No abstract

A quantitative and qualitative empirical evaluation is presented to show the effect of providing some sensor support during driving lessons as a tele-operator learns to drive a mobile robot. Different levels of sensor support were provided and the effect on training was measured. Different levels of force feedback were provided through a joystick.Results are presented and conclusions drawn about the way that teleoperators behave during driving tasks.

Methods arc described to create more accurate sub sets of user data by introducing dead bands into data clusters. User data is collected and then mined. That produces clusters of data. Dead bands arc then generated to delineate and describe the data in the clusters more accurately. This is accomplished by classifying data inside the newly created dead bands as NOT being in either of two or more clusters. For example, three clusters arc generated f r om two. If the t w o were YES and NO then another set of DON'T KNOW is introduced. The new set improves the precision of choices made using data in the YES and the NO clusters. Dead bands arc introduced by establishing a radius f r om the corners of2-D shapes containing the clusters or by establishing a horizontal or vertical line in parallel with the edges. Each radius or edge encompasses 80% of user data nearest to the corner or edge of the data set. 20% arc outside and excluded f r om their original set. If lines do not overlap, then a dead-band is created to contain user data that is not as confident. That increases the likelihood of accurate decisions being made about the new sets of user data. Case studies are described to demonstrate that.

This paper considers the hardware and software implementation of a Near-Field Communication (NFC) reading system which was used to collect student attendance information in a two-year study with University Engineering students. Learning analytics has become popular with an increase in attendance monitoring activity across the higher education sector. This paper presents a bespoke in-house attendance monitoring system and has been created in the School of Engineering to conduct research to consider the benefits of such a system for staff and students. The paper highlights some of the technical decisions and challenges faced during the implementation of the trial system and considers the accessibility of the system for powered wheelchair users. Electronic card reader hardware was purchased and installed in selected rooms in the University as part of a faculty of technology learning and teaching grant looking at Attendance monitoring. The study has concluded that the introduction of an electronic card reader system at the University of Portsmouth would not cause unacceptable delays for students entering classrooms and would contribute positively to student experience, however accessibility measures would need to be put in place to enable powered wheelchair users to engage with the system at all locations across the University premises.