Digital Divide in Learning Services in Ghana’s Basic School

Quaicoe, James Sunney; Pata, Kai

doi:10.1007/978-3-319-47440-3_9

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Distinctive to the previous researches on smart ecosystem and digital maturity tools, we use qualitative data collected by the external evaluaters to develop the instrument for the study. Qualitative data gave us a better opportunity to see the threats and opportunities in the digital school ecosystems [15]. The data was then quantified that enabled us to explore the interplay of the school regims and external socio-technical landscape and develop the model of smart digital learning ecosystem.…”

Section: Discussionmentioning

confidence: 99%

“…On one hand, we rely on gathering and interpreting qualitative data from schools where different stakeholders' voices are combined by an external evaluator. We assume that such an approach to the evaluation helps to find and explore threats and opportunities in the ecosystem [15]. On the other hand, digital learning ecosystems are complex systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Factors Determining Digital Learning Ecosystem Smartness in Schools

Jeladze¹,

Pata²,

Quaicoe³

2017

IxD&A

Self Cite

View full text Add to dashboard Cite

This paper discusses the factors that determine school’s digital learning ecosystem smartness. A dataset was collected from 52 schools in Ghana, Georgia and Estonia. Qualitative school observations and interviews were transformed to the quantitative categories and compound variables using the grid-based approach. We found three distinctive digital learning ecosystem types that described some possible developmental stages in the ecosystem. Discriminant analysis revealed two functions. Most dominant compound variables in the first function were the top-down external provision of digital resources and ICT incentives. The second function characterizes with bottom-up proactiveness of the schools. Path modelling between the compound variables revealed the growing complexity in connectivity among the mediating, transformative and flow components, that determines the smartness of learning ecosystem. Such interconnected components form specific fitness niches which have been co-created in organizations through collective effort, making school ecosystem responsive to the socio-technical regime and externally provided opportunities in the countries.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%