Learning in the Pandemic: How the possibility to play video games during class and attend lessons without getting out of bed affects time-on-task

This Innovate Practice work-in-progress paper presents findings around how distance learning, due to the the COVID-19 pandemic, affected students as measured by time-on-task in programming.In this qualitative study, we examine a group of 36 second year upper-secondary students in Programming 1 during a nine week period in Spring 2021. During this time, they alternated between one whole week of distance learning followed by two weeks of in school instruction. For the Programming 1 lessons, students used an online platform to write, edit and run code in. We analyzed the log data from the platform to estimate time-on-task for each student for every lesson both at home and at school.We observed that students were affected differently by distance learning as measured by time-on-task. 12 students had more average time-on-task at school. 15 students had more average time-on-task at home. Nine students had less than five minutes difference on average.In addition to the analysis of time-on-task, students were given a survey in Fall 2021 to follow up on their experiences with in-school teaching and distance learning. In the survey, students were asked questions about their study environment at home during distance learning. From the responses, 13 students described their study environment as 'in bed' despite having access to a table and chair in a room for themselves and twenty-three students described their study environment as 'playing video games during online lectures'. Not surprisingly, students that said they were playing video games during online lectures had a lower average time-on-task by about ten minutes than their peers. Interestingly, students that said they participated in class in bed had a higher average time-on-task by about ten minutes than their peers.Correlating responses from the survey and time-on-task data, we reason about how students' study environments at home affected their time-on-task and how distance learning has affected students in the pandemic. © 2022 IEEE.

Successfully Inverting the Classroom - Recommendations to Teach (fully online): Practical marginal conditions to succeed lecturing science implementing the inverted classroom method

Inverting the classroom is a method to let students study the science of engineering materials on their own and then take time to discuss their questions and do extended hands-on lectures or exercises in class - or in the case of the covid-19 pandemic during plenary online sessions. First year mechanical engineering students use different materials to study and comprehend the principle underlying science in theory and then the plenary lectures offer the opportunity to apply their knowledge and transfer different aspects of material science to get the bigger picture. To meet the course learning outcome and overcome the diversity of a first year class many practical leads have to be fulfilled. Also, a sufficient number and variety of teaching materials, lecture videos, lightboard videos and micro-module lectures are provided aiming at different learning skills of the students. Students may individually choose, combine and study from alone or in study groups from a distance. Exercises, worked solutions, self-assessed tests and peer-instruction during present time help students to check on their learning progress. However, the self-study periods and (online) plenary sessions need to be guided carefully. If the inverted classroom teaching method follows a holistic approach it has been proven to be successful and increase the fun of learning (online) throughout the first year. © 2022 Association for Computing Machinery. All rights reserved.

A Comparison of Machine Learning Algorithms for Internet Cost in Satun Education Sandbox

Satun Province is a pilot in driving innovation in education in the area. Also known as Satun Education Sandbox. Sixteen schools participate in the project located in Satun Province, the south of Thailand. During the Covid-19 situation, many students study at home. The emergence of online learning increases the internet cost per family. Parents and teachers are aware of this cost. Moreover, the Satun Education Sandbox committee need to draw the strategies from the existed data. In this research, the data was collected from a survey of 2,594 people including teachers and students about the media that used in the teaching and learning during Covid-19 and the internet cost per family. This paper will compare machine learning algorithm, including LR, LDA, k-NN, CART, SVM, Naïve Bayes, SVM, RF, and MLP algorithms, to foresight the cost. After cleaning and some more precise configuration of the results, the details of the data set are described in detail. The model can then predict the expected cost of household internet use at home. It will be helpful for the Ministry of Education plan for further assistance to students in online learning. © 2022 IEEE.

COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing

The COVID-19 pandemic continues to affect the daily life of college students, impacting their social life, education, stress levels and overall mental well-being. We study and assess behavioral changes of N=180 undergraduate college students one year prior to the pandemic as a baseline and then during the first year of the pandemic using mobile phone sensing and behavioral inference. We observe that certain groups of students experience the pandemic very differently. Furthermore, we explore the association of self-reported COVID-19 concern with students' behavior and mental health. We find that heightened COVID-19 concern is correlated with increased depression, anxiety and stress. We evaluate the performance of different deep learning models to classify student COVID-19 concerns with an AUROC and F1 score of 0.70 and 0.71, respectively. Our study spans a two-year period and provides a number of important insights into the life of college students during this period. © 2022 Owner/Author.

University students' fully digital study of mathematics: An identification of student-groups via their resources usage and a characterization by personal and affective characteristics

We investigated university students' study of mathematics in the digital setting context of the COVID-19 pandemic. We gathered data from a survey of 89 students enrolled in a 'Linear Algebra 1' course including affective variables, learning strategies, social relatedness and resources considered useful. The results indicate students' high effort and self-regulation and a high variation in affective characteristics and social relatedness. All the traditional aspects of mathematics teaching (lecture, tutorials and lecture notes) were rated as particularly useful. In addition, the videos from external resources were rated as equally useful as the teaching team's videos. In contrast, traditional literature such as textbooks was rarely considered useful. The most useful resource rated was communication with peers, underlining the important role of social learning despite fully digital learning environments. Finally, a cluster analysis based on students' rated usefulness of the resources led to three different user-types. Whereas the 'digitals' find the external digital resources very useful (videos, webpages, etc.), the 'traditionalists' rate the digitalized traditional resources best (lecture, tutorials, etc.). All resources receive uniformly good ratings from a third group ('all resource users'). We reflect on our findings in light of the pandemic and describe directions for future research. © 2021 The Author(s) 2021. Published by Oxford University Press on behalf of The Institute of Mathematics and its Applications. All rights reserved. For permissions, please email: journals.permissions@oup.com.