Matching Items (4)
Filtering by
- All Subjects: Evaluation
- Genre: Academic theses
- Creators: Baral, Chitta
- Creators: Amrein-Beardsley, Audrey
- Creators: Bullock, Donna
- Status: Published
Description
ABSTRACT A review of studies selected from the Educational Resource Information Center (ERIC) covering the years 1985 through 2011 revealed three key evaluation components to analyze within a comprehensive teacher evaluation program: (a) designing, planning, and implementing instruction; (b) learning environments; and (c) parent and peer surveys. In this dissertation, these three components are investigated in the context of two research questions: 1. What is the relationship, if any, between comprehensive teacher evaluation scores and student standardized test scores? 2. How do teachers and administrators experience the comprehensive evaluation process and how do they use their experiences to inform instruction? The methodology for the study included a mixed-method case study at a charter school located in a middle-class neighborhood within a large metropolitan area of the southwestern United States, which included a comparison of teachers' average evaluation scores in the areas of instruction and environment, peer survey scores, parent survey scores, and students' standardized test (SST) benchmark scores over a two-year period as the quantitative data for the study. I also completed in-depth interviews with classroom teachers, mentor teachers, the master teacher, and the school principal; I used these interviews for the qualitative portion of my study. All three teachers had similar evaluation scores; however, when comparing student scores among the teachers, differences were evident. While no direct correlations between student achievement data and teacher evaluation scores are possible, the qualitative data suggest that there were variations among the teachers and administrators in how they experienced or "bought into" the comprehensive teacher evaluation, but they all used evaluation information to inform their instruction. This dissertation contributes to current research by suggesting that comprehensive teacher evaluation has the potential to change teachers' and principals' perceptions of teacher evaluation as inefficient and unproductive to a system that can enhance instruction and ultimately improve student achievement.
ContributorsBullock, Donna (Author) / Mccarty, Teresa (Thesis advisor) / Powers, Jeanne (Thesis advisor) / Stafford, Catherine (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT
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.
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.
ContributorsSloat, Edward F. (Author) / Wetzel, Keith (Thesis advisor) / Amrein-Beardsley, Audrey (Thesis advisor) / Ewbank, Ann (Committee member) / Shough, Lori (Committee member) / Arizona State University (Publisher)
Created2015
Description
Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions.
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.
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.
ContributorsZhang, Lishang (Author) / VanLehn, Kurt (Thesis advisor) / Baral, Chitta (Committee member) / Hsiao, Ihan (Committee member) / Wright, Christian (Committee member) / Arizona State University (Publisher)
Created2015
Description
In natural language processing, language models have achieved remarkable success over the last few years. The Transformers are at the core of most of these models. Their success can be mainly attributed to an enormous amount of curated data they are trained on. Even though such language models are trained on massive curated data, they often need specific extracted knowledge to understand better and reason. This is because often relevant knowledge may be implicit or missing, which hampers machine reasoning. Apart from that, manual knowledge curation is time-consuming and erroneous. Hence, finding fast and effective methods to extract such knowledge from data is important for improving language models. This leads to finding ideal ways to utilize such knowledge by incorporating them into language models. Successful knowledge extraction and integration lead to an important question of knowledge evaluation of such models by developing tools or introducing challenging test suites to learn about their limitations and improve them further. So to improve the transformer-based models, understanding the role of knowledge becomes important. In the pursuit to improve language models with knowledge, in this dissertation I study three broad research directions spanning across the natural language, biomedical and cybersecurity domains: (1) Knowledge Extraction (KX) - How can transformer-based language models be leveraged to extract knowledge from data? (2) Knowledge Integration (KI) - How can such specific knowledge be used to improve such models? (3) Knowledge Evaluation (KE) - How can language models be evaluated for specific skills and understand their limitations?
I propose methods to extract explicit textual, implicit structural, missing textual, and missing structural knowledge from natural language and binary programs using transformer-based language models. I develop ways to improve the language model’s multi-step and commonsense reasoning abilities using external knowledge. Finally, I develop challenging datasets which assess their numerical reasoning skills in both in-domain and out-of-domain settings.
ContributorsPal, Kuntal Kumar (Author) / Baral, Chitta (Thesis advisor) / Wang, Ruoyu (Committee member) / Blanco, Eduardo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023