FW891: Maximum Likelihood Estimation for Natural Resources and Ecology
FW891 teaches complex, high-level, hands-on statistical modeling concepts at a distance, by using media-rich interactive learning experiences that personalize the content for students with differing backgrounds, experience, skill-levels and learning styles.
Multimodal, flexible content gives students myriad opportunities to personalize their learning experience to match their preferred learning style, need for extra help, and interest in more advanced content.
- Interactive Lecture Videos
- Interactive guided practice exercises with progressive hints
- Detailed animations that clarify concepts and procedures
- Built in quick self-assessments
- Personalized navigation (opportunities to dig deeper, practice more, or skip over already familiar content)
- Interactivity throughout the lectures that helps keep students actively engaged
- Background and Advanced Links
- I Need Help Link –
Each unit interface contains a prominent link to set up a personal meeting with the instructor. A short survey asks whether the student wants to meet in person or online, what times they have available, and a description of their question or the problem that they need help with.
The course is designed to accommodate students with diverse backgrounds including:
- Students in both master’s (2 in 1st year) and doctoral program (3 in 1st year, 4 in 3rd year).
- Students working in the field for many years vs. full-time students.
- Students with high-level statistical training and those with minimal training.
- Students who frequently use the statistical software (R) and those who have never heard of it or barely use it.
- Students who have taken prior online courses and those who for whom this is their first online course.
The course utilizes low-stakes and authentic assessment. Each unit contains two graded assessments: 1. a multiple choice quiz that covers the concepts covered in the unit and 2. a “real-world” case where the students must find the solution (by developing the appropriate model and running the model to arrive at the solution).
Each unit is interlaced with non-graded assessments so the students can check their own understanding as they progress. These assessments are embedded within lecture videos and are also presented in a “Do you remember the previous units?” quiz, which serves as a quick review of prior content so students are consistently exposed to older material through the entire course. Students constantly practice and see material several times to strengthen retention and recall.
Course content is highly accessible, including close captioning of video lectures, printable text handouts, and ALT text of equations.
Angie Leslie, online course content developer
Jim Bence Faculty, QFC co-director Developer and lead instructor
Matt Catalano Post-Doc R specialist, instructor
Travis Brenden Faculty, associate-director Support instructor