Determining Geo-location of Tweets Using Cognitive Intelligence

2021 ◽  
pp. 49-57
Author(s):  
Aishwarya Asesh
Keyword(s):  
Cognitive Intelligence

scholarly journals IQ and digital inequality: An empirical investigation

2021 ◽  
pp. 146144482110240
Author(s):  
Alexander JAM van Deursen ◽  
Jan AGM van Dijk
Keyword(s):  
Intelligence Quotient ◽  
Online Survey ◽  
Empirical Investigation ◽  
Internet Access ◽  
Normative Reasons ◽  
Cultural Resources ◽  
Digital Inequality ◽  
Cognitive Intelligence ◽  
Positive Effects ◽  
Current Contribution

Cognitive intelligence is rarely discussed in the context of digital inequality for practical and normative reasons: substantial difficulties around measurements and the fact that it cannot (easily) be changed. In the current contribution, cognitive intelligence is studied in relation to resources and appropriation theory which explains digital inequality as a process of four successive phases of Internet access: motivational, material, skills, and usage. For the measurement of cognitive intelligence, we build on considerable efforts devoted to developing alternatives to cumbersome intelligence quotient (IQ) tests of intelligence. We conducted a two-wave online survey in the Netherlands, resulting in a sample of 1733 respondents. The importance of IQ was confirmed with direct positive effects on education, economic, social, and cultural resources, and on Internet attitude and skills. The results reveal several details that can enhance our understanding of the specific mechanisms through which IQ and education operate in digital inequalities.

Cognitive intelligence

BMJ ◽  
2002 ◽  
Vol 325 (7376) ◽  
pp. 193S-193
Author(s):  
B. McMullen
Keyword(s):  
Cognitive Intelligence

Emotional Intelligence

2019 ◽  
pp. 193-204
Author(s):  
Stacy M. Lauderdale-Littin ◽  
Carol McArthur-Amedeo
Keyword(s):  
United States ◽  
Emotional Intelligence ◽  
Training Programs ◽  
Gifted Students ◽  
The United States ◽  
Preservice Teacher Training ◽  
Cognitive Intelligence ◽  
Teacher Training Programs ◽  
Understanding Emotions

Within the United States, almost 50% of teachers leave the field of education within the first five years. Teachers who remain in the field have been shown to be able to demonstrate career competency skills. These skills are related to emotional intelligence (EI), which refers to competencies in recognizing, managing, communicating, and understanding emotions in one's self and others. Previous literature suggests gifted students, due to specific characteristics associated with giftedness, struggle with EI, which impacts their ability to utilize the skills they have, including cognitive intelligence. For gifted individuals entering the field of education, difficulty with EI could potentially impact their ability to feel successful and remain in the field long term. This chapter provides information and resources related to meeting the emotional intelligence needs of gifted students in preservice teacher training programs.

Cultural Intelligence and Experiential Learning Powering Faculty Intercultural Leadership Development

2016 ◽  
pp. 354-375
Author(s):  
Althia Ellis
Keyword(s):  
Leadership Development ◽  
Experiential Learning ◽  
Learning Outcomes ◽  
Cultural Intelligence ◽  
Process Model ◽  
Learning Experience ◽  
Diverse Students ◽  
Culturally Diverse Students ◽  
Cognitive Intelligence ◽  
Intercultural Leadership

A look at today's higher education institutions shows an increasing number of culturally diverse students. The ability of faculty to value these learners can serve as an unmatched resource to enrich the learning experience for students and enhance the intercultural leadership development of faculty. We will explore how the integration of research in experiential learning and cultural intelligence (CQ) can help develop a process model for faculty to turn their interactions with diverse students into learning outcomes (Ng, Van Dyne, & Ang, 2009). The application of cultural intelligence, which offers a four-factor framework (metacognitive intelligence, cognitive intelligence, motivational intelligence, and behavioral intelligence) might increase the likelihood that faculty who interact with diverse students will engage in the four-stage theory of experiential learning: experience, reflection, conceptualization, and experimentation (Ng, Van Dyne, & Ang, 2009; Mezirow, 1997). The experience will impact experiential and learning outcomes, and can lead to multiple advantages.

Cognitive Intelligence

2016 ◽  
Vol 10 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Yingxu Wang ◽  
Bernard Widrow ◽  
Lotfi A. Zadeh ◽  
Newton Howard ◽  
Sally Wood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computational Intelligence ◽  
Cognitive Systems ◽  
Cognitive Computing ◽  
Cognitive Intelligence ◽  
Basic Studies ◽  
Deep Reasoning ◽  
Intelligence Theories ◽  
The Brain

The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

Bug Model Based Intelligent Recommender System with Exclusive Curriculum Sequencing for Learner-Centric Tutoring

2019 ◽  
Vol 14 (4) ◽  
pp. 1-25
Author(s):  
Ninni Singh ◽  
Neelu Jyothi Ahuja
Keyword(s):  
Recommender System ◽  
Curriculum Design ◽  
Intelligent Tutoring System ◽  
Learning Gain ◽  
Course Content ◽  
Cognitive Intelligence ◽  
Face To Face ◽  
Human Tutor ◽  
Overall Performance ◽  
Enhanced Learning

Face to face human tutoring in classroom environments amply facilitates human tutor-learner interactions wherein the tutor gets opportunity to exercise his cognitive intelligence to understand learner's pre-knowledge level, learning pattern, specific learning difficulties, and be able to offer course content well-aligned to the learner's requirements and tutor in a manner that best suits the learner. Reaching this level in an intelligent tutoring system is a challenge even today given the advanced developments in the field. This article focuses on ITS, mimicking a human tutor in terms of providing a curriculum sequence exclusive for the learner. Unsuitable courseware disorients the learner and thus degrades the overall performance. A bug model approach has been used for curriculum design and its re-alignment as per requirements and is demonstrated through a prototype tutoring recommender system, SeisTutor, developed for this purpose. The experimental results indicate an enhanced learning gain through a curriculum recommender approach of SeisTutor as opposed to its absence.

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