Problematic smartphone use (PSU) has been consistently shown to relate to dysfunctional behaviors and negative daily life outcomes, including in academic context. One explanatory factor could be ...procrastination - yet it has not been studied how procrastination is related to PSU. The aim of this research was to study that relationship. Participants were 366 Estonian university students who responded to the Estonian Smartphone Addiction Proneness Scale, Aitken Procrastination Inventory, and items regarding social media use in lectures via an online survey. Correlation analysis and structural equation modelling were used to investigate the relationships between procrastination, PSU, and social media use in lectures. The results showed that procrastination and PSU and were positively correlated. Furthermore, social media use in lectures completely mediated that relationship, suggesting that students who tend to procrastinate may engage in more social media use in lectures, and that may be a driver of PSU. In addition to theoretical contribution, this study could contribute to discussions on ICT use in educational context.
•Relation between procrastination and problematic smartphone use (PSU) was studied.•In addition, social media use in lectures (SMUL) was included.•Procrastination, PSU and SMUL were positively correlated.•SMUL completely mediated the association between PSU and procrastination.•This is the first study to analyze these relationships and to propose this model.
Although associations between problematic smartphone use (PSU), or impaired functioning in different aspects of life due to excessive smartphone use, and psychopathology are well-established, the ...body of research including objectively measured smartphone use (OMSU) is relatively small. In addition, the majority of these studies relies on cross-sectional self-report data. Finally, there are no studies investigating the effect of excessive smartphone use related notifications on self-reported PSU and OMSU. The aim of the current study was to fill that gap. The effective sample comprised 73 people (age M = 26.5, SD = 8.5; 82% women) who were randomly assigned into one of three groups: control, active experimental, or passive experimental. All groups filled out a PSU measure at Time 1. Then, active and passive experimental group used a smartphone use tracking application for three weeks. After receiving their data, active experimental group received instructions for setting up their app so that it would prompt notifications based on their personal smartphone use patterns for the next month of smartphone use, while smartphone use tracking for passive experimental group continued without prompting notifications (Time 2). Finally, all groups of participants were asked about their PSU again (Time 3). Contrary to our expectations, notifications regarding excessive smartphone use did not lower self-reported PSU, nor participants’ screen time or the frequency of phone-checking behavior.
•The aim was to examine the effect of pop-up notifications on smartphone use.•Self-reported problematic (PSU) and objectively measured smartphone use (OMSU) were DVs.•Three participant groups were measured over two months (Time 1, Time 2, Time 3).•Notifications regarding excessive smartphone use had no effect on PSU or OMSU.
Social comparison on social networking sites Verduyn, Philippe; Gugushvili, Nino; Massar, Karlijn ...
Current opinion in psychology,
December 2020, 2020-12-00, Letnik:
36
Journal Article
Recenzirano
Odprti dostop
Because of the rise of social networking sites (SNSs), social comparisons take place at an unprecedented rate and scale. There is a growing concern that these online social comparisons negatively ...impact people’s subjective well-being (SWB). In this paper, we review research on (a) the antecedents of social comparisons on SNSs, (b) the consequences of social comparisons on SNSs for SWB and, (c) social comparison as a mechanism explaining (mediator) or affecting (moderator) the relationship between SNSs and SWB. The occurrence of social comparisons on SNSs depends on who uses the SNS and on how the SNS is being used with passive use in particular resulting in increased levels of social comparison. Moreover, social comparison on SNSs may occasionally result in an increase in SWB but typically negative effects are found as people tend to engage in contrasting upward social comparisons. Finally, several studies show that social comparison is a key mechanism explaining the relationship between use of SNSs and SWB and that users with a tendency to engage in social comparison are especially likely to be negatively impacted by SNSs. The dynamic, cyclical processes that result from this pattern of findings are discussed.
Several studies have shown that problematic smartphone use (PSU) is related to detrimental outcomes, such as worse psychological well-being, higher cognitive distraction, and poorer academic ...outcomes. In addition, many studies have shown that PSU is strongly related to social media use. Despite this, the relationships between PSU, as well as the frequency of social media use in lectures, and different approaches to learning have not been previously studied. In our study, we hypothesized that both PSU and the frequency of social media use in lectures are negatively correlated with a deep approach to learning (defined as learning for understanding) and positively correlated with a surface approach to learning (defined as superficial learning). The study participants were 415 Estonian university students aged 19-46 years (78.8% females; age M = 23.37, SD = 4.19); the effective sample comprised 405 participants aged 19-46 years (79.0% females; age M = 23.33, SD = 4.21). In addition to basic socio-demographics, participants were asked about the frequency of their social media use in lectures, and they filled out the Estonian Smartphone Addiction Proneness Scale and the Estonian Revised Study Process Questionnaire. Bivariate correlation analysis showed that PSU and the frequency of social media use in lectures were negatively correlated with a deep approach to learning and positively correlated with a surface approach to learning. Mediation analysis showed that social media use in lectures completely mediates the relationship between PSU and approaches to learning. These results indicate that the frequency of social media use in lectures might explain the relationships between poorer academic outcomes and PSU.
Test-taking motivation (TTM) has been found to have a profound effect on low-stakes test results. From the components of TTM test-taking effort has been shown to be the strongest predictor of test ...performance. This article presents an overview of methods and instruments used to measure TTM and effect sizes between test-taking effort and performance found with these instruments. Altogether 104 articles were included in the qualitative synthesis based on literature search in EBSCO Discovery database. Effect sizes for the relationship between test-taking effort and performance were available in 28 studies. The average correlation between self-reported effort and performance was r = .33 and the average correlation between Response Time Effort and performance was r = .72, indicating that these two types of measures could be distinctly different. Educational level was a significant moderator of the effect sizes: the average correlation between test-taking effort and performance was stronger for university students than for school students. An overview of interventions aimed to enhance TTM and their effect is given.
•The average correlation between self-reported effort and test score is r = 0.33•The average correlation between Response Time Effort and test score is r = 0.72•Educational level moderates the relationship between self-reported effort and test performance.
Background
Although mathematics anxiety and self-efficacy are relatively well-researched, there are several uninvestigated terrains. In particular, there is little research on how mathematics anxiety ...and mathematics self-efficacy are associated with deep (more comprehensive) and surface (more superficial) approaches to learning among STEM and social sciences students. The aim of the current work was to provide insights into this domain.
Results
Bivariate correlation analysis revealed that mathematics anxiety had a very high negative correlation with mathematics self-efficacy. However, while mathematics anxiety correlated positively with surface approach to learning in the STEM student sample, this association was not statistically significant in the social sciences student sample. Controlled for age and gender, regression analysis showed that lower mathematics self-efficacy and female gender predicted higher mathematics anxiety, while only mathematics self-efficacy predicted mathematics anxiety in the social sciences student sample. Interestingly, approaches to learning were not statistically significant predictors in multivariate analyses when mathematics self-efficacy was included.
Conclusions
The results suggest that mathematics self-efficacy plays a large role in mathematics anxiety. Therefore, one potential takeaway from the results of the current study is that perhaps improving students’ mathematics self-efficacy could also be helpful in reducing mathematics anxiety. Since the current study was cross-sectional, it could also be that reducing students’ mathematics anxiety could be helpful in boosting their mathematics self-efficacy. Future studies should aim to clarify the causal link in this relationship.
Background
The excessive use of Internet-based technologies has received a considerable attention over the past years. Despite this, there is relatively little research on how general Internet usage ...patterns at and outside of school as well as on weekends may be associated with mathematics achievement. Moreover, only a handful of studies have implemented a longitudinal or repeated-measures approach on this research question. The aim of the current study was to fill that gap. Specifically, we investigated the potential associations of Internet use at and outside of school as well as on weekends with mathematics test performance in both high- and low-stakes testing conditions over a period of 3 years in a representative sample of Estonian teenagers.
Methods
PISA 2015 survey data in conjunction with national educational registry data were used for the current study. Specifically, Internet use at and outside of school as well as on weekends were queried during the PISA 2015 survey. In addition, the data set included PISA mathematics test results from 4113 Estonian 9th-grade students. Furthermore, 3758 of these students also had a 9th-grade national mathematics exam score from a couple of months after the PISA survey. Finally, of these students, the results of 12th-grade mathematics national exam scores were available for 1612 and 1174 students for “wide” (comprehensive) and “narrow” (less comprehensive) mathematics exams, respectively.
Results
The results showed that the rather low-stakes PISA mathematics test scores correlated well with the high-stakes national mathematics exam scores obtained from the 9th (completed a couple of months after the PISA survey) and 12th grade (completed approximately 3 years after the PISA survey), with correlation values ranging from
r
= .438 to .557. Furthermore, socioeconomic status index was positively correlated with all mathematics scores (ranging from
r
= .162 to .305). Controlled for age and gender, the results also showed that students who reported using Internet the longest tended to have, on average, the lowest mathematics scores in all tests across 3 years. Although effect sizes were generally small, they seemed to be more pronounced in Internet use at school.
Conclusions
Based on these results, one may notice that significantly longer time spent on Internet use at and outside of school as well as on weekends may be associated with poorer mathematics performance. These results are somewhat in line with research outlining the potentially negative associations between longer time spent on digital technology use and daily life outcomes.
Abstract
Estonian students achieved high scores in the latest Programme for International Student Assessment surveys. At the same time, there needs to be more knowledge about the teachers guiding ...these students, as this could provide insights into effective teaching methods that can be replicated in other educational contexts. According to the Teaching and Learning International Survey, Estonian teachers' average age is among the highest in the world, and the shortage of young, qualified mathematics teachers is well-documented. The present study aimed to map the motivating and demotivating factors for mathematics teachers to continue working in this profession. The effective sample comprised 164 Estonian mathematics teachers who responded to items regarding self-efficacy and job satisfaction and open-ended questions about motivating and demotivating factors regarding their work. The results showed that
students
,
salary and vacation
, and
job environment
are both motivating and demotivating for mathematics teachers. On the one hand, helping the students to succeed (and witnessing the progress), satisfying salaries and a good job climate motivate the teachers. And at the same time, students' low motivation, poor salary, and straining work conditions (e.g., very high workload) serve as demotivating factors. We showed that mathematics teachers' work experience is an essential factor to be considered when thinking about motivating and demotivating factors for teachers, as well as their self-efficacy and job satisfaction. The reasons, possible impact, and potential interventions on an educational policy level are discussed.
The aim of the current work was to investigate relations between problematic smartphone use (PSU) severity and intolerance of uncertainty, a transdiagnostic psychopathology construct reflecting ...individual differences in reacting to uncertain situations and events. In addition, it was tested if use of social and/or non-social smartphone use mediated associations between intolerance of uncertainty and PSU. The effective sample comprised 261 college students. Participants completed a web survey using the Smartphone Addiction Scale-Short Version (measuring PSU), Social and Process Smartphone Use Scale, and Intolerance of Uncertainty Scale-Short Form. The survey was administered twice, with approximately one month separating two measurement waves. In this paper, the measures of intolerance of uncertainty and social/non-social smartphone use from Time 1 and the PSU score from Time 2 were used. Correlation analyses showed that intolerance of uncertainty and both social and non-social smartphone use are related to Time 2 levels of PSU. In a structural equation model, intolerance of uncertainty was positively associated with non-social smartphone use, but not with social smartphone use. Non-social smartphone use was related to Time 2 PSU severity. Mediation analysis showed that only non-social smartphone use mediated the relationship between intolerance of uncertainty and levels of PSU. The study contributes to PSU research by demonstrating that intolerance of uncertainty and PSU are associated, and that non-social smartphone use may drive that relationship. This study emphasizes the need to understand the potential causes for excessive technology use.
•Problematic smartphone use (PSU) and intolerance of uncertainty (IU) were examined.•261 college students responded to a web survey twice, with one month apart.•IU was related to non-social smartphone use and PSU.•Non-social smartphone use mediated relations between IU and PSU.
The role of epistemic beliefs in science (EBS) and socio-economic status (SES) on mathematics and science test results on both student- and school-level data was investigated via a secondary analysis ...of Estonian Programme for International Student Assessment (PISA) 2015 survey data. The effective sample comprised 3991 students (52% girls, 48% boys) from 81 schools. Complementing bivariate correlation analysis, two-level regression models were computed where mathematics and science test scores were predicted from student- and school-level EBS as well as SES. Mathematics and science test scores had a medium-sized correlation with both EBS as well as SES on the student-level data. These correlations were larger on the school-level data. Multilevel analyses showed that both higher mathematics and science scores were predicted by male gender, higher student-level SES and EBS, and higher school-level SES. Higher school-level EBS significantly predicted better science test scores, but this was not the case with mathematics.
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