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The study explores the perspectives of college students on the pedagogical shift as well as frequent transitions between online and offline learning modes during the COVID-19 pandemic in Kerala, the most literate state in India.

A descriptive cross-sectional study was conducted among 1,366 college students in Kerala during December 2021. A pre-tested questionnaire was sent using Google Forms to students of arts and science colleges. The authors analyzed quantitative data using descriptive statistics and qualitative data using thematic content analysis.

The reported advantages of online learning were increased technical skill, flexibility in study time, effectiveness in bridging the gap of the missed academic period and provision of attending more educational webinars. Students expressed concerns of increased workload, difficulty in concentration due to family circumstances, academic incompetency, uncleared doubts and addiction to mobile phones and social media during the online classes. The main advantages reported for switching to an offline learning mode were enhanced social interaction, effective learning, better concentration and reduced stress. The reported challenges of offline classes were fear of getting the disease, concern of maintaining social distancing and difficulty in wearing masks during the classes. The shift in offline to online learning and vice versa was perceived as a difficult process for the students as it took a considerable time for them to adjust to the switching process of learning.

Students' concerns regarding transition between different learning modes provide important information to educators to better understand and support the needs of students during the pandemic situations.

The purpose of this paper is to present mobile solutions that aid in accessing open educational resources (OERs) in areas that have limited bandwidth resulting in poor internet connectivity and a gap between those with ready access to the online OERs and those without such access.

A system architecture was designed to support the repository-based, mobile-friendly, and hybrid online/offline characteristics of OERs. In a hybrid online/offline setup, the learner connects to the internet to obtain OERs from the repository via a process called syncing. Afterward, he may view any OER content regardless of whether he is online or offline. Mobile solutions based on Browser-Like Android App (BLAP)/HTTrack and Worona/Corona approaches were successfully implemented and evaluated by 139 respondents using the System Usability Scale.

BLAP/HTTrack and Worona/Corona solutions were well received. These were found to be both relatively usable, acquiring above-average usability scores of 73.2374 and 71.6546, respectively.

The findings of this study aim to contribute to the literature of hybrid online/offline model that addresses low bandwidth access of OERs in developing countries, which is not historically well provided.

The mobile solutions were designed to help those learners who experience internet connectivity difficulties in accessing OERs efficiently and updating them conveniently.

This study aims to propose an offline exploratory method that consists of two stages: first, the authors focus on completing the kinematics model of the system by analyzing the Jacobians in the vicinity of the starting point and deducing a virtual input to effectively navigate the system along the non-holonomic constraint. Second, the authors explore the sensorimotor space in a predetermined pattern and obtain an approximate mapping from sensor space to chained form that facilitates controllability.

In this paper, the authors tackle the controller acquisition problem of unknown sensorimotor model in non-holonomic driftless systems. This feature is interesting to simplify and speed up the process of setting up industrial mobile robots with feedback controllers.

The authors validate the approach for the test case of the unicycle by controlling the system with time-state control policy. The authors present simulated and experimental results that show the effectiveness of the proposed method, and a comparison with the proximal policy optimization algorithm.

This research indicates clearly that feedback control of non-holonomic systems with uncertain kinematics and unknown sensor configuration is possible.

The purpose of this paper is to propose a design of online-merge-offline (OMO) classroom for open education with design principles related to practical issues of teachers’ teaching, students’ learning and schools’ management.

Three stages were covered: drafted an OMO classroom framework, built a sample classroom and explored end-users’ experience. First, authors searched for and reviewed previous studies and related cases to draft an OMO framework. Second, a classroom, consisted of wireless devices, cloud-based services, Internet of Things terminals, ergonomics furniture, and comprehensive data management and analysis services, was built in Shanghai Open University. Third, invited 11 students’, 18 teachers’ and 9 school managers’ perspectives were collected and analysed by surveys and interviews.

All student participants responded positively in terms of learning experience in the classroom. They not only engaged in classroom activities such, but also accessed needed learning materials and interacted with teachers and peers anytime anywhere via mobile devices. Similarly, most teachers (90 per cent) made positive responses because of flexibility of teaching strategies and learning activities and expressed willingness to use the classroom in the future (94.4 per cent). In addition, more than 78 per cent of managers positively commented on the design of classroom, interaction effects and effective management. Visualised data allowed them to timely monitor status of facilities, comprehensively understand users’ behaviour and issues, make necessary decision with scientific evidence.

The framework and classroom not only provide teachers, students, school managers and researcher with a better understanding of innovative open education, but also indicate the key role of objective-oriented and data-driven issues for further work.

To meet needs of teachers, students, managers and researchers in today’s open education, an OMO classroom was built in Shanghai Open University based on the proposed Objective-Oriented Pedagogy-Space-Technology (OPST) framework. The framework provides readers (especially teachers and administrators of open-education institutes, staff of information centres and ed-tech researchers) with a better understanding of innovative instruction and effective management, and the originally designed classroom can be a practical and illuminating example.

The current corona virus pandemic had a major impact upon the health, economy and education worldwide. In India, the government as a part of the nationwide lockdown closed all educational institutions, as a consequence of which, the teaching-learning, examination and placement scenario of students were affected. UNESCO estimated that about 32 crore students have been affected in India. The adoption of an online e-learning approach tried to minimize the academic loss and stress on students. However, the online teaching transition moved on an untested and unprecedented scale in a very short span of period. The present study analyses the perception and level of acceptance of transition from offline to online learning amongst the students of higher education in Punjab.

The analysis was carried out by surveying 200 students of higher education from Punjab belonging to different regions. An appropriate pre-tested questionnaire was framed on Google Forms. A five-point Likert scale was employed to determine the perception of students regarding quality of audio and video lectures, instructor's content, preparation, interactiveness, discussion regarding doubts and assessing likes and dislikes of students. The data were coded-decoded in SPSS by using descriptive statistics and factor analysis.

The study revealed the level of acceptance of online study among the students of higher education in Punjab. It is found that both online and offline modes of learning process have their own pros and cons. The sudden transition from regular mode to virtual mode was not an easy task for students either. The study revealed that both online and offline study modes have been accepted by students depending upon the availability of IT infrastructure as almost half of the respondents perceived online mode and the other half offline.

Punjab is a recognized state of northern India. In Punjab, the quality of higher education is good and students' satisfaction is always the priority of the educational institutions. At the time of the pandemic, this sudden transition from regular to virtual mode impacted education with students coming from diverse backgrounds like urban areas, rural areas, remote areas, rural-urban areas etc. So, in the light of this sudden transition, it is important to study the viewpoint of the students of Punjab. So, the present study was conducted to understand the perception of the students toward online learning during this ongoing pandemic. For this purpose, the study employed factor analysis. Application of factor analysis in this area is unique.

The purpose of this study is mainly to find out the EFL learners' attitude towards corrective feedback. This paper also investigates types of learners who prefer the online or offline corrective feedback, and how feedback should be tailored to the needs of the learners.

The study was conducted on sixty female participants who were students of levels 7 and 8 of the B. A. program (English) at King Khalid University, Saudi Arabia. They were administered a questionnaire comprising eight questions about corrective feedback from teachers individually and independently so that they could identify their own choices without any influence from other participants. The items in the questionnaire were closed items.

It was found that both types of feedbacks are essential to enhance learners' linguistic accuracy. Learners have shown their positive attitude towards teachers' corrective feedback because they consider it a motivating learning tool. Not only that the learners have expressed the view that corrective feedback is very useful in enhancing the learning process for EFL learners. On the other hand, some of the learners are not serious about going through corrective feedback given by teachers because sometimes they are unable to differentiate between what helps or hampers progress towards language learning. However, they preferred both online (immediate/automated) feedback and offline (delayed) feedback. In general, the results state that the learners have expressed the view that corrective feedback is very useful in enhancing the learning process for EFL learners.

This study has some limitations. The first one is the sample size. Only students from levels 7 and 8 (undergraduates) were taken into consideration. The second limitation is that the researchers focused on only one university in Saudi Arabia. The third limitation is that no male students participated in this study. The results might be different if the male students participated as well.

One vital point in employing CF in the language classrooms is timing. Considering the timing of corrective feedback, teachers face the problem of whether CF should be immediate (online) or delayed (offline).

Students' satisfaction with e-learning outcomes is a vital component in determining the adoption of e-learning systems. Understanding the antecedent factors impacting students' satisfaction with e-learning outcomes helps decision-makers at the higher education to take the necessary actions to enhance the quality of students' performance, especially during the exceptional times, such as the COVID-19 pandemic.

This paper aims to determine and analyse the antecedent factors influencing students' satisfaction with e-learning outcomes during the pandemic. Structural equation modelling (SEM) is used to analysis a proposed research model.

The SEM results show that digital communities in e-learning, information technology (quality and accessibility) and the online course design quality directly influence students' satisfaction with e-learning outcomes. More interestingly, results show that the COVID-19-related factors (1) awareness of the COVID-19, (2) perceived challenges (negatively) and (3) the educational institutions' preparedness also influence e-learning outcomes.

The results suggest that the decision-makers at the educational institutions should consider adopting a blended learning system approach to deliver e-learning during the emergency, such as the COVID-19 outbreak situation.

This is one of the first studies in higher education context, which seek to identify the antecedent factors that influence students' satisfaction with e-learning outcomes during the COVID-19 pandemic.

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

During the COVID-19 pandemic, educational institutions were forced to shut down, causing massive disruption of the education system. This paper aims to determine the critical factors for the intention to participate in e-learning during COVID-19.

Data were collected by surveying 131 university students and structural equation modelling technique using PLS-SEM was employed to analysis the data.

The results showed that the COVID-19 related factors such as perceived challenges and COVID-19 awareness not only directly impact students' intention but also such effects are mediated through perceived usefulness and perceived ease of use of e-learning systems. However, the results showed that the educational institution's preparedness does not directly impact the intention of students to participate in e-learning during COVID-19. The results also showed that the gender and length of the use of e-learning systems impact students' e-learning systems use.

These results demonstrated that, regardless of how well the educational institutions are prepared to promote the use of e-learning systems, other COVID-19-related challenges play a crucial role in forming the intention of students to participate in e-learning during the COVID-19 pandemic. Theoretical and practical implications are provided.