The COVID‐19 pandemic forced universities around the world to shut down their campuses indefinitely and move their educational activities onto online platforms. The universities were not prepared for such a transition and their online teaching‐learning process evolved gradually. We conducted a survey in which we asked undergraduate students in an Indian university about their opinion on different aspects of online education during the ongoing pandemic. We received responses from 358 students. The students felt that they learn better in physical classrooms (65.9%) and by attending MOOCs (39.9%) than through online education. The students, however, felt that the professors have improved their online teaching skills since the beginning of the pandemic (68.1%) and online education is useful right now (77.9%). The students appreciated the software and online study materials being used to support online education. However, the students felt that online education is stressful and affecting their health and social life. This pandemic has led to a widespread adoption of online education and the lessons we learn now will be helpful in the future.

Automata theory is an important subject in computer science and quite consequently, simulation of automata for pedagogical purposes is an important topic in computer science education research. This article reviews the major initiatives in the field of simulation of automata in the last five decades with emphasis on those automata simulators actually used at universities for teaching. A classification of the automata simulators on the basis of their design paradigms has been developed where they have been classified broadly into language based automata simulators and visualization centric automata simulators. Some salient trends in the research on simulation of automata are also identified. The article concludes with an advocacy for continuing research on simulation of automata and integration of automata simulators in teaching.

Children up to two years of age could be entertained and kept busy by showing them YouTube clips on smartphones, but did not learn anything from the videos.

We found that drawing apps were appropriate for young children aged two to four and may be used by them along with other drawing media.

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Wearable devices such as smartwatches, wristbands, GPS shoes are increasingly used for fitness and wellness as they allow users to monitor their daily health. These devices have sensors for accumulating user activity data. Clinical actigraph devices fall in the category of wearable devices worn on the wrist determined to estimate sleep parameters by recording movements during sleep. This study aims to predict sleep quality from wearable sensors using deep learning techniques. Three sleep indicators are proposed which are calculated using the data collected automatically from wearable devices. These sleep indicators are Daily Sleep Quality, Weekly Sleep Quality, and Sleep Consistency. Two deep learning models namely Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) have been implemented to predict sleep quality on the basis of the proposed indicators. Two datasets have been used to validate the work proposed in this study which include a dataset comprising sleep parameters using commercial wearable devices and another dataset consisting of sleep data using clinical actigraph device. Systematic Minority Oversampling Technique has been applied for data augmentation with the intent to increase data instances and to alleviate class imbalance. CNN is observed to outperform MLP in predicting sleep quality with the highest accuracy of 97.30%. This study also evaluates the worth of each sleep attribute using Information Gain algorithm in order to identify the most important attributes which contribute to the sleep quality. It has been concluded that in bed awake percentage contributes maximum to the Daily Sleep Quality, average sleep efficiency contributes maximum to the Weekly Sleep Quality and standard deviation of midpoint of in bed and out of bed times contributes maximum to the Sleep Consistency.

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