Best in eLearning 2012-13 – Earth, Atmospheric and Environmental Sciences

Title: EART30551 Meteorology
Tutor: Professor David Schultz

1. Course Overview

EART30551 is a third year Meteorology course from the school of Earth and Atmospheric Sciences. The unit has 42 students and received 5 nominations

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2. Comments from Students

Student 1
The tutor provided an online weather prediction assessment where we were given the opportunity to use weather maps and charts to predict the weather ourselves. I found this made the course more exciting and enabled you to see how weather prediction maps could be both accurate and inaccurate at times. The e-learning resource could be found on the EART30551 page on blackboard under course content.

Student 2
He used an online forecasting contest system (http://metcast.seaes.manchester.ac.uk/ ) to encourage us to forecast the weather weekly. He also provided us with useful links (such as Uniweather, wetter3.de, meteox.com, the whitworth observatory, etc) that supported the forecasting and helped to put into context what we were learning in class. In the process, this also supported the forecasting contest, and stimulated independent learning in terms of forecasting. Detailed feedback regarding coursework was given over email, and grades for the class tests and assignments were published in the form of histograms on Blackboard to allow us to gauge our standing in class. Furthermore, personalised feedback was given whenever I asked for help over email. A range of interactive materials were used to support powerpoint lectures. For example, Uniweather was one of the forecasting tools used during lectures. This helped me understand and apply Uniweather to my own forecasting exercises.

Student 3
We had a weekly online MetCast (Weather forecasting competition), which was really fun and helped to apply what we learnt in lectures. The personalised feedback we received on the writing assignment was very useful and I improved my grade by 45%! Lecture slides were always on blackboard on time and there was always supporting information and extra information. The lecturer replied to emails quickly which I found very helpful, especially when I had a question to ask and was stuck. I enjoyed the module thoroughly!!

Student 4
Set up a weekly weather prediction test online, forcing me to use the online weather forecast and data provided by the university, this can all be found online at uniweather, and the Whitworth Observatory website. This allowed me to build knowledge by looking at the data and forecasts and having to interpret and extrapolate the results, this created the most interactive elearning experience I have ever had.

Student 5

Dr Schultz introduced a stricter marking scheme to anything we have ever experienced, giving myself personally an insight into the higher standard expected at a higher academic level. I am now much more careful about the details of my work, having been shown that little errors make a big difference. He was also infectiously enthusiastic about the course, which resulted in me learning much more than I would have.

3. Comments from Professor David Schultz

eLearning in EART30551 has two components.  The first component is Uniweather, the first real-time UK computer weather forecasting system for students.   Uniweather was co-developed with Leeds University and provides daily forecasts using similar technology to what the Met Office uses.  As such, students see the strengths and weaknesses of the Uniweather model every day.  The model is used in our weather map discussions within lectures, and the archived output can be revisited in the discussions or the student projects.  Each student is responsible for picking a past weather event and discussing it within a short written report.  Having access to such tools helps students become familiar with how, not only of weather prediction, but how environmental prediction is done in general, using a state-of-the-art model used by potential employers.  No other UK university gives students this direct contact with models.

The second component is the forecast contest MetCast, codeveloped with eLearning staff.  MetCast gets students looking at the weather and relating it to their lecture material, outside of lecture.  Students enter into MetCast a forecast for the maximum temperature and probability of precipitation for the campus weather station for the next day.  MetCast calculates, ranks, and displays the results.  Student evaluations show that many students were engaged by the weather discussions using Uniweather and the contest, leading to synthesis of knowledge and practical application of the lecture material.  An article about MetCast was published in the peer-reviewed Journal of Science Education and Technology.

 

 

Best in eLearning 2012-13 – Earth, Atmospheric and Environmental Sciences

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