A mixed-methods study to identify effective practices in the teaching of writing in a digital learning environment in low income schools

2018 ◽  
Vol 119 ◽  
pp. 14-30 ◽  
Author(s):  
Rebecca Jesson ◽  
Stuart McNaughton ◽  
Naomi Rosedale ◽  
Tong Zhu ◽  
Victoria Cockle
Keyword(s):  
Mixed Methods ◽  
Learning Environment ◽  
Low Income ◽  
Mixed Methods Study ◽  
Effective Practices ◽  
Digital Learning ◽  
Digital Learning Environment ◽  
Teaching Of Writing

scholarly journals Learning with Friction—Students’ Gestures and Enactment in Relation to a GeoGebra Simulation

2021 ◽  
Author(s):  
Lorena Solvang ◽  
Jesper Haglund
Keyword(s):  
Learning Environment ◽  
Video Data ◽  
Digital Learning ◽  
School Students ◽  
Representational Competence ◽  
Physics Students ◽  
Digital Learning Environment ◽  
Upper Secondary ◽  
Collaborative Learning Environment ◽  
Why Questions

AbstractThe present study contributes to the understanding of physics students’ representational competence by examining specific bodily practices (e.g. gestures, enactment) of students’ interaction and constructions of representations in relation to a digital learning environment. We present and analyse video data of upper-secondary school students’ interaction with a GeoGebra simulation of friction. Our analysis is based on the assumption that, in a collaborative learning environment, students use their bodies as means of dealing with interpretational problems, and that exploring students’ gestures and enactment can be used to analyse their sensemaking processes. This study shows that specific features of the simulation—features connected with microscopic aspects of friction—triggered students to ask what-if and why questions and consequently, to learn about the representation. During this sense-making process, students improvised their own representations to make their ideas more explicit. The findings extend current research on students’ representational competence by bringing attention to the role of students’ generation of improvised representations in the processes of learning with and about representations.

scholarly journals SETTING UP A DIGITAL LEARNING ENVIRONMENT IN A PEDAGOGICAL UNIVERSITY

2019 ◽  
Vol 15 (34) ◽  
pp. 624
Author(s):  
Vladimir N. Ivanov ◽  
Ekaterina A. Ilyina ◽  
Alexander A. Kirillov ◽  
Galina A. Alexandrova ◽  
Nikolay I. Stepanov ◽  
...  
Keyword(s):  
Learning Environment ◽  
Digital Learning ◽  
Digital Learning Environment

Este artigo tem como objetivo descobrir as particularidades de desenvolvimento do sistema de gerenciamento de universidades digitais da Rússia na Rússia no contexto da digitalização universal e identificar as oportunidades para o desenvolvimento de elementos do ambiente de aprendizado digital das universidades. O principal método de pesquisa da questão é uma análise comparativa do nível de competências digitais de estudantes de educação profissional na Rússia e nos estados membros da União Europeia. Os autores do artigo descobriram as particularidades dos processos transformacionais da educação moderna, revelaram o papel principal do desenvolvimento das tecnologias da informação e da comunicação, determinaram o lugar da Rússia no espaço de aprendizado mundial e analisaram a dinâmica da posição das instituições de ensino superior russas no país. a classificação mundial da universidade.

scholarly journals An intelligent agent model for learning group development in the digital learning environment: A systematic literature review

2020 ◽  
Vol 9 (3) ◽  
pp. 1159-1166
Author(s):  
Budi Laksono Putro ◽  
Yusep Rosmansyah ◽  
Suhardi Suhardi
Keyword(s):  
Literature Review ◽  
Learning Environment ◽  
Systematic Literature Review ◽  
Intelligent Agents ◽  
Intelligent Agent ◽  
Group Development ◽  
Collaborative Problem Solving ◽  
Digital Learning ◽  
Agent Model ◽  
Digital Learning Environment

Group development is the first and most important step for the success of collaborative problem solving (CPS) learning in the digital learning environment (DLE). A literacy study is needed for studies in the intelligent agent domain for group development of collaborative learning in DLE. This paper is a systematic literature review (SLR) of intelligent agents for group formation from 2001 to 2019. This paper aims to find answers to 4 (four) research questions, namely: 1) What components to develop intelligent agents for group development; 2) What is the intelligent agent model for group development; 3) How are the metrics for measuring intelligent agent performance; and 4) How is the Framework for developing intelligent agent. The components of the intelligent agent model consist of: member attributes, group attributes (group constraints), and intelligent techniques. This research refers to Srba and Bielikova's group development model. The stages of the model are formation, performing and closing. An intelligent agent model at the formation stage. A performance metric for the intelligent agent at the performance stage. The framework for developing an intelligent agent is a reference to the stages of development, component selection techniques, and performance measurement of an intelligent agent.

The Next-Generation CBE Architecture

2017 ◽  
pp. 134-156 ◽  
Author(s):  
Jon Mott ◽  
Rob Nyland ◽  
Greg Williams ◽  
Michael Atkinson ◽  
Arin Ceglia
Keyword(s):  
Learning Environment ◽  
Financial Aid ◽  
Digital Learning ◽  
Data Standards ◽  
Backward Design ◽  
Digital Learning Environment ◽  
Competency Based Education ◽  
Authentic Assessments ◽  
Competency Based ◽  
And Performance

Institutions looking to adopt competency-based education often struggle with the technological challenges of supporting this model. In response, this chapter proposes a “born-CBE” infrastructure intentionally designed to support the data exchanges and workflows required by CBE. This modular infrastructure contains a system of record, a digital learning environment, a recommendation engine, a financial aid processor, a competency dashboard, and a competency transcript. In order for these components to work together cohesively, data standards for interoperability (LIS, OneRoster, and LTI) are essential. The authors then discuss the essential capabilities of the learning infrastructure. These include support for backward design, authentic assessments, various learning workflows, personalization and adaptivity, and learning and performance analytics.

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