Scaling for Meaning Making and Cultural Inclusivity

2021 ◽  
pp. 267-278
Author(s):  
Samaa Haniya
Keyword(s):  
Learning Environments ◽  
Large Scale ◽  
Meaning Making ◽  
Diverse Learners ◽  
Digital Learning ◽  
Learning Tools ◽  
Learning Gap ◽  
Large Scale Learning ◽  
Multiple Paths ◽  
Global Movement

With the rapid growth of the global movement and technological advancements, learners are becoming more diverse than ever before. Diverse learners come from different backgrounds, including cultures, race, ethnicity, socioeconomic status, gender, language, or personality traits. These differences even increase in lecture halls and large-scale learning environments and MOOCs, which makes it difficult to accommodate the varying learners' needs. Failing to address their needs and teach inclusively may constitute a common challenge for diverse learners to understand and communicate adequately in the classrooms. To bridge the learning gap, there is a need for pedagogical transformation to ensure effective, meaningful, and inclusive learning for all. This can happen by adopting innovative pedagogies and integrating digital learning tools to calibrate different educational options and integrate multiple paths of learning to meet these variations. This chapter will present the concept of inclusive pedagogy and explore its principles in large-scale learning environments.

Learner-Modelling

2021 ◽  
pp. 24-37
Keyword(s):  
Community Development ◽  
Learning Environments ◽  
Large Scale ◽  
Development Model ◽  
Original Research ◽  
Digital Learning ◽  
Context Model ◽  
Model Learning ◽  
The Uk ◽  
Digital Learning Environments

This chapter focuses on the Open Context Model of Learning, namely that of a Community Development Model of Learning. However, this sector-based model of learning emerged from research carried out in 2002 into how people learned in UK online centres, which were the first wholly digital learning environments, developed in the UK. This chapter goes beyond examining digitally enabled learning within a single context by asking, “How do people learn?” especially as the original research had started with the question “How do people learn in UK online centres?” The chapter also asks, “How do we model learning?” The education system itself has never “modelled learning” it offers content-based courses. The design of large-scale computerisation technology projects has been based on a systems analysis approach that includes the concept of “user-modelling.” The chapter shows how this can be done from the research conceptualisation of these processes from three perspectives: 1) learner (interest-driven learning), 2) learning location (lifecycles), 3) large-scale (context-responsive) system.

A DOMAIN-INDEPENDENT, TRANSFERABLE AND TIMELY ANALYSIS APPROACH TO ASSESS STUDENT COLLABORATION

2013 ◽  
Vol 22 (04) ◽  
pp. 1350020 ◽  
Author(s):  
ANTONIO R. ANAYA ◽  
JESÚS G. BOTICARIO
Keyword(s):  
Collaborative Learning ◽  
Learning Environments ◽  
Large Scale ◽  
Learning Experience ◽  
Learning Tools ◽  
Student Interactions ◽  
Student Collaboration ◽  
E Learning ◽  
The Relationship ◽  
Domain Independent

Collaborative learning environments require intensive, regular and frequent analysis of the increasing amount of interaction data generated by students to assess that collaborative learning takes place. To support timely assessments that may benefit students and teachers the method of analysis must provide meaningful evaluations while the interactions take place. This research proposes machine learning-based techniques to infer the relationship between student collaboration and some quantitative domain-independent statistical indicators derived from large-scale evaluation analysis of student interactions. This paper (i) compares a set of metrics to identify the most suitable to assess student collaboration, (ii) reports on student evaluations of the metacognitive tools that display collaboration assessments from a new collaborative learning experience and (iii) extends previous findings to clarify modeling and usage issues. The advantages of the approach are: (1) it is based on domain-independent and generally observable features, (2) it provides regular and frequent data mining analysis with minimal teacher or student intervention, thereby supporting metacognition for the learners and corrective actions for the teachers, and (3) it can be easily transferred to other e-learning environments and include transferability features that are intended to facilitate its usage in other collaborative and social learning tools.

scholarly journals Notes on Corpoliteracy: Bodies in Post-digital Educational Contexts

2021 ◽  
pp. 167-182
Author(s):  
Gila Kolb
Keyword(s):  
Learning Environments ◽  
Teaching Practices ◽  
Power Relations ◽  
Teaching And Learning ◽  
Digital Art ◽  
Digital Learning ◽  
Conceptions Of Learning ◽  
Global Pandemic ◽  
Art Teaching ◽  
Digital Learning Environments

AbstractThis chapter demonstrates the potential to challenge power relations, and reconsider teaching practices and conceptions of learning bodies. How do bodies in a digital learning setting perform are read and observed? How they can be included in learning settings? Since teaching and learning increasingly take part in digital learning environments, especially since the outbreak of the COVID-19 global pandemic, digital art teaching needs rethinking toward the knowledge of learning bodies and of the perception of learning in the digital realm: a digital corpoliteracy.

scholarly journals Measuring the value of a digital supplemental resource

2021 ◽  
Vol 45 (4) ◽  
pp. 685-693
Author(s):  
Yvonne M. Baptiste ◽  
Samuel Abramovich ◽  
Cherylea J. Browne
Keyword(s):  
Factor Analysis ◽  
Science Education ◽  
Construct Validity ◽  
Student Perceptions ◽  
Large Scale ◽  
Quantitative Methods ◽  
Student Perception ◽  
Digital Learning ◽  
Internal Reliability ◽  
Traditional Component

Supplemental resources in science education are made available to students based on the belief that they will improve course-based student learning. This belief is ubiquitous, with supplemental resources being a traditional component of physiology education. In addition, the recent large-scale transition to remote learning caused by the Covid-19 pandemic suggests an increased relevance and necessity of digital versions of supplemental resources. However, the use of a supplemental resource is entirely dependent on whether students view it as beneficial. If students in a specific course do not perceive a supplemental resource as useful, there is little reason to believe the resources will be used and are worthy of investment. Consequently, measurement of student perception regarding the effectiveness of any digital learning tool is essential for educators and institutions in order to prioritize resources and make meaningful recommendations to students. In this study, a survey was used to determine student perceptions of a digital, supplemental resource. Quantitative methods, including exploratory factor analysis, were performed on data collected from the survey to examine the dimensionality and functionality of this survey. The findings from this study were used to devise an improved, standardized (i.e., reliable and valid) survey that can be used and adapted by physi3ology researchers and educators to determine student perception of a digital supplemental resource. The survey, with known construct validity and internal reliability, can provide useful information for administrators, instructors, and designers of digital supplemental resources.

scholarly journals SLINGER: large-scale learning for predicting gene expression

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Kévin Vervier ◽  
Jacob J. Michaelson
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Large Scale Learning
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