“Lifelong learning Ecologies: Linking formal and informal contexts of learning in the digital era”
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Résumé
The use of digital technologies in education has generated a sense of “continuum” across several contexts of learning, underpinning new forms of formal, non-formal and informal learning (Kop & Fournier, 2010; Siemens, 2008). As a result, education researchers have created new constructs and explored new ontologies to support their efforts to investigate learning experiences that span multiple contexts. In this regard, the concepts of formal, informal and non-formal learning formulated early in the 1980s attempted to go beyond the traditional definition of learning processes taking place in structured and institutionalized educational settings (Livingstone, 2001; Mocker, 1983). Researchers examining emergent educational phenomena, generated theories and constructs to embrace such complexity. Ubiquitous learning (Virtanen, Haavisto, Liikanen, & Kääriäinen, 2018), seamless learning (Wong & Looi, 2011) and expanded contexts of learning and personal learning environments (Attwell, 2007; Dabbagh & Kitsantas, 2012) dealt with forms of learning that (a) exploit emergent technological affordances, (b) go beyond a single context and (c) are personalized and self-directed. More recent technological advances, like augmented reality, intelligent tutoring systems and mobile devices, to mention a few, facilitated closer connections between the virtual and physical world, generating new forms of continuity (Adams Becker et al., 2017). However, the conceptualization of emergent learning experiences still seems to be fragmented in the broader literature. The “Learning Ecologies for Lifelong Learning” construct emerges as a means to provide an integrated conceptualization of learning as a complex phenomenon bridging formal, non-formal and informal learning experiences. This construct provides a framework to understand how individuals select, experience, navigate and participate in learning experiences that span multiple contexts. The concept of “ecology” features prominently here (Bateson, 1972 [1999]; Bronfenbrenner, 1994). This concept has afforded researchers a way to investigate learning as an aspect of the human experience that is complex and multilayered. More recently, the concept has been used in educational technology research to characterize the units of analysis containing sets of contexts found in physical or virtual spaces and providing opportunities for learning (Barron, 2006). However, the concept has been adopted in highly diversified ways. For instance, the idea that temporal and spatial dimensions of learning are connecting past and present moments, and linking life actions to significant experiences was embraced by Lemke (2002 2004). In contrast, the idea of community and network in learning has been explored by Siemens (2003, 2007, 2008) within the context of connectivism. While learning ecologies have mostly described combinations of formal, non-formal and informal learning (Wilkinson, Kemmis, Hardy, & Edwards-Groves, 2009) some studies have applied the term only to formal learning (Richardson, 2002). Moreover, the term has been used in several areas of study in education, including technologies and gender (Barron, 2004), ICT skills development (Barron, 2006), collaborative learning (Hodgson & Spours, 2009), designs for learning with technologies (Luckin, 2010), resources to learn for people who are homeless (Strohmayer, Comber, & Balaam, 2015), teachers’ professional development (Sangrà, Gonzalez-Sanmamed, & Guitert, 2013; van den Beemt & Diepstraten, 2016), personalized learning and lifelong learning (Maina & García, 2016), youth civic engagement (Adedayo Ige, 2017) and ubiquitous learning in higher education (Díez-Gutiérrez & Díaz-Nafría, 2018). While already applied in several ways, the concept of Learning Ecologies for Lifelong Learning has to overcome a variety of issues to reach its full potential. Firstly, there is an ontological problem that consists of the diversified ways into which learning ecologies are defined as an empirical phenomenon (technological resources, digital spaces, learning networks, etc.) and in some cases based on subsidiary theories. Secondly, the differing definitions of learning ecologies encompass the adoption of a variety of instruments and research methods. While the diversification of methods should not be considered as a problem per sé, they should be framed, characterized and discussed, aiming at including new educational research approaches such as those using publicly available data (Kimmons & Veletsianos, 2018), among others. Therefore, the potential to promote innovation through the application of the concept to educational processes and products, such as instructional design, diagnosing learning needs and empowering self-directed learning processes, seems to be underexploited. This Special Section brings together original perspectives to the construct of Lifelong Learning Ecologies by collating a number of articles which have adopted the term to conduct empirical research in the field of educational technologies. Overall, the studies in this section encompass a perspective into which learning is understood not only in the present and at a granular level (eg, activities, resources’ consumption, digital connections), but also attempt to understand the broader picture of the learner’s experience, as a sense-making process that is poly-contextual (from the physical to the digital context and back) as well as a process towards developing expertise along a personal timeline. The papers in this Special Section employ the concept of learning ecologies as a useful framework to analyse what people actually do to learn, exploring and characterizing the components of an individual’s learning ecology, as well as investigating how such components are used by or impact the participant in a variety of professional contexts. These papers adopt several methods in order to introduce an international, updated perspective of an already existing concept, which power could be exploited to overcome traditional and rather rigid schemes characterizing learning. The papers have been organized into three sections. The first defines the boundaries of learning ecologies as a topic of research and as a construct with potential to address both basic and applied research. Sangrà, Raffaghelli and Guitert in their paper Learning ecologies through a lens: ontological, methodological and applicative issues. A systematic review of the literature reports on a review of 85 papers on the more recent literature about learning ecologies. The authors find that the term is blurry and suggest that there is room for further applied research, which could emerge as a result of more empirical, design-based studies. Further elaborating on the understanding of the concept, González-Sanmamed, Muñoz-Carril and Santos-Caamaño, in their paper Key components of learning ecologies: A Delphi assessment, describe a model for identifying the components of learning ecologies. Their contribution reinforces the idea of ecologies as a framework for analysing learning in the digital era. The second section dives into more specific cases of people’s learning ecologies, capturing the potential and the affordances of different tools and environments which can provide further opportunities for learning. Greenhow, Li and May focus on the use of Twitter as a core component of cultivating relationships between groups of professionals who develop their learning ecologies. By analysing more than 20 000 tweets produced around professional conferencing contexts, the authors explore the transitions from non-formal to informal learning. Going beyond social networks, Ranieri, Giampaolo and Bruni highlight the importance of empowering learners to be able to consciously shape their learning ecologies, especially for professional purposes and development. These authors study the use of an e-portfolio as a suitable instrument to document and visualize the different elements of everyone’s learning ecology. Persico et al., report on an extensive qualitative and participatory research through which they identify tensions and issues regarding the relationship between digital games and learning along the formal to informal continuum. They point out the need of being more aware of the strengths and weaknesses of games for lifelong, lifewide and life-deep learning. The last paper in this section comes from Veletsianos, Johnson and Belikov who examine academics’ use of social media situated in their own learning ecologies. Their work contributes to a better understanding of how social media are integrated in academics’ lives, and how broader forces impact upon how academics use social media. The last section focuses on different collectives and their strategies to manage their learning ecologies. Peters and Romero-Carbonell explore and develop the notion of lifelong learning ecologies in the context of online higher education as a way to examine engagement between professional and academic practices, and suggest that much of the current and past research on learning in online higher education neglects the productive and generative engagement of student learning across a continuum of contexts, practices and trajectories. He and Li study stress the role of digital competence in informal learning contexts, and focus on the importance of cultural differences when facing strategies regarding the learning ecologies of Chinese and Belgian students. In the area of adults’ education, Nygren et al.’s paper identifies formal and informal activities that adults perform when applying skills for technology-rich environments. Using a large sample extracted from the PIAAC (OECD Adults’ Skills Survey), these authors suggest that the recognition of this situation would support the design more appropriate learning environments. These papers suggest that the learning ecologies concept may be useful in supporting and extending our understanding of emergent educational phenomena. By overcoming the artificial boundaries between analogue and digital contexts for learning, between formal, non-formal and informal contexts, between personal or group learning, we begin to explore how an ecological lens might support and enrich our understanding of learning, teaching and scholarship. Such a lens might inform design practices guiding the use of educational resources, devices, technologies and networks in teaching and learning.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle