Predictors Of Academic Performance Research Articles

Learner Autonomy during COVID-19: The Case of Moroccan EFL Undergraduates com

<em>Learner autonomy is one of the predictors of academic performance. Many researchers have argued that moving from secondary to higher school is a big transition. Shifting the focus from teacher-centeredness to learner-centeredness, learners in higher school display less reliance on their teachers to carry on their learning. Throughout this process, learners develop their autonomy and boost their independence. However, numerous studies revealed that most students are unaware of this shift. The purpose of this paper is to assess the autonomy of Moroccan EFL undergraduate students during the Covid-19 pandemic. Equally, this study aims at developing awareness of some strategies and techniques students use to increase their autonomy and self-independence. To achieve this purpose, the study employed a quantitative approach. A Likert scale-based questionnaire was administered to 100 (55 males and 45 females) EFL Moroccan undergraduate students. The research data was generated and analyzed using SPSS. Statistical analysis revealed that during the Covid-19 pandemic (69%) of EFL students were not aware of their role as independent and autonomous learners. Likewise, only (30%) of them were involved in syllabus design. Nevertheless (49%) of EFL students revealed their readiness to be part of decision-making concerning the teaching-learning process. The study results suggest that the use of the internet, self-managerial skills, peer and teacher collaboration are among the effective strategies students employ to increase their autonomy and self-independence alike.</em>

Visuospatial ability and student approach to learning as predictors of academic performance on written versus laboratory-based assessments in human anatomy.

As hours devoted to human anatomy curricula fall under threat and curricular delivery methods remain in flux, many new teaching innovations are emerging, which require comprehensive evaluation to ensure evidence-based teaching is maintained. Although grades are the predominant measure of 'learning', alternative metrics can assess more nuanced and meaningful outcomes. Two common predictors of students' three-dimensional understanding of the body and depth of learning are visuospatial abilities and approaches to learning, respectively. This study evaluated and compared the relative predictive power of these metrics on written and laboratory-based assessments in a human anatomy course. Deep approaches to learning and visuospatial abilities were expected to positively correlate with overall performance, with visuospatial abilities being the more salient predictor, especially on laboratory-based assessments. Additionally, visuospatial abilities were expected to positively correlate with deep learning approaches and negatively correlate with surface learning approaches. Multiple linear regression models controlling for covariates found that both visuospatial abilities (p = 0.049; p = 0.014) and deep learning approaches (p = 0.001; p = 0.001) were independent significant predictors of final and laboratory-based grades, while only deep learning approaches were significantly predictive of written grades (p = 0.007). There was no significant relationship between visuospatial abilities and approaches to learning. Given these findings and the increased reliance on visuospatially demanding digital learning activities in anatomy, both metrics should be considered when evaluating the impact of teaching innovations. Further, educators should design learning resources and environments that train visuospatial abilities and promote deeper learning approaches to maximize students' success.

Correlates of Sleep Quality: A Pilot Descriptive Cross-Sectional Survey among Undergraduate Students in a Ghanaian University

Background and Objective: A gap exists in evidence of the association between sleep quality and the academic performance of university students in sub-Saharan Africa. A limited number of studies have been conducted in this regard, which have mainly focused on medical students, thus neglecting the larger population of undergraduate non-medical students.
 Materials and Methods: This pilot study evaluated the correlates of sleep quality and the academic performance of 500 randomly sampled undergraduate non-medical students at Kwame Nkrumah University of Science and Technology, Ghana. Data was collected using the Pittsburgh Sleep Quality Index (PSQI) and a demographic questionnaire. The participants' demographic characteristics, academic performance, and sleep quality were first analyzed, descriptively, using SPSS software. Relationships between participants’ sleep quality and demographic factors, and sleep quality and academic performance were determined using Spearman’s rank correlation. The causation between these variables was then determined using simple linear regression.
 Results: Poor quality of sleep was prevalent among undergraduate students with a mean (SD) PSQI score of 6.43 (2.78). The respondent’s sleep quality had a weak correlation with their academic performance (rs = -0.146; P = 0.001), although poor quality of sleep was a predictor of academic performance [R2 = 0.022; 95% CI (-0.640, -0.168)].
 Conclusion: Health education and sleep promotion interventions should be used to encourage good sleep quality and practices among students. Furthermore, the findings point to the need for further studies using a larger population.

Stress, Anxiety and Depression, as Predictors of Academic Performance among Undergraduate University Students in North Western Nigeria

Stress, Anxiety and Depression, as Predictors of Academic Performance among Undergraduate University Students in North Western Nigeria

INTERAKSI ANTARA KEPUASAN KERJA, KUALITI HIDUP DAN KESIHATAN MENTAL SEBAGAI PERAMAL KEPADA PRESTASI AKADEMIK DALAM KALANGAN PELAJAR SEKOLAH MENENGAH KEBANGSAAN MEMBAKUT II SEMASA PANDEMIK COVID-19

The spread of the COVID-19 epidemic has caused a number of negative impacts such as in terms of mental and physical health issues, performance instability especially among school and university students, job dissatisfaction in doing tasks and completing tasks according to the requirements and time set as well as a decrease in quality live as long as this pandemic happens continuously. Among the objectives of this study is to identify gender and area of residence as not a factor in the interaction of job satisfaction, quality of life, mental health, and academic performance. Second, the study identified the factors of job satisfaction, quality of life, and mental health as predictors of academic performance at the Membakut II National School during the covid-19 pandemic. The selection of the sample for this study uses a random sampling method involving 225 respondents among students from grades one to six at Membakut II Secondary School. The research design is using quantitative methods. The instrument of this study is to use a questionnaire distributed through the Whatsapp application in the form of a Google Form Survey. Data were collected and analyzed using Statistical Package for The Social Science software version 26 (SPSS 26.0) using 2-way manova and multiple regression. This study found that Gender and place of residence are not factors in job satisfaction, quality of life, mental health, and academic performance among Membakut II Middle School students during the Covid-19 pandemic. In addition, the regression analysis found that the factors of job satisfaction, quality of life, and mental health are not predictors of academic performance at Sekolah Kebangsaan Membakut II during the covid-19 pandemic recorded as much as 0.17 = p<0.00 and F (3.221) = 9.137. The results of this study need to be emphasized as a guide to ensure that students get satisfaction in terms of work, especially in order to improve academic performance with better quality and maintain physical and mental health during the COVID-19 Pandemic.

Predictors of Academic Performance among Potential Medical Students: A Cross-Sectional Study from Rawalpindi

Objective: To compare the potential medical students regarding cognitive intelligence, emotional intelligence, social intelligence and academic performance.
 Study Design: Cross-Sectional study
 Place and Duration of Study: Army Medical College, Rawalpindi Pakistan, in October 2020.
 Methodology: The sample of the study comprised 512 male potential Medical students who completed their High School Education and came for the Entry test at Army Medical colleges with an age range of 17-21 years. All participants were assessed through the group administration of an assessment pack comprised of all tests.
 Results: Results revealed that among all 512 students, 390 were finally recommended as selected candidates, and the rest, 122 were finally not recommended. A comparison between recommended and not-recommended groups on study variables was drawn. And result revealed significant mean differences in the variables of Cognitive Intelligence (t=-3.592 and p<0.001) and Emotional intelligence (t=-3.969 and p<0.001) and non-significant differences in the variable of social intelligence and academic performance (t=-1.609; p<0.001; t=-0.900; p<0.001).
 Conclusion: Hence, academic performance is proven to be associated with emotional intelligence, IQ and personality characteristics. Better emotional regulation enables the students to develop better peer relations and invest energies in demanding academics owing to their cognitive and non-cognitive skills.

Locus of Control, Achievement Motivation and Academic Self Efficacy as Determinants of Secondary School Students’ Academic Performance in Ondo State, Nigeria

This study examined locus of control, achievement motivation and academic self-efficiency as determinants of secondary school students’ academic performance in Ondo State. The study adopted ex-post facto and descriptive research of survey type designs. The population of the study comprises all secondary school students in three Senatorial Districts of Ondo State. Multi-state sampling procedure was used to select 900 sample respondents for the study. Instrument used for data collection were Questionnaire on Locus of Control, Achievement Motivation, Self-Efficacy and Students Academic Performance Questionnaire (LCAMSESPQ) and Academic Performance Proforma. The validity of the instruments was done by expert in Tests and Measurement. The reliability of the questionnaire was determined through test re-test method. Data collected were analysed using descriptive and inferential statistics. All the hypotheses were tested at 0.05 level of significance. The result showed that there was significant relationship among locus of control, achievement motivation, academic self-efficacy and academic performance of secondary school students (R= 0.456, P< 0.05). Locus of control, achievement motivation and academic self-efficacy jointly accounted for 20.8% of the total variables in academic performance (R2 = 0.208, F (3,899) = 78.226, p<0.05). Locus of control which was the best predictor of academic performance accounted for 55.6% (β= 0.556, p<0.05) of academic performance and closely followed by achievement motivation with (β= 0.463, p<0.05), Academic Self-efficacy was the least predictor, with β= 0.233, p<0.05. Based on the findings, it was concluded that Locus of Control, Achievement Motivation and Academic Self-Efficacy are influential to students’ academic performance. It was recommended among others that teachers should endeavour to create adequate time to develop strong internal locus of control in students by encouraging good study habits, lower academic procrastination, low test anxiety both at home and in school in order to boost their locus of control and consequently enhance their academic performance.

Forecasting Student Academic Performance Using Neuro-Fuzzy Model

To achieve objective and precise forecasting of students’ academic performance while keeping up with technological improvements and changes, this paper discusses a neuro-fuzzy technique for the prediction of student academic performance. The technique comprises a Neural Network (NN), Fuzzy Logic (FL), Knowledge Base (KB) and Rule Base (RB) subcomponents. The NN subcomponent uses a three-layered feed-forward architecture to acquire a large collection of interconnected units in some pattern for communications. The FL subcomponent is an extension of Boolean logic and it was used for establishing accurate selection processes and precise solutions to multivariable problems. The KB component forms the database of multi-level information while the RB comprises a set of if-then statements for decision-making. The inference engine subcomponent applies a pre-defined procedure on input from the rule base and fuzzy logic interfaces for final prediction. The proposed technique performs predefined procedures that are based on some input sets that store multi-level information derived from several pre-specified scores. Results from the implementation of the technique established its practical function.

Gender differences in self-efficacy partially explain the female underprediction effect

Women tend to perform better in college than their admission test scores predict. The observed differential prediction of men's and women's academic performance based on academic tests is known as the female underprediction effect. Prior research demonstrates that gender differences in trait-level conscientiousness explain some of the observed female underprediction effect. The current study examined the effects of the facets of conscientiousness (i.e., self-efficacy, orderliness, dutifulness, achievement-striving, self-discipline, and cautiousness) in mediation analyses which were expected to partially explain the relationship between gender and academic performance after controlling for test scores. The results show that the relationship between gender and GPA is mediated by trait-level conscientiousness and, more specifically, that the effect is mediated by the facet of self-efficacy.

Predictive Analysis on Students’ Academic Performance in Mathematics

This research aimed to determine the predictors of academic performance in mathematics of Grade 10 students using descriptive correlational design. The respondents were 435 Grade 10 students from the three identified public high schools in Lapu-Lapu City and Liloan, Cebu, Philippines. A survey questionnaire was used to describe student-related factors, teacher-related factors, and environment-related factors while the First Quarter Grades were used to measure students’ academic performance in mathematics. Data gathered were treated statistically using frequency count, percentage, weighted mean, and multiple regression. The results showed that most of the respondents were 14 to 15 years old and were female; most of the parents were high school graduates and had a combined family monthly income of 10,000 pesos and below. The respondents had satisfactory performance. Also, teaching skills and instructional materials used by the teacher are significant predictors of academic performance in mathematics. However, the students’ interest, study habits, teacher’s personality, school environment and home environment of the students were not significant predictors of the mathematics performance of the students. It was concluded that the teacher-related factors as to teaching skills and instructional materials used can predict the academic performance of the students. The researchers recommended that the proposed intervention plan could be utilized and monitored.

A Regression Model and a Combination of Academic and Non-Academic Features to Predict Student Academic Performance

Predicting academic performance provides an effective way for students and faculties to monitor their academic progress. The identification of the most significant features was a key outcome of this research, and the college/university databases from online learning platforms are the main academic data sets used to ascertain performance. However, previous research emphasized the addition of other significant features in the prediction of academic performance. Universities’ organizational features include non-academic essential data used in determining student success, but no research has utilized this data to predict student academic performance. Generally, to evaluate binary classification, the relationship between the predicted classifications and the true classifications is analyzed, this approach can lead to the loss of important information from the data. Furthermore, to avoid such loss, this research proposes a regression model by comparing six regression algorithms, and combining academic and non-academic features for prediction student academic performance. Among the various models examined, the gradient-boosted trees regression model demonstrated the lowest error rate. The proposed features have been observed to have a significant impact on academic performance, making them suitable for use in predictions.

Learning Environment and Online Assignment Behaviors as Predictors of the Academic Performance of Low Socioeconomic Status (SES) Junior High Students

The COVID-19 pandemic profoundly impacted the methods of instruction for K-12 and higher education institutions around the world. Schools and families were challenged to adapt to online-based modes of instruction with minimal preparation. This study examined the importance of the learning environment and commitment to assignments as predictors of the academic performance of junior high students during the COVID-19 pandemic. We obtained data from 328 seventh through ninth grade students enrolled in an urban school district in the Delta region of Arkansas. Students used the same online learning platform, Lincoln Learning Solutions, receiving instruction in virtual, mixed, or on-site learning environments. We examined the total time spent in each course, overall assignment completion, and average grades for science, mathematics, and ELA. Our results show that when we considered only the learning environment, students in the on-site environment consistently outperformed their peers in the virtual learning environment across all subjects and grade levels, except for mathematics at the eighth-grade level. However, when we also considered students’ online assignment behaviors (time spent working on assignments, and number of assignments completed), there was a significant improvement in the prediction of academic performance for mathematics in the seventh and ninth grade, for science at the eighth-grade level, and for ELA at the ninth-grade level.

Autonomy support, personality, and mindset in predicting academic performance among early adolescents: The mediating role of self‐determined motivation

AbstractThere is a growing interest on the role of noncognitive factors such as personality traits and implicit beliefs in accounting for individual differences in academic performance. Autonomy support from primary socializing agents too is an important tool for enhancing student motivation and other learning outcomes. In this study, we tested a model hypothesizing that the broad personality traits of conscientiousness and openness, growth mindset, and perceived teacher and parent autonomy support would be distal predictors of academic performance, with self‐determined motivation proximally accounting for performance outcomes. Middle school students (N = 234) completed an online Qualtrics survey. Students' year‐end grade point averages for all subjects were used as an indicator of their academic performance. We found that conscientiousness had direct and indirect effects (through autonomous motivation) on academic performance. Openness, growth mindset, and parent autonomy support were indirectly associated with academic performance through their effects on autonomous motivation. The effect of teacher autonomy support on academic performance was mediated by both controlled motivation and autonomous motivation.

Cognitive flexibility and academic performance of children in care and children from a community sample: the contrasting mediator effect of task persistence

ABSTRACT Objective This study investigated the role of cognitive flexibility and temperament as predictors of academic performance, in children in care and children from a community sample, longitudinally. Also, it examined the mediating role of child’s temperament in the relationship between cognitive flexibility and academic performance, as well as between-group differences. Method Participants were 46 children in care and 48 children from a community sample, aged 6 to 10 years. Cognitive flexibility, temperament, and academic performance were assessed with the Raven’s Colored Progressive Matrices set B (RCPM-B), the Portuguese version of School-Age Temperament Inventory (SATI), and the competence academic scale (CAS) of the Portuguese version of the Social Skills Rating System – Teacher Form (SSRS-T), respectively. Results Cognitive flexibility was a significant predictor of academic performance only for children in care. In both groups, negative reactivity and task persistence predicted academic performance, and children’s task persistence mediated the relationship between cognitive flexibility and academic performance. However, a between group difference was observed in this mediation: in addition to the mediation effect observed in both groups, a direct effect was also found in the in-care group. Conclusions These findings highlight the importance of promoting cognitive and task persistence competencies in normative and at-risk populations.

Academic integrity during examinations, age and gender as predictors of academic performance among high school students

The study sought to determine if students academic integrity during examinations, age and gender predict their academic performance and examine its implications for adopting preventive actions against cheating in examinations. It was based on the Modified Theory of Planned Behaviour. Analysis of data collected with the aid of Academic Integrity Measurement Instrument (AIMI) led to significant findings that Academic Integrity during examinations and Age significantly predict Academic Performance. There was significant predictive relationship between Age and Academic Integrity during examinations as well as between Age and Academic Performance. Also, there was a statistically significant difference between male and female students in the Academic Integrity during examinations as well as in their Academic Performances. Whereas mean Academic Integrity score of female students were slightly higher than that of the males; the mean Academic Performance of male students was higher than that of the female students. These findings have implications for test development, administration and students’ academic integrity policies and practices. Test developers and administrators should consider inclusion of a segment in the question papers that assess students’ academic integrity especially in developing countries like Nigeria. The essence of such inclusion is to determine the relationship between students academic integrity quotient and their academic performance. In other words, the authors strongly recommend to the examination bodies and stakeholders to consider the possibility of determining the proportion of a student’s academic performance attributable to academic dishonesty or honesty.

A Prediction Model for Online Student Academic Performance Using Machine Learning

Academic data mining impacts a large number of educational institutions, significantly, playing a prime role in accumulating, studying, and analyzing the academic data. The accumulated academic data can be processed and analyzed for various purposes such as predicting the student academic performance and broadening the retention rate. The prediction of students’ academic performance at the initial stage helps the students to identify their lacking subjects. Accordingly, the predicted results can assist the students to focus more on their deficient subjects so as to improvise their academic performance. Currently, numerous machine learning techniques are being used by the academic institutions to extract and analyze the valuable information ranging from predicting students’ academic performance to the identification of fast and slow learners. This paper intends to propose an ensemble model, using the voting method for preclusive prediction of the student academic performance. The predicted results are being further utilized by the poor performers to concentrate more on their deficit courses. Hence, the instructors can focus on creating and implementing novel strategies or amending the existing pedagogical tools and approaches to aid the slow learners in improvising their performance. The proposed model has been tested on the academic data of an educational institution using the RapidMiner tool.

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.

Is good dietary diversity a predictor of academic success?

Interest in identifying factors influencing educational success is growing. Many studies have examined factors that may affect academic performance. It is often observed that a group of students sharing the same external variables (school environment) yet have different results, which states that individual variables have more impact on the determination of academic performance. Therefore, the present study aims to substantiate this controversy by investigating the association between dietary habits and academic performance in a population of adolescents attending school in Eastern Morocco Methods: Cross-sectional study based on a self-administered questionnaire. Items included sociodemographic variables, eating and sleeping habits, physical activity practice and the last semester grade point average of mathematics (GPAm) and first foreign language (GPAf) subjects Results: Dietary diversity score (DDS) is a significant predictor of academic performance. Weight status and sleep duration was found to be strongly associated with academic achievement. Absenteeism and smoking are negatively associated with school achievement Conclusions: Findings indicate associations between dietary diversity and academic performance of students in eastern Morocco which support creation of effective nutrition education policies to eradicate the double burden of malnutrition on the one side leanness and on the other side fighting against obesity by promoting good diet in terms of quality and quantity and encouraging physical exercise. It is also essential to review the lifestyle of adolescents by raising awareness against the scourge of smoking near schools. The results of these actions are likely to be healthier schools and improved behavioral development and academic performance of children. (www.actabiomedica.it).

Multi-Class Phased Prediction of Academic Performance and Dropout in Higher Education

The application of intelligent systems in the higher education sector is an active field of research, powered by the abundance of available data and by the urgency to define effective, data-driven strategies to overcome students’ dropout and improve students’ academic performance. This work applies machine learning techniques to develop prediction models that can contribute to the early detection of students at risk of dropping out or not finishing their degree in due time. It also evaluates the best moment for performing the prediction along the student’s enrollment year. The models are built on data of undergraduate students from a Polytechnic University in Portugal, enrolled between 2009 and 2017, comprising academic, social–demographic, and macroeconomic information at three different phases during the first academic year of the students. Five machine learning algorithms are used to train prediction models at each phase, and the most relevant features for the top performing models are identified. Results show that the best models use Random Forest, either incorporating strategies to deal with the imbalanced nature of the data or using such strategies at the data level. The best results are obtained at the end of the first semester, when some information about the academic performance after enrollment is already available. The overall results compare fairly with some similar works that address the early prediction of students’ dropout or academic performance.

An Investigation of the Relationship between Academic Buoyancy and Academic Performance among Senior High School Students: A Quantitative Research Approach

Academic buoyancy is a student’s ability to effectively deal with academic setbacks and challenges. This study aimed to determine whether academic buoyancy predicts academic performance among high school students. The objective of this study was to investigate the relationship between academic buoyancy and academic performance among high school students. Participants were 142 high school students who completed a questionnaire that included measures of academic buoyancy and grade point average (GPA). Data were analyzed using simple linear regression and frequency distribution. The results showed that academic buoyancy predicted academic performance among high school students. About 11.6% of the variation in academic performance was explained by academic buoyancy. The average GPA of the participants was 86.51, and the average academic buoyancy score was 4.69. The findings suggest that academic buoyancy is a valuable predictor of academic performance among high school students. Teachers and educators can use this knowledge to help students develop their academic buoyancy and improve their academic performance.