This resource can be used in providing real-life activity for students by conducting survey. Results of their survey will be organized and presented through text, graphs and tables with research ethics observed.
Students create a spreadsheet to quantify, analyze and determine the experiences and views towards technology of the Computer Commuter users.
Students create the layout of the spreadsheet that best suits the data in the survey. Questions are in different formats: multiple choice and short answer. The student must determine how to set up the spreadsheet to make the data easy and efficiently understandable.
The ETD+ Virtual Workshop Series, taught by Dr. Katherine Skinner, is a set of free introductory training resources on crucial data curation and digital longevity techniques. Focusing on the Electronic Thesis and Dissertation (ETD) as a mile-marker in a student’s research trajectory, it provides in-time advice to students and faculty about avoiding common digital loss scenarios for the ETD and all of its affiliated files.
About the ETDplus Project
The ETDplus project is helping institutions ensure the longevity and availability of ETD research data and complex digital objects (e.g., software, multimedia files) that comprise an integral component of student theses and dissertations. The project was generously funded by the Institute of Museum and Library Services (IMLS) and led by the Educopia Institute, in collaboration with the NDLTD, HBCU Alliance, bepress, ProQuest, and the libraries of Carnegie Mellon, Colorado State, Indiana State, Morehouse, Oregon State, Penn State, Purdue, University of Louisville, University of Tennessee, the University of North Texas, and Virginia Tech.
Acknowledgements
This project was made possible in part by the Institute of Museum and Library Services.
This video lesson is an example of ''teaching for understanding'' in lieu of providing students with formulas for determining the height of a dropped (or projected) object at any time during its fall. The concept presented here of creating a chart to organize and analyze data collected in a simple experiment is broadly useful. During the classroom breaks in this video, students will enjoy timing objects in free fall and balls rolling down ramps as a way of learning how to carefully conduct experiments and analyze the results. The beauty of this lesson is the simplicity of using only the time it takes for an object dropped from a measured height to strike the ground. There are no math prerequisites for this lesson and no needed supplies, other than a blackboard and chalk. It can be completed in one 50-60-minute classroom period.
How researchers structure their data varies by disciplines and research questions. Still, there are general guidelines for structuring data that make it more likely to be usable in the future. The following questions should be considered for any project that gathers data. These questions should be considered first at the planning stage, again as data is being gathered and stored, and once more prior to final deposit into a digital archive or repository.
1. What are the data organization standards for your field? For example, there are often standards for labeling data fields that will make your data machinereadable. There may also be specific variables and coding guidelines that you can use that will make your work interoperable with other datasets. Lastly, there may be accepted hierarchies and directory structures in your discipline that you can build upon.
2. What are the data export options in the software you are using? If using proprietary and/or highly specialized software to analyze large data sets, export the data in a format that is likely to be supported in the future, and that will be accessible from other software programs. This usually means choosing an open format that is not proprietary. Remember that you may not have access to the same software in the future, and not all software upgrades can read old file types.
3. What forms of the data will be needed for future access? Consider the various forms the data may take, and the scale of the data involved. You may need to preserve not only the underlying raw data, but also the resulting analyses you have created from it.
After a car and pedestrian accident occurs near the local school, concerned students, parents, and neighbors launch a neighborhood safety project. Students consider potential hazards and then collect traffic and pedestrian data that might shed light on the situation. A survey is conducted to determine how children in the neighborhood travel between home and school, and students challenge their classmates to increase their use of human-powered (foot and pedal) transportation. Students use spreadsheets to enter and represent data, analyze their observations and survey data to determine the most significant problems, and study possible solutions. They develop a proposal for improving traffic safety, create slideshows and brochures, and present their ideas to the local city council.
This unit plan was originally developed by the Intel® Teach program as an exemplary unit plan demonstrating some of the best attributes of teaching with technology.
Using actual wildlife injury data from a local wildlife rescue center, students learn what animal species have been injured and the causes of injury. Students use spreadsheet software to sort, organize, and evaluate their findings for recommendations to reduce human-caused injury to wildlife. Students prepare and present a summary of their findings and recommendations to the local Audubon Society, The Humane Society, neighborhood associations, and other interested groups. At the end of each public presentation, students gather public reaction to the data and collect ideas on how to reduce injury to wildlife. These recommendations are compiled into a newsletter and wiki for dissemination to a wider audience.
This unit plan was originally developed by the Intel® Teach program as an exemplary unit plan demonstrating some of the best attributes of teaching with technology.
This course focuses on a number of qualitative social science methods that can be productively used in media studies research including interviewing, participant observation, focus groups, cultural probes, visual sociology, and ethnography. The emphasis will primarily be on understanding and learning concrete techniques that can be evaluated for their usefulness in any given project and utilized as needed. Data organization and analysis will be addressed. Several advanced critical thematics will also be covered, including ethics, reciprocity, “studying up,” and risk. The course will be taught via a combination of lectures, class discussions, group exercises, and assignments. This course requires a willingness to work hands-on with learning various social science methods and a commitment to the preparation for such (including reading, discussion, and reflection).