Data manipulation and statistical analysis are increasingly important learning goals for undergraduate biology majors, yet many biology students approach statistics courses with apprehension. We designed a set of activities to use engaging subject matter, bird feeder webcams located throughout the Western Hemisphere, to foster a growth mindset by gradually building on data analysis and experimental design skills, using the American Statistical Association’s educational guidelines.

Engaging laboratory activities are vital to students’ academic success and retention within STEM fields (Gasper & Gardner, 2013; Thiry et al., 2012), especially for students from historically marginalized backgrounds (Jones et al., 2016). Ineffective course design leads to achievement gaps in data and analytical skills, the basis of biostatistics, which are crucial learning goals for life science majors (Cottone & Yoon, 2020). While recent technological advances have created pedagogical opportunities for educators, students continue to enter statistically oriented classes with negative attitudes (Bateiha et al., 2020).

The pedagogical approaches used throughout this activity work to sidestep many of the barriers that may keep students from succeeding in biostatistics courses. These activities excite students’ interest in biostatistics, using an approachable subject, engaging laboratory activities, and instruction that fosters a growth mindset in students. Vital to achieving these goals was the focus on encouraging students’ inquiry-based learning, which has shown to significantly increase the effective retention of key learning outcomes in introductory biostatistics courses (Metz, 2017).

Conducting a relatable and engaging activity to introduce students to data collection and analysis will encourage students to develop a growth mindset.

Furthermore, the pedagogical approaches lead students to surpass the learning goals for a successful implementation of statistics in the classroom set out in the American Statistical Association’s Guidelines for Assessment and Instruction in Statistical Education (GAISE) College Report (GAISE, 2016). Lab 1 of this activity works to familiarize students with the scientific questions for which statistics are useful, in addition to the best practices used to collect ethical and sound data. Lab 2 builds upon these skills through an exploration of the students’ collected data. Lab 2 sees students producing and interpreting graphical models while familiarizing themselves with the central roles of variability and randomness, ultimately leading to statistically sound conclusions and analysis of the initial hypothesis.

These activities use an international set of bird feeder webcams to engage introductory biostatistics students in recording biological data, making observations and developing hypotheses to explain them, testing hypotheses, thinking broadly about how patterns may vary among ecosystems, and overall, they create a solid foundation from which students can explore the ever-evolving world of biostatistics.

Many groups of organisms reach maximum diversity in tropical regions, but the reason why this pattern is so common remains a subject of debate among biologists (e.g., Hillebrand, 2004). A leading hypothesis contends that the greater length and stability of the growing season in the tropics leads to higher net primary production, thus providing a diversity of resources to consumers year-round (Hawkins et al., 2003). This is particularly true for resources that are only available seasonally in temperate climates, such as fruit.

While few plant species have yearlong fruit production, conditions in the tropics allow different plants to fruit throughout the year, while in temperate zones few if any plants produce fruit during cold winters. The consumption of fruit by temperate organisms is only viable if consumers migrate, hibernate, or switch diets during winters when fruit is not available, therefore limiting which groups of organisms can persist on diets of fruit in temperate zones. While these constraints affect a number of groups of animals, they are particularly evident in birds.

Frugivorous birds, those that consume primarily fruit, are more diverse in tropical regions than temperate regions (Kissling et al., 2009; Kissling et al., 2012). On the American continents, frugivorous birds like toucans, parrots, and tanagers are abundant in tropical forests, but largely absent from temperate regions. This well-described latitudinal pattern in frugivore diversity is observable using available data sets, but recent technological advances allow students to use webcam streams to make real-time visual observations of this pattern regardless of their location. By testing the hypothesis that the diversity of frugivorous birds is related to latitude, with data collected themselves, students lead an international research project on charismatic organisms that offers an engaging and low-stress entry into data collection, experimental design, and statistical analysis.

Wild birds make ideal subjects for laboratory activities in introductory biology and biostatistics classes because some locally common birds should be familiar to most students regardless of their background, and some birds’ presence and behaviors are easily observable at any time of year. These features have also led to a network of bird observation platforms, such as apps that aid in the identification of birds and in recording observations while bird-watching and viewing live webcam streams of bird feeding stations (e.g., Sullivan et al., 2009). Webcam streams posted in the past have yielded numerous opportunities for integration into the classroom (Eichhorst, 2018), but a recent expansion of the geographical areas covered by live webcam streams offers exciting new opportunities to test questions about latitudinal diversity patterns. Further, the use of webcam streams across a range of biomes allows students to directly observe and collect data on diverse ecosystems and organisms regardless of where they are taking the class or their ability to travel.

Following is a series of laboratory activities spanning the first two weeks of an undergraduate introductory biological statistics course based on the questions and opportunities described above. These activities serve as an introduction to observational studies and the R computing environment (R Core Team, 2020). These activities engage students with descriptive statistics and the basics of working with data sets. Students develop skills in collecting, analyzing, and cleaning data—and then culminating descriptive statistics. Students broaden their horizons for ecosystems and organisms across the Western Hemisphere. In Lab 1, students learn how to collect and enter observational data. Then, in Lab 2, students manipulate those data and produce summary tables and/or figures. Given that many students enter statistics courses with a negative outlook, our lessons work to gradually introduce students to the exciting world of biological statistics by encouraging a growth mindset and strengthening their investigative and analytical prowess.

Lab Objective

Lab 1 focuses on the following GAISE goals:

  • Goal 2: Students should be able to recognize questions for which the investigative process in statistics would be useful and should be able to answer questions using the investigative process.

  • Goal 9: Students should demonstrate an awareness of ethical issues associated with sound statistical practice.

Instructor Preparation

Prior to class, instructors created the data entry sheet (see the Bird Feeder Data Entry Sheet in the Supplemental Material available with the online version of this article), checked the webcams to make sure they were functional, and familiarized themselves with the Bird ID Guide (in the Supplemental Material online) and adjusted it as necessary.

Student Engagement

When we employed these labs in the classroom, the instructor began by prompting the students with the question, Why are many groups of organisms more diverse in the tropics than in temperate or arctic regions? To encourage active learning, the instructor applied the think-pair-share collaborative learning strategy. The instructor then took note of the hypotheses so that the students could, in upcoming labs, revisit and revise their initial hypotheses as they garnered insights from their data.

The instructor next introduced the students to bird feeder webcams, sharing the exciting opportunity to collect data on bird behavior from many diverse locations. The instructor briefly introduced the locations the students would study. Antonina, Brazil; El Valle de Antón, Panamá; Fort Davis, Texas, USA; Ithaca, New York, USA; and Manitouwadge, Ontario, Canada were chosen as the sites, to cover a broad latitudinal gradient within the Western Hemisphere (see Lab 1 in the Supplemental Material online). The instructor then explained the use of a “point count” study for data collection and then led students through the protocol as a practice (see the Supplemental Material online). Throughout the 10-minute collection period, the instructor paused the video stream upon the arrival of each bird. This allowed the students to practice using the Bird ID Guide (available online) to identify the species, and to learn how to interact with and upload the species to the data set, using a four-letter “alpha-code.” If possible, demonstrating this protocol on a bird feeder array set up outside near the classroom would help the students further connect with the material. If you do incorporate local data collection at the college/university level, note that Institutional Animal Care and Use Committee (IACUC) approval is likely needed. After the mock collection, the students used the shared data sheet to sign up for time slots and conduct their own data collections from each feeder stream.

The instructor prompted students to take note of other observations that could be helpful during data analysis. In doing so, the instructor discussed the value of recording observations that may not be central to the study question. For example, although the lab activity does not explicitly ask students to analyze variation in weather conditions among locations, students may find that noting weather conditions during the observation periods is helpful in interpreting their results. If one site experiences a large storm during the observation period, this could affect the number and/or species of birds observed at that site for reasons unrelated to the hypothesis the students are testing. Having students collect additional data or record any interesting observations they make is particularly valuable for developing follow-up activities that allow the students to generate and test a new research question.

Lab Objective

Lab 2 focuses on the following GAISE goals:

  • Goal 3: Students should be able to produce graphical displays and numerical summaries and interpret what these do and do not reveal.

  • Goal 4: Students should recognize and be able to explain the central role of variability in the field of statistics.

  • Goal 5: Students should recognize and be able to explain the central role of randomness in designing studies and drawing conclusions.

  • Goal 8: Students should be able to interpret and draw conclusions from standard output from statistical software.

Instructor Preparation

Students in the Middlebury College biostatistics course in which this activity was used had already learned to enter data and perform simple statistical analyses (t-tests and linear correlation tests) in Microsoft Excel in an introductory ecology and evolutionary biology prerequisite course. However, if students have not previously had this experience, the instructor should be sure to have introduced the concept of statistical hypothesis testing with p values before this exercise. In our biostatistics course, the instructor reviewed these concepts and introduced the R programming software in the accompanying lecture section during the two weeks prior to this lab. Instructors directed the students to download, install, and troubleshoot any issues with the statistical language or program prior to arriving at this lab. The instructors recommend that students use the user-friendly RStudio interface and the {introverse} R package to help them learn how the functions referred to in the posted script work and troubleshoot them as they work through it (Spielman, 2021). The instructors also selected a required textbook for the class that provides well-annotated R code examples for all of the concepts discussed in the book (Whitlock & Schluter, 2020), which provides an additional resource for helping students connect the concepts to the coding. The instructor should also check over the data before class to be aware of any data input errors and to check if any unexpected species were encountered.

Student Engagement

First, the instructor applied the think-pair-share method by having the students discuss the challenges, successes, and any other feelings or questions they had from the first lab. The instructor encouraged the students to think carefully about what they observed and how it might influence the conclusions they could draw in this discussion, to emphasize the importance scientists place upon open discussions of successes and failures as a key element of the scientific process.

Students first organized their data and then calculated the summary statistics of species richness and abundance at each site. The students then graphed how these responses varied with latitude and performed linear correlation analyses to statistically test their hypotheses. Finally, the instructor had the students revisit their initial hypotheses and compare these first insights with the knowledge they had gained through the data collection, coding, and analysis. With these results, the instructor started a class discussion by asking the students if and how they would revise their hypotheses based upon what their scientific study had shown them.

To follow up the discussion, students implemented a separate table of frugivory data (Wilman et al., 2014), which was merged with the data they collected, to see to what degree the percent frugivory varied among sites. Students then repeated their calculation of the summary statistics and response variables, made graphs and performed linear correlation analyses, and again determined whether the data supported their hypothesis. With their analysis and conclusions solidified, the students then independently answered the questions in the Lab 2 handout (see the Supplemental Material online) to evaluate their progress in meeting the learning objectives.

After the completion of this series of lab activities, students were asked to rate how comfortable they felt using R, on a scale of 1–5, with 5 being the most comfortable. They were also asked to select which components of the class helped them learn how to work with and analyze data in R. These results indicated that 96% of students felt more comfortable using R than they did at the beginning of the course, and over half indicated that these activities were some of the most helpful aspects of learning how to use R. Notably, students who initially rated their comfort level in R as being higher did not find these lab activities to be helpful. These results suggested to us that these activities did foster a growth mindset in this statistics course, particularly in students with limited prior experience with using statistical coding software to analyze data. These activities would be less helpful or would need to be modified for an advanced statistics course in which students had prior experience working with statistical software to analyze data. Further, the results also highlight that this activity alone is not sufficient to build confidence in using R but, rather, should be combined with additional activities like in-class R coding sessions and problem sets that provide students a variety of types of opportunities to become more comfortable using R to analyze biological data. Student responses indicated that 82% of students felt confident in designing a research study and collecting data without the help of a professor or TA, and 65% of students felt confident in organizing their data and performing statistical tests without the help of a professor or TA.

We believe that conducting a relatable and engaging activity to introduce students to data collection and analysis will encourage students to develop a growth mindset. One important consideration is that this activity could be especially designed to foster a growth mindset if students work together in peer groups and a peer TA is available to help them, as having a student who other students can relate to and who can share how they overcame their own initial trepidation in the course is one of the main factors responsible for promoting a growth mindset in undergraduate students (Limeri et al., 2020). Further, this activity may foster a growth mindset beyond the core course learning objectives by inviting students to engage with birds, which in turn can promote positive interest in and attitudes toward animals (Hummel et al., 2015).

Although developed for an introductory biostatistics course, the resources presented here could be modified for use in a variety of biology classroom settings. From ornithology classes that may have students learn about bird identification and behavior, to primary school classes that focus on the biological questions and scientific method rather than the statistical aspects, there are many ways in which these activities can be curated to a diverse range of classrooms. We suggest expanding beyond the locations included in the Bird ID Guide by including web camera locations that students may find interesting. We suggest using a webcam near your location if available, as students will become familiar with common birds in your area. This may be valuable in opening opportunities for students to generate follow-up or different questions based on their observations and designing local experiments with bird feeders. If pursuing activities that involve the setup of bird feeders, check with your institution’s IACUC committee about whether the proposed work would need IACUC approval. If students are particularly excited about engaging with birds during this activity, it would be valuable to share copies of regional bird field guides and the Merlin Bird ID app from Cornell University. These tools could be effectively incorporated if you build local bird-watching activities into a class.

Additionally, for students who are interested in exploring larger global patterns in bird diversity, follow-up activities or independent projects using the community-generated eBird platform allow for innumerable possibilities (Sullivan et al., 2009). Data on eBird are submitted by bird-watching enthusiasts around the globe and can be accessed through the auk R package (Strimas-Mackey et al., 2018). With some modifications for the specific class objectives, these labs, activities, and resources can thus connect students to global ecological patterns, and their use can engage them in statistical analysis, experimental design, bird ecology and evolution, and a wide variety of other topics.

We thank students in the BIOL 211 class at Middlebury College for their feedback on this activity. Observation at bird feeders was approved by the Middlebury College Institutional Animal Care and Use Committee (IACUC Protocol #331-21). The Supplemental Material, including the bird identification guide, used media from Macaulay Library at the Cornell Lab of Ornithology.

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Supplementary data