Pedagogical research in science education has shown that students effectively learn science by doing science. As a result, there is increased interest in bringing research-like experiences into the classroom, particularly for laboratory courses. This lesson describes a structured inquiry laboratory module focused on the examination of muscle cell differentiation. Muscle differentiation is a complex process that provides a unique opportunity for undergraduate students to explore various aspects of cell biology in the laboratory. The students engage in a project spanning eight weeks in which they utilize three complementary techniques (including fluorescence microscopy, Western blotting, and reverse transcriptase-based polymerase chain reaction) to examine the morphological and genetic changes that occur during muscle cell differentiation in culture. The instructor assesses students on the quality of their laboratory notebooks, including how thoroughly they document each experiment, as well as on their participation in discussions regarding experimental design, techniques, and results. Ultimately, students compile their work into an individually written research report, the format of which parallels typical journal articles published in the field of cell biology. The design of this module allows students to explore fundamental cell biology concepts while learning key experimental techniques. In addition, the instructor teaches this module in a structured inquiry-based manner to engage students in learning through investigation and discovery.

Primary Image: Myogenin and desmin expression in C2C12 myotubes. Visualized by immunofluorescence for myogenin, a muscle-specific regulatory transcription factor (green) and desmin, a muscle-specific cytoskeletal intermediate filament protein (red). 400X total magnification.

Pedagogical research in science education has shown that students effectively learn science by doing science. As a result, there is increased interest in bringing research-like experiences into the classroom, particularly for laboratory courses. This lesson describes a structured inquiry laboratory module focused on the examination of muscle cell differentiation. Muscle differentiation is a complex process that provides a unique opportunity for undergraduate students to explore various aspects of cell biology in the laboratory. The students engage in a project spanning eight weeks in which they utilize three complementary techniques (including fluorescence microscopy, Western blotting, and reverse transcriptase-based polymerase chain reaction) to examine the morphological and genetic changes that occur during muscle cell differentiation in culture. The instructor assesses students on the quality of their laboratory notebooks, including how thoroughly they document each experiment, as well as on their participation in discussions regarding experimental design, techniques, and results. Ultimately, students compile their work into an individually written research report, the format of which parallels typical journal articles published in the field of cell biology. The design of this module allows students to explore fundamental cell biology concepts while learning key experimental techniques. In addition, the instructor teaches this module in a structured inquiry-based manner to engage students in learning through investigation and discovery.

Primary Image: Myogenin and desmin expression in C2C12 myotubes. Visualized by immunofluorescence for myogenin, a muscle-specific regulatory transcription factor (green) and desmin, a muscle-specific cytoskeletal intermediate filament protein (red). 400X total magnification.

Students analyze sea surface temperature (SST) data from NOAA to predict coral bleaching at four locations in the Bahamas. They then compare their predictions to authentic research collected about coral mortality and temperature fluctuations

This module introduces how to determine whether observation is significantly different from expectation in the context of understanding Chi-square Test. It is intended for an introductory biology audience.

The students will practice identifying the appropriate basic statistical tests when given a scenario and learn how to run and interpret those statistical tests in R.

Lesson on what it means for biological processes to be Poissonian, published in CourseSource

In this lesson, students will have the opportunity to work through a chi-squared test of independence between two categorical variables.

A guide to helping intro bio students develop a deeper understanding of key statistical concepts by translating among numeric, graphical, verbal, and symbolic representations.

This module utilizes a user-friendly database exploring data selection, box-and-whisker plot, and correlation analysis. It also guides students on how to make a poster of their data and conclusions.

Students are introduced to concepts of sampling distributions and hypothesis testing using a simulation applet, elementary hypothesis tests, t-tests, and p-values as they compare a given fish population for methylmercury levels (using real and hypothetical data) against real-world mercury standards.

Conquer basic math and statistics used in biology while exploring classroom-ready resources. Concepts include central tendency and variation, spreadsheet skills, graphing, and data analysis with Chi-Square and T-Tests

Students typically find linear regression analysis of data sets in a biology classroom challenging. These activities could be used in a Biology, Chemistry, Mathematics, or Statistics course. The collection provides student activity files with Excel instructions and Instructor Activity files with Excel instructions and solutions to problems. Students will be able to perform linear regression analysis, find correlation coefficient, create a scatter plot and find the r-square using MS Excel 365. Students will be able to interpret data sets, describe the relationship between biological variables, and predict the value of an output variable based on the input of an predictor variable.

Students are introduced to concepts of hypothesis testing using elementary hypothesis tests, t-tests, and p-values as they compare a given fish population for methylmercury levels (using real and hypothetical data) against real-world mercury standards.

This is a teaching resource that uses fascinating images and videos of leaf cutter ants foraging in Panama to provide an engaging context for students to learn about mathematics and statistics.

statsTeachR is an open-access, online repository of modular lesson plans, a.k.a. "modules", for teaching statistics using R at the undergraduate and graduate level.

Part of the 2015 SMB Minisymposium: Topics in Biomathematics Education

Topics include measures of average (mean, median, and mode), variability (range and standard deviation), uncertainty (standard error and 95% confidence interval), Chi-square analysis, student t-test, Hardy-Weinberg equation, and frequency calculations.

Generative AIs (e.g., ChatGPT have arrived on college campuses. STEM faculty have begun grappling with this disruptive technology and have responded in a various way from grudging acceptance to heroic efforts to forestall cheating to innovative incorporation. We provide a comprehensive survey of responses and a set of best practices.

As instructors, we continually look for new ways to create equitable learning environments and support learning for all students in our courses. Recently, we have explored ways that we can increase structure to better support students. We have identified four evidence-based elements that we include in our course design and implementation: 1) structured assessments and feedback; 2) structured out-of-class learning; 3) structured class time using inclusive practices; and 4) structured assignments using transparent design. In this essay, we identify some relevant literature to address each of these levels of structure and describe our experiences with implementation at each level to support equitable classroom environments.