Predictors Of Academic Performance Research Articles

Schools: First Promoters of a Good Visual Health

Abstract Vision plays an important role in the physical, cognitive and social development of children. Uncorrected vision problems can harm a child's development and interfere with learning. Visual functioning is a strong predictor of academic performance in school-aged children. Early detection and treatment are essential. The objective of our work is to establish the prevalence of visual disorders in children and to investigate the motivations for a consultation. This is a descriptive study carried out at the Vision Clinic of Tetouan-Morocco, including all pediatric patients (<16 years old) who presented to the consultation of the same doctor, between July 2022 and December 2022. All patients underwent a complete ophthalmological examination with measurement of refraction after cycloplegia. The study involved 450 children, including 254 girls and 196 boys. The average age was 9 years old. One of two children was consulting for the first time in ophthalmology. The average age at the first consultation is 8.5 years. The ophthalmological examination was requested by the school in 46% of the cases. In 34%, it is requested following observations made by the parents. In 10%, it is the child who reports the visual disturbance (these children were all over 8 years old). In 3% of cases, the ophthalmological examination is requested by the attending physician. 93% of these children required optical correction after measurement of refraction under cycloplegia and 45% had amblyopia of variable importance. One out of three amblyopic children was over 10 years old when the visual impairment was diagnosed. This work highlights the essential role of schools in the detection of visual impairment in children. Routine vision screening and eye exams are critical to detect vision problems before a child's development is compromised. Schools and teachers are the first and essential actors in a solid public health approach to children's vision and eye health. Key messages • Systematic screening for ametropia in children of school age & Awareness of parents for the recognition of visual disorders. • A child who does not complain is not necessarily in good visual health.

Predict students performance based on machine learning algorithms

Several studies have used models for students academic performance prediction to improve teaching quality. The aim of this study is to use machine learning algorithms to forecast students performances using their daily study behaviors and the extent of parents concerns about their childrens studies to generate synchronous predictions with daily teaching activities. The data includes study attitudes, behaviors, demographic features, and parents concerns about the students. The preprocessed dataset after feature engineering was used to train the models (i.e Support Vector Machine, Decision Tree, Random Forest, And K Nearest Neighbor). Random Forest has the best performance among the algorithms applied. The impact of students daily study behavior is highly related to academic achievement, and parents impact is also an influencing factor on childrens performances. This study could encourage and motivate parents to care more about their childrens studies with their favorable actions and behaviors. Besides, this study would help students realize the importance of their daily performance and realize it is essential to their final exam grades.

The interplay between self-regulation, learning flow, academic stress and learning engagement as predictors for academic performance in a blended learning environment: A cross-sectional survey

AimTo examine the correlations between self-regulation, learning flow, academic stress and learning engagement as predicting variables for academic achievement in a blended learning environment in Namibia. DesignCross-sectional survey. MethodsData were collected from 166 randomly selected undergraduate nursing students through an online survey between January and February 2023, and were analysed using IBM SPSS AMOS version 28.0. The data were explored through factor, parallel and confirmatory factor analyses. The relationship between the study factors and the total score of the scale was analysed using the Pearson correlation coefficient. ResultsThe results indicate that the two factors identified in the factor analysis are consistent with the theoretical proposition in this research. Factor 1 comprises items C1 to C24, which pertain to self-regulation (SR), while factor 2 consists of items D1 to D9, which relate to learning flow (LR). The findings demonstrate that self-regulation significantly predicts both flow and stress, as well as learning engagement. Additionally, there is a significant relationship between stress and self-regulated learning, as well as between stress and learning flow (r = 0.23–0.26; p= < .05). However, none of the study constructs were found to predict academic achievement. ConclusionAlthough self-regulation significantly predicted flow, stress and learning engagement, a non-significant association exists between all the study constructs and academic achievement. The results of this study have significant implications for improving the development of a positive learning environment that fosters active student engagement. Future studies should investigate correlation by conducting large-scale studies. ImpactThis study makes a valuable contribution to the current body of literature concerning academic achievement within the context of undergraduate nursing education. The insignificant relationship between the study variables and academic achievement indicate that these elements are not of considerable significance in enhancing educational achievements in blended learning surroundings in Namibia. Patient or public contributionOne hundred and sixty-six undergraduate nursing students participated in the survey. The data collected were analysed and interpreted by a skilled statistician.

Application of The Learning Strategies and Motivation Questionnaire (LEMO) at the University: Reliability and Relation to First-year GPA

As part of efforts to enhance academic achievement in higher education, incoming first-year students are becoming more and more subjected to surveys and assessments, e.g., regarding motivation and learning strategies. The Learning Strategies and Motivation Questionnaire (LEMO; Donche, Van Petegem, Van de Mosselaer, &amp; Vermunt, 2010) is one of these surveys, applied mostly in professional bachelor programmes. The current study examines the reliability and predictive validity of the LEMO questionnaire in a sample of 416 first-year university students. All 13 scales were included in the study, i.e. Concrete Processing, Analysing, Memorising, Critical Processing, Relating-Structuring, External Regulation, Self-Regulation, Lack of Regulation, Amotivation, Controlled Motivation, Autonomous Motivation, Self-Efficacy, and Learning Together. In line with its reliability in previous studies, Cronbach’s alfa of most LEMO scales was below .70, which is the minimum threshold for scientific research, as was the Composite Reliability of eight of the 13 LEMO-scales. A confirmatory factor analysis showed that several factor loadings were below .70, resulting in an average variance extracted (AVE) below .50 for 11 of the 13 scales. Most scales had no or only a limited correlation to first-year GPA (FYGPA). Only Self-Efficacy and Analysing correlated ≥ .20 with FYGPA. These two scales explained 10.4% of the variance in study success. Hereby, Self-Efficacy is the most important predictor. The other 11 scales had no significant contribution to the prediction of academic performance in addition to Self-Efficacy and Analysing (ΔR2 = 3.4%, n.s.). Additional analyses showed that the correlation between the LEMO scales and FYGPA varied according to Bachelor programme.

Street Hawking and Truancy as Predictor of Academic Performance Amongst Secondary School Students in Ondo State, Nigeria

The paper examined street hawking as predictor of truancy among secondary school students in Ondo State, Nigeria. The population of the study comprised all the secondary school teachers. A simple random sampling technique was used to select 200 teachers. The instrument for data collection was self-constructed questionnaire titled “Street Hawking and Truancy” on four-point Likert scale. Pearson product movement correlation was used to determine the correlation coefficient which was 0.76. Three hypotheses were formulated to guide the study. The hypotheses were tested using analysis. The result indicated that there was a significant relationship between street hawking and truancy amongst secondary school students. The result also showed that there was a significant relationship between street hawking and academic performance of secondary school students, and finally the result revealed that there was a significant relationship between street hawking and academic performance of secondary school students. The study concluded that there was a positive relationship between street hawking, truancy and academic performance due to so many factors such as inability to pay school fee, unauthorized extra fees being charged by school authorities, poor socio-economic status of the students. The study recommended amongst others that the state’s constituted law enforcement agents should further intensify efforts towards apprehending any student found roaming about the streets during official school hours.

Using Grades in Core Curriculum Chain Courses as Predictors of Academic Performance in Subsequent Courses: A Study at Qatar University

The study aimed to investigate students’ final grades in a linked course or chain course to determine any effect on students’ academic performance in their next linked course. Courses with high D, F, and W grades were selected with their subsequent linked courses to form a chain so that each chain consisted of two courses or more. More specifically, Arabic ( n = 11,780), English ( n = 7,714), and mathematical ( n = 1,367) chains were investigated through the years 2012 to 2016. High positive correlations between the grades of the chain courses and the transitional grade are C. Factors were examined to identify any effects on final grades. Findings indicate that 60% to 75% of low-performing students in one link of any chain will demonstrate low performance in the following link.

UTME Scores as Predictor of Academic Performance (CGPA) in Tertiary Institution in Akwa Ibom State and Cross River State, Nigeria

The study investigated whether UTME Scores are predictor of Student Academic Performance (CGPA) in Tertiary Institutions in Akwa Ibom State and Cross River State. To achieve this purpose three research questions and three hypotheses were formulated and tested with simple linear regression analysis. The study adopted correlational design. The sample of the study were made up of two thousand, eight hundred and fifty (2,850) drawn through simple random sampling. The data for the study were collected from admissions office of the two universities. The result revealed that all the three null hypotheses were rejected, indicating that UTME scores do significantly predict students' academic performance CGPA. Based on these findings, some recommendations were made to include improving on computer based method of conducting their examination. This will help to curb the high rate of examination malpractice in the system.

Investigating the relationship between emotional intelligence and self-esteem with educational performance in paramedical students

BackgroundEmotional intelligence means the "ability" of a person to express, recognize, and manage "emotions" in themselves and others. Self-confidence means seeing oneself as capable, competent, lovable, and unique. Emotional intelligence can be effective in advancing goals for a person. Academic performance is one of the important indicators in student evaluation, which can also measure the extent to which a person has achieved the goal of acquiring knowledge. The aim of this study was to investigate the emotional intelligence of paramedical students of Hamedan University of Medical Sciences and its relationship with their academic performance. MethodsThe present study is a cross-sectional study of correlation. And it was done on 300 students of the School of Medical Sciences, who have passed at least two semesters of their studies and were selected by random sampling. In this study, students' demographic information was collected and Shering's standard questionnaire was used for emotional intelligence, Rosenberg's self-esteem questionnaire was used. ResultsThere was a positive and significant relationship between students' academic performance and self-esteem (p ≥ 0.01). The results of the study also showed that there was no significant relationship between students' academic performance and emotional intelligence (p = 0.01). ConclusionThe results of our research showed that among the predicted variables, only self-esteem was able to predict academic performance in students. Self-esteem is the best predictor of academic performance.

Academic burnout among accounting majors: the roles of self-compassion, test anxiety, and maladaptive perfectionism

ABSTRACT Academic burnout, a significant negative predictor of academic performance, appears to be increasing. We examine antecedents of academic burnout among accounting majors – cognitive test anxiety, maladaptive perfectionism, and self-compassion. Based on a survey of 159 accounting majors across three years, we find that more than 85% of accounting majors report modest to high levels of burnout. Cognitive test anxiety and maladaptive perfectionism are at higher levels than observed in earlier samples of college students. Academic burnout is higher among those with higher levels of cognitive test anxiety and somewhat lower among those with higher levels of self-compassion. Although maladaptive perfectionism is not found to be related to academic burnout, it does lead to higher levels of cognitive test anxiety. Higher levels of self-compassion lead to lower levels of both cognitive test anxiety and maladaptive perfectionism, and is particularly critical to alleviating academic burnout when examining the test anxiety subfactor for freezing up (i.e. when students are unable to organize thoughts during an exam due to anxiety). In all, our study provides a first step in unpacking the antecedents of academic burnout among accounting majors.

&lt;p&gt;Psychometric properties of the Teaching Competence Assessment Scale in Mathematics. Satisfaction and academic performance prediction model&lt;/p&gt;

Abstract: The purposes of this study were: (i) to provide reliability and validity evidence regarding the Teaching Competence Questionnaire for the Math class in the Mexican context; (ii) to analyze factorial invariance according to sex in the Teaching Competence Questionnaire; (iii) to study the relationship between student perceptions of Math teaching competencies, satisfaction with school, and academic performance. A sample was selected from the total number of third-grade secondary school Mexican students. The prediction model showed that the students' perception of their Math teachers’ competence does predict satisfaction with Math class, as well as their academic performance in Math Resumen: Los objetivos de este estudio fueron: (i) proporcionar evidencia de confiabilidad y validez del Cuestionario de Competencias Docentes para la clase de Matemáticas en el contexto mexicano; (ii) analizar la invariancia factorial según sexo en el Cuestionario de Competencia Docente; (iii) estudiar la relación entre las percepciones de los estudiantes sobre las competencias de enseñanza de Matemáticas, la satisfacción con la escuela y el rendimiento académico. Se seleccionó una muestra del total de estudiantes mexicanos de tercer grado de secundaria. El modelo de predicción mostró que la percepción de los estudiantes sobre la competencia de sus profesores de Matemáticas sí predice la satisfacción con la clase de Matemáticas, así como su rendimiento académico en Matemáticas

Associations of ADHD-Risk and Motor Competence With School Functioning

Objectives: This study investigated the unique associations of motor competence and ADHD risk status on school functioning. Additionally, this study examined the factor structure of the Bruininks-Oseretsky Test of Motor Proficiency, Short Form (BOT2-SF). Methods: Participants included early elementary school children (N = 202, Mage = 6.83, SD = 0.96, Male = 53.5%, ADHD-risk = 46.5%). Child assessments of motor competence and teacher and parent reports of school functioning were used. Results: A two-factor model of the BOT2-SF with correlated fine and gross motor factors and correlated residuals best fit the data. Fine and gross motor competence were unique positive predictors of academic performance, after accounting for the effects of ADHD-risk on academic performance. ADHD-risk was the only unique predictor of adaptive school behavior. Conclusion: Findings suggest that assessing for ADHD and motor competence in children demonstrating school functioning problems may better allow for targeted intervention in school.

MBBS admission criteria as predictor of academic performance in a medical college of Pakistan.

To determine the relationship between scores obtained by students in assessments considered as admission criteria (cognitive and non-cognitive) with their academic performance in medical college. This correlational study used the data of students who got admission in Shalamar Medical and Dental College (SMDC) in 2015. Spearman correlation and Multiple regression tests were carried out to determine the relationship between admission criteria (Matric, FSC, MDCAT and MMI) and academic performance in medical college (pre-clinical and clinical years). There was significant positive correlation between scores obtained in MDCAT and pre-clinical years. When combined, MDCAT and MMI scores showed a significant positive correlation with scores obtained by students in clinical years. Scores obtained by students in pre-clinical years strongly correlated with their performance in clinical years. While scores obtained by students in FSC showed negative correlation with clinical year scores, significantly. It is concluded that the tools used for admission criteria should include both cognitive and non-cognitive elements. MDCAT is a good predictor of academic performance in pre-clinical years however it can only predict performance in clinical years when combined with MMI since MMI assesses the non-cognitive attributes (communication, empathy, ethics etc.) required in those years. FSC should not be given weightage as admission criteria owing to a lot of variability in the exams and scoring of different academic boards of the country.

Substance use and sexual cognitive orientation as predictors of academic performance among pregnant adolescents in Nigeria

Despite extensive research on the subject, the persistent prevalence of unwanted pregnancies and school dropouts among teenage girls remains a significant concern. This study aims to elucidate the complex dynamics underlying substance use, adolescent psycho-cognitive predispositions, and academic achievement among pregnant teenagers in selected metropolitan areas of Nigeria. Using a cross-sectional study design, we enrolled 400 consenting pregnant teenagers from three chosen states in Nigeria (Niger state, Lagos, and Port Harcourt) between January and April 2021, employing the snowball sampling technique. Data were collected through a validated self-administered questionnaire and subjected to analysis using Structural Equation Modeling with Smart-Pls to illustrate path analysis representing the dynamics of the phenomenon. The findings revealed that substance use, including alcohol (β=-0.224, p&lt;0.001) and psychoactive drugs (β=0.211, p&lt;0.001), significantly influenced sexual-cognitive orientation, encompassing knowledge/perception (β=-0.229, p&lt;0.001) and attitude (β=-0.171, p&lt;0.001). Furthermore, sexual-cognitive orientation (β=0.443, p&lt;0.001) and attitude (β=-0.168, p&lt;0.001) significantly predicted academic performance. Substance use with abuse potential was found to impair the cognitive ability to make decisions regarding safe sex, and sexual activity negatively affected the academic performance of pregnant students. This study highlights the need for a multi-level approach, addressing individual and environmental factors, to facilitate positive lifestyle modifications related to sexual cognitive orientation and harm reduction, ultimately improving the academic performance of pregnant teenagers.

Predicting learning success: research problems and challenges

The article is devoted to the problems of learning success prediction. The aim of the work is to discuss current tasks and possible difficulties related to the development of services for predicting learning success in the digital environment of an educational institution. Among the variety of forecasting tasks arising in educational analytics, two main directions were identified and examined in detail: prediction of student dropout and prediction of academic performance for courses of the curriculum. The article discusses examples of creating and using predictive models in the educational process by secondary and higher education organizations. It is noted that despite the large number of studies in this problem field, there are only few examples of successfully implemented regional or at least organizational-level forecasting systems. The authors believe that the main obstacles to building a well-scalable system for supporting learning success based on predictive models are difficulties with data unification, lack of policy of using personal data in learning analytics, lack of feedback mechanisms and activities for correcting learning behavior. Solving each of these problems is a separate serious scientific task. The prospects for using the results of the research are indicated.

Developmental trajectories of late preterm infants and predictors of academic performance.

The trajectories of late preterm development from infancy to kindergarten reading and math, and predictors of academic resilience and risk are unknown. Sample included 1200 late preterm infants (LPIs) from the Early Childhood Longitudinal Study, Birth Cohort. Objective measurements of development at 9 and 24 months (Bayley-SFR) and reading and math academic achievement at preschool and kindergarten were standardized; trajectories of late preterm development from 9 months to kindergarten reading and math were identified using latent class growth analysis. Multinomial logistic regression [aOR, 95% CI] identified predictors of academic resilience and risk. Four trajectory groups were observed for reading and three for math. More optimal trajectories (in reading and math) and academic resilience were associated with experiencing sensitive parenting and preschool attendance. Suboptimal (at-risk) trajectories (in reading or math) and an increased odds of academic risk were associated with <high school education, twin/multiple gestation, prenatal smoking and male sex. LPI trajectories varied from infancy to kindergarten. More sensitive parenting and preschool attendance predicted academic resilience, and should be encouraged. Select risk factors (prenatal smoking, twin/multiple gestation, male sex, <high school education) predicted academic risk, and can help identify LPIs who might benefit from closer monitoring prior to school entry. Late preterm infants (LPIs) have developmental risks compared to full terms. LPI trajectories of early reading and math varied from infancy to kindergarten. We identified predictors of academic resilience and risk, which can help inform anticipatory guidance provided to LPIs prior to kindergarten. Promotive factors (sensitive parenting and preschool enrollment) predicted academic resilience. Select maternal and neonatal characteristics (<high school education, male sex, prenatal smoking, and multiple gestation) predicted academic risk at kindergarten. LPIs account for the majority of preterm infants. Encouraging preschool enrollment and fostering sensitive parenting may promote early academic achievement in LPIs, which has population-level impacts.

Writing Strategy, Influence of Social Media, Motivation for Reading: A Structural Model in Interpersonal Communication Skills

Aim: To identify the Model appropriate for evaluating students' interpersonal communication abilities.&#x0D; Study Design: Quantitative Causal Analysis.&#x0D; Place and Duration of Study: Region XII - SOCCSKSARGEN, Philippines, during the second semester of 2022&#x0D; Methodology: The data were collected using survey questionnaires to the 400 public senior high school students selected through stratified random sampling. Mean was used to evaluate the student's academic achievement, self-efficacy in language acquisition, test-taking techniques, and reading motivation. The association between reading motivation, self-efficacy in language learning, test-taking techniques, and students' academic performance was determined using Pearson's Product Moment association. Multiple regression was performed to identify the significant predictors of academic performance. The structural equation model was used to choose the best Model. Analysis of Moment Structure (AMOS) was used to conduct the Goodness of Fit Statistics for Alternative Models.&#x0D; Results: The findings showed that students had a high level of writing strategy; a moderate level of social media influence; a high level of motivation to read; and an excellent level of interpersonal skills. Moreover, the writing approach, social media influence, and reading motivation were significantly correlated with students' interpersonal communication skills. The best fit for the data was Model 5, which had consistent indices across all five analyzed models.&#x0D; Conclusion: The study's findings supported Vygotsky's Social Cultural Theory of Writing, emphasizing the role of motivation and social impact in students' communication skills development, and highlighted the importance of communication in fostering learning processes in the brain.

Quantitative Analysis and Prediction of Academic Performance of Students Using Machine Learning

Academic performance evaluation is essential to enhance educational affection and improve educational quality and level. However, evaluating academic performance is difficult due to the complexity and nonlinear education process and learning behavior. Recently, machine learning technology has been adopted in Educational Data Mining (EDM) to predict and evaluate students’ academic performance. This study developed a quantitative prediction model of academic performance and investigated the performance of various machine learning algorithms and the influencing factors based on the collected educational data. The results conclude that machine learning provided an excellent tool to characterize educational behavior and represent the nonlinear relationship between academic performance and its influencing factors. Although the performance of various methods has some differences, all could be used to capture the complex and implicit educational law and behavior. Furthermore, machine learning methods that fully consider various factors have better prediction and generalization performance. In order to characterize the educational law well and evaluate accurately the academic performance, it is necessary to consider as many influencing factors as possible in the machine learning model.

Academic Performance in Institutionalized and Noninstitutionalized Children: The Role of Cognitive Ability and Negative Lability.

More research is needed to understand the factors that contribute to low academic achievement in institutionalized children. The aim of this study was to investigate the relationship between cognitive and emotion regulation skills and academic performance, by comparing institutionalized and noninstitutionalized Portuguese children. The sample comprised 94 participants (46 institutionalized (22 boys) and 48 noninstitutionalized (23 boys) children), aged between 6 and 10 years, matched for age and sex. We used Raven's Colored Progressive Matrices (RCPM) to measure cognitive abilities. Emotional regulation and negative lability were assessed using the Emotion Regulation Questionnaire (ERC). Academic performance was assessed with the Competence Academic Scale (CAS) of the Portuguese version of the Social Skills Rating System-Teacher Form (SSRS-T). Institutionalized children exhibited poorer academic performance than their noninstitutionalized counterparts (effect size, η2 = 0.174). Cognitive ability (β = 0.28) and negative lability (β = -0.28) were significant predictors of academic performance. In addition to institutionalization, cognitive ability, and the challenges of managing negative emotions may contribute to the observed differences in academic performance. Interventions aimed at fostering cognitive and emotional competencies may play a protective role for institutionalized children facing academic and social difficulties.

Academic stress and stress coping style in nursing undergraduates: Predictors of academic performance in a public tertiary institution in Nigeria

Background: The students’ ability to withstand the effect of stress is significantly determined by their coping ability. Their inability to cope effectively with stress may lead to low academic performance. This study aimed to investigate academic pressure and coping style as predictors of the academic performance of nursing undergraduates. Methods: This study adopted a cross-sectional descriptive research design. The target population was nursing undergraduates at the Federal University of Birnin-Kebbi. A sample size of 118 was selected as respondents. Data were collected and analyzed using Statistical Package for the Social Science (SPSS) version 26. Frequencies and percentages, Pearson product moment correlation, and multiple linear regression were used in data analysis. Results: The respondents’ mean age was 25.09 (SD=4.53). Females were 55.9%. There was a weak negative correlation between cumulative grade point average (CGPA) and mean academic stress (AS) (r=-0.141, P=0.065), a weak positive correlation between CGPA and mean stress coping style (SCS) (r=0.237, P=0.005), and a weak positive correlation between mean AS and mean SCS was 0.008 (P=0.466). Mean AS was not a significant predictor of academic performance t (115)=-1.589, P=0.115. Mean SCS was a significant predictor of academic performance t (115)=2.656, P=0.009. Conclusion: The mean AS was not a significant predictor of academic performance. However, the mean SCS was a significant predictor of academic performance with a positive and significant correlation.

K Nearest Neighbour and Improved Artificial Neural Network Techiniques for Student Academic Performance Prediction

Educational Data Mining has been an emerging topic nowadays due to the growth of educational data. This field makes it possible to develop methods in order to find out hidden patterns from educational data. The methods extracted from Educational Data Mining discipline are then used to understand students including their learning behavior as well as to predict their academic performance. In recent work used artificial neural network to predict academic performance. However in recent work does not used any proper method for missing value imputation and this will affect prediction performance. To mitigate the above mentioned issue in this work introduced an improved framework for student academic performance prediction. In which pre-processing is done by using data formatting, K Nearest Neighbour (KNN) based missing data imputation, min max normalization based data normalization and data filtering methods. Once the data is enhanced using pre-processing, it will be send to the classifier for Student academic performance prediction which is done by using Improved Artificial Neural Network (IANN) method. Experimental results demonstrate the effectiveness of the proposed model interms of precision, recall and f-measure.