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Journal ArticleDOI

Using clickers in class. The role of interactivity, active collaborative learning and engagement in learning performance

TLDR
The results reveal that the high level of interactivity with peers and with the teacher that is promoted by the use of clickers positively influences active collaborative learning and engagement, which, in turn, improves student learning performance.
Abstract
As more and more educational institutions are integrating new technologies (e.g. audience response systems) into their learning systems to support the learning process, it is becoming increasingly necessary to have a thorough understanding of the underlying mechanisms of these advanced technologies and their consequences on student learning performance. In this study, our primary objective is to investigate the effect of clickers (i.e. audience response systems) on student learning performance. To do so, we develop a conceptual framework in which we propose that interactivity, active collaborative learning and engagement are three key underlying forces that explain the positive effects and benefits of clickers in enhancing student learning performance. We test these relationships empirically in a university class setting using data from a survey answered by students in a social sciences degree. The results provide strong support for our proposed framework and they reveal that the high level of interactivity with peers and with the teacher that is promoted by the use of clickers positively influences active collaborative learning and engagement, which, in turn, improves student learning performance. These results show the importance of clickers in improving the student learning experience and recommend their use in educational settings to support the learning process. Highlights? To investigate the impact of clickers on student learning performance. ? Interactivity as a result of using clickers promotes active collaborative learning. ? Interactivity as a result of using clickers promotes engagement. ? Engagement improves students' learning performance. ? Active collaborative learning improves students' engagement and learning performance.

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Citations
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Journal ArticleDOI

Measuring student engagement in technology-mediated learning

TL;DR: This review examines existing approaches to measure engagement in technology-mediated learning, identifies strengths and limitations of existing measures, and outlines potential approaches to improve the measurement of student engagement.
Journal ArticleDOI

Social networking, knowledge sharing, and student learning

TL;DR: The results show that there are significant positive relationships between both chatting and online discussion and file sharing and knowledge sharing, and entertainment and enjoyment with student learning.
Journal ArticleDOI

Mobile-based assessment: integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance

TL;DR: The proposed Mobile Based Assessment - Motivational and Acceptance Model (MBA-MAM), a combined model that explains and predicts Behavioral Intention to Use Mobile-based Assessment, is proposed, explaining and predicting students’ intention to use MBA in terms of both acceptance and motivational factors.
Journal ArticleDOI

A meta-analysis of the effects of audience response systems (clicker-based technologies) on cognition and affect

TL;DR: It is revealed that knowledge domain, class size, and the use of clicker questions, are among factors that significantly moderated the summary effect sizes observed among the studies in the meta-analysis.
Journal ArticleDOI

The effects of learner-generated videos for YouTube on learning outcomes and satisfaction

TL;DR: The findings showed that active participation had a direct influence on the perceived acquisition of cross-curricular competencies and on academic performance and that the use of YouTube as a teaching vehicle has a positive impact on students' learning outcomes and satisfaction.
References
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Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
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Multivariate Data Analysis

TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
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Structural equation modeling in practice: a review and recommended two-step approach

TL;DR: In this paper, the authors provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development, and present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests.
Journal ArticleDOI

On the evaluation of structural equation models

TL;DR: In this article, structural equation models with latent variables are defined, critiqued, and illustrated, and an overall program for model evaluation is proposed based upon an interpretation of converging and diverging evidence.
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