COVID-19 pandemic has disrupted teaching in a vriety of institutions. It has tested the readiness of academic institutions to deal with such abrupt crisis. Online learning has become the main method ...of instruction during the pandemic in Jordan. After 4 months of online education, two online surveys were distributed to investigate faculty’s and Students’ perception of the learning process that took place over that period of time with no face to face education. In this regard, the study aimed to identify both faculty’s and students’ perceptions of online learning, utilizing two surveys one distributed to 50 faculty members and another 280 students were selected randomly to explore the effectiveness, challenges, and advantages of online education in Jordan. The analysis showed that the common online platforms in Jordan were Zoom, Microsoft Teams offering online interactive classes, and WhatsApp in communication with students outside the class. The study found that both faculty and students agreed that online education is useful during the current pandemic. At the same time, its efficacy is less effective than face-to-face learning and teaching. Faculty and students indicated that online learning challenges lie in adapting to online education, especially for deaf and hard of hearing students, lack of interaction and motivation, technical and Internet issues, data privacy, and security. They also agreed on the advantages of online learning. The benefits were mainly self-learning, low costs, convenience, and flexibility. Even though online learning works as a temporary alternative due to COVID-19, it could not substitute face-to-face learning. The study recommends that blended learning would help in providing a rigorous learning environment.
Due to COVID-19, higher education institutions transitioned to online learning. This study explored college students’ perceptions of their adoption, use, and acceptance of emergency online learning. ...The factors analyzed were attitude, affect, and motivation; perceived behavioral control (ease of use of technology, self-efficacy, and accessibility), and cognitive engagement. Quantitative and qualitative data were collected from 270 students. The findings present how attitude, motivation, self-efficacy, and use of technology play a significant role in the cognitive engagement and academic performance of students. Also, participants preferred face-to-face learning over online learning. This study presents suggestions on how to improve the acceptance of emergency online learning.
Educational institutes across the world have closed due to the COVID-19 pandemic jeopardizing the academic calendars. Most educational institutes have shifted to online learning platforms to keep the ...academic activities going. However, the questions about the preparedness, designing and effectiveness of e-learning is still not clearly understood, particularly for a developing country like India, where the technical constraints like suitability of devices and bandwidth availability poses a serious challenge. In this study, we focus on understanding Agricultural Student’s perception and preference towards the online learning through an online survey of 307 students. We also explored the student’s preferences for various attributes of online classes, which will be helpful to design effective online learning environment. The results indicated that majority of the respondents (70%) are ready to opt for online classes to manage the curriculum during this pandemic. Majority of the students preferred to use smart phone for online learning. Using content analysis, we found that students prefer recorded classes with quiz at the end of each class to improve the effectiveness of learning. The students opined that flexibility and convenience of online classes makes it attractive option, whereas broadband connectivity issues in rural areas makes it a challenge for students to make use of online learning initiatives. However, in agricultural education system where many courses are practical oriented, shifting completely to online mode may not be possible and need to device a hybrid mode, the insights from this article can be helpful in designing the curriculum for the new normal.
Without question, the global spread of COVID-19 poses a challenge to the higher education landscape at a magnitude we have not seen since the emergence of technology supported and online instruction. ...The impact of this hits entrepreneurship education classrooms especially hard. Thus, in this editorial, we discuss how the pandemic is impacting entrepreneurship education globally and call for additional scholarship and the development of additional resources for online entrepreneurship education.
Due to COVID-19, universities have shifted to offer online learning for their students from traditional face-to-face learning. Despite various efforts made by university administrators for their ...students' online learning during the COVID-19 pandemic, not much has been identified about how students perceived online learning and what factors affected their online learning engagement and outcomes. Examining students' motivation, self-efficacy, and anxiety as key factors for their online learning engagement and outcomes, this study conducted a self-administered online survey with college students in three countries: the U.S., South Korea, and Colombia. This study used SEM to test hypotheses and conducted a multi-group analysis to find differences among students. The findings indicated that students' self-efficacy and anxiety significantly impacted their online learning engagement, influencing online learning outcomes. Although students were highly engaged in online learning, their perceived online learning was not so effective and rigorous compared to face-to-face learning.
Background
Due to the global COVID‐19 pandemic, online learning became the only way to learn during this unprecedented crisis. This study began with a simple but vital question: What factors ...influenced the success of online learning during the COVID‐19 pandemic with a focus on online learning self‐efficacy?
Objectives
The purpose of this study was to examine the structural relationship among self‐efficacy (SE) in time management, SE in technology use, SE in an online learning environment, and learning engagement.
Methods
The participants of the study were 1205 undergraduates who were enrolled in a residential undergraduate program in South Korea in spring semester, 2020. The online survey was administered to collect data for this research and the survey results were analyzed using structural equation modeling.
Results and Conclusions
SE in technology use had a significant but negative influence on learning engagement and had a positive impact on SE in an online learning environment. SE in time management had a significant positive impact on SE in an online learning environment and learning engagement. SE in an online learning environment also significantly influenced learning engagement.
Implications
SE in technology use itself did not enhance learning engagement. In addition, indirect effects of SE in technology use and SE in time management on learning engagement through SE in an online learning environment were confirmed in this study. This indicates the influential role of SE in an online learning environment on learning engagement of online learners.
Lay Description
What is already known about this topic
COVID‐19 forced almost all students to learn online.
Time management plays a major role in the success of online learning.
Technology self‐efficacy is a requirement in online learning.
The learning engagement variable explains learning achievement and attitudes.
What this paper adds
Self‐efficacy (SE) in time management and SE in technology use enhanced SE in an online learning environment.
SE in technology use itself did not enhance learning engagement.
SE in time management and in SE an online learning environment significantly influenced learning engagement.
SE in an online learning environment mediated the relationship between SE in technology use and learning engagement as well as between SE in time management and learning engagement.
Implications for practice and/or policy
It is important to promote students' online learning self‐efficacy to enhance learning engagement in online learning.
Practitioners should be aware that SE in technology use improves learning engagement through SE in an online learning environment.
SE in time management should be promoted for successful online learning.
Online Learning: Are You Ready to Adopt? Sulistiyani, Endang; Meutia, Nur Shabrina; Firmansyah, Ardhi Dwi
Procedia computer science,
2024, 2024-00-00, Volume:
234
Journal Article
Peer reviewed
Open access
Online learning is a new paradigm in the education sector. UNUSA is one of the universities that still not optimal in online learning adoption. This study aims to identify elements and levels of ...adoption readiness. The result is four elements including technology, organization, infrastructure, and human resources. Overall, there are several differences in the level of readiness from the perspective of lecturers and students. There are three dimensions with the same index of readiness. The dimension with the highest score is technology, which is 3.84. While the lowest dimension of readiness is financial.
We prove novel algorithmic guarantees for several online problems in the smoothed analysis model. In this model, at each time step an adversary chooses an input distribution with density function ...bounded above pointwise by \(\tfrac{1}{\sigma }\) times that of the uniform distribution; nature then samples an input from this distribution. Here, σ is a parameter that interpolates between the extremes of worst-case and average case analysis. Crucially, our results hold for adaptive adversaries that can base their choice of input distribution on the decisions of the algorithm and the realizations of the inputs in the previous time steps. An adaptive adversary can nontrivially correlate inputs at different time steps with each other and with the algorithm’s current state; this appears to rule out the standard proof approaches in smoothed analysis. This paper presents a general technique for proving smoothed algorithmic guarantees against adaptive adversaries, in effect reducing the setting of an adaptive adversary to the much simpler case of an oblivious adversary (i.e., an adversary that commits in advance to the entire sequence of input distributions). We apply this technique to prove strong smoothed guarantees for three different problems: (1)Online learning: We consider the online prediction problem, where instances are generated from an adaptive sequence of σ-smooth distributions and the hypothesis class has VC dimension d. We bound the regret by \(\tilde{O}(\sqrt {T d\ln (1/\sigma)} + d\ln (T/\sigma))\) and provide a near-matching lower bound. Our result shows that under smoothed analysis, learnability against adaptive adversaries is characterized by the finiteness of the VC dimension. This is as opposed to the worst-case analysis, where online learnability is characterized by Littlestone dimension (which is infinite even in the extremely restricted case of one-dimensional threshold functions). Our results fully answer an open question of Rakhlin et al. 64. (2)Online discrepancy minimization: We consider the setting of the online Komlós problem, where the input is generated from an adaptive sequence of σ-smooth and isotropic distributions on the ℓ2 unit ball. We bound the ℓ∞ norm of the discrepancy vector by \(\tilde{O}(\ln ^2(\frac{nT}{\sigma }))\) . This is as opposed to the worst-case analysis, where the tight discrepancy bound is \(\Theta (\sqrt {T/n})\) . We show such \(\mathrm{polylog}(nT/\sigma)\) discrepancy guarantees are not achievable for non-isotropic σ-smooth distributions. (3)Dispersion in online optimization: We consider online optimization with piecewise Lipschitz functions where functions with ℓ discontinuities are chosen by a smoothed adaptive adversary and show that the resulting sequence is \(({\sigma }/{\sqrt {T\ell }}, \tilde{O}(\sqrt {T\ell }))\) -dispersed. That is, every ball of radius \({\sigma }/{\sqrt {T\ell }}\) is split by \(\tilde{O}(\sqrt {T\ell })\) of the partitions made by these functions. This result matches the dispersion parameters of Balcan et al. 13 for oblivious smooth adversaries, up to logarithmic factors. On the other hand, worst-case sequences are trivially (0, T)-dispersed.1
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