A Review on the analysis of Behavioral pattern of Students during pandemic COVID-19

The widespread of (covid-19) has become the major reason for many physical illnesses in addition to psychological encounters to the whole world. The psychological challenges brought in due to the Covid-19 pandemic have resulted in decrease in the learning curve of students to a very large extent risking the academic ability of students due to psychological/mental health. Hence it is a challenge to identify valid cues for disorientation in the learning ability of the student at the right time and to suggest necessary support and guidance. This paper aims to describe about the work done so far and analyzes the future challenges to be addressed based on the learning curve of a student and gives an insight of how a student can be identified to be psychologically disturbed. © 2023 IEEE.

Exploring Large Digital Bodies for the Study of Human Behavior.

Internet access has become a fundamental component of contemporary society, with major impacts in many areas that offer opportunities for new research insights. The search and deposition of information in digital media form large sets of data known as digital corpora, which can be used to generate structured data, representing repositories of knowledge and evidence of human culture. This information offers opportunities for scientific investigations that contribute to the understanding of human behavior on a large scale, reaching human populations/individuals that would normally be difficult to access. These tools can help access social and cultural varieties worldwide. In this article, we briefly review the potential of these corpora in the study of human behavior. Therefore, we propose Culturomics of Human Behavior as an approach to understand, explain, and predict human behavior using digital corpora.

Monintoring Student Depression by Mobile Devices with Autoencoder Networks

The timely assessment of mental health is difficult since we lack the objective measurements of symptoms, especially for the Covid-19 pandemic quarantined students. Fortunately, smart phones can capture the real-world data such as the GPS traces and the phone active time et.al that link the behavioral patterns to the mental health. However, recent studies are based on a very small size of participants and only collect fewer phone features, which means that the effective predicting models which require various features are hardly adopted. In this paper, we develop an android application to record multidimensional data of users as well as a PHQ-9 and a SAS questionary, and we distribute it to 176 college students to collect larger scale data when in quarantine period. To address the unprecise problem of handcrafted feature extraction, we design an autoencoder machine learning model to monitor the student mental health. Extensive experiments indicate that the performance of the proposed method improves its F-1 score for PHQ-9 and SAS by 5% and 6% to the state of the current studies, respectively. © 2023 IEEE.

Understanding Residents' Behavior for Smart City Management by Sequential and Periodic Pattern Mining

Understanding the residents’routine and repetitive behavior patterns is important for city planners and strategic partners to enact appropriate city management policies. However, the existing approaches reported in smart city management areas often rely on clustering or machine learning, which are ineffective in capturing such behavioral patterns. Aiming to address this research gap, this article proposes an analytical framework, adopting sequential and periodic pattern mining techniques, to effectively discover residents’routine behavior patterns. The effectiveness of the proposed framework is demonstrated in a case study of American public behavior based on a large-scale venue check-in dataset. The dataset was collected in 2020 (during the global pandemic due to COVID-19) and contains 257 561 check-in data of 3995 residents. The findings uncovered interesting behavioral patterns and venue visit information of residents in the United States during the pandemic, which could help the public and crisis management in cities. IEEE

Definition of Metrics for Work Efficiency Monitoring Based on Multi-System Usage Behaviour Analysis

this paper describes a workflow efficiency estimation solution with appropriate metrics that could be used to obtain mathematical evaluations of work activities of IT systems users. Such evaluations are of great importance in current World state where COVID pandemic forced many employees to transfer their work activities from the office to home environment. Managers and administration of organizations could use analysis of these metrics and patterns for optimizing their day-to-day workflows. © 2022 IEEE Computer Society. All rights reserved.

Predicting student performance using sequence classification with time-based windows.

A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity of online education. Among the major advantages of e-learning is not only improving students' learning experience and widening their educational prospects, but also an opportunity to gain insights into students' learning processes with learning analytics. This study contributes to the topic of improving and understanding e-learning processes in the following ways. First, we demonstrate that accurate predictive models can be built based on sequential patterns derived from students' behavioral data, which are able to identify underperforming students early in the course. Second, we investigate the specificity-generalizability trade-off in building such predictive models by investigating whether predictive models should be built for every course individually based on course-specific sequential patterns, or across several courses based on more general behavioral patterns. Finally, we present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable to predict student performance with high levels of accuracy, reaching 90% for course-specific models.

Attention Deficit Hyperactivity Disorder Assessment Based on Patient Behavior Exhibited in a Car Video Game: A Pilot Study.

Symptoms of Attention Deficit Hyperactivity Disorder (ADHD) include excessive activity, difficulty sustaining attention, and inability to act in a reflective manner. Early diagnosis and treatment of ADHD is key but may be influenced by the observation and communication skills of caregivers, and the experience of the medical professional. Attempts to obtain additional measures to support the medical diagnosis, such as reaction time when performing a task, can be found in the literature. We propose an information recording system that allows to study in detail the behavior shown by children already diagnosed with ADHD during a car driving video game. We continuously record the participants' activity throughout the task and calculate the error committed. Studying the trajectory graphs, some children showed uniform patterns, others lost attention from one point onwards, and others alternated attention/inattention intervals. Results show a dependence between the age of the children and their performance. Moreover, by analyzing the positions by age over time using clustering, we show that it is possible to classify children according to their performance. Future studies will examine whether this detailed information about each child's performance pattern can be used to fine-tune treatment.

Characterization of Domestic Violence through Self-disclosure in Social Media: A Case Study of the Time of COVID-19

Domestic violence (DV) can lead to physical, psychological, and/or emotional consequences for its victims. Social media provides a new platform for DV victims to share their personal experiences and seek needed support. The anonymity of social media can potentially provide comfort and safety for victims to disclose their victimization experience. Despite a few efforts in detecting DV from social media, they have focused on differentiating DV-from non-DV-related content, or classifying DV-related content into a few general categories. By conducting an in-depth analysis of the content of DV self-disclosure in social media, this study characterizes DV in multiple aspects for the first time, including victim, perpetrator, relationship, and abuse. Moreover, it identifies the attributes to describe each aspect in detail. Furthermore, we use the social media data generated during the COVID-19 pandemic as a case study to understand the patterns of DV. The research findings of this study have implications for increasing the awareness of DV and designing support for DV victims. © 2021 IEEE.

Managers as Agents of Change: Ready for Uncertainty in Digital Transformation

The global crisis caused by the spread of COVID-19, the consequences of which could not hurt business entities operating in the Samara region and satisfying the needs of its inhabitants and organizations. Managers, acting as agents of change in companies, are often forced to make decisions in conditions of uncertainty. And the current process of digital transformation of society cannot but be accompanied by an increase in the degree of uncertainty in the external environment of business entities. Therefore, it becomes relevant to assess the readiness of future managers to change in conditions of uncertainty, as well as to highlight behaviors and responses characteristic of them in such situations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.