Developing research methods, data analysis and statistics course (RMDAS): Engaging students in curriculum development

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In this post, Dr Louise Connelly and Dr Jessica Martin, based at The Royal (Dick) School of Veterinary Studies, describe the findings from a PTAS -funded project (2017-2018), which involved postgraduate students in the curriculum design of a fully-online research methods, data analysis and statistics (RMDAS) 10-credit course…

As educators, we can collaborate with students to design and deliver research methods and statistics courses that are less daunting; ensure that the student academic needs are met, and increase student self-efficacy. The project has enabled an exploration of students’ needs, anxieties, and learning preferences via a questionnaire (n=32 responses), interviews (n=6), and review of an existing RMDAS course. The findings indicate that students lack confidence, as 65% of those surveyed were ‘not/somewhat confident about data analysis’; and 72% were ‘not/somewhat confident about statistics’.

It is important that educators understand which area of the course students might find daunting, and address this through effective course design and support processes. In the survey, 53% of respondents stated that the ‘most daunting/scary’ aspect of research methods and statistics is ‘correctly applying stats to a write-up/explanation of the stats output’, with 25% stating that using a stats package/technology would be daunting. Both of these areas are easily addressed by considering the design of the course and resources/support offered.

Our online MSc courses typically include a reading list, discussion boards, and lectures in MP3, MP4, and PDF format, to ensure accessibility and flexibility of learning. However, for coding, statistics, and research methods, a more innovative approach is needed. Some students stated that non-assessed multiple choice questions (MCQs) are useful: “Yeah, I think they’re good, yeah, short little [quizzes]” (Interview participant 5), as it “gives you a feeling of how you’re doing” (Interview participant 3). Providing assurances is also important, as it helps to reduce anxiety about the topic, as one student stated: “I think what’s really important, what I liked about the course is…that it was explicitly said that don’t worry if you don’t have a clue about statistics, we’ll get you through.” (Interview participant 3, reflecting on the existing course).

Overall, the feedback from the students aligned with existing research. Where it differed was the different approaches we need to consider in order to effectively design and deliver a fully online course for a diverse student cohort.


The findings from the research have provided a useful insight into the challenges of delivering this subject, as well as understanding the fears and expectations of students. A few areas where course design can be improved or where students have benefited from existing teaching and assessment approaches. We now propose the following in RMDAS courses:

  • Design and deliver courses that are less daunting and which help to address students’ anxiety and confidence issues.
  • Use technology as an enabler not a barrier.
  • Use formative assignments, such as non-assessed MCQs.
  • Run live synchronous coding sessions (in Blackboard Collaborate. They are also recorded for watching at a later point).
  • Provide R (stats package) exercises and supporting documents.
  • Provide a glossary of terms.

Our future plans include:

  • Open sharing of code (there is a strong community of this in the Rstats world with GitHub etc.). Use of a shared folder structure and encourage students to copy and comment on each other’s code.
  • Review student course feedback and consider further changes/improvements.

This has been a valuable research project, as it has helped us understand the challenges, concerns, and levels of previous experience of our varied student cohort. In doing so, we have developed and delivered an RMDAS course that meets the needs of the students, as well as providing a course that aligns with the requirements for completing a dissertation.

For further information and final project report, please see here.

Louise Connelly

Louise Connelly is a Senior E-Learning Developer at the R(D)SVS. She provides pedagogical advice and develop innovative approaches for CPD resources, online MSc programmes, and other digital educational resources, such as apps. My research interests include social media, e-professionalism, student transitions, and digital education.

Jessica Martin

Dr Jessica Martin is a Lecturer in Statistics and Animal Welfare at the R(D)SVS. She is interested in the integration of behavioural, physiological and data science to investigate animal welfare issues. She teaches research methods and data analysis subjects to science and vet students.

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