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Purpose – To help teacher educators understand how to more fully prepare pre-service teachers (PSTs) for meaningful and effective instruction with multimodal texts and the underlying technologies.

Design – This mixed methods investigation employed designed-based research in that as the authors observed and gathered data on PSTs’ outcomes within the context of a literacy methods course, the authors also engaged in an iterative process of collaborative design to develop a sustainable instructional model across three academic semesters with three cohorts of PSTs. The authors analyzed pre- and post-PST surveys measuring their knowledge of, disposition toward, and self-efficacy with technology and technology in teaching as well their intent to use technology in their future teaching. The authors also coded and analyzed PST lesson plans completed across each semester for instances of meaningful integration of multimodal texts and the underlying technology, and sound literacy instruction. Finally, the authors closely examined differences in how the course was shaped and “reshaped” across all three iterations and noted any differences in PST outcomes related to these shifts.

Findings – Overall findings suggest that enrollment in the literacy methods course improved both PSTs’ self-efficacy and knowledge about teaching with technology while also supporting PSTs’ ability to develop sound literacy instructional plans. Moreover, strategic positioning of multimodal texts and technology, in which integration is seamless, can help PSTs meaningfully and effectively weave multimodal text sets into their literacy lesson plans.

Practical Implications – This chapter contributes to the literature on integrating multimodal texts and the underlying technologies into PST programs by providing explicit, research-based recommendations for how teacher educators can meaningfully and seamlessly infuse multimodal text sets into core curricula and instructional practices.

This qualitative research study focused upon collaboration between regular and special education teachers in middle school inclusive social studies classrooms. Data sources included interviews, observations and a review of Individualized Education Plans (IEPs). Two pairs of regular and special education teachers (high and low collaborators) were selected from three schools in different counties. Major findings included a description of the ways teachers formed and maintained their relationships, the role of administrators, and obstacles that needed to be overcome. Lack of time was identified as the greatest obstacle. IEPs were not found to be useful. Teacher use of accommodations and strategies tended to be global, rather than individualized. Perceptions of role were examined by teacher type.

This chapter concerns itself with the development of a regression model for determining the executive compensation of the AT&T CEO. The data observations for this model consist of a list of 21 comparable companies selected by the compensation committee of AT&T, its institutional investors, and AT&T advisors. A set of 24 financial variables for each of the companies is compiled as the data source for the regression model.

This chapter concerns itself with the development of a regression model for an executive compensation forecasting of the top-level executives of MetLife. The data observations consist of a list of 12 comparable corporations selected from comparable financial institutions. A set of 28 financial variables from each of the corporations is compiled as the data source of this regression model.

Forecasting sales for an innovation before the product's introduction is a necessary but difficult task. Forecasting is a crucial analytic tool when assessing the business case for internal or external investments in new technologies. For early stage investments or internal business cases for new products, it is essential to have some understanding of the likely diffusion of the technology. Diffusion of innovation models are important tools for effectively assessing the merits of investing in technologies that are new or novel and do not have prima facie, predictable patterns of user uptake. Most new product forecasting models require the estimation of parameters for use in the models. In this chapter, we evaluate three techniques to determine the parameters of the Bass diffusion model by using an example of a new movie.