•Procrastination was negatively correlated with performance in a meta-analysis.•Choice of procrastination measure affects its association with performance.•Performance indicator choice affects the ...procrastination–performance association.•Use of self-report data masks the procrastination–performance association.•Demographic variables affect the procrastination–performance relationship.
Previous findings on the relationship between procrastination and academic performance are inconsistent. We conducted a meta-analysis of 33 relevant studies involving a total of 38,529 participants to synthesize these findings. This analysis revealed that procrastination was negatively correlated with academic performance; this relationship was influenced by the choice of measures or indicators. The use of self-report scales interfered with detection of a significant relationship between procrastination and academic performance. The demographic characteristics of participants in individual studies also affected the observed relationship. Implications of this meta-analysis are discussed.
Objective:
to study the indicators of the functional state of the body and working capacity in the dynamics of learning in elementary school children with different school success.
Materials and ...methods:
the study included 573 elementary school students. Group I – 82 students with excellent academic performance Group II – 491 children with good academic performance. The vegetative index of Kerdo (VIC), the Rufier test, the Genchi test, and the adaptive index (AP) were determined by the method of F. Halberg. The mental performance of younger schoolchildren was studied using Schulte tables.
Results:
the values of VIC in group I children were 2.5–3.5 times higher. The difference in IR between the groups increased, amounting to 32.88% in the 4
th
grade. The Gencha sample was characterized by lower values (by 1–2 seconds) in group I children. The difference in AP in the 1
st
grade was 4.73 sec., then in the 4th grade –6.86 sec. The Schulte tables showed higher values of work efficiency, workability and endurance coefficient along with a decrease in speed and strength indicators and physical endurance.
Conclusions:
by the end of primary school, the vegetative balance in group I children is between normo- and sympathicotonia. The Gencha test made it possible to establish insufficient resistance of the body to hypoxia. Children of group II demonstrated better adaptive capabilities of the CCC, speed and strength indicators and physical endurance. The data obtained must be taken into account in the pedagogical process with younger schoolchildren and require the development of differentiated recreational activities.
This review integrates 12 years of research on the relationship between academic self-efficacy and university student's academic performance, and known cognitive and motivational variables that ...explain this relationship. Previous reviews report moderate correlations between these variables, but few discuss mediating and moderating factors that impact this relationship. Systematic searches were conducted in April 2015 of psychological, educational, and relevant online databases for studies investigating academic self-efficacy and performance in university populations published between September 2003 and April 2015. Fifty-nine papers were eligible. Academic self-efficacy moderately correlated with academic performance. Several mediating and moderating factors were identified, including effort regulation, deep processing strategies and goal orientations. Given the paucity of longitudinal studies identified in this review, further research into how these variables relate over time is necessary in order to establish causality and uncover the complex interaction between academic self-efficacy, performance, and motivational and cognitive variables that impact it.
•Academic self-efficacy moderately correlated with academic performance.•Mediating and moderating factors were identified (effort regulation, deep processing strategies and goal orientations).•Causality between academic self-efficacy and performance remains to be established.•Future research should focus on longitudinal intervention-based studies.
Adequate sleep is integral to better mental health and facilitates students' learning. We aimed to assess sleep quality among medical students and to see whether it was associated with their mental ...health (e.g., depression, anxiety, and stress) and academic performance.
A total of 206 responded, and 95 of them had complete data on demography, lifestyle, academic performance, sleep quality (Pittsburgh Sleep Quality Index), and mental health (Depression Anxiety Stress Scales). The prevalence of poor sleep was 63.2%; it was higher among students who were physically inactive and had more screen time. Poor sleepers demonstrated higher academic performance than sufficient sleepers (p = 0.04). The prevalence of depression, anxiety, and stress were 42%, 53%, and 31% respectively. Sleep quality was significantly associated with depression (p = 0. 03), anxiety (p = 0.007), and stress (p = 0.01).
While it has been consistently demonstrated that academic self-efficacy and performance are positively correlated in groups of students, little is known about whether individual students' academic ...self-efficacy levels align with their own performance abilities. At the same time, researchers contest whether self-efficacy should align with performance abilities to be of most benefit to students. In this study, we applied procedures used in the meta-cognitive calibration paradigm to investigate the alignment between academic self-efficacy and academic performance (i.e., self-efficacy calibration) in higher education. Undergraduate students (n = 207) completed five self-efficacy questionnaires with regard to academic performance outcomes in one subject over a semester (two written assignments, two exams, and the subject overall). Five corresponding grades were also collected. We calculated two types of self-efficacy calibration scores: self-efficacy accuracy (the deviation between self-efficacy and performance) and self-efficacy bias (the signed difference i.e., valence; over- and under-efficaciousness). Miscalibration of self-efficacy beliefs was prevalent, consistent with findings regarding meta-cognitive calibration. Under-efficaciousness was common at task level (for written assignments and exams), while over-efficaciousness was pronounced at domain level (for the subject overall). Self-efficacy exceeded performance for low-achievers, while it fell short of performance for high-achievers. A key finding was that self-efficacy bias predicted academic performance on similar subsequent tasks, with under-efficacious students performing better than accurate or over-efficacious students. Findings suggest self-efficacy is not a self-fulfilling prophecy; instead, over-efficacious students may experience negative impacts on academic self-regulation and performance.
•Academic self-efficacy beliefs are often mismatched with performance capacity.•Students were under-efficacious overall at task level (assignments, exams).•Students were over-efficacious overall at course level (subject grades).•Stronger students were under-efficacious; weaker students were over-efficacious.•Under-efficaciousness at T1 predicted better performance on similar tasks at T2.
Data mining offers strong techniques for different sectors involving education. In the education field the research is developing rapidly increasing due to huge number of student’s information which ...can be used to invent valuable pattern pertaining learning behavior of students. The institutions of education can utilize educational data mining to examine the performance of students which can support the institution in recognizing the student’s performance. In data mining classification is a familiar technique that has been implemented widely to find the performance of students. In this study a new prediction algorithm for evaluating student’s performance in academia has been developed based on both classification and clustering techniques and been ested on a real time basis with student dataset of various academic disciplines of higher educational institutions in Kerala, India. The result proves that the hybrid algorithm combining clustering and classification approaches yields results that are far superior in terms of achieving accuracy in prediction of academic performance of the students.
PDF
