Abstract The current study aims to develop an efficient, reliable and valid assessment, the affective states for online learning scale (ASOLS), for measuring learners’ affective states during online ...learning using a sample of 173 young learners. The assessment consists of 15 items which assess five affective states, including concentration, motivation, perseverance, engagement, and self-initiative. To improve efficiency, five items (one for each affective state) are randomly selected and presented every 30 min during online learning. In addition, 14 among the participants were further invited to perform on-site online learning, and their affective states were validated by observations conducted by two psychologists. The ASOLS was found to be reliable and valid, with high internal consistency reliabilities and good construct, convergent and criterion validity. Confirmatory factor analyses showed that the hypothesized five-factor structure demonstrated a satisfactory fit to the data. Moreover, engagement was found to be positively associated with learning performance. Our findings suggest that the ASOLS provides a useful tool for teachers to identify students in upper primary and junior secondary schools with deficits in affective states and offer appropriate remedy or support. It can also be used to evaluate the effectiveness of interventions aimed at enhancing students’ affective states during online learning.
This report describes the adaptations made to one initial teacher education course at a Hong Kong university designed for face-to-face instruction that was required to be delivered exclusively online ...due to the suspension of face-to-face classes caused by the COVID-19 pandemic. It describes the adaptations the tutor made, and the challenges faced adapting to the new mode of delivery. It is hoped that others can learn from the author's experience and be prepared for the suspension of face-to-face classes caused by the COVID-19 pandemic or other health emergencies.
Recently, much attention has been given to e-learning in higher education as it provides better access to learning resources online, utilising technology - regardless of learners' geographical ...locations and timescale - to enhance learning. It has now become part of the mainstream in education in the health sciences, including medical, dental, public health, nursing, and other allied health professionals. Despite growing evidence claiming that e-learning is as effective as traditional means of learning, there is very limited evidence available about what works, and when and how e-learning enhances teaching and learning. This systematic review aimed to identify and synthesise the factors - enablers and barriers - affecting e-learning in health sciences education (el-HSE) that have been reported in the medical literature.
A systemic review of articles published on e-learning in health sciences education (el-HSE) was performed in MEDLINE, EMBASE, Allied & Complementary Medicine, DH-DATA, PsycINFO, CINAHL, and Global Health, from 1980 through 2019, using 'Textword' and 'Thesaurus' search terms. All original articles fulfilling the following criteria were included: (1) e-learning was implemented in health sciences education, and (2) the investigation of the factors - enablers and barriers - about el-HSE related to learning performance or outcomes. Following the PRISMA guidelines, both relevant published and unpublished papers were searched. Data were extracted and quality appraised using QualSyst tools, and synthesised performing thematic analysis.
Out of 985 records identified, a total of 162 citations were screened, of which 57 were found to be of relevance to this study. The primary evidence base comprises 24 papers, with two broad categories identified, enablers and barriers, under eight separate themes: facilitate learning; learning in practice; systematic approach to learning; integration of e-learning into curricula; poor motivation and expectation; resource-intensive; not suitable for all disciplines or contents, and lack of IT skills.
This study has identified the factors which impact on e-learning: interaction and collaboration between learners and facilitators; considering learners' motivation and expectations; utilising user-friendly technology; and putting learners at the centre of pedagogy. There is significant scope for better understanding of the issues related to enablers and facilitators associated with e-learning, and developing appropriate policies and initiatives to establish when, how and where they fit best, creating a broader framework for making e-learning effective.
The outbreak of the COVID-19 pandemic has dramatically shaped higher education and seen the distinct rise of e-learning as a compulsory element of the modern educational landscape. Accordingly, this ...study highlights the factors which have influenced how students perceive their academic performance during this emergency changeover to e-learning. The empirical analysis is performed on a sample of 10,092 higher education students from 10 countries across 4 continents during the pandemic's first wave through an online survey. A structural equation model revealed the quality of e-learning was mainly derived from service quality, the teacher's active role in the process of online education, and the overall system quality, while the students' digital competencies and online interactions with their colleagues and teachers were considered to be slightly less important factors. The impact of e-learning quality on the students' performance was strongly mediated by their satisfaction with e-learning. In general, the model gave quite consistent results across countries, gender, study fields, and levels of study. The findings provide a basis for policy recommendations to support decision-makers incorporate e-learning issues in the current and any new similar circumstances.
The number of adult learners who participate in online learning has rapidly grown in the last two decades due to online learning's many advantages. In spite of the growth, the high dropout rate in ...online learning has been of concern to many higher education institutions and organizations. The purpose of this study was to determine whether persistent learners and dropouts are different in individual characteristics (i.e., age, gender, and educational level), external factors (i.e., family and organizational supports), and internal factors (i.e., satisfaction and relevance as sub-dimensions of motivation). Quantitative data were collected from 147 learners who had dropped out of or finished one of the online courses offered from a large Midwestern university. Dropouts and persistent learners showed statistical differences in perceptions of family and organizational support, and satisfaction and relevance. It was also shown that the theoretical framework, which includes family support, organizational support, satisfaction, and relevance in addition to individual characteristics, is able to predict learners' decision to drop out or persist. Organizational support and relevance were shown to be particularly predictive. The results imply that lower dropout rates can be achieved if online program developers or instructors find ways to enhance the relevance of the course. It also implies that adult learners need to be supported by their organizations in order for them to finish online courses that they register for.
‘Panic-gogy’ is a term that describes the educational situation during the pandemic due to the transformation phenomenon from face-to-face learning to distance learning. Various types of research are ...used to uncover the constraints of this phenomenon, but not many researchers use phenomenological studies with parents as participants. Therefore, we used a phenomenological study to describe parents’ views on the constraints, expectations, and approvals regarding the preparation of distance learning modules at the junior high school level (aged 13-15 years). Data collection was carried out using semi-structured interviews. Data were analyzed using NVivo-12-assisted thematic analysis. The main findings are that most parents experience problems. Namely, children do not understand mathematics material, incomplete explanations of material from teachers, internet disturbances, and quota limitations, and children cannot learn mathematics optimally during the distance learning period. Most parents want face-to-face learning to be carried out immediately, teachers to provide detailed explanations, and use digital learning platforms. In addition, 85% of parents agree that mathematics teachers should develop distance learning modules. However, because the pandemic is still not over, this study recommends using blended learning to maintain the quality of mathematics learning.
Abstract
During the COVID-19 pandemic, distance learning became the predominant teaching method at most universities, exposing students and teachers alike to novel and unexpected challenges and ...learning opportunities. Our study is situated in the context of higher physics education at a large Swedish university and adopts a mixed-methods approach to explore how students perceive shifts to distance learning. Quantitative student survey responses comparing distance learning during the pandemic with previous in-person learning are analyzed with k-means cluster analysis and with a random-intercept multilevel linear model. Combined analyses produce a consistent picture of students who report having experienced the greatest challenges. They are on average younger, report being less autonomous in their learning, and find it harder than peers to ask questions to the instructor. They are also less likely to have access to a place where they can study without interruptions. Variation across courses is small with students being largely subjected to the same set of challenges. Qualitative data from semi-structured focus group interviews and open-ended questions supports these findings, provides a deeper understanding of the struggles, and reveals possibilities for future interventions. Students report an overall collapse of structure in their learning that takes place along multiple dimensions. Our findings highlight a fundamental role played by informal peer-to-peer and student-instructor interactions, and by the exchange of what we refer to as “structural information.” We discuss implications for teachers and institutions regarding the possibility of providing support structures, such as study spaces, as well as fostering student autonomy.
HVAC systems are the major energy consumers in commercial buildings in the United States. These systems are operated to provide comfortable thermal conditions for building occupants. The common ...practice of defining operational settings for HVAC systems is to use fixed set points, which assume occupants have static comfort requirements. However, thermal comfort has been shown to vary from person to person and also change over time due to climatic variations or acclimation. In this paper, we introduce an online learning approach for modeling and quantifying personalized thermal comfort. In this approach, we fit a probability distribution to each comfort condition (i.e., uncomfortably warm, comfortable, and uncomfortably cool) data set and define the overall comfort of an individual through combing these distributions in a Bayesian network. In order to identify comfort variations over time, Kolmogorov–Smirnov test is used on the joint probability distributions. In order to identify comfortable environmental conditions, a Bayesian optimal classifier is trained using online learning. In order to validate the approach, we collected data from 33 subjects, and an average accuracy of 70.14% and specificity of 76.74% were achieved. In practice, this approach could transform the comfort objectives to constrain functions and prevents pareto optimality problems.
•An adaptive stochastic modeling for quantifying personalized thermal comfort is proposed.•It transforms the comfort objectives to constraints and prevents pareto optimality problems.•A Bayesian optimal classifier is trained on a Bayesian network for comfort factors.•The online learning technique detects time dependent variations in thermal preferences.
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