Components of learning and assessment in linear algebra

Linear algebra provides theory and technology that are the cornerstones of a range of cutting edge mathematical applications, from designing computer games to complex industrial problems, as well as more traditional applications in statistics and mathematical modelling. Once past introductions to matrices and vectors, the challenges of balancing theory, applications and computational work across mathematical and statistical topics and problems are considerable, particularly given the diversity of abilities and interests in typical cohorts. This paper considers two such cohorts in a second level linear algebra course in different years. The course objectives and materials were almost the same, but some changes were made in the assessment package. In addition to considering effects of these changes, the links with achievement in first year courses are analysed, together with achievement in a following computational mathematics course. Some results that may initially appear surprising provide insight into the components of student learning in linear algebra.

Relative entropy rate based multiple hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimisation problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterising multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.

Illegal pedestrian crossing at signalised intersections : Incidence and relative risk

Illegal pedestrian behaviour is common and is reported as a factor in many pedestrian crashes. Since walking is being promoted for its health and environmental benefits, minimisation of its associated risks is of interest. The risk associated with illegal road crossing is unclear, and better information would assist in setting a rationale for enforcement and priorities for public education. An observation survey of pedestrian behaviour was conducted at signalised intersections in the Brisbane CBD (Queensland, Australia) on typical workdays, using behavioural categories that were identifiable in police crash reports. The survey confirmed high levels of crossing against the lights, or close enough to the lights that they should legally have been used. Measures of exposure for crossing legally, against the lights, and close to the lights were generated by weighting the observation data. Relative risk ratios were calculated for these categories using crash data from the observation sites and adjacent midblocks. Crossing against the lights and crossing close to the lights both exhibited a crash risk per crossing event approximately eight times that of legal crossing at signalised intersections. The implications of these results for enforcement and education are discussed, along with the limitations of the study.