Critical success factors for transforming pedagogy with mobile <scp>W</scp>eb 2.0

Cochrane, Thomas

doi:10.1111/j.1467-8535.2012.01384.x

Cited by 154 publications

(153 citation statements)

References 34 publications

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“…Closer to home, ownership of multiple devices is affirmed by Research New Zealand (2014), who report 90 percent of respondents either own or have access to at least four mobile devices. Cochrane (2014) noted the focus of mobile Web 2.0 projects has moved from providing students with institutionally loaned wireless mobile devices to appropriating the affordances of student-owned devices. This study seeks to understand the level of ownership of mobile devices among students at a provincial polytechnic.…”

Section: Byod: Ownership Of Mobile Technologymentioning

confidence: 99%

Devices, distractions and digital literacy: ‘Bring your own device’ to polytech

Drew¹,

Forbes²

2017

tandc

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Section: Byod: Ownership Of Mobile Technologymentioning

confidence: 99%

Devices, distractions and digital literacy: ‘Bring your own device’ to polytech

Drew¹,

Forbes²

2017

tandc

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“…Messinger (2012) sees that the lack of practical frameworks in m-learning limits the widespread adoption of mobile learning. Other researchers listed some factors affecting the widespread adoption of mobile learning, such as lack of theoretical and pedagogical grounding and lack of teacher support and training (Cochrane, 2014;Peng, Su, Chou, & Tsai 2009). Looi, Sun, Seow, and Chia (2014) found limited research on the investigation of curricular based implementations of mobile learning and devices.…”

Section: Pedagogical Limitations Of M-learningmentioning

confidence: 99%

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Al-Hunaiyyan

Bimba

Idris

et al. 2017

IJIKM

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Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

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“…It is also observed that the considered web learning systems [9,12] should not only consider technical or non-technical skillsset separately. They seem to be taken care together jointly.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Approach to Assess Web Learner's Joint Skills

Mehrabani¹,

Das²

2015

IJMECS

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Abstract-Skill assessment is an important but complicated task in the entire web based teaching and learning process. The learner's performance assessment has a strong influence on learners' approaches to learn and their learning outcomes like professional acceptability on desired skills. Most educators focus either on assessing a learner's technical skill set or nontechnical skill set, individually, rather than focusing on both the aspects. This paper bridges the gap by applying fuzzy logic approach to analyze a learner's joint skills incorporating both skills-set.An already proven e-commerce website's evaluation technique has been chosen and applied in two situations of learner's skill assessment through case studies namely: technical skills evaluation, and non-technical skills evaluation. Experiments show that the learner's success depends on both sets of skill attributes. This work then proposed a novel method for skill assessment considering two (instead of one) sets of skill attributes invoking parallel or joint application of the technique. This new technique has also been analysed through a case study.

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Critical success factors for transforming pedagogy with mobile Web 2.0

Cited by 154 publications

References 34 publications

Devices, distractions and digital literacy: ‘Bring your own device’ to polytech

Devices, distractions and digital literacy: ‘Bring your own device’ to polytech

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

A Fuzzy Logic Approach to Assess Web Learner's Joint Skills

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