Adult Learning in the Transformational Environments of the Digital Revolution: Connecting Theory and Practice

The purpose of this study was to investigate the learning preferences and the post-secondary educational experiences of a group of Net-Gen adult learners, aged between 18 and 35, currently working in the knowledge economy workplace, and their assessment of how adequately they were prepared to meet the requirements of the knowledge economy workplace. This study utilized an explanatory mixed-method research design. Participants completed a questionnaire providing information on their self-reported learning style preferences, their use of digital tools for formal and informal learning, their use of digital technologies in postsecondary educational experiences, and their use of digital technologies in their workplace. Four volunteers from the questionnaire respondents were selected to participate in interviews based on the diversity of their experiences in higher education, including digital environments, and the diversity of their knowledge economy workplaces. Data collected from the questionnaire were analyzed for descriptive and demographic statistics, and categorized so that common patterns could be identified from information gathered from the online questionnaire and interviews. Findings based on this study indicated that these Net-Gen adult learners were fluent with all types of digital technologies in collaborative environments, expecting their educational experiences to provide a similar experience. Participants clearly expressed an understanding that digital/collaborative aptitudes are essential to successful employment in the knowledge economy workplace. The findings of this study indicated that the majority of participants felt that their post-secondary educational experiences did not adequately prepare them to meet the expectations of this type of working environment.

The impact of a digital children's literature program on primary students' reading motivation

This qualitative study stemmed from a concern of the perceived decline in students' reading motivation after the early years of schooling, which has been attributed to the disconnect between the media students are accustomed to using outside the classroom and the media they predominantly use within the classroom. This research documented the effectiveness of a digital children's literature program and a postreading multimedia program on eight grade 1 students' reading motivation, word recognition, and comprehension abilities. Eight students were given ten 25-minute sessions with the software program over 15 weeks. Preprogram, interim-program, and postprogram qualitative data were collected from students, teachers, and parents through questionnaires, interviews, standardized reading assessment tools, classroom observations, field notes, and student behaviour observation checklists. Findings are summarized into 3 themes. The motivational aspects and constructivist styles of instruction in the digital reading programs may have contributed to 5 student participants' increased participation in online storybook reading at home. Qualitative data revealed that the digital children's literature program and multimedia postreading activities seemed to have a positive influence on the majority of grade 1 student participants' reading motivation, word recognition, and listening comprehension skills. These findings suggest the promise of multimedia and Internet-based reading software programs in supporting students with reading andlor behavioural difficulties. In keeping with current educational initiatives and efforts, increased use of media literacy practices in the grade 1 curriculum is suggested.

QUANTIFICATION AND EXTRACTION OF SURFACE FEATURES FROM DIGITAL TERRAIN MODELS

Digital Terrain Models (DTMs) are important in geology and geomorphology, since elevation data contains a lot of information pertaining to geomorphological processes that influence the topography. The first derivative of topography is attitude; the second is curvature. GIS tools were developed for derivation of strike, dip, curvature and curvature orientation from Digital Elevation Models (DEMs). A method for displaying both strike and dip simultaneously as colour-coded visualization (AVA) was implemented. A plug-in for calculating strike and dip via Least Squares Regression was created first using VB.NET. Further research produced a more computationally efficient solution, convolution filtering, which was implemented as Python scripts. These scripts were also used for calculation of curvature and curvature orientation. The application of these tools was demonstrated by performing morphometric studies on datasets from Earth and Mars. The tools show promise, however more work is needed to explore their full potential and possible uses.