The cellular phone age is arrived. Use of these medications is not "quite" safe if one does not know the risks involved, which may include cancer and other health problems. There are studies available on the malignancies generated by electromagnetic energy from mobile phones; nevertheless, more study is necessary on the negative physical and psychological ramifications, especially on heavy users such as college students. The goal of this research was to learn how much professional course students at urban institutions use their mobile phones and how it affects their mental health. Elements and processes: High school and college students from both urban and rural locations were selected at random and given a questionnaire about the impact of mobile phone usage on mental health. Results: The most common symptom reported was a headache (51.47 percent), followed by irritability (50.79) and anger (50.79%). Disorders of cognition such as apathy, poor school performance, inability to sleep, and anxiety are also common. The most frequent cell phone users are young people, thus it is important that they be made aware of the potential mental health risks associated with heavy cell phone use and encouraged to take preventative steps. This is because young people are the population that utilizes mobile phones the most. Several options have been recommended, including decreasing reliance on technology, decreasing time spent conversing, and increasing time spent messaging.
Early diagnosis of mental problems and the development of effective therapies rely on accurate assessment and continuing monitoring of mental health. Potentially life-saving treatments may be used by more individuals suffering from mental illness if early warning signs could be identified. However, conventional medical practices are infamously bad at identifying early warning signs. Patients and medical professionals conduct face-to-face interactions and assessments at predefined intervals under the standard style of care. Patients' reports of their daily activities and symptoms are the only data considered in such assessments by medical professionals. Statistics of this nature may not accurately reflect patients' day-to-day functionality due to the inherent biases in self-reporting. Smartphone-based mobile sensing offers the potential to go beyond the confines of traditional approaches to mental health diagnosis by providing more comprehensive, real-time data on patients' behaviours, habits, and symptoms. Possible solution: expanding beyond the limitations of current approaches to assessing mental health. Approximately 77% of U.S. adults have a mobile, citing 2018 data from Pew Research1.
Smartphones not only include sensors that can track their users' habits and whereabouts, but their owners also tend to keep them on them virtually constantly. Together, they allow us to see into people's everyday activities, which was previously unattainable. Users might be prompted to answer questions about their stress, emotions, and moods via their mobile devices as part of research studies. These surveys and questionnaires might be about the users' stress, emotions, and moods. Research investigating the use of smartphone sensing to track the effects of mental health difficulties is now underway, and the collection of behavioral data via cellphones has already begun. In this article, the authors conducted a study on the relationship between the authors' activity levels throughout the day and their scores on tests of cognitive function relevant to the manic-depressive spectrum discuss the correlations they found between the two variables. In an exploratory study on mobile mental health for people with schizophrenia, Ben-Zeev et al. look at the viability and social acceptability of using mobile devices for intervention and self-management. Participants in the study are persons who have been diagnosed with schizophrenia. The authors observed that people who used smartphones equipped with passive sensing apps reported feeling less worried. Participants have also shown an interest in receiving feedback on their current health status and suggestions for improvement. The authors provide findings from a study that link the PHQ-9 (patient health questionnaire-9) depression screening survey to a wide range of movability-related variables. The length of time spent at home and the normalized entropy are examples of these features.
BACKGROUND OF THE STUDY
The sensing app, which is downloaded and installed on the smartphone, and the backend service, which is stored in the cloud, are two of the most crucial parts of any smartphone sensing system. The sensor software is really installed on the mobile device in question. The sensing application takes readings from a range of various sensors, as well as other apps and the activity log of the user's phone, and then it transmits this information to a server. The data gathered by the sensing programme isn't only from the sensing app itself. Having access to aspects of the backend service that are normally unavailable to users considerably simplifies the data collection process. In addition to storing data collected from the supplied sensors in a database, the backend service also offers options for managing participants and keeping track of compliance records.
Smartphones are beneficial in many ways because they combine sensing, computation, and communication capabilities into a single, portable device. These days' smartphones are equipped with a wide range of sensors that can track not just the user's behaviour but also their immediate surroundings. As an example, a smartphone's operating system records lock/unlock events that can be used to infer the user's physical activities (such as sitting, standing, walking, running, and driving), while a global positioning system (GPS) can be used to record the user's mobility (such as locations visited, distance travelled, and mobility routines). The sensing application makes use of the various sensors that are already incorporated into the mobile device in order to collect data on the actions that users conduct. Machine learning algorithms then analyze this information to infer habits and routines of the users. In specifically, a sensor app will monitor where a person goes, who they interact with, how long they spend on their phone, how long they sleep, and whether or not they suffer from any self-reported emotional or mental illnesses (EMAs). Analyzing motion requires the observer and the tracked to work together. Activity detection using mobile devices and wearable sensors is the subject of a lot of current research. Numerous concerted attempts have been made to determine whether a device is stationary, walking, running, driving, or cycling from data obtained from accelerometer streams in order to build physical activity classifiers for smartphones. These efforts have been made to use acquired data to infer if a device is walking, running, driving, or cycling. Characteristics are derived from information that has been preprocessed by the accelerometer, and the activity is inferred via a decision tree. The activity classifier's high accuracy of 94% indicates how well it functions.
PROBLEM STATEMENT
“In the lives of students throughout the globe, mobile phones have become an indispensable tool. Nowadays, it's not uncommon to see students bringing their incredibly expensive and advanced mobile devices to class. These devices often include apps, cameras, microphones, and web browsers that allow them to access the internet and various social media platforms. Students use these devices to chat, stream videos, download music, upload photos, and play games, many of which contain explicit content.”
They were able to store information for watching whenever and wherever it looked convenient for them, due to the flexibility and storage space of some of these gadgets. They were able to do this whenever and wherever they wanted. Using the personal identification numbers (PINs) and passwords that are installed on these mobile phones, these contents are shielded from the prying eyes of parents and teachers. It is as a consequence of this that the majority of the mobile phones belonging to these adolescents include pornographic content. The majority of students from Taraba State did not get a passing grade in either the English Language or Mathematics components of the West African Examination Council (WAEC) examination that appeared in 2014. It is probable that the extensive usage of mobile phones as a method of communication is one reason for this observed phenomenon. They switched their attention away from their studies and towards their mobile devices, which they used excessively in the classroom, in the dormitory, and even while playing football. There are a number of variables that may have a negative impact on the academic performance and accomplishments of students. Some of these problems include inefficient teaching tactics, limited classroom supplies, and poor supervision from parents and guardians. The habits that these youngsters have of using their mobile phones, both during school hours and after school hours, are having a significant impact on the academic achievement of these children. Their participation in activities such as free night calls, chatting, instant messaging, social networking, exam malpractice, and other activities is included in this category. In light of this, the objective of this study was to investigate the ways in which the use of mobile phones influences the academic performance of pupils in the state of Jalin and Taraba in Nigeria, which is located in Malaysia [1].
To find out the influence of mobile phone usage on academic performance among senior secondary schools students.
To determine the influence of mobile phone usage on academic performance of senior secondary school students of different ages.
To find out the influence of mobile phone usage on academic performance among senior secondary school students of different socio economic status.
To find out the influence of mobile phone usage on academic performance among male and female senior secondary school students.
To determine the influence of the frequency of mobile phone usage on academic performance among senior secondary school students.
Concerning the use of personal cell phones in secondary school classrooms, two camps have formed: those who believe that students' ability to multitask and access these devices will cause them to perform poorly academically and encourage cheating, and those who believe that students can actually benefit academically by using these devices to engage with course material in new and exciting ways [2]. Whether or whether this negatively impacts children's capacity to learn is still an open question, despite efforts by educational institutions to both limit and progressively allow student use of mobile phones in classrooms. The potential effects of students' mobile phone use on their academic performance has been the subject of several discussions and research in the field of education since the middle of the last decade. The primary topic that has guided these research and conversations is the appropriateness of allowing kids to use mobile phones in school. In the 1990s, the government outlawed pagers and the first forms of mobile phones. Things that are essential for a student to succeed in college weren't widely known about mobile phones before the 1999 Columbine High School massacre. Researchers first started looking at the consequences of mobile phone use on students' grades in 2009. The subsequent discussions on the pros and cons of allowing, regulating, or outright banning mobile phones have been more heated. Much of the prior study on this topic has focused on only one group of students, from kindergarten all the way up to senior year of high school. The great bulk of the research have shown this to be true. As part of our analysis, we set out to determine if and how a restriction on students' usage of mobile phones in the classroom might influence their overall performance. These days, it's not uncommon for schools to let individual teachers decide how they'll handle student phone use in the classroom; these teachers often take the institution-wide policy as a guide [3]. This is in sharp contrast to earlier rules that often implemented a "zero tolerance" approach to address the problem of pupils using their phones in class. Teachers would certainly have an easier time keeping classrooms under control and kids would be more encouraged to engage in class if they were not allowed to use mobile phones on school premises.
This study will employ a descriptive quantitative research technique to examine the impact that mobile phones have on students' academic achievement. This will be accomplished by keeping track of and evaluating the mean student scores on a standardized reading test that is administered every other week. The academic performance of the student will be monitored, and the effect that the laws regarding mobile phones have had on that performance will be examined.
The study was conducted on govt. employee, healthcare and professional, engineer, businessman, pvt. Employee and consultant in global skills.
Sampling: A pilot study will be conducted with the questionnaire using a group of 20 dementia patients from China and final study will be conducted with the questionnaire on sample of 557 people .A total of questionnaires will be distributed among patients selected in a systematic random sampling. All the completed questionnaires will be considered for the study and any incomplete questionnaire will be rejected by the researcher.
Data and Measurement: Primary data for the research study will be collected through questionnaire survey. The questionnaire will be divided into two parts – (A) Demographic information (B) Factor determining the role of HRM in improving quality of life in dementia patients . Secondary data will be collected from multiple sources, primarily internet resources.
Statistical Software: MS-Excel and SPSS 25 will be used for Statistical analysis.
Statistical tools: Descriptive analysis will be applied to understand the basic nature of the data. Validity and reliability of the data will be tested through Cronbach alpha. The study will implement ANOVA, T and F test for data analysis.
Dependent Variable
Historically, policymakers and educators have put a premium on students' grades as a measure of their overall well-being and potential for personal development. But recently, they've started to pay more attention to the students' interpersonal dynamics and social and emotional development [4].The fact that these aspects are more strongly linked to academic achievement may explain this change in focus. This shift in emphasis could be associated with the realisation that these characteristics significantly impact academic achievement. Especially in the realm of intellectual achievement. With the new year 2019 upon us, the OECD will expand its standard set of economic statistics to include social and emotional data as well. It will be instantly effective that these new additions are made. This is occurring right now, which is an indication that something is changing. Chernyshenko et al. state that having accomplished one's goals in life requires a person to have emotional control, competence in activities (which includes desire, persistence, and self-discipline), and compound skills. Six high-quality empirical investigations have been carried out on this subject. The intricacies of the factors that contribute to academic success are the subject of these research. Some of these factors are directly linked to academic success, while others are unconnected but nevertheless important. Although some of these traits are relevant even if they have nothing to do with academic success, others do have an impact. The pupils' academic achievements are considered by both Colmar, Liem, Connor, and Martin, and Martinez, Youssef-Morgan, Chambel, and Marques-Pinto. Academic buoyancy, or the propensity for students to quickly recover from failures, was not identified as a significant predictor of academic success in the study by Colmar and colleagues.
Independent Variable
Smartphone Self-Efficacy
Modifying educational practices to include information and communication technology (ICT) into a classroom environment has seen a renaissance of interest since the start of the Fourth Industrial Revolution. Smart phones and other portable electronic gadgets are quickly becoming commonplace in many IT-related contexts. A recent study by [5]. found that mobile phone service has almost attained total global coverage. Several countries that are seen of being economically developed have a far higher proportion. A large portion of society now considers cellphones an essential device due to the wide variety of tasks that may be accomplished with them. This is particularly true in relation to the domains of instruction and discourse. Smartphone use is on the increase in both professional and informal situations, according to researchers [6] This is likely due to the fact that cellphones are seen as devices that are both user-friendly and efficient, as shown by [6] Researchers have looked at the impact of cellphones on students' performance in the classroom. The extensive content transferability of cellphones is one of the benefits of utilising cellphones in educational contexts, according to [7]. Another advantage of smartphones in these contexts that they said was the ease with which information could be shared and learned new things. Students nowadays anticipate being exposed to a wide range of interesting forms of media over their whole educational journey. As a solution to the issue of disruptive smartphone use in the classroom, [7] suggest using mobile devices, namely smartphones, in the teaching process. in particular, the writers zero down on potential ways to achieve this goal. A study by [8]found that kids' goals, needs, and preferences could vary greatly depending on how often they use their phones. The best way for teachers to keep their classes running smoothly and prevent students from being distracted or losing attention is to establish ground rules and regulations. Acceptability of tablet technology, learning excitement and engagement, and the repercussions of teacher preparedness and technical proficiency were among the many factors evaluated by [7] in relation to digital usage in the classroom.
H01: There is no significant relationship between Smartphone self-efficacy and Academic Achievement.
H1: There is significant relationship between Smartphone self-efficacy and Academic Achievement.
RESULT
In the demographics section of the questionnaire, students were asked questions such as their gender, age range, and academic level. The reason behind the demographic questions is to determine or find out basic personal information of the respondents.
Demographic Information
The results shown in Table indicate that the majority of respondents were female. According to the findings, there were a greater number of women than there were males who took part in the study. According to the findings of the poll, the vast majority of respondents are young people (somewhere between the ages of 21 and 24). This would seem to suggest that the majority of the population was comprised of students. According to the findings, the vast majority of the students who participated in the poll were in their third year at the time it was being carried out. There is a disproportionate number of replies from students in their third year of undergraduate study. These students have more expertise with technology (such as smartphones).
The statistical method known as regression analysis is used to investigate how one variable is anticipated to change in relation to another variable, or, to put it another way, how the value of the second variable may be derived from the value of the first variable. ion Analysis
Regression analysis is a statistical method that is used to analyse the likely change in a variable with regard to the amount of change in the other one. This implies that the value of the unidentified variable may be calculated from the identified value of the other variable. In other words, regression analysis allows one to identify the value of the unidentified variable based on the identified value of the other variable.
Regression Analysis
The reliability analysis scale that is being utilised has to be adjusted to accommodate the most recent finding. A course of action that the discoverer may take with his reliable observations if, and only if, two perceptions are equal with regard to the findings that are being measured.
Reliability
CONCLUSION
There is a steady uptick in studies investigating the potential negative effects of mobile use on mental health. Smartphones provide a strategy that is both practical and unobtrusive, allowing for the continuous collecting of behavioral data from people. Scientists in the domains of psychology and mental health are beginning to use smartphones to investigate the manifestations of mental illnesses like schizophrenia, bipolar disorder, anxiety, sadness, PTSD, and personality disorders. Part one of this thesis detailed our efforts to build the sensor system Student Life and put it to use collecting data from college students. Afterwards, this data was examined. We proved that the data collected from students' phones via the Student Life sensor system can be utilized to make accurate predictions about students' mental and academic health. Teachers might use these forecasts to evaluate their students' academic progress. Several student actions were used to draw conclusions on those actions, as they significantly impacted campus life. Classroom actions like these have a significant impact on students' emotional and intellectual development. Our group decided it would be beneficial to apply the same mental health sensing technology to a population with more serious mental disorders after the successful completion of the Student Life project. Since the Student Life initiative was a smashing success, this choice was made. Patients with schizophrenia are being studied in the Crosscheck study, a randomized controlled experiment that aims to detect potential relapses by tracking the patients' symptoms. The research is taking place at Cambridge University. To a greater extent, this topic is explored in the second half of this thesis. We modified the Student Life sensing system to build the Crosscheck sensing system, and then we gave it to those who suffer from schizophrenia. By examining data from patients' smartphones and contacting them personally, we were able to identify high-risk people during the research.
LIMITATION OF THE STUDY
We are cognizant of the fact that the research had several flaws and restrictions. We might have used a more trustworthy sample technique, and respondents' comprehension of the topics and language may have impacted their responses. It is possible that parents, teachers, and participants did not remember screen-related activities precisely. Due to the reliance on parent replies for the computation of screen time, it is possible that parents overlooked their children's screen time. There was no examination of media content, and the research sample was small. The research may not have been reflective of Malaysia as a whole as it only covered a small portion of the nation.
WAEC Chief Examiner report .(2014). Investigating the Use of Smartphones for Learning Purposes by Australian Dental Students. Retrieved on September 19, 2018, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC411 4424/
Kiema, K. (2015). As Schools Lift Bans on Cell Phones, Educators Weigh Pros and Cons | NEA. National Education Association. Quantitative Research Methods Proposal Page 47 IMPACTS OF STUDENT CELL PHONES https://www.nea.org/advocating-for-change/new-from-nea/schools-lift-bans-cell-phones-e ducators-weigh-pros-and-cons
Beland, & Murphy, R. (2016). Ill Communication: Technology, distraction & student performance. Labour Economics, 41, 61–76. https://doi.org/10.1016/j.labeco.2016.04.004
Chernyshenko, Doffing, E. A. (2018, May). Effective Technology Policies: How To Create And Implement An Effective Policy For Personal Devices In Secondary Schools. DigitalCommons@Hamline. Retrieved July 25,
Sarker et al. ,Ann, K., Cassandra, G., Leonie, S., and Oliver, D. (2019). How to reduce test anxiety and academic procrastination through inquiry of cognitive appraisals: a pilot study investigating the role of academic self-efficacy. Front. Psychol. 10:1917. doi: 10.3389/fpsyg.2019.01917
Kent, M., Looi et al. and Yi et al. (2016). Adding to the mix: Students use of Facebook groups and blackboard discussion forums in higher education. Knowledge Management & E-Learning, 8(3), 444–463.
Anshari et al., Tikoria, J., & Agariya, A. K. (2017). ICT enabled classroom effectiveness scale development and validation: A case of multi-campus university. Knowledge Management & E-Learning (KM&EL), 9(1), 111–127.
Bluestein , Kim ,Tulane, S., Vaterlaus, J. M., & Cho, Lee Beckert, T. E. (2017). An A in Their Social Lives, but an F in School: Adolescent Perceptions of Texting in School. Youth & Society, 49(6), 711–732.
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