatisticians, public health practitioners, and epidemiologists. Each team member brings a unique perspective and set of skills to the communication of data analysis. Together, they can identify patterns and trends in the data that might be missed by one person working alone.

Furthermore, public health policy and practice are often informed by data analysis. Results must be translated and communicated effectively to decision-makers, practitioners, and the public. A students might be a researcher or policymaker in a future career. A team whose members have different backgrounds and communication skills can help ensure that the results are presented in an understandable and usable way.

To help students gain the skills required for collaboration, we incorporated group work in the introductory data analysis course for the MPH program. We recognized that each team member brings their knowledge and skill set to the workforce and designed group work to simulate that experience. Students were required to work on individual assignments that would prepare them for the group work so each member would have something to contribute once the group work began. For example, to ensure that each member had something to contribute when students met up, they would first analyze the data individually before working as teams to write a report.

One student’s feedback (edited for grammar) was:

The group project portion of this course accurately reflected my experience as a professional working in teams (in a positive way), which is often not the case in class group projects.

Unstructured Assignments

Letting students have input in their assignments can be a powerful way to promote ownership in the learning process. It enables students to incorporate workforce skills they feel are essential. One way to do this is to provide unstructured assignments that give students the freedom to explore a topic or problem in a way that is meaningful to them.

For example, the final project in our Introduction to R Programming course is formatted as an unstructured assignment. Students can choose their research question and data set to analyze for the final project. Students in the class come from various backgrounds; some are already in careers and learning R to aid them with their current positions.

Letting students have input in the assignments can also help personalize the learning experience and make it more meaningful for each student.

These assignments do not have to be large projects; they can be smaller assignments. For example, we wanted to replicate tools and platforms used in the real world to make the material more authentic and relevant, so we left how the students interacted with the tools unstructured.

One tool we replicated for the Introduction to R Programming course was coding forums like Stack Exchange using a discussion board. This provided students with a less-intimidating environment to ask and answer coding-related questions while exposing them to the types of interactions and problem-solving they will encounter in the workforce.

By mimicking real-world tools and settings in the classroom, educators can help make the material more relatable and relevant for students, as well as help prepare them for the challenges and situations they will encounter in their careers.

Student feedback from two students (edited for grammar):

I really enjoyed this course. It was very helpful to have this course online and the class discussion board/code clinic because that is often the environment in which R learning/troubleshooting occurs.

This course was very well-designed, and it was helpful to have it as a distance learning/online course because, outside of the classroom setting, most R learning/staying up to date and R troubleshooting occurs in an online/virtual environment. Thus, having this course in a distance learning/online format helps prepare students for learning and working in R beyond the timeframe and scope of this course.

Student-Directed Content Delivery

Learning new concepts and applying them outside the classroom is an essential workforce skill for students planning to analyze data in their careers. One way to help students gain the skills to learn independently is to involve them in finding and selecting content for the course. By involving students in the delivery of course content, educators can create a more dynamic and engaging learning experience.

For example, in our Generalized Linear Models course, students are introduced to a topic by reading or viewing an online resource and then find additional resources (articles, videos, or other formats) to share with the class. The instructor leads a class discussion to discuss the main concepts and resources. This approach can help personalize each student’s learning experience and make it more meaningful and relevant to their interests and goals.

Student feedback (summary of three students at mid-semester):

The course achieved more of a practical tone than courses that approached similar methods and concepts. As a result, the learning is more transferrable to particular specializations [we] may pursue in later coursework or other programs.

Evaluate and Adapt

Today’s workforce skills may not be tomorrow’s skills, so we plan to continually monitor the workforce skills we incorporate in the classroom and make changes as workforce needs change. Regularly reviewing and updating course materials with this in mind can also help to ensure that the courses are relevant and valuable for future students in their careers.

One way we plan to do this is to keep in touch with students who have taken the courses. Sharing examples of work from former students who have gone on to similar careers can be a valuable way to make the material more authentic and relevant for current students. Providing examples can help demonstrate the practical application of the skills and knowledge that current students are learning and give them a sense of the types of opportunities and challenges they may encounter in their careers.

For example, one of the students who took our introductory data analysis course for MPH students and created the report now works for a nonprofit group where they were part of a team to create a report similar in style and content to the one made in the classroom. We can share that report with current students to further emphasize how the skills they are learning in the classroom translate to their careers.

In addition, it can be helpful to have former students speak to a class about their experiences and how the course helped prepare them for their current jobs. These discussions can give current students a sense of the available career opportunities and give them a valuable perspective on how their learning applies to real-world scenarios. Such peer perspectives are a valuable addition to classroom instruction.

Final Thoughts

Incorporating workforce development skills into course curricula is crucial for preparing students for success in their future careers. These skills, such as independent learning, teamwork, and communication, are highly valued by employers and essential for success in today’s rapidly changing job market. By infusing these skills into the classroom, educators can equip their students with the knowledge and abilities they will need to succeed in their chosen fields. By making the material more authentic, instructors will help students develop a better understanding of how it applies to their careers and find additional value in their coursework.

Further Reading

Centers for Disease Control and Prevention (CDC). 2020. Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: Centers for Disease Control (CDC), U.S. Department of Health and Human Services.

Ellis, A. R. 2022. Development and implementation of a fully online introductory data analysis course to better prepare students for the public health workforce. Pedagogy in Health Promotion 8(4):309–314.

Tomlinson, M. 2017. Student perceptions of themselves as ‘consumers’ of higher education. British Journal of Sociology of Education 38(4):450–467.

Vance, E. A. 2021. Using Team-Based Learning to Teach Data Science. Journal of Statistics and Data Science Education 29(3), 277–296.

Wiggins, G. 1990. The case for authentic assessment. Practical assessment, research, and evaluation 2(1):2.

Wilhelm, J., Wilhelm, R., and Cole, M. 2019. Creating project-based STEM environments. New York, NY: Springer International Publishing.

About the Author

Amanda Ellis is an assistant professor in the Department of Biostatistics at the University of Kentucky. She is currently serving as vice chair of the department and director of graduate studies for the Master of Science in Biostatistics Program. Her focus is on graduate education, course, and curriculum development.

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