Development of an instrument to measure Faculty’s information and communication technology access (FICTA)

The phenomenon of “digital divide” is complex and multidimensional, extending beyond issues of physical access. The purpose of this study was to develop a scale to measure a range of factors related to digital divide among higher education faculty and to evaluate its reliability and validity. Faculty’s Information and Communication Technology Access (FICTA) scale was tested and validated with 322 faculty teaching in public and private sector universities. Principal components analysis with varimax rotation confirmed an 8-factor solution corresponding to various dimensions of ICT access. The 57-item FICTA scale demonstrated good psychometric properties and offers researchers a tool to examine faculty’s access to ICT at four levels – motivational, physical, skills, and usage access.     

Contextual Factors Influencing Access to Teaching Computational Thinking

This study attempts to determine whether teachers' access to computational thinking (CT) and CT technologies varies by rurality (rural versus urban) of the school county and grade level taught (primary versus secondary). A total of 81 teachers from West Virginia, Georgia, and Oklahoma participated in this survey study. Overall, teachers found CT and CT tools useful and relevant to their teaching and had access to an essential level of technology in schools supporting computational thinking. However, teachers' CT skills and their teaching of CT were limited and varied depending on rurality and grade level. For instance, primary school teachers in rural settings reported significantly lower levels of CT skills than those in urban settings. Findings and implications are discussed.     

E-government discourses: An inductive analysis

This article offers a new perspective on e-government by documenting the categories of e-government discourses, and evaluating them within a public value framework. Understanding e-government discourses is significant, since these discourses represent contested visions of e-government, and one can derive a feel for public sentiment about e-government from the discourses used in the media. The findings are accumulated through an inductive analysis of 85 newspaper articles, published during the year of 2010, in three top-selling, ideologically different, nationally circulating Turkish newspapers. In these 85 articles, 98 discourses presented by 90 policy actors are found. Five positive and four negative discourse categories and their relationships emerged from the analysis of the data. The results show that, government reform efforts shaped by the New Public Management movement and Turkey's harmonization efforts with the global political system in general, and with the European Union in particular, are influential in the presentation of e-government projects to the Turkish public through newspapers.     

Discovering socially similar users in social media datasets based on their socially important locations

Socially similar social media users can be defined as users whose frequently visited locations in their social media histories are similar. Discovering socially similar social media users is important for several applications, such as, community detection, friendship analysis, location recommendation, urban planning, and anomaly user and behavior detection. Discovering socially similar users is challenging due to dataset size and dimensions, spam behaviors of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar social media users out of social media datasets using spatial and temporal information. However, most of these studies rely on trajectory pattern mining methods or take into account semantic information of social media datasets. Limited number of studies focus on discovering similar users based on their social media location histories. In this study, to discover socially similar users, frequently visited or socially important locations of social media users are taken into account instead of all locations that users visited. A new interest measure, which is based on Levenshtein distance, was proposed to quantify user similarity based on their socially important locations and two algorithms were developed using the proposed method and interest measure. The algorithms were experimentally evaluated on a real-life Twitter dataset. The results show that the proposed algorithms could successfully discover similar social media users based on their socially important locations.     

Problem Solving and Teaching How to Solve Problems in Technology-Rich Contexts

ABSTRACT

Problem solving is perhaps the key characteristic that makes us human. Given the kinds of problems that we face in a competitive economy and society, the new generation of learners is ever more required to have problem-solving abilities. By drawing from the literature on technological pedagogical content knowledge, design thinking, general and specific methods of problem solving, and role of technologies for solving problems, this article highlights the importance of problem solving for future teachers and discusses strategies that can help them become good problem solvers and understand the requirements of teaching their students problem solving in technology-rich contexts. This article consists of two main parts. Part 1 focuses on strategies required to help preservice teachers to be better problem solvers, and Part 2 summarizes approaches to introduce preservice teachers to the methods of teaching problem solving. The strategies reviewed provide a tangible guidance for teacher education programs regarding how to promote future teachers’ problem-solving skills.