Learning Agency in New Learning Environments

2011 ◽  
pp. 236-252
Author(s):  
Hitendra Pillay ◽  
John A. Clarke ◽  
Peter G. Taylor
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Individual Characteristics ◽  
Potential Contribution ◽  
University Campus ◽  
New Learning Environments ◽  
New Learning ◽  
Contextual Elements ◽  
E Learning ◽  
Learner Agency

The Bandurian concept of learner agency was originally embedded in a para-digm where behavior, self and environment influenced each other significantly.However, evolution of the concept has focused almost exclusively on individualsas the locus of agency ignoring the potential contribution of context. It is arguedthat learning environments should be considered truly reciprocal with individualsthrough mutual and iterative influence by contextual elements and by individuallearners. It is postulated that learner agency be broadened to a more inclusiveconcept of learning agency. This concept is explored empirically with data col-lected on an e-learning university campus from 125 students about theirapproaches to learning, perceptions of their learning environments, and episte-mological reflections on themselves as learners. Results indicate that students’behavior cannot be explained by individual characteristics but by the influencesof the technology-rich learning environment and peers, suggesting that individu-als’approach to learning arises from mutual interactions between individual andcontextual agency.

scholarly journals An Ontology to Model e-portfolio and Social Relationship in Web 2.0 Informal Learning Environments

2010 ◽  
Vol 5 (4) ◽  
pp. 578 ◽  
Author(s):  
Davide Taibi ◽  
Manuel Gentile ◽  
Giovanni Fulantelli ◽  
Mario Allegra
Keyword(s):  
Social Networks ◽  
Web 2.0 ◽  
Informal Learning ◽  
Learning Environments ◽  
Learning Opportunities ◽  
Learning Contexts ◽  
New Learning Environments ◽  
New Learning ◽  
E Learning ◽  
Definition Of

<p>Web 2.0 applications and the increasingly use of social networks have been creating new informal learning opportunities. Students interact and collaborate using new learning environments which are structurally different from traditional e-learning environments. In these informal unstructured learning contexts the boundaries between the learning contexts and social spheres disappear, and the definition of the students competences appears more and more important. In this paper we propose a semantic web approach in order to create the basis for a software platform to model learner profiles. <br />In particular we propose to extend the FOAF ontology, used to describe people and their personal relationships, with an ontology related to the IMS Learning Portfolio used to model students’ competencies. This ontology could be a fundamental layer for a new Web 2.0 learning environment in which students’ informal learning activities carried out in social networks can be managed and evaluated.</p>

scholarly journals Factors Affecting Students’ Preferences for Online and Blended Learning: Motivational Vs. Cognitive

2020 ◽  
Vol 22 (2) ◽  
pp. 72-86 ◽  
Author(s):  
Sinan Keskin ◽  
Halil Yurdugül
Keyword(s):  
Learning Environment ◽  
Blended Learning ◽  
Learning Environments ◽  
Self Efficacy ◽  
Teaching And Learning ◽  
Learning Motivation ◽  
Task Value ◽  
Individual Factors ◽  
E Learning ◽  
The Individual

AbstractToday’s educational institutions are expected to create learning opportunities independent of time and place, to offer easily accessible learning environments and interpersonal communication opportunities. Accordingly, higher education institutions develop strategies to meet these expectations through teaching strategies, such as e-learning, blended learning, mobile learning, etc., by using teaching technologies. These new technology-based teaching strategies are mainly shaped by decision-makers in education. This study seeks to analyse the individual factors that affect learners’ mode of teaching and learning delivery preferences. In this study, blended and online learning is considered as preferences of learners’ mode of teaching and learning delivery. The individual factors discussed in this research are cognitive learning strategies, e-learning readiness, and motivation. The data were obtained from the pre-service teachers at the end of the academic semester when they experienced online and blended learning. Data were analysed using optimal scaling analysis. The analysis method provides a two-dimensional centroid graph which shows the correlations between the variable categories. According to study findings, there is a correlation between the preferences of the learning environment, and the constructs of self-efficacy, e-learning motivation, and task value. It can be said that the motivational variables are more effective in the learning environment preference. The students with high task value, e-learning motivation, and self-efficacy preferred studying in blended learning environments. Cognitive strategies, self-directed learning, learner control, and test anxiety factors are independent of the learners’ learning delivery preferences.

scholarly journals Pedarchitecture: Which Learning Environments for the Personalisation of Teaching and Learning? An Educational Architecture for the Schools of the Future

2020 ◽  
pp. 85-107
Author(s):  
Mariagrazia Francesca Marcarini
Keyword(s):  
Learning Environments ◽  
Teacher Collaboration ◽  
Teaching And Learning ◽  
Personal Responsibility ◽  
First Grade ◽  
Traditional Learning ◽  
New Learning Environments ◽  
New Learning ◽  
Teachers And Students ◽  
Improved Learning

AbstractThis project investigates how to overcome traditional learning environment’s rigidity; those established practices that may hinder full use of what we might call new learning environments. It addresses how teachers adapt their teaching to changing learning environments, what impact new educational spaces have on teachers and students, how to organise students with different criteria, and how learning environments can be redesigned in old schools with limited investments. The research studies four schools: in Denmark, the Hellerup Folkeskole in Gentofte and the Ørestad Gymnasium in Copenhagen; in Italy, the Enrico Fermi High School in Mantua and IC3 Piersanti Mattarella secondary first grade in Modena. New learning environments are intended to enhance teacher collaboration and stimulate the exchange of new teaching methods, enabling learning personalisation. This is often facilitated by team teaching, which in this chapter is seen as a “bridge-culture” concept, offering a wider vision including structural and organisational details. The chapter discusses how this strategy lead to students improved learning skills, them taking on greater personal responsibility and displaying aptitude to study in different ways. In this sample of “architecture feeds pedagogy” schools, some key concepts are explored that might guide future learning environments design: readability, “semantic-topical”, flexibility, invisible pedagogy and affordances.

Securing the Infrastructure for Service-Based E-Learning Environments

2011 ◽  
pp. 1-24
Author(s):  
Simon Schwingel ◽  
Gottfried Vossen ◽  
Peter Westerkamp
Keyword(s):  
Web Services ◽  
Learning Environment ◽  
Learning Environments ◽  
User Authentication ◽  
Learning Objects ◽  
Learning System ◽  
Learning Material ◽  
Single Sign On ◽  
User Data ◽  
E Learning

E-learning environments and their system functionalities resemble one another to a large extent. Recent standardization efforts in e-learning concentrate on the reuse of learning material only, but not on the reuse of application or system functionalities. The LearnServe system, under development at the University of Muenster, builds on the assumption that a typical learning system is a collection of activities or processes that interact with learners and suitably chosen content, the latter in the form of learning objects. This enables us to divide the main functionality of an e-learning system into a number of stand-alone applications or services. The realization of these applications based on the emerging technical paradigm of Web services then renders a wide reuse of functionality possible, thereby giving learners a higher flexibility of choosing content and functionalities to be included in their learning environment. In such a scenario, it must be possible to maintain user identity and data across service and server boundaries. This chapter presents an architecture for implementing user authentication and the manipulation of user data across several Web services. In particular, it demonstrates how to exploit the SPML and SAML standards so that cross-domain single sign-on can be offered to the users of a service-based learning environment. The chapter also discusses how this is being integrated into LearnServe.

Online Learning Environments

2011 ◽  
pp. 678-685 ◽  
Author(s):  
Riu Hu ◽  
Shuyan Wang
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Learning Environments ◽  
Communication Systems ◽  
The Internet ◽  
Electronic Communications ◽  
Online Learning Environments ◽  
Computer Communication ◽  
E Learning ◽  
The Web

Online learning, which was defined as a learning environment using computer communication systems for learning delivery and interaction (Harasim, 1990), has been involved into all facets of society’s education. Online learning can be considered as a subset of the category of e-learning because it refers specifically to learning that is occurring via the Internet or Intranet. Online learning environment normally refers to learning via electronic communications, coursework, and/or information posted on the Web, and through other instructional activities by using Internet.

Using Infographics for Teaching

2021 ◽  
pp. 105-126
Author(s):  
Kyriaki Skenteridou ◽  
Theodosios Tsiakis
Keyword(s):  
Educational Technology ◽  
Environmental Education ◽  
Learning Environments ◽  
Information Dissemination ◽  
Teaching Tool ◽  
Historical Maps ◽  
Visual Aids ◽  
Modern Age ◽  
New Learning Environments ◽  
New Learning

Outstanding advances in educational technology are significantly influencing new learning environments, where it is necessary for teachers to respond and for learners to be able to adapt to the modern age of knowledge and information dissemination. The development of ICT has catalyzed the ability of all types of data to be reproduced visually (visualization). The term visualization refers to the use of various visual aids, which makes a subject more eloquent. This is especially useful for teaching a variety of special courses (environmental education), geography (maps, atlases), history (historical maps, atlases). Geography is a comprehensive and one of the most demanding subjects, as its study deals with a variety of different topics. This course can be made more effective and produce more permanent results through the use of innovative tools. One of these tools is information. In the context of the present study, the use of infographics, a pioneering visual tool transformed into a reliable teaching tool-guide in the classroom, is presented.

AI and AR

2021 ◽  
pp. 216-231
Author(s):  
Anita M. Cassard ◽  
Brian W. Sloboda
Keyword(s):  
Artificial Intelligence ◽  
Distance Learning ◽  
Augmented Reality ◽  
Learning Environments ◽  
Virtual Learning Environments ◽  
Learning Assessment ◽  
New Learning Environments ◽  
New Learning ◽  
Available Technology ◽  
The Impact

This chapter presents some of the possibilities and approaches that are used in the application of AI (artificial intelligence) and AR (augmented reality) in the new learning environments. AI will add another dimension to distance learning or eLearning that in some cases already includes AR (augmented reality) virtual learning environments. Because of this advent in available technology and the impact it will have on learning, assessment of newly structured parameters and their impact on student outcomes is crucial when measuring student learning. For some of us there might be a concern about the domination of AI as seen in the movie The Terminator, but we can take ease in the notion that it is not only AI versus humans. A new version of human augmented intelligence (HI) is being developed as we speak.

Social Software and E-Learning

2007 ◽  
pp. 228-247
Author(s):  
Jon Dron
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Web 2.0 ◽  
Learning Environments ◽  
Social Software ◽  
Learning Trajectory ◽  
Online Learning Environment ◽  
Networked Learning ◽  
E Learning

This book offers an exploration of the ways that a learning trajectory is determined, and, in particular, how an online learning environment can affect that trajectory. It provides suggestions about how, primarily through technologies that underlie what is vulgarly known as “Web 2.0,” networked learning environments should be constructed to give control to learners if they need it, as they need it, and when they need it.

scholarly journals After the Move to a New Campus—Effects on Students’ Satisfaction with the Physical and Learning Environment

2020 ◽  
Vol 10 (12) ◽  
pp. 370
Author(s):  
Andreia P. Costa ◽  
Georges Steffgen
Keyword(s):  
Higher Education ◽  
Learning Environment ◽  
Learning Environments ◽  
Physical Environment ◽  
Empirical Studies ◽  
University Campus ◽  
Evaluation Questionnaire ◽  
Students Satisfaction

Few empirical studies in higher education consider the importance of the physical environment on students’ satisfaction with the learning environment. The present study first examined the effects of a move to a new campus on students’ satisfaction with the physical and learning environments. Then, it examined how students’ satisfaction with a physical environment affects students’ satisfaction with the learning environment. It was hypothesised that the move to a new and modern university campus with better study facilities would increase students’ satisfaction both with the physical and learning environment, and that these two would be linked. Results contained 771 students’ assessments of the Bachelor Evaluation Questionnaire, which included students’ satisfaction with five aspects of their learning environment as well as five items assessing satisfaction with the physical environment. Findings showed that students were overall more satisfied with the physical environment in the new campus than in the old campus. These differences were even greater when comparing only students in their last study year than students of all study years. Furthermore, results showed that students’ satisfaction with lecturers and teaching was predicted by increased satisfaction with classrooms. The implications of these findings for the need to design physical learning environments are discussed.

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