scholarly journals Mind the gap: the reality of remote learning during COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sarah Osborne ◽  
Kate Hogarth
Keyword(s):  
Learning Environment ◽  
Student Learning ◽  
Sense Of Community ◽  
Tertiary Education ◽  
Full Potential ◽  
Pedagogical Strategies ◽  
Content Type ◽  
Expectations Gap ◽  
Difficult Time ◽  
Remote Learning

Purpose Students, faculty staff and universities thrive and reach their full potential through planning and a sense of community. In a few short weeks, COVID-19 unravelled months of planning, separated the university community and shifted tertiary education to remote learning. This created a triangulated expectations–performance gap as to what could be reasonably implemented to support student learning, support educators and provide a continued sense of community. The purpose of this paper is to consider how educators could implement strategies to close the expectations gap created by COVID-19 remote learning. Design/methodology/approach The authors consider the expectations gap through pre-COVID-19 pedagogical strategies and teaching methods, then outline how we modified them into COVID-19 teaching approaches and designs. Findings The authors found that although expectations differ between university administration, students and faculty staff, there are a number of paths educators can take to close the expectations gap, facilitate interaction and engagement while gently encouraging self-driven student learning in a difficult time. Originality/value The practical exemplars identify steps educators can take as support mechanisms for student learners to embrace and take control of their own education in the remote learning environment and convey the importance of maintaining a sense of belonging. This creates an improved teaching environment for educators and an enhanced learning environment for students.

Setting the Stage for Success in an Online Learning Environment

2021 ◽  
pp. 53-59
Author(s):  
Maria Orlando ◽  
Linda Howard
Keyword(s):  
Higher Education ◽  
Academic Achievement ◽  
Online Learning ◽  
Learning Environment ◽  
Student Learning ◽  
Online Learning Environment ◽  
Traditional Classrooms ◽  
Pedagogical Strategies ◽  
Self Regulated Learning ◽  
Regulated Learning

Online learning in higher education has become increasingly popular because of the convenience and flexibility that it provides. Self-regulated learning, which requires students to plan, monitor, and assess their own learning, has been recognized as a key predictor of academic achievement and motivation in student learning. Few students, however, naturally do this. In order to guide students to become self-regulated learners and for these experiences to be successful for both the students and the instructor, instructors should use pedagogical strategies that differ slightly from those they have used in traditional classrooms. The purpose of this chapter is two-fold: to identify some of the challenges presented to students in an online learning environment and to give an overview of some possible solutions that an instructor can implement in order to address those challenges and give students tools to help them to become self-regulated learners.

scholarly journals Factors that influence students’ satisfaction with their physical learning environments

2016 ◽  
Vol 34 (3) ◽  
pp. 256-275 ◽  
Author(s):  
Hannah Kira Wilson ◽  
Alison Cotgrave
Keyword(s):  
Higher Education ◽  
Learning Environment ◽  
Sense Of Community ◽  
Personality Profiles ◽  
Learning Spaces ◽  
Content Type ◽  
Physical Learning Environment ◽  
New Perspective ◽  
The Relationship ◽  
Students Satisfaction

Purpose – The purpose of this paper is to identify personality types between different university disciplines, and to establish whether there are differing requirements in the design of physical learning environment. Also to identify features of the learning environment that can support a sense of community. This paper seeks to investigate the relationship between student’s personality and preferences of features of the built environment. Design/methodology/approach – Quantitative questionnaires were distributed in three university disciplines based on the variables personality, elements of the physical learning environment and features that could support a sense of community. Findings – The analysis revealed that there are differences in preferred features within the physical learning environment for the three university disciplines within a large UK-based university. It can also be seen that there are differences in personality profiles between these three university disciplines. Features of the environment that could support a sense of community have been also identified. Research limitations/implications – Those who are responsible for the design and refurbishment of higher education institutions may find this research useful to improve the facilities for students. To support the development of appropriate physical learning spaces through the understanding of students’ requirements. Originality/value – This paper presents a new perspective on how the development of higher education facilities can be designed to increase student experience by identifying specific features of the physical learning environment students prefer.

Modeling students’ problem solving performance in the computer-based mathematics learning environment

2017 ◽  
Vol 34 (5) ◽  
pp. 385-395 ◽  
Author(s):  
Young-Jin Lee
Keyword(s):  
Logistic Regression ◽  
Problem Solving ◽  
Learning Environment ◽  
Student Learning ◽  
Mathematics Learning ◽  
Quantitative Model ◽  
Content Type ◽  
Computer Based Learning ◽  
Instructional Scaffolding ◽  
Computer Based

Purpose The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment. Design/methodology/approach Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can solve a mathematics problem correctly based on how well they solved other problems in the past. The usefulness of the model was evaluated by comparing the predicted probability of correct problem solving to the actual problem solving performance on the data set that was not used in the model building process. Findings The regularized logistic regression model showed a better predictive power than the standard Bayesian Knowledge Tracing model, the most frequently used quantitative model of student learning in the Educational Data Mining research. Originality/value Providing instructional scaffolding is critical in order to facilitate student learning. However, most computer-based learning environments use heuristics or rely on the discretion of students when they determine whether instructional scaffolding needs be provided. The predictive model of problem solving performance of students can be used as a quantitative guideline that can help make a better decision on when to provide instructional supports and guidance in the computer-based learning environment, which can potentially maximize the learning outcome of students.

Setting the Stage for Success in an Online Learning Environment

2018 ◽  
pp. 1-9 ◽  
Author(s):  
Maria Orlando ◽  
Linda Howard
Keyword(s):  
Higher Education ◽  
Academic Achievement ◽  
Online Learning ◽  
Learning Environment ◽  
Student Learning ◽  
Online Learning Environment ◽  
Traditional Classrooms ◽  
Pedagogical Strategies ◽  
Self Regulated Learning ◽  
Regulated Learning

Online learning in higher education has become increasingly popular because of the convenience and flexibility that it provides. Self-regulated learning, which requires students to plan, monitor, and assess their own learning, has been recognized as a key predictor of academic achievement and motivation in student learning. Few students, however, naturally do this. In order to guide students to become self-regulated learners and for these experiences to be successful for both the students and the instructor, instructors should use pedagogical strategies that differ slightly from those they have used in traditional classrooms. The purpose of this chapter is two-fold: to identify some of the challenges presented to students in an online learning environment and to give an overview of some possible solutions that an instructor can implement in order to address those challenges and give students tools to help them to become self-regulated learners.

scholarly journals Remote elementary education: a comparative analysis of learner development (part 1)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stefan Kleinke ◽  
David Cross
Keyword(s):  
Elementary Education ◽  
Learning Environment ◽  
Learning Environments ◽  
Standardized Test ◽  
Local Politics ◽  
Group Differences ◽  
Content Type ◽  
Significant Difference ◽  
Subject Areas ◽  
Remote Learning

PurposeThe purpose of this two-part research was to investigate the effect of remote learning on student progress in elementary education. Part one, presented in this paper, examined achievement differences between learners in a fully remote learning environment and those in a hybrid setting.Design/methodology/approachA quantitative, quasi-experimental study with factorial design was used to investigate group differences in student achievement between the different learning environments. Ex-post-facto data from standardized test scores were utilized to examine in which ways the learning environment may have affected learner progress in two distinct subject areas crucial to elementary education: English language (ELA) and math.FindingsFindings revealed a significant difference between the two learning environments in both subject areas. While preexisting group differences, selection biases and testing inconsistencies could be effectively ruled out as potential causes for the observed differences, other factors such as developmental and environmental differences between the learning environments seemed to be influential. Therefore, the follow-on research aimed at further investigating and confirming the influence of such factors and will be presented in a Part 2 paper.Practical implicationsKnowledge of the observed differences in learning achievements between the different environments, as well as the factors likely causing them, may aid educators and school administrators in their decision processes when faced with difficult circumstances such as during the pandemic.Originality/valueWhen the SARS-CoV-2 virus started to rapidly spread around the globe, educators across the world were looking for alternatives to classroom instruction. Remote learning became an essential tool. However, in contrast to e-learning in postsecondary education, for which an abundance of research has been conducted, relatively little is known about the efficacy of such approaches in elementary education. Lacking this type of information, it seems that educators and administrators are facing difficult decisions when trying to align the often conflicting demands of public health, local politics and parent pressure with what may be best for student learning.

scholarly journals Learning environment and primary school efficiency

2019 ◽  
Vol 33 (4) ◽  
pp. 678-697 ◽  
Author(s):  
Boon L. Lee ◽  
Andrew Worthington ◽  
Clevo Wilson
Keyword(s):  
Learning Environment ◽  
Primary School ◽  
Student Learning ◽  
Primary Schools ◽  
Educational Background ◽  
Teaching Staff ◽  
Full Time ◽  
Teaching Environment ◽  
School Efficiency ◽  
Content Type

Purpose Existing studies of school efficiency primarily specify teacher inputs as the number of teachers and perhaps the student-teacher ratio. As a result, there is no direct qualitative recognition of the learning environment. The purpose of this paper is to incorporate the learning environment directly into the assessment of school efficiency. Design/methodology/approach The authors employ data envelopment analysis to derive efficiency scores and the double-bootstrap truncated regression approach in Simar and Wilson’s (2007) Journal of Econometrics to quantify the sources of efficiency in 430 Queensland state primary schools. In the first stage, the outputs of student National Assessment Program-Literacy and Numeracy scores and the inputs of full-time equivalent teaching staff and cumulative capital expenditure per student are used to measure efficiency. In the second stage, the authors specify an index of community socio-educational advantage, class size, the share of teachers with postgraduate qualifications, funds spent on professional development, and surveyed opinions from parents/caregivers, students, staff and principals on the learning environment to explain these measures of efficiency. Findings Socio-economic background and the teaching environment affect school efficiency. Although not all variables related to teacher contribution are significant, there is evidence to suggest that teachers have a positive influence on student performance hence school efficiency. Teachers ability to clearly explain the requirements of schoolwork tasks and listening to student opinions sets an ideal student engagement environment which can have a profound impact on student learning. Practical implications From a policy perspective, policy makers should target resources at inefficient schools aimed at enhancing student learning through teacher development and, at the same time, providing financial and non-financial educational assistance to students and their families from a low socio-educational background. Originality/value This is the first large-scale primary school efficiency analysis to incorporate the Simar and Wilson (2007) approach to explaining the determinants of efficiency, including teaching environment from the perspective of students, teachers and other stakeholders.

scholarly journals Perceptions of a Remote Learning Pathology Elective for Advanced Clinical Medical Students

2021 ◽  
Vol 8 ◽  
pp. 237428952110068
Author(s):  
Kara S. Tanaka ◽  
Rageshree Ramachandran
Keyword(s):  
Medical Students ◽  
Learning Environment ◽  
Online Survey ◽  
Learning Goals ◽  
Asynchronous Learning ◽  
Self Directed Learning ◽  
Anatomic Pathology ◽  
Institutional Review ◽  
Clinical Clerkships ◽  
Remote Learning

In mid-March 2020, our institution removed most medical students from in-person clinical clerkships due to the COVID-19 pandemic. The Department of Pathology responded by transitioning a fourth-year clinical elective to an all-remote format composed of synchronous didactics, daily clinical sign-out utilizing digital microscopy, and asynchronous learning materials. Thirty-seven medical students completed 2- or 4-week anatomic pathology electives tailored to meet their career goals and allowing them to progress toward graduation. Institutional Review Board approval was granted to survey students’ perceptions of engagement in the remote learning environment. Quantitative and qualitative data were collected using a standardized school-wide end-of-rotation survey, an online survey developed by the authors, and students’ self-directed learning goals. End-of-rotation data showed the remote pathology course performed well (4.88 of possible 5) when compared to all advanced clinical clerkships (4.51, n = 156 courses), all elective rotations (4.41, n = 50 courses), and the traditional in-person pathology elective (4.73). Core strengths in the virtual environment included high educational value, flexibility of content and schedule, organization, tailoring to an individual’s learning goals, and a positive education environment. Deficits included the inability to gross surgical specimens, inadequate observation or feedback about students’ skills, and impaired social connections. Areas for improvement included requests for in-person experiences and development of themed tracks for career exploration. Many aspects of anatomic pathology appear well-suited to the remote learning environment. While the remote model may not be sufficient for students pursuing careers in pathology, it can be adapted to increase nonpathologists’ understanding of interdisciplinary clinical collaboration with pathologists.

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