1,721,079 research outputs found
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges
No full textA Student Primer on How to Thrive in Engineering Education during and beyond COVID-19
No full textIn this paper, we strive to provide a primer for students on how to thrive and learn effectively in engineering education in the volatile, uncertain, complex, and ambiguous (VUCA) times following the onset of the COVID-19 global pandemic, which has disrupted the educational enterprise massively with universities physically closing in many parts of the world and students and faculty transitioning to remote learning. Although the immediate audience assumed in this paper comprises engineering students (such as those enrolled in electrical, electronics, or computer engineering programs) studying in an outcome-based education (OBE) environment—the global educational paradigm mandated by the Washington Accord that aims to standardize engineering competencies in terms of the attained student learning outcomes—the presented ideas are more general and broadly useful for learners of all types. We will describe seven evidence-based steps that the students can adopt to thrive in OBE settings in these challenging times. The main contribution of this paper is practical: we present a synthesis of the vast research literature on effective student learning in normal, online, and disrupted settings to present practical insights that students can leverage to substantially improve their learning. At the end of the paper, we also present a discussion of important issues related to remote teaching and online education such as ensuring equity and the handling of lab work for engineers in such settings (e.g., through simulators and virtual labs)
A Student Primer On How to Thrive in Post-COVID-19 Engineering Education
No full textIn this paper, we strive to provide a primer for students on how to thrive and learn effectively in engineering education in the volatile, uncertain, complex, and ambiguous (VUCA) times following the onset of the COVID-19 global pandemic, which has disrupted the educational enterprise massively with universities physically closing in many parts of the world and students and faculty transitioning to remote learning. While our immediate focus in this paper is on engineering education functioning in an outcome-based education (OBE) environment, the global paradigm mandated by the Washington Accord that aims to standardize engineering competencies in terms of the attained student learning outcomes, our ideas are general and broadly useful for learners of all types. We will describe seven evidence-based steps that the students can adopt to thrive in OBE settings in these challenging times. The main contribution of this paper is practical: we present a synthesis of the vast research literature on effective student learning in normal, online, and disrupted settings to present practical insights that students can leverage to substantially improve their learning
Client-side architecture for mobile service QoS monitoring using Generalized Extreme Value theorem
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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