Quantitative Course Best Practices

"What I hear, I forget; what I see, I remember; what I do, I understand." –Chinese proverb

Quantitative courses develop practical skills and concepts that are unique compared to qualitative programs. In his article on quantitative research pedagogy in technical writing curricula, Michael Albers argues for the significance of teaching data analysis with a focus on critical thinking: “Teaching quantitative data analysis is not teaching number crunching, but teaching a way of critical thinking for how to analyze the data. The goal of data analysis is to reveal the underlying patterns, trends, and relationships…” (Albers, 2017, p. 215). Albers’ approach is a useful principle to guide all quantitative course development.

When designing an online quantitative course, focus on authentic tasks and applications of key course concepts and skills. Teach students how to use specific tools and techniques to retrieve numerical data results and draw conclusions that can provide actionable decisions for real-world problems. The following are a few best practice approaches that can optimize this type of learning.

Discussions may be commonly associated with qualitative learning, but in quantitative courses, they are useful in helping students form connections with their peers and instructor and challenging students to provide solutions using words, tables, and figures through concise communication (Chen et al., 2018; Strandberg & Campbell, 2014). Consider posing questions that can be approached using different methods. For example, ask students to practice writing lines of code and then share one tip with peers on how to make this process more efficient. Another option is to assign a designated student in each discussion board to provide a summary of the different techniques, listing out the pros and cons. Alternatively, incorporate group discussions, where students rotate the “expert” role and teach their peers how to solve a problem. Keep in mind that discussions do not always need to be graded. Using ungraded end-of-module Q&A forums creates a safe space for students to ask questions and learn from their peers.

Quantitative topics often involve applying meticulous and complex processes to solve problems. Students should be provided numerous opportunities to master targeted skills before they approach summative assessments (Prince et al., 2020). For example, when teaching statistical concepts and analysis techniques, provide students with simplified data sets to use for practice. Follow this up with a Q&A board where they can ask questions if they are stuck. If students need additional support, the Q&A is also a useful tool for instructors to share timely resources. This will help build the skills and confidence to complete these tasks in a graded assignment. Additionally, students are actively involved in the learning process through interactions with their peers, and this type of engagement can lead to better retention of content. Consider incorporating pair or group learning exercises where students help each other solve a set of problems. For auto-graded quizzes, allow multiple attempts and use question banks so that students can master skills through a comprehensive variety of questions, in a low-stakes environment. Incorporate self-graded interactive questions that are presented after a new skill or technique has been introduced. For all practice opportunities, remember to provide a range of questions that increase in complexity, and offer detailed, step-by-step feedback that shows how to solve the problems.

Incorporating high-interest, current, and relevant real-world examples serves to demonstrate how the materials can be applicable across different contexts. For example, if students are calculating descriptive statistics for a data set, provide recent data on a familiar topic for them to use and illustrate how analysis of such data is useful in the real world. For ongoing learning that allows students to build topics together, incorporate research projects that utilize several different skills and ultimately focus on the bigger picture. Activities should help students develop skills such as organization, presentation, and discussion of quantitative results (Strandberg & Campbell, 2014).

Research indicates that video is a useful technology for online learning “…especially, as an asynchronous replacement…for face-to-face learning” (Ghilay, 2018, p. 16). For online learners, videos provide the additional benefit of playback and are increasingly mobile.

Research conducted by Everspring’s Learning Design team indicates that fundamental tutorials have the most viewership and engagement. These videos should showcase mathematical or analytical skills, ideally demonstrated in course-relevant software such as Excel or RStudio. When recording tutorial videos, consider the following:

Break up large processes into smaller videos focusing on targeted skills. Brame (2017) explains that segmenting allows “…learners to engage with small pieces of new information and gives them control over the flow of new information” (p. 2). This approach will also help keep videos short and retain student attention. For example, if you are teaching a complex data analysis procedure in RStudio, you might record a three-part tutorial series, working within the R interface. The first video can demonstrate how to load and read the data, and how to select the variables for analysis. The second video can focus on specific considerations for and steps involved in the analysis procedure, and the third video can demonstrate how to review the analysis and draw conclusions from your findings.

A study by Szpunar et al. (2013) found that students who viewed videos interspersed with connected practice questions outperformed students who completed tasks unrelated to the video content. Try incorporating follow-up activities such as providing a mock data set and having students practice the techniques demonstrated in a tutorial video to reinforce the learning.

In a world that is increasingly reliant on data, quantitative courses have become popular among graduate students (Zhou & Gao, 2021). Instructors should focus on designing courses that provide students with opportunities to learn and master core skills and techniques. This can be done by using high-interest examples to identify analytical trends and draw conclusions that can be utilized in critical decision-making processes.

Albers, M. J. (2017). Quantitative data analysis—In the graduate curriculum. Journal of Technical Writing and Communication, 47(2), 215–233.

Brame, C. J. (2017). Effective educational videos: Principles and guidelines for maximizing student learning from video content. CBE—Life Sciences Education, 15(4), 1–6.

Chen, B., Bastedo, K., & Howard, W. (2018). Exploring design elements for online STEM courses: Active learning, engagement & assessment design. Online Learning, 22(2), 59–75.

Ghilay, Y. (2018). Video-based learning of quantitative courses in higher education. Journal of Educational Technology, 15(2), 16–27.

Prince, M., Felder, R., & Brent, R. (2020). Active student engagement in online STEM classes: ­Approaches and recommendations. Advances in Engineering Education, 8(4), 1–25.

Strandberg, A. G., & Campbell, K. (2014). Online teaching best practices to better engage students with quantitative material. Journal of Instructional Pedagogies, 15, 1–14.

Szpunar, K. K., Khan, N. Y., & Schacter, D. L. (2013). Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences of the United States of America, 110(16), 6313–6317.

Zhou, E., & Gao, J. (2021). Graduate enrollment and degrees: 2010 to 2020. Council of Graduate Schools.