Thesis (M.A.)--York University, 2006. / Includes bibliographical references (p. 150-[154])

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Report (M.F.)--Virginia Polytechnic Institute and State University, 1994. / Includes bibliographical references (leaves 136-149). Also available via the Internet.

Learning theory and educational data analytics can be said to coevolve, that is, to refine and improve each other reciprocally, each aspect providing a necessary element for the growth and advancement of the other. In this three-paper dissertation, I explore this process of coevolution between learning theory and data analytics in the context of digital game-based learning. From the theoretical side, I describe a framework based on a general theory of cognition (the two-system or dual-system model) that can be applied to digital game environments. The main hypothesis in this framework is that certain patterns of action in the game-space indicate the use of certain epistemic stances that have analogues within the two-system model. The proposed Two Stance/Two Model Framework (2SM) provides (a) improved explanatory power regarding intrapersonal variation in learning from games, (b) more complete theory regarding individual needs, goals, and agency, (c) a more extensive account of collaboration and community, and (d) improved perspective on knowledge-rich interactions in online affinity spaces. From the methodological side, I applied techniques of statistical computing (affinity clustering and sequence mining) to detect the stances of the 2SM as they appear in a physics learning game. The 2SM theorized that slow modes of solution would correlate to higher learning gains; students who use mainly fast iterative solution strategies did achieve lower learning gains than students who preferred slow, elaborated solutions. A second finding was that, as play progresses, students generally improve their performance in game areas that highlight physics concepts, but that this improvement is strongly moderated by their prior knowledge of physics. This dissertation further contributes to the existing knowledge of digital game-based learning by demonstrating how an analysis of the collected actions of players can be applied in a reliable and comprehensive fashion to research questions that are otherwise challenging to investigate.

Thesis (Ed.M.)--Boston University

No description available.

The three papers in this dissertation contribute to research that seeks to characterize the complex and multi-dimensional relation between the physical environment and human learning. The first paper outlines a new approach to describe, represent, and interpret peopleâs interaction as they move within and across physical environments. I call this approach interaction geography. It encompasses Mondrian Transcription, a method to map peopleâs movement and conversation over space and time, and the Interaction Geography Slicer (IGS), a dynamic visualization tool that supports new forms of interaction and multi-modal analysis. The second paper extends this work to provide a conceptual framework to expand interaction geography in studies of learning. I show how interaction geography offers resources to integrate four historically separate research perspectives in order to study how peopleâs interaction, movement, and responses to, and actions on, the physical environment lead people to learn. The third paper adapts and uses the IGS to visualize and discuss data about New York Cityâs Stop-And-Frisk Program. I show how the IGS provides new ways to view, interact with, and query large-scale data sets of stop-and-frisk and crime data over space and through time to support analyses of and public discussion about a controversial social and political issue.

M.B.A. / The main thrust of this research is its critical perspective on the current situation faced by corporate South Africa. This involves the deliberate attempt to make a contribution in dismantling the deeply rooted problems of the past. The research has specifically focused on the transformation of all organisations in the country. The study then chooses to argue that for corporate South Africa to undergo a smooth planned change and overcome resistance to transformation, it has to go via the cultural diversity route. There is a need to prioritise and find new solutions. There is much work to be done. The major motivation behind the study was the very perception that South Africa is a multicultural country having all members aspiring to have their voices heard. The study then further argues that it will be best timing to activate economic transformation simultaneously with political transformation with particular emphasis on corporate South Africa. Hence the need to develop an integrated and formalised methodology for managing corporate planned change in its entirety. The development of the model is thus intended to provide management with a holistic methodology to internalise, think, plan and in the process of its implementation, incorporate all members of the organisation in decision making. This work has attempted through conceptual literature, to create an outcome that will influence other disciplines in different spheres, generate constructive arguments and activate further research. The work also intends to create a new perception that South Africa be seen as doing something concrete- about its corporate impasses.

Though researchers and policymakers have called for an increase in text complexity in reading instruction, little research exists on whether scaffolding can support students reading complex texts, and almost no research has evaluated reading interventions for late high school students. In addition, research has experimentally verified scaffoldingâs effectiveness in supporting comprehension and described possible mechanisms, but has not determined the effectiveness of different types of scaffolding. To address these gaps, the present study randomly assigned eleventh-grade students to either an eight-session intervention in which small groups of students (n=82) read complex texts with tutors or a comparison condition in which students (n=71) learned individually with computerized ACT preparation software. In addition, for intervention groups, tutorsâ interactional scaffolding adaptations to support their studentsâ comprehension was tracked, and multilevel regression analyses were used to investigate possible associations between types of scaffolding and student comprehension. Results showed that the intervention groupâs growth on a standardized measure of passage-based comprehension was statistically significant (p<0.03) and practically meaningful (d=0.17) for students who attended at least six sessions. Regression models examining links between types of scaffolding and outcomes found that scaffolds helping students determine the structure of the passage and scaffolds helping students unpack complex syntax were positively associated with comprehension outcomes, while scaffolds that involved rereading the text and understanding academic-register vocabulary words were negatively associated. The experimental results offer a causal argument to show that scaffolding can support readers of complex texts, and present a possible scaffolding-based model for comprehension interventions for late high school students. The correlational evidence linking scaffolds and outcomes suggests that the kinds of scaffolding matter for student outcomes, and that scaffolding which supports students in unraveling the syntactic and structural complexities of text may be helpful, while scaffolding that encourages rereading of text or prompting for knowledge of rare vocabulary commonly used in academic texts may be less helpful. Implications for research are presented. Overall, the study presents evidence for how scaffolding can support high school students reading complex texts.

Since the enactment of No Child Left Behind, U.S. education has been dominated by test-based accountability policies and subsequent calls for data-driven decision-making (DDDM). DDDM is often framed as a method for making instruction more rational and scientific. Yet there is little clarity or consensus around the DDDM process: What data do teachers use? How do they interpret data? Though data can be used for instructional improvement, the high pressure associated with test-based accountability often results in data use that has distortive effects on teaching and learning. In this dissertation, I build on the literature on educatorsâ data use in practice to investigate the tensions between test-based accountability policies and instructional improvement. In Paper 1, I examine the existing data use literature to identify distortive data use practices and offer recommendations for using evidence of student learning in more responsive ways. Paper 2 is an analysis of the ways that test-based accountability policy shapes the data use practices of a middle-school mathematics teacher workgroup. The logic of accountability policy constrains their data use practices in ways that reinforce systemic oppression and limit opportunities for more equitable instruction. In Paper 3, I analyze the epistemic underpinnings of teacherâs data use through a comparative case study of two middle-school mathematics teacher workgroups. The workgroups take different epistemic stances on data, which shape their data use practices, what they consider evidence of learning, and the instructional responses they design. The educators who use data as an indicator of learning are better positioned for instructional improvement than those who use data as a measurement of learning. These analyses inform the development of more productive data use practices. Despite the various calls for DDDM, there are few efforts to prepare teachers or instructional coaches to engage in nuanced discussions of data. By identifying potential pitfalls of data use and articulating ways to use data for instructional improvement, I provide recommendations that can support more ambitious and equitable instruction.