Context and deep learning design

Boyle, Tom; Ravenscroft, Andrew

doi:10.1016/j.compedu.2012.06.007

Cited by 34 publications

(22 citation statements)

References 12 publications

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“…The focus of majority of the studies carried out on mobile learning so far has been on the context. In this respect, it is seen that most studies have been conducted on subjects like design of mobile learning (Boyle and Ravenscroft, 2012) educational material related to the learner"s location, associating in-class learning with out-ofclass learning, and relationship between learners, technologies, and society. One of new trends in mobile learning is the subject of seamless learning.…”

Section: Introductionmentioning

confidence: 99%

Seamless Learning: A Scoping Systematic Review Study

Durak¹,

Çankaya²

2018

Journal of Education and e-Learning Research

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In recent years, a new concept called seamless learning has appeared. This concept depends on the premise that learning should be maintained without interruption in different environments. Seamless learning is also important not only for meeting the expectations of the new generation whose life styles and learning needs are different but also for supporting the new paradigms in education. In this respect, the present study aimed to demonstrate the results of content analysis on the articles related to concept of Seamless Learning in Scopus database between 2009 and 2018. A total of 58 papers were examined to determine the concept list, top journal list, most cited papers, research methods and models, participants, data collection tools and variables in these articles. The study is considered to be important as no detailed content analysis has been conducted on the concept of seamless learning concept. The research results revealed a considerable increase in the number of studies on the concept of seamless learning by year. Singapore was the leading country in seamless learning research. It was seen in these studies that conceptual/descriptive methods were prominent as the research methods; that K-12 students were preferred more as participants; and that the most common data collection tools included interview and academic achievement tests. Lastly, in the studies examined, academic performance and perception were among the most popular dependent variables.

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Section: Introductionmentioning

confidence: 99%

Seamless Learning: A Scoping Systematic Review Study

Durak¹,

Çankaya²

2018

Journal of Education and e-Learning Research

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“…To begin the analytical process, discern the original goal of the tool as a means for understanding how your goals compare to those of the original design. [19] If the goals are different, consider what modifications need to be made or whether a different innovation might better suit students' instructional needs. For example, in McLaughlin et al's work, their goals were to develop students as critical thinkers and problem solvers, engage students and instructors in the learning process, and stimulate higher order thinking.…”

Section: The Why: Why Use This Innovation?mentioning

confidence: 99%

A Framework for Thinking About Transferring Teaching Innovations into New Settings

Gilliland

Wyatt

2017

Med.Sci.Educ.

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As a result of health science educators' shift to more active formats of teaching and learning, many educators are implementing innovative teaching strategies that were designed in other educational contexts. In some cases, this transfer from one context to another is smooth and unproblematic, but in others, educators must make informed decisions about how to adjust the innovation or incoming context to fit their needs. This paper presents a framework that draws on principles of design-based research to guide educators in analyzing and adapting teaching tools to fit new contexts.

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“…Then all the LOs so created (or adapted) run as stand-alone Web based LOs. This approach has been borrowed from the systemic grammar (Boyle and Ravenscroft, 2012); however, it can be also viewed as the template-based approach in terms of software generative reuse (Sametinger, 1997).…”

Section: Introductionmentioning

confidence: 99%

Stage-Based Generative Learning Object Model for Automated Content Adaptation

Štuikys¹,

Burbaitė²,

Bespalova³

et al. 2017

BJMC

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This paper introduces a Stage-Based (SB) Generative Learning Object (GLO) model to specify the learning content. Capabilities of the model are the content automatic generation and adaptation. Externally, our model has a similar structure as the known two-level generic models (i.e. metadata and content implementation). The internal structure, however, is quite different in both parts. The use of the external parameterization technology based on pre-programming predefines the internal structure. The SB model implements the deep internal staging by allocating parameters and functions (objects) into predefined stages according to the given context. The essence of the approach is the SB de-activation and activation of the objects within the specification. That ensures the automatic SB generation and flexibility for adaptation. We analyze the SB model capabilities, the use scenarios and processes, present a case study and extended results of using and evaluation in the robot-oriented computer science education.

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Context and deep learning design

Cited by 34 publications

References 12 publications

Seamless Learning: A Scoping Systematic Review Study

Seamless Learning: A Scoping Systematic Review Study

A Framework for Thinking About Transferring Teaching Innovations into New Settings

Stage-Based Generative Learning Object Model for Automated Content Adaptation

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