diff --git a/content/about/audience.md b/content/about/audience.md index 8fcb64f..cf27ebd 100644 --- a/content/about/audience.md +++ b/content/about/audience.md @@ -5,16 +5,16 @@ summary: "Description of intended audience for this material" --- The long-term goal of this site is to help two large groups of people: 1) Students in a classroom at a college or university; and 2) Self-guided learners, or folks who aren’t taking a formal class and are interested in learning online on their own time. -We are starting with the classroom students and then attempting to build on the material generated to make it maximally useful to the broader audience of folks who want to learn about ecological forecasting and dynamics. +We are starting with the classroom students and then building on the material generated to make it maximally useful to the broader audience of folks who want to learn about ecological forecasting and dynamics. * The material is designed to be accessible to graduate students and advanced undergraduates * It assumes a basic ability to read and engage with the primary scientific literature, but provides guidance for engaging with each paper to help students who are learning how to do this -* It assumes a basic understanding of R, including loading tabular data, working with variables, loading packages, and running functions. Some experience with `dplyr` and `ggplot2` is also helpful. If you need a basic introduction to R we recommend checking out the Data Carpentry lesson materials on [Data Analysis and Visualization in R for Ecologists](https://datacarpentry.org/R-ecology-lesson/). +* It assumes a basic understanding of R, including loading tabular data, working with variables, loading packages, and running functions. Some experience with `dplyr` and `ggplot2` is also helpful. If you need a basic introduction to R, we recommend checking out the Data Carpentry lesson materials on [Data Analysis and Visualization in R for Ecologists](https://datacarpentry.org/R-ecology-lesson/). Examples of folks who we are trying to help: -Maya: An advanced undergraduate in natural resources who wants to understand what ecological forecasting is and how it might be applied in conservation and management. She has used basic R in some of her other courses and has just started reading the primary scientific literature in a classroom context. +Maya: An advanced undergraduate in natural resources who wants to understand what ecological forecasting is and how it might be applied in conservation and management. Maya has used basic R in some of their other courses and has just started reading the primary scientific literature in a classroom context. -Juniper: A graduate student with a thesis related to how populations change through time, but who doesn't know anything about how to model time-series. They want to learn how to build and analyze time-series models for their thesis projects and find the idea of forecasting interesting. +Juniper: A graduate student with a thesis related to how populations change through time, but who doesn't yet know how to model time-series. Juniper wants to learn how to build and analyze time-series models for their thesis projects and finds the idea of forecasting interesting. -Jaylen: A professor who understands that ecological forecasting is becoming important for students to learn and wants to develop either a full course or a seminar on the topic. He understands the main concepts, but doesn't know what the best papers would be best for teaching and doesn't have the time to develop a bunch of R tutorials. \ No newline at end of file +Jaylen: A professor who understands that ecological forecasting is becoming important for students to learn and wants to develop either a full course or a seminar on the topic. Jaylen understands the main concepts, but doesn't know what the best papers would be best for teaching and doesn't have the time to develop a bunch of R tutorials. \ No newline at end of file diff --git a/content/getting-started/_index.md b/content/getting-started/_index.md index cc2da82..1576cd6 100644 --- a/content/getting-started/_index.md +++ b/content/getting-started/_index.md @@ -56,14 +56,12 @@ The course website is written in Hugo using the [Wowchemy Documentation theme](h The easiest way to create your own version of the course is the create a deployed course on Netlify via this template. You need a GitHub account to do this. -Follow the Wowchemy instructions for [Creating a site with Hugo and GitHub](https://wowchemy.com/docs/getting-started/hugo-github-quickstart/), -but instead of using the "Choose a template" button [click this template link](https://app.netlify.com/start/deploy?repository=https://github.com/weecology/forecasting-course). +[Click this template link](https://app.netlify.com/start/deploy?repository=https://github.com/weecology/forecasting-course) to create a copy of the GitHub repository in your GitHub account. Then follow the Wowchemy instructions for [Creating a site with Hugo and GitHub](https://wowchemy.com/docs/getting-started/hugo-github-quickstart/), skipping the "Choose a template" button on that page. -This will create a GitHub repository in your GitHub account and live version of the site. You can then edit files in the GitHub repository and they will automatically deploy to the website. -Edit `config/_default/params.yaml` to match your version of course. -In particular update the repository url to match the new repository you created. +Edit `config/_default/params.yaml` to match your version of the course. +In particular, update the repository URL to match the new repository you created. This will ensure that the `Edit this page` links on each page direct you to your version of the material. #### Locally @@ -86,7 +84,7 @@ hugo server ### Modifying the Site * Most content is stored in one folder per lesson in the [`content/lessons` folder](https://github.com/weecology/forecasting-course/tree/main/content/lessons) -* To add a new lesson make a copy of the [lesson template folder](https://github.com/weecology/forecasting-course/tree/main/content/lessons/LessonTemplate) and modifying the pages in the resulting folder using [markdown](https://www.markdownguide.org/) +* To add a new lesson make a copy of the [lesson template folder](https://github.com/weecology/forecasting-course/tree/main/content/lessons/LessonTemplate) and modify the pages in the resulting folder using [markdown](https://www.markdownguide.org/) * To modify a lesson edit the markdown files in that lesson folder with the appropriate name. If you followed the instructions on installing on Netlify above, the easiest way to do this is to go to the page you want to edit on the deployed site and click the `Edit this page` link at the bottom. * To modify the schedule edit `content/schedule/schedule.md`. In the `lessons` section list the titles of the lessons you want to teach in the order you want to teach them. If you want to include specific dates for each lesson then edit the `dates` section to include those dates in the same order. @@ -96,6 +94,6 @@ Contributions are always welcome! * [Open an issue](https://github.com/weecology/forecasting-course/issues/new) to say Hi or if there’s anything we can do to help! * Contributions of new lessons are welcome as Pull Requests or we can work with you to add new material and data to the site -* If you want to create a modified copy of the course including the website either following the instructions for installing on Netlify above or fork/copy the repository and [connect it to Netlify](https://wowchemy.com/docs/hugo-tutorials/deployment/) to automatically build the site. +* If you want to create a modified copy of the course including the website either follow the instructions for installing on Netlify above or fork/copy the repository and [connect it to Netlify](https://wowchemy.com/docs/hugo-tutorials/deployment/) to automatically build the site. For more information see our [CONTRIBUTING page](https://github.com/weecology/forecasting-course/tree/main/CONTRIBUTING.md) \ No newline at end of file diff --git a/content/syllabus/_index.md b/content/syllabus/_index.md index 94b2358..60dea9f 100644 --- a/content/syllabus/_index.md +++ b/content/syllabus/_index.md @@ -8,7 +8,7 @@ weight: 20 | Instructor | Dr. Morgan Ernest (she/her) | Dr. Ethan White (he/him) | |-----------------|-----------------------------|--------------------------| -| Office Location | on-line | online | +| Office Location | online | online | | Email | | | #### **Times and Locations** @@ -44,11 +44,11 @@ discussion. #### **Course Participation** The course is designed so that students may participate synchronously or asynchronously in the course. -Students may shift between synchronous and aynschronous participation as needed. +Students may shift between synchronous and asynchronous participation as needed. ##### Paper discussions: Synchronous participation: During the assigned course time, synchronous discussions of assigned papers -will occur over zoom. In-class group discussion about the assigned papers paper. +will occur over zoom. This discussion is generally centered around the discussion questions that are provided in advance but may also expand beyond them. Our goal is to produce a classroom environment where everyone is comfortable participating in class discussions. We will try to manage discussions so @@ -60,16 +60,16 @@ Asynchronous participation: Students may opt to participate asynchronously by pr ##### R-tutorials -Most weeks we have an R-tutorial session on Thursdays. R-tutorials consist of a video that all students (synchronous and asynchronous) students are required to watch before the thursday class time. Synchronous class time on R-tutorial thursdays is dedicated for students to ask questions, get clarification, or help with their r code. Asynchronous students are encouraged to post their questions to the course discussion board on canvas. +Most weeks we have an R-tutorial session on Thursdays. R-tutorials consist of a video that all students (synchronous and asynchronous) students are required to watch before the Thursday class time. Synchronous class time on R-tutorial Thursdays is dedicated for students to ask questions, get clarification, or help with their R code. Asynchronous students are encouraged to post their questions to the course discussion board on canvas. #### **Course Grading** -* 50% of grade will be based on paper paper discussion -* 50% of the grade will be based on completing R tutorials +* 50% of grade will be based on paper discussions +* 50% of the grade will be based on completing R-tutorials #### **Attendance Policy** -Two class days can be missed without impacts on your grade without the need +Two class days can be missed without impacts on your grade -- there is no need to submit make-up work, though we recommend that students attempt any missed class activities on their own time because additional class activities or discussions may rely on that knowledge. diff --git a/paper.md b/paper.md index 6b771d7..afb6372 100644 --- a/paper.md +++ b/paper.md @@ -33,11 +33,11 @@ bibliography: paper.bib # Summary -Ecological Forecasting and Dynamics' is a semester-long course to introduce students to the fundamentals of ecological forecasting & dynamics. This course implements paper-based discussion to introduce students to concepts and ideas and R-based tutorials for hands-on application and training. The course material includes a reading list with prompting questions for discussions, teachers notes for guiding discussions, lecture notes for live coding demonstrations, and video presentations of all R tutorials. This course material can be used either as self-directed learning or as all or part of a college or university course. Individual learners have access to all of the necessary material - including discussion questions and instructor notes - on the website. The course focuses on papers with an open-access or free-to-read version where possible, though some materials still rely on access to closed-access papers. The course is structured around two sessions per week, with most weeks consisting of a one hour paper discussion session and a 1-2 hour session focused on applications in R. R tutorials use publicly available ecological datasets to provide realistic applications. Because the material is organized around content themes, instructors can modify and remix materials based on their course goals and student levels of background knowledge. These course materials have been taught for several years at the authors’ university and have also generated significant online engagement with course videos tens of thousands of times. +'Ecological Forecasting and Dynamics' is a semester-long course to introduce students to the fundamentals of ecological forecasting & dynamics. This course implements paper-based discussion to introduce students to concepts and ideas and R-based tutorials for hands-on application and training. The course material includes a reading list with prompting questions for discussions, teachers notes for guiding discussions, lecture notes for live coding demonstrations, and video presentations of all R tutorials. This course material can be used either as self-directed learning or as all or part of a college or university course. Individual learners have access to all of the necessary material - including discussion questions and instructor notes - on the website. The course focuses on papers with an open-access or free-to-read version where possible, though some materials still rely on access to closed-access papers. The course is structured around two sessions per week, with most weeks consisting of a one hour paper discussion session and a 1-2 hour session focused on applications in R. R tutorials use publicly available ecological datasets to provide realistic applications. Because the material is organized around content themes, instructors can modify and remix materials based on their course goals and student levels of background knowledge. These course materials have been taught for several years at the authors’ university and have also generated significant online engagement with course videos tens of thousands of times. # Statement of Need -Ecological forecasting is an emerging field that aims to project the current state of nature into uncertain futures. This goal of understanding and modeling nature benefits from traditional ecological approaches that assess processes by modeling known outcomes from short-term experiments or historical data, but also involves unique tools, methods, and ways of thinking [@houlahan2017; @dietze2018; @white2019]. Many ecologists have limited exposure to all of the core concepts necessary to engage in forecasting [@brewer2003; @dietze2018] including: 1) understanding of ecological dynamics [@wolkovich2014]; the iterative cycle of model fitting, evaluation, and improvement [@dietze2018]; and 3) assessing and communicating uncertainty in forecasts [@brewer2003]. Building a community of practice around ecological forecasting requires courses that provide students with foundational conceptual knowledge relevant to ecology in conjunction with active training in methodologies and approaches [@dietze2018]. However, ecological forecasting is still a small field with few practitioners, creating a potential educational bottleneck. The ‘Ecological Forecasting and Dynamics’ course provides training in the fundamentals of ecological forecasting that will allow students to engage critically with the field and provide tools for students to deploy as they develop as forecasters. These materials can be used by instructors as modifiable core materials for their own courses or by individual students as an independent self-guided course. +Ecological forecasting is an emerging field that aims to project the current state of nature into uncertain futures. This goal of understanding and modeling nature benefits from traditional ecological approaches that assess processes by modeling known outcomes from short-term experiments or historical data, but also involves unique tools, methods, and ways of thinking [@houlahan2017; @dietze2018; @white2019]. Many ecologists have limited exposure to all of the core concepts necessary to engage in forecasting [@brewer2003; @dietze2018] including: 1) understanding of ecological dynamics [@wolkovich2014]; the iterative cycle of model fitting, evaluation, and improvement [@dietze2018]; and 3) assessing and communicating uncertainty in forecasts [@brewer2003]. Building a community of practice around ecological forecasting requires courses that provide students with foundational conceptual knowledge relevant to ecology in conjunction with active training in methodologies and approaches [@dietze2018]. However, ecological forecasting is still a small field with few practitioners, creating a potential educational bottleneck. The ‘Ecological Forecasting and Dynamics’ course provides training in the fundamentals of ecological forecasting that will allow students to engage critically with the field and provide tools for students to deploy as they develop as forecasters. These materials can be used by instructors as modifiable core materials for their own courses or by individual students as an independent self-guided course. # Audience @@ -47,11 +47,11 @@ The discussion material is primarily designed to be used in a classroom environm Examples of folks who we are trying to help: -Maya: An advanced undergraduate in natural resources who wants to understand what ecological forecasting is and how it might be applied in conservation and management. She has used basic R in some of her other courses and has just started reading the primary scientific literature in a classroom context. +Maya: An advanced undergraduate in natural resources who wants to understand what ecological forecasting is and how it might be applied in conservation and management. Maya has used basic R in some of their other courses and has just started reading the primary scientific literature in a classroom context. -Juniper: A graduate student with a thesis related to how populations change through time, but who doesn't yet know how to model time-series. They want to learn how to build and analyze time-series models for their thesis projects and find the idea of forecasting interesting. +Juniper: A graduate student with a thesis related to how populations change through time, but who doesn't yet know how to model time-series. Juniper wants to learn how to build and analyze time-series models for their thesis projects and finds the idea of forecasting interesting. -Jaylen: A professor who understands that ecological forecasting is becoming important for students to learn and wants to develop either a full course or a seminar on the topic. He understands the main concepts, but doesn't know the best papers for teaching and doesn't have the time to develop a suite of R tutorials. +Jaylen: A professor who understands that ecological forecasting is becoming important for students to learn and wants to develop either a full course or a seminar on the topic. Jaylen understands the main concepts, but doesn't know what the best papers would be best for teaching and doesn't have the time to develop a bunch of R tutorials. # Features @@ -59,7 +59,7 @@ Jaylen: A professor who understands that ecological forecasting is becoming impo The course combines two key components for developing a community of practice around ecological forecasting: 1) learning and engaging with the background and current state of knowledge in the field; and 2) developing the quantitative tool set for using time-series data to make and evaluate forecasts. A standard week in the course starts with discussing a paper on the core topic being covered and ends with an R tutorial demonstrating an application or implementation of that topic. -For discussion sessions,students read a paper in advance and are given a list of discussion questions to help them focus on key aspects of the paper and prepare for group discussions. The instructors then lead a group discussion on the paper, guiding the students through the discussion questions and integrating mini-lectures where appropriate to address common points of confusion about the paper (e.g., walking through a complicated modeling approach). A typical part of the discussion will involve the students reflecting on how the concepts apply to questions or systems they are familiar with. Based on Constructivism and other learning theories, this step of integrating the material into their existing knowledge will help students in constructing richer cognitive maps that better support retention and application [@bada2015]. +For discussion sessions, students read a paper in advance and are given a list of discussion questions to help them focus on key aspects of the paper and prepare for group discussions. The instructors then lead a group discussion on the paper, guiding the students through the discussion questions and integrating mini-lectures where appropriate to address common points of confusion about the paper (e.g., walking through a complicated modeling approach). A typical part of the discussion will involve the students reflecting on how the concepts apply to questions or systems they are familiar with. Based on Constructivism and other learning theories, this step of integrating the material into their existing knowledge will help students in constructing richer cognitive maps that better support retention and application [@bada2015]. In the second session of the week a live-coding R tutorial is presented on a topic related to the paper discussion. The R tutorials are designed to build from zero knowledge of time-series and forecasting in R. They assume that students have a working version of R and RStudio and a small amount of experience with basic R is beneficial for fully understanding all of the steps. However, it is possible to follow and work with the tutorials with no R background. The tutorials follow a general progression of: @@ -78,7 +78,7 @@ All of the course materials are available online at