Mindmapping as an adult learning notetaking strategy to increase the encoding of information in a corporate training course

A study was conducted to investigate the effectiveness, as measured by performance on course posttests, of mindmapping versus traditional notetaking in a corporate training class. The purpose of this study was to increase knowledge concerning the effectiveness of mindmapping as an information encoding tool to enhance the effectiveness of learning. Corporations invest billions of dollars, annually, in training programs. Given this increased demand for effective and efficient workplace learning, continual reliance on traditional notetaking is questionable for the high-speed and continual learning required on workers.^ An experimental, posttest-only control group design was used to test the following hypotheses: (1) there is no significant difference in posttest scores on an achievement test, administered immediately after the course, between adult learners using mindmapping versus traditional notetaking methods in a training lecture, and (2) there is no significant difference in posttest scores on an achievement test, administered 30 days after the course, between adult learners using mindmapping versus traditional notetaking methods in a training lecture. After a 1.5 hour instruction on mindmapping, the treatment group used mindmapping throughout the course. The control group used traditional notetaking. T-tests were used to determine if there were significant differences between mean posttest scores between the two groups. In addition, an attitudinal survey, brain hemisphere dominance survey, course dynamics observations, and course evaluations were used to investigate preference for mindmapping, its perceived effect on test performance, and the effectiveness of mindmapping instruction.^ This study's principal finding was that although the mindmapping group did not perform significantly higher on posttests administered immediately and 30 days after the course, than the traditional notetaking group, the mindmapping group did score higher on both posttests and reported higher ratings of the course on every evaluation criteria. Lower educated, right brain dominant learners reported a significantly positive learning experience. These results suggest that mindmapping enhances and reinforces the preconditions of learning. Recommendations for future study are provided. ^

Data partitioning and replication management in distributed geographic information system (GIS) database

This research presents several components encompassing the scope of the objective of Data Partitioning and Replication Management in Distributed GIS Database. Modern Geographic Information Systems (GIS) databases are often large and complicated. Therefore data partitioning and replication management problems need to be addresses in development of an efficient and scalable solution. ^ Part of the research is to study the patterns of geographical raster data processing and to propose the algorithms to improve availability of such data. These algorithms and approaches are targeting granularity of geographic data objects as well as data partitioning in geographic databases to achieve high data availability and Quality of Service(QoS) considering distributed data delivery and processing. To achieve this goal a dynamic, real-time approach for mosaicking digital images of different temporal and spatial characteristics into tiles is proposed. This dynamic approach reuses digital images upon demand and generates mosaicked tiles only for the required region according to user's requirements such as resolution, temporal range, and target bands to reduce redundancy in storage and to utilize available computing and storage resources more efficiently. ^ Another part of the research pursued methods for efficient acquiring of GIS data from external heterogeneous databases and Web services as well as end-user GIS data delivery enhancements, automation and 3D virtual reality presentation. ^ There are vast numbers of computing, network, and storage resources idling or not fully utilized available on the Internet. Proposed "Crawling Distributed Operating System "(CDOS) approach employs such resources and creates benefits for the hosts that lend their CPU, network, and storage resources to be used in GIS database context. ^ The results of this dissertation demonstrate effective ways to develop a highly scalable GIS database. The approach developed in this dissertation has resulted in creation of TerraFly GIS database that is used by US government, researchers, and general public to facilitate Web access to remotely-sensed imagery and GIS vector information. ^

Improved Annual Average Daily Traffic (AADT) estimation for local roads using parcel-level travel demand modeling

Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. By definition, AADT is the average 24-hour volume at a highway location over a full year. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. For example, AADTs are used by state Departments of Transportation (DOTs) to calculate the crash rates of all local roads in order to identify the top five percent of hazardous locations for annual reporting to the U.S. DOT. ^ This dissertation develops a new method for estimating AADTs for local roads using travel demand modeling. A major component of the new method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The model uses the tax parcel data together with the trip generation rates and equations provided by the ITE Trip Generation Report. The generated trips are then distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The all-or-nothing assignment method is then used to assign the trips onto the roadway network to estimate the final AADTs. The entire process was implemented in the Cube demand modeling system with extensive spatial data processing using ArcGIS. ^ To evaluate the performance of the new method, data from several study areas in Broward County in Florida were used. The estimated AADTs were compared with those from two existing methods using actual traffic counts as the ground truths. The results show that the new method performs better than both existing methods. One limitation with the new method is that it relies on Cube which limits the number of zones to 32,000. Accordingly, a study area exceeding this limit must be partitioned into smaller areas. Because AADT estimates for roads near the boundary areas were found to be less accurate, further research could examine the best way to partition a study area to minimize the impact.^