Designating a Laboratory or Field Work Experience Course: [L]
When you request L designation for a course on the Garnet Gateway, you will be given the following information and be asked to respond to the questions below.
To qualify for designation as a laboratory or fieldwork course, at least one-quarter of the total number of hours the class meets should be devoted to laboratory or fieldwork activities. In addition, the laboratory or fieldwork component should provide students with hands-on experience in making their own measurements or observations, engage students in evaluating the quality of data or observations, and encourage students to think critically about how conclusions can be drawn from available scientific evidence.
Please check the criteria listed below which will be met by your proposed ‘L’ course. To qualify as an ‘L’ course, the Registrar anticipates that at least three of these criteria should be met. If your proposed course does not meet at least three of the criteria but you think it should still qualify for an ‘L’ designation, please use the comments box to provide an explanation. Note that scientific evidence as used here is defined as data collected or observations made in a systematic way. If more than one instructor will be teaching the proposed course, all must agree that the course should be designated as an ‘L’ course.
1. This course includes a laboratory or field work component that engages students in the design of experiments or in making their own measurements or observations.
2. This course includes a laboratory or field work component that engages students in the consideration of the factors which render data or observations valid for use as scientific evidence.
3. This course includes a laboratory or field work component that teaches students how to critically evaluate data or observations and think critically about the conclusions that can be drawn from data or observations.
4. This course includes a laboratory or field work component that demonstrates the predictability and reproducibility of outcomes, based upon prior measurements or observations.