Remote laboratories comprise experimental apparatus that can be observed and controlled in real time, from a remote location. When used in undergraduate education, remote laboratories allow more active learning to be included in a course than would otherwise be possible. This is because remote laboratories use less physical space, fewer staff hours, and can be accessed outside of ordinary working hours, as compared to traditional laboratory exercises. Hence, they permit deeper engagement with the subject due to the extra time that can be spent in active learning. Their open-ended nature allows exploration, which develops inquiry skills. Their ready accessibility supports widening participation, and allows sessions missed from caring commitments to be done online. So far, evidence suggests that online laboratories produce as good or better outcomes as traditional laboratories, possibly due to visualisations of hidden factors such as fields, flows and stresses, and that it is better to use them in combination with traditional laboratories rather than as a total replacement.
When laboratory work moves online, it is no longer possible for the instructor to supervise the class by walking around the lab, because the activities may be conducted in any location at any time. Students are expected to benefit from guidance and feedback during their activities. In prior work, open-ended simulation environments have been tapped to provide a data stream that can be analysed in order to give guidance to the teacher on which groups may require an intervention. In remote laboratories, the analysis will also rely in part on analysing measurement data that is being produced by the activity, and student responses to that data. Therefore, our research questions are to find out the degree to which we can unambiguously determine what is happening in an open-ended activity that is generating measurement data, and how best to guide the student. It is known from other work in a related area that a simple pre-planned explanation can achieve similarly beneficially results as a partial analysis of a student’s typed responses to questions. We expect good baseline performance from relatively straightforward analyses triggering pre-planned prompts and explanations, with considerable scope to gain additional benefits from more sophisticated approaches that draw on artificial intelligence techniques.
For an introduction to the concept of remote labs please see these videos from lead supervisor’s award-winning large-scale remote laboratory work at his previous employer:
For an introduction to the evaluation of online technical work in education settings, please see this TedX talk from the second supervisor:
The image comes from a webapp developed for use in a course, which is described here:
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
An undergraduate (or Masters) degree in informatics/computer science or engineering. All candidates must have previous experience of programming. It is desirable to have taken an introductory course in artificial intelligence or signal processing and be willing to develop further skills in these areas. Candidates with a main degree in (digital) education or psychology and with demonstrated coding skills are also encouraged to express interest.
Tuition fees + stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate)