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- All Subjects: Evaluation
- Creators: Amrein-Beardsley, Audrey
- Creators: Anselin, Luc
- Creators: Danzig, Arnold
- Creators: Ewbank, Ann
- Member of: ASU Electronic Theses and Dissertations
- Status: Published
Choropleth maps are a common form of online cartographic visualization. They reveal patterns in spatial distributions of a variable by associating colors with data values measured at areal units. Although this capability of pattern revelation has popularized the use of choropleth maps, existing methods for their online delivery are limited in supporting dynamic map generation from large areal data. This limitation has become increasingly problematic in online choropleth mapping as access to small area statistics, such as high-resolution census data and real-time aggregates of geospatial data streams, has never been easier due to advances in geospatial web technologies. The current literature shows that the challenge of large areal data can be mitigated through tiled maps where pre-processed map data are hierarchically partitioned into tiny rectangular images or map chunks for efficient data transmission. Various approaches have emerged lately to enable this tile-based choropleth mapping, yet little empirical evidence exists on their ability to handle spatial data with large numbers of areal units, thus complicating technical decision making in the development of online choropleth mapping applications. To fill this knowledge gap, this dissertation study conducts a scalability evaluation of three tile-based methods discussed in the literature: raster, scalable vector graphics (SVG), and HTML5 Canvas. For the evaluation, the study develops two test applications, generates map tiles from five different boundaries of the United States, and measures the response times of the applications under multiple test operations. While specific to the experimental setups of the study, the evaluation results show that the raster method scales better across various types of user interaction than the other methods. Empirical evidence also points to the superior scalability of Canvas to SVG in dynamic rendering of vector tiles, but not necessarily for partial updates of the tiles. These findings indicate that the raster method is better suited for dynamic choropleth rendering from large areal data, while Canvas would be more suitable than SVG when such rendering frequently involves complete updates of vector shapes.
This study examines validity evidence of a state policy-directed teacher evaluation system implemented in Arizona during school year 2012-2013. The purpose was to evaluate the warrant for making high stakes, consequential judgments of teacher competence based on value-added (VAM) estimates of instructional impact and observations of professional practice (PP). The research also explores educator influence (voice) in evaluation design and the role information brokers have in local decision making. Findings are situated in an evidentiary and policy context at both the LEA and state policy levels.
The study employs a single-phase, concurrent, mixed-methods research design triangulating multiple sources of qualitative and quantitative evidence onto a single (unified) validation construct: Teacher Instructional Quality. It focuses on assessing the characteristics of metrics used to construct quantitative ratings of instructional competence and the alignment of stakeholder perspectives to facets implicit in the evaluation framework. Validity examinations include assembly of criterion, content, reliability, consequential and construct articulation evidences. Perceptual perspectives were obtained from teachers, principals, district leadership, and state policy decision makers. Data for this study came from a large suburban public school district in metropolitan Phoenix, Arizona.
Study findings suggest that the evaluation framework is insufficient for supporting high stakes, consequential inferences of teacher instructional quality. This is based, in part on the following: (1) Weak associations between VAM and PP metrics; (2) Unstable VAM measures across time and between tested content areas; (3) Less than adequate scale reliabilities; (4) Lack of coherence between theorized and empirical PP factor structures; (5) Omission/underrepresentation of important instructional attributes/effects; (6) Stakeholder concerns over rater consistency, bias, and the inability of test scores to adequately represent instructional competence; (7) Negative sentiments regarding the system's ability to improve instructional competence and/or student learning; (8) Concerns regarding unintended consequences including increased stress, lower morale, harm to professional identity, and restricted learning opportunities; and (9) The general lack of empowerment and educator exclusion from the decision making process. Study findings also highlight the value of information brokers in policy decision making and the importance of having access to unbiased empirical information during the design and implementation phases of important change initiatives.