Beyond basic study skills: The use of technology for success in college
ABSTRACT Technology has become a fundamental component of both education and work. Yet regardless of perceived benefits, in many cases students do not use technology effectively. One challenge educators confront is how to motivate students to effectively use the technological mediums provided in their classes. The goal of the current study is twofold: to use the Technology Acceptance Model (TAM) to examine two motivators of behavior, ease of use and perceived need, and to assess how they affect students’ likelihood of effectively using technology. Second, we evaluate how the match between expectations of the use of technology and the actual student use affect actual classroom performance. To test our hypotheses, college students (N = 384) in introductory psychology classes completed a survey. We also obtained the instructor’s perceptions of the need for technology in their class and students’ final class grades. Results showed that ease of use and perceived need of technology were related to the frequency of computer use and intentions to use technology. Additionally, findings suggested that technology use, specifically technology deemed important by the instructor, was related to academic success (i.e., final grade in class).
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ABSTRACT: Coinciding with the development and growth of the Internet, there has been a dramatic increase in the application of the Learning Management Systems (LMS) in higher education. University and college campuses should consider evaluating each LMS to ensure that the system meets the requirements and demands of the institution. Therefore, the purpose of this study is to present a model which incorporates the concepts and findings from research on LMS application in higher education. The alternative model was modified based on the Technology Acceptance Model (TAM). In addition, five categories of LMS features for higher education are discussed including: (1) transmitting course content; (2) evaluating students; (3) evaluating courses and instructors; (4) creating class discussions; and (5) creating computer-based instruction. This study reviews and discusses prior research and provides several recommendations including a model of development and design of an LMS for future implementation in higher educational environments.
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ABSTRACT: This case study investigates the use of online blogs as a teaching tool. A collaborative blog was implemented in parallel classes on group processes in the United States and Germany. Our goal was to connect American and German graduate students by helping them to talk about group communication and meeting behaviors. Collected data included transcripts of the messages, as well as students’ evaluations of the blog (collected at the end of the project). Quantitative analyses assessed students’ participation rates and the content of their postings. Qualitative analysis examined the use of the blog as a teaching and learning tool. The results showed that students interacted more on the blog than was required by the instructor. Students valued blogging as a new learning experience. We discuss the pedagogical implications of blog usage for teaching about groups and provide recommendations for instructors interested in using blogs in their own courses.Small Group Research 06/2013; 44(4). DOI:10.1177/1046496413487020 · 1.35 Impact Factor
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ABSTRACT: The present study examines the relationship between technology self-efficacy among university students and gender roles. Previous research has based differences in technology self-efficacy on biological sex and found significant differences. University students were asked to complete a survey dealing with gender roles and technology self-efficacy. The current study shows that gender roles, specifically masculinity, is the source of this difference in technology self-efficacy, and not biological sex alone. Further, masculinity predicts technology self-efficacy above and beyond what can be explained by other contributing factors such as previous computer hassles and perceived structural technology support.Computers in Human Behavior 07/2013; 29(4):1779–1786. DOI:10.1016/j.chb.2013.02.012 · 2.69 Impact Factor