Mobilizing education

Frías-Martínez, Vanessa; Virseda, Jesus; Gomero, Aldo

doi:10.1145/2371574.2371642

Cited by 16 publications

(5 citation statements)

References 8 publications

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“…Another challenge associated with the types of learning, especially in combination with technology, is the false perception that informal learning environments are mostly intended for entertainment and only secondary as means of education (Eshach, 2007;Greenfield, 2009;Sefton-Green, 2004). However, with the increasing awareness that informal contexts are essential for knowledge acquisition and building (Greenfield, 2009), supported by a number of successful informal learning studies (Falk & Storksdieck, 2005), informal learning contexts should not be neglected; on the contrary, they should be more deeply exploited.…”

Section: Formal and Informal Learning In Different Settingsmentioning

confidence: 99%

A review of research on bridging the gap between formal and informal learning with technology in primary school contexts

Jagušt

Botički

2018

Computer Assisted Learning

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This study reviews empirical research articles published in the field of technology‐enhanced learning in the out‐of‐class contexts in primary schools between the years 2007 and 2016 and explores how the body of research has connected formal and informal learning experiences, referred to in the paper as bridging the gap. The review focuses on 43 selected experiments from 41 research papers, which are in detail examined and classified using the 3 criteria: (a) the Bloom's taxonomy for learning, (b) the intentionality to physical settings classification used to differentiate types of learning in various environments, and (c) the characteristics of seamless learning design. The findings confirm that technology can enhance learning in and out of classroom, especially by impacting student interest, motivation, and engagement. The close examination of the subset of studies with cognitive gains shows that they successfully bridged the gap between learning spaces and that such bridging positively correlates with the number of steps in the learning activity design. The successful bridging of the gap between learning spaces could further benefit from including more online social learning activities into the designed learning process and from involving teachers as cocreators of the learning process and resources.

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Section: Formal and Informal Learning In Different Settingsmentioning

confidence: 99%

A review of research on bridging the gap between formal and informal learning with technology in primary school contexts

Jagušt

Botički

2018

Computer Assisted Learning

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“…• Context-based learning (Frias-Martinez et al, 2012;Köhlmann et al, 2012;Zender et al, 2014;Lemcke et al, 2015;Lamrani and Abdelwahed, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…There are also native applications that only run on a mobile device and take advantage of the device's special features. The first example (Frias-Martinez et al, 2012) shows how the application named 'EducaMovil' can helps to improve knowledge acquisition in the classroom. The second is an experiment presented by Nordby et al (2016), which consists in giving tools to students to create an overarching pervasive game on a blackboard; through it, children help prevent climate change with various small actions and must solve problems around the school.…”

Section: Related Workmentioning

confidence: 99%

Using pervasive games as learning tools in educational contexts: a systematic review

Arango‐López

Collazos

Velas

et al. 2018

IJLT

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Technology is now a vital part of our society and with such a significant impact on the educational system, information and communication technologies (ICT) cannot be dissociated from the educational process (Ramos and de Andrade, 2016). While the implementation of digital technologies in society and more specifically among young people represents a challenge for teachers, it might also provide the opportunity to improve academic performance. In this context, pervasive games have established a new trend and present a new way for people to interact with each other in a real environment by means of virtual worlds and the elements they bring to the table. Students are therefore able to enjoy a learning process with fun graphics on their mobile devices, as well as stories or other features common to pervasive games. In this paper, we focus our attention on the results of pervasive learning games, results which we had previously obtained from a systematic literature review (Arango-López et al., 2017a), as well as from a review of specific literature to identify different research projects for improving the learning process.

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“…Another mobility feature used in crime prediction contexts is the origin-destination matrix (OD) that characterizes human mobility (flows) between census tracts. Human mobility data has been used to characterize human behaviors in the built environment [23][24][25][26][27], for public safety [10,14], during epidemics and disasters [28][29][30][31][32][33][34][35], as well as to support decision making for socio-economic development [36][37][38][39][40][41]. In this paper, we will focus on deep learning crime prediction models that exploit the predictive power past crime data and OD mobility matrices [10].…”

Section: Crime Prediction With Mobility Patternsmentioning

confidence: 99%

Enhancing short-term crime prediction with human mobility flows and deep learning architectures

Abrar

Awasthi

et al. 2022

EPJ Data Sci.

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Place-based short-term crime prediction models leverage the spatio-temporal patterns of historical crimes to predict aggregate volumes of crime incidents at specific locations over time. Under the umbrella of the crime opportunity theory, that suggests that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based short-term crime models. Researchers have used call detail records (CDR), data from location-based services such as Foursquare or from social media to characterize human mobility; and have shown that mobility metrics, together with historical crime data, can improve short-term crime prediction accuracy. In this paper, we propose to use a publicly available fine-grained human mobility dataset from a location intelligence company to explore the effects of human mobility features on short-term crime prediction. For that purpose, we conduct a comprehensive evaluation across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we show that adding human mobility flow features to historical crimes can improve the F1 scores for a variety of neural crime prediction models across cities and types of crimes, with improvements ranging from 2% to 7%. Our analysis also shows that some neural architectures can slightly improve the crime prediction performance when compared to non-neural regression models by at most 2%.

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Mobilizing education

Cited by 16 publications

References 8 publications

A review of research on bridging the gap between formal and informal learning with technology in primary school contexts

A review of research on bridging the gap between formal and informal learning with technology in primary school contexts

Using pervasive games as learning tools in educational contexts: a systematic review

Enhancing short-term crime prediction with human mobility flows and deep learning architectures

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