Effects of structured writing versus nonstructured writing as an effective and efficient teaching and learning strategy

This research investigated the effectiveness and efficiency of structured writing as compared to traditional nonstructured writing as a teaching and learning strategy in a training session for teachers.^ Structured writing is a method of identifying, interrelating, sequencing, and graphically displaying information on fields of a page or computer. It is an alternative for improving training and educational outcomes by providing an effective and efficient documentation methodology.^ The problem focuses upon the contradiction between: (a) the supportive research and theory to modify traditional methods of written documents and information presentation and (b) the existing paradigm to continue with traditional communication methods.^ A MANOVA was used to determine significant difference between a control and an experimental group in a posttest only experimental design. The experimental group received the treatment of structured writing materials during a training session. Two variables were analyzed. They were: (a) effectiveness; correct items on a posttest, and (b) efficiency; time spent on test.^ The quantitative data showed a difference for the experimental group on the two dependent variables. The experimental group completed the posttest in 2 minutes less time while scoring 1.5 more items correct. An interview with the training facilitators revealed that the structured writing materials were "user friendly." ^

Reduced-parameter model of head-related transfer functions for synthesized spatial audio

Digital systems can generate left and right audio channels that create the effect of virtual sound source placement (spatialization) by processing an audio signal through pairs of Head-Related Transfer Functions (HRTFs) or, equivalently, Head-Related Impulse Responses (HRIRs). The spatialization effect is better when individually-measured HRTFs or HRIRs are used than when generic ones (e.g., from a mannequin) are used. However, the measurement process is not available to the majority of users. There is ongoing interest to find mechanisms to customize HRTFs or HRIRs to a specific user, in order to achieve an improved spatialization effect for that subject. Unfortunately, the current models used for HRTFs and HRIRs contain over a hundred parameters and none of those parameters can be easily related to the characteristics of the subject. This dissertation proposes an alternative model for the representation of HRTFs, which contains at most 30 parameters, all of which have a defined functional significance. It also presents methods to obtain the value of parameters in the model to make it approximately equivalent to an individually-measured HRTF. This conversion is achieved by the systematic deconstruction of HRIR sequences through an augmented version of the Hankel Total Least Squares (HTLS) decomposition approach. An average 95% match (fit) was observed between the original HRIRs and those re-constructed from the Damped and Delayed Sinusoids (DDSs) found by the decomposition process, for ipsilateral source locations. The dissertation also introduces and evaluates an HRIR customization procedure, based on a multilinear model implemented through a 3-mode tensor, for mapping of anatomical data from the subjects to the HRIR sequences at different sound source locations. This model uses the Higher-Order Singular Value Decomposition (HOSVD) method to represent the HRIRs and is capable of generating customized HRIRs from easily attainable anatomical measurements of a new intended user of the system. Listening tests were performed to compare the spatialization performance of customized, generic and individually-measured HRIRs when they are used for synthesized spatial audio. Statistical analysis of the results confirms that the type of HRIRs used for spatialization is a significant factor in the spatialization success, with the customized HRIRs yielding better results than generic HRIRs.