Studies in televised instruction, the role of visuals in verbal learning.

"AIR-C48-1/63-TR."

New Techniques in Deep Representation Learning

Thesis (Ph.D.)--University of Washington, 2016-06

Essays on the Analysis of Dynamic Games

Thesis (Ph.D.)--University of Washington, 2016-06

Teaching College Writing in a High School Setting: The Impact of Concurrent Enrollment on Teacher Learning and Practice

Thesis (Ph.D.)--University of Washington, 2016-06

Socio-Historical Factors Mediating Collaborative Teaching and Learning: A Design-Based Investigation and Intervention

Thesis (Ph.D.)--University of Washington, 2016-06

Using the R package Shiny to create web applications that facilitate quantitative gene expression analysis

Thesis (Master's)--University of Washington, 2016-06

Principals' and teachers' perceptions of learning disabilities: A study from Nara Prefecture, Japan

In this study, perceptions of learning disabilities were obtained from 128 principals and 123 teachers in the Nara Prefecture, Japan. A factor analysis indicated that five factors underlie perceptions of learning disabilities: changes in the family and social situation, insufficient knowledge of and support for learning disabilities, teachers' abilities and professional development, teachers' situation and governmental issues. Teachers' situation was perceived to be the main factor, whereas the least important factor was governmental issues. Teachers mainly indicated agreement on the factor of insufficient knowledge of and support for students with learning disabilities. Principals were more aware of governmental issues than teachers.

Learning mathematics in a classroom community of inquiry

This article considers the question of what specific actions a teacher might take to create a culture of inquiry in a secondary school mathematics classroom. Sociocultural theories of learning provide the framework for examining teaching and learning practices in a single classroom over a two-year period. The notion of the zone of proximal development (ZPD) is invoked as a fundamental framework for explaining learning as increasing participation in a community of practice characterized by mathematical inquiry. The analysis draws on classroom observation and interviews with students and the teacher to show how the teacher established norms and practices that emphasized mathematical sense-making and justification of ideas and arguments and to illustrate the learning practices that students developed in response to these expectations.

Multivariate genetic analysis of cognitive abilities in an adolescent twin sample

The sources of covariation among cognitive measures of Inspection Time, Choice Reaction Time, Delayed Response Speed and Accuracy, and IQ were examined in a classical twin design that included 245 monozygotic (MZ) and 298 dizygotic (DZ) twin pairs. Results indicated that a factor model comprising additive genetic and unique environmental effects was the most parsimonious. In this model, a general genetic cognitive factor emerged with factor loadings ranging from 0.28 to 0.64. Three other genetic factors explained the remaining genetic covariation between various speed and Delayed Response measures with IQ. However, a large proportion of the genetic variation in verbal (54%) and performance (25%) IQ was unrelated to these lower order cognitive measures. The independent genetic IQ variation may reflect information processes not captured by the elementary cognitive tasks, Inspection Time and Choice Reaction Time, nor our working memory task, Delayed Response. Unique environmental effects were mostly nonoverlapping, and partly represented test measurement error.

A conceptual model of personalized virtual learning environments

The Virtual Learning Environment (VLE) is one of the fastest growing areas in educational technology research and development. In order to achieve learning effectiveness, ideal VLEs should be able to identify learning needs and customize solutions, with or without an instructor to supplement instruction. They are called Personalized VLEs (PVLEs). In order to achieve PVLEs success, comprehensive conceptual models corresponding to PVLEs are essential. Such conceptual modeling development is important because it facilitates early detection and correction of system development errors. Therefore, in order to capture the PVLEs knowledge explicitly, this paper focuses on the development of conceptual models for PVLEs, including models of knowledge primitives in terms of learner, curriculum, and situational models, models of VLEs in general pedagogical bases, and particularly, the definition of the ontology of PVLEs on the constructivist pedagogical principle. Based on those comprehensive conceptual models, a prototyped multiagent-based PVLE has been implemented. A field experiment was conducted to investigate the learning achievements by comparing personalized and non-personalized systems. The result indicates that the PVLE we developed under our comprehensive ontology successfully provides significant learning achievements. These comprehensive models also provide a solid knowledge representation framework for PVLEs development practice, guiding the analysis, design, and development of PVLEs. (c) 2005 Elsevier Ltd. All rights reserved.

Does industry matter? Examining the role of industry structure and organizational learning in innovation and brand performance

A manager's perception of industry structure (dynamism) has the potential to impact various organizational strategies and behaviors. This may be particularly so with regard to perceptions driving organizational learning orientations and innovation based marketing strategy. The position taken here suggests that firms operating within a competitive industry tend to pursue innovative ways of performing value-creating activities, which requires the development of learning capabilities. The results of a study of SMEs suggest that market focused learning, relative to other learning capabilities plays a key role in the relationships between industry structure, innovation and brand performance. The findings also show that market focused learning and internally focused learning influence innovation and that innovation influences a brand's performance. (c) 2005 Elsevier Inc. All rights reserved.

Analysis of articulatory dynamics in dysarthria following brain injury in childhood using electromagnetic articulography and electropalatography

Two physiological assessments, electromagnetic articulography (EMA) and electropalatography (EPG), were used simultaneously to investigate the articulatory dynamics in an 18-year-old male with dysarthria 9 years following traumatic brain injury (TBI). Eight words consisting of /t, s, integral, k/ in word initial and word final positions were produced up to 10 times. A nonneurologically impaired male served as a control subject. Six parameters were analyzed using EMA: velocity, acceleration, deceleration, distance, duration, and motion path of tongue movements. Using EPG, the pattern and amount of tongue-to-palate contact and the duration of the closure/constriction phase of each consonant produced were assessed. Timing disturbances in the TBI speaker's speech were highlighted in perceptual assessments in the form of prolonged phonemes and a reduced speech rate. EMA analysis revealed that the approach and release phase durations of the consonant productions were within normal limits. Kinematic strategies such as decreased velocity and decreased distances traveled by the tongue, however, may have counterbalanced each other to produce these appropriate results. EPG examination revealed significantly longer closure/constriction phase periods, which may have contributed to the prolonged phonemes and reduced speech rate observed. The implications of these findings for the development of treatment programs for dysarthria subsequent to TBI will be highlighted.

The applicability of recurrent neural networks for biological sequence analysis

Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.

Highlighting hybridity: A critical discourse analysis of teacher talk in science classrooms

There is evidence that alienation from science is linked to the dominant discourse practices of science classrooms (cf. Lemke, J. L. (1990). Talking Science: Language, Learning, and Values. Norwood, NJ: Ablex). Yet, in secondary science education it is particularly hard to find evidence of curriculum reform that includes explicit changes in pedagogic discourses to accommodate the needs of students from a wide range of backgrounds. However, such evidence does exist and needs to be highlighted wherever it is found to help address social justice concerns in science education. In this article, I show how critical discourse analysis can be used to explore a way of challenging the dominant discourse in teacher-student interactions in science classrooms. My findings suggest a new way of moving toward more socially just science curricula in middle years and secondary classrooms by using hybrid discourses that can serve emancipatory purposes. © 2005 Wiley Periodicals. Inc.