The various remote robotic labs (RRL), including MIT's iLab, are different in some important aspects. Because our approach is so different, educators often have trouble understanding the differences compared to approaches with which they may be familiar. This explanation should help to clear up any questions.
A RRL provides data automatically. You set up your parameters, whatever those may be, and push a virtual button. Often, you see nothing transpire at the remote site. After a brief pause, you're handed a sheaf of data electronically. For advanced students, that may be just fine, but for ordinary students, all of the trouble of setting up the RRL has been wasted. You might as well have stored the data from yesterday (or last year) along with any imagery and provided that. In that event, you could have just provided this information locally. The students wouldn't know the difference and probably wouldn't even care.
RRLs have limited range. They cannot do Sordaria crossing over or seed germination experiments. You can imagine doing tides, but the real-time aspect is lost because students are not there in real time the entire time that data are being captured. And so it goes. You cannot base an entire biology or chemistry course on just RRLs.
RRLs have limited access. If you attempt to scale RRLs, you must have more pieces of expensive or unique equipment. Depending on the precise experiment being run, the time that the machine is available controls how many students can use it during a given hour-long period. It's not unlimited. You know that you cannot deliver to a million students per hour and probably not even to a thousand.
Our approach takes the online hands-on lab (OHOL) path. We toss out the pretense of real-time experiments. (I say pretense because there's always a delay between data capture and arrival at the student workstation.) In its place, we open up entire new vistas of learning science.
The OHOL way does not deliver data automatically. Students truly must interact to take their own data. As in the tides example, those data are different for different students doing the same experiment with the same parameters.
With OHOL, you have a visual experience. With tides, you watch the actual tides and then measure them yourself.
An OHOL can be created for any experiment you can record on video and take data from. The data may be quantitative, semi-quantitative, or qualitative. They are your data, not those of a machine. The experiment videos may be from a high-speed camera or from a time-lapse camera. They may even combine multiple cameras as with the shadows lab where one camera follows the Sun with a fish-eye lens and the other tracks the path of a shadow.
What do OHOLs and RRLs have in common? None of the data are invented. They all come from the real world. The various forms of real wet labs also have this feature. However, only manual wet labs and OHOLs are truly hands-on in the sense that you take your own data point by point.
Our technology allows for an unlimited number of scenarios. We're only limited by our imagination and our resources. We have done as many as 100 experiments to create one lab. The number of experiments available is also a function of the pedagogy. Students can be confused by having 30 experiments available. Some will think that they must do every one rather than exercise judgment (actually think) despite our telling them otherwise. It becomes the instructor's task to handle this issue because instructors control grades, and students who do every single experiment available are doing so because they think they'll improve their grades. The instructor must convince them that lack of thought will reduce their grades. Our best efforts cannot do so because we do not hand out grades.
There's much more to this picture. For example, we insist on students making predictions before beginning experiments. We provide introductory (pre-lab or formative) assessments and summary (post-lab or summative) assessments. We provide extensive background resources and an online lab report that can be customized for your classes.
The above is not to say that RRLs have no value. On the contrary they are the go-to labs of the future for college engineering courses. They open up the use of expensive equipment that many schools cannot afford to undergraduate engineering students. They have limited use for college science courses. The limitations are those of the medium that requires complete automation and relatively quick experiment completion. They're of little value in K-12 education. You can find better ways to learn any science concept at that level, with the possible exception of advanced or honors courses and then, as with college science, only with a very few investigations.
© 2013 by Smart Science Education Inc., U.S.A. www.smartscience.net
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My B.S. is in chemistry from Caltech. My PhD is from Columbia University. I was a postdoctoral fellow at Colorado State and a professor at Northeastern University. I even was the chair of the 3,500-member Northeastern Section of the American Chemical Society.
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