This Remote Learning Plan was created by Kelly Garcia in collaboration with Andrew Easton as part of the 2019-20 ESU-NDE Digital Age Pedagogy Project. Educators worked with coaches to create Remote Learning Plans as a result of the COVID-19 pandemic.Remote Learning Plan is designed for secondary Spanish students. Students will talk about healthy/unhealthy lifestyle decisions.

As a result of the COVID-19 pandemic, it is increasingly likely that Remote Learning may continue into the 2020-21 academic year, either in pockets or statewide. Nebraska Educational Service Units are working in collaboration with the Nebraska Department of Education to help Districts navigate these uncertain times. The purpose of this professional learning module is to help educators plan high quality remote learning experiences. It is expected to take approximately 1 hour to complete this professional learning module on Remote Learning Planning. Upon completion, you are invited to share the Remote Learning Plan you create to receive a Certificate of Completion.  

Learning Management Systems (LMS) specialists from the Washington Association of Educational Service Districts have put together a list of key words and their meanings to help families become familiar with the basic vocabulary around LMS. Available in multiple languages.

A java code for computing modular exponents using the algorithm of repeated squares.

This exercise introduces learners to the practice of pre-reading. While designed for FYE, it can be used in any course with a research component. 

For decades, the media have given enormous attention to sensational claims that vague lights in the sky are actually extra-terrestrial spacecraft. Recently, there has been a flurry of misleading publicity about UFOs on military photographs. A sober examination of these claims reveals that there is a lot LESS to them than first meets the eye: when there is enough evidence, UFO claims can be explained by terrestrial or celestial phenomena (including lights from human craft and re-entering space junk). This up-to-date guide provides key resources available free on the Web, to help scientists, educators, students, and journalists learn about the skeptical perspective (and the background stories) behind these claims.

This lesson in part of Software Carpentry workshop and teach novice programmers to write modular code and best practices for using R for data analysis. an introduction to R for non-programmers using gapminder data The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis. The lesson contains more material than can be taught in a day. The instructor notes page has some suggested lesson plans suitable for a one or half day workshop. A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

The reliability of experimental findings depends on the rigour of experimental design. Here we show limited reporting of measures to reduce the risk of bias in a random sample of life sciences publications, significantly lower reporting of randomisation in work published in journals of high impact, and very limited reporting of measures to reduce the risk of bias in publications from leading United Kingdom institutions. Ascertainment of differences between institutions might serve both as a measure of research quality and as a tool for institutional efforts to improve research quality.

A module that uses Euler poles to describe the relative motion between the Baja Microplate and North American Plate, highlighting how the obliquity of rifting changes along strike in the Gulf of California.

A module in which students use field and geophysical measurements and observations of Gulf of California basins to calculate isostasy and its controls.

Students conduct an experiment to determine the relationship between the speed of a wooden toy car at the bottom of an incline and the height at which it is released. They observe how the photogate-based speedometer instrument "clocks" the average speed of an object (the train). They gather data and create graphs plotting the measured speed against start height. After the experiment, as an optional extension activity, students design brakes to moderate the speed of the cart at the bottom of the hill to within a specified speed range.

Contains files to print and play SEEK!, a card game to help students improve their information literacy - in particular, the ability to construct a search strategy.

Students listen to a story and answer questions about a family in Central or South America that barters to get the ingredients for chicken sancocho, a kind of stew. The students complete sentences that record the various trades carried out by the family to obtain all of the ingredients for the sancocho. They also participate in trading activities that illustrate money's advantages over barter.

See Note at End. This is a guide to science fiction stories and novels which are based on reasonably good science (and can thus be recommended in introductory astronomy courses.) The stories are organized by astronomical topic. While most of the stories are available only in print, a number are now published electronically free of charge, and links to those are included.

NOTE: An updated version of this resource can be found at:
http://bit.ly/astronomyscifi

This course focuses on early-stage biotechnology companies with particular emphasis on understanding the underlying science, technology, and disease targets—together with the application of novel business structures and financing methods—to facilitate drug discovery, clinical development, and greater patient access to new therapies.   
The course was created for MITx as a collaboration between the Whitehead Institute and the Sloan School of Management and is now archived on the Open Learning Library (OLL), which is free to use. You have the option to sign up and enroll in each module if you want to track your progress, or you can view and use all the materials without enrolling.

This is the textbook of a graduate course given in 2024 at the University of Twente on "Scientific computing with open-source software".

Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.

For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).

This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.

Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.

For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).

This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.