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Modern information and communications technologies provide a significant challenge for the design, development, and deployment of learning environments, instructional systems and performance technologies.
However, the potential for technologies to foster learning self-regulation processes in students and the deployment of learning strategies requires a suitable technopedagogic design of tasks and activities, not just depending on ICT characteristics (Area, 2007).
Drent and Meelissen (2008) argue that the deployment of learning technologies into the teaching and learning environment brings benefits for students, such that they are able to develop skills for searching and assessing information, collaboration, communication and problem solving.
This burgeoning source of data also presents challenges for the design and deployment of learning analytics systems related to interoperability and privacy as well as changing pedagogical and organisational models.
From an educational perspective, it also seems imperative that appropriate pedagogical and organisational models also inform the design and deployment of learning analytics systems to ensure productive learning and teaching as well as enable quality review.
The foundations for the design, development and deployment of learning environments have been drawn somewhat eclectically from such areas as the psychology of learning, instructional design, human-computer interaction, communication theory, cultural anthropology, media research, performance technology, and so on (Richey, Klein, & Tracey, [2011]; Seel, [2012]; Spector, [2012]).
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In education, standardization has played a pivotal role in promoting the principle of systems interoperability in deployment and use of learning technologies allowing the sector to move in smarter direction.
The proposed loosely coupled technical platforms and services together with the Go-Lab design principles have already enabled a smooth and progressive deployment of inquiry learning spaces to develop a shared understanding of the deployment challenges among the interdisciplinary partners in the project and with the initial pilot teachers.
In this paper we explore how GLUE -PS (a system for the deployment and run-time manaGLUE -PSf learning desystemacross DLEs) supports orchestration, through its teacher usage in three authentic university courses and one teacher workshop.
And each board powered on average eight synthetic sensors, according to Harrison, who says the average accuracy across all of those deployments — after about a week of learning signals — was a pretty impressive 98percentnt.
It is plausible that reported risky driving following deployment is a reflection of learned behaviours on deployment which continue afterwards.
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