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- All Subjects: Evaluation
- Creators: Baral, Chitta
- Creators: Amrein-Beardsley, Audrey
- Creators: Armfield, Jessica Ann
- Status: Published
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.
To boost students’ learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student’s current competence so that a suitable question could be selected based on the student’s previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group.
To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators.
A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from Amazon Mechanical Turk, it turned out that the two types of questions performed very closely on all the three measures.
Preventing heat-associated morbidity and mortality is a public health priority in Maricopa County, Arizona (United States). The objective of this project was to evaluate Maricopa County cooling centers and gain insight into their capacity to provide relief for the public during extreme heat events. During the summer of 2014, 53 cooling centers were evaluated to assess facility and visitor characteristics. Maricopa County staff collected data by directly observing daily operations and by surveying managers and visitors. The cooling centers in Maricopa County were often housed within community, senior, or religious centers, which offered various services for at least 1500 individuals daily. Many visitors were unemployed and/or homeless. Many learned about a cooling center by word of mouth or by having seen the cooling center’s location. The cooling centers provide a valuable service and reach some of the region’s most vulnerable populations. This project is among the first to systematically evaluate cooling centers from a public health perspective and provides helpful insight to community leaders who are implementing or improving their own network of cooling centers.