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Journal articles on the topic 'Student performance'

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Published: 4 June 2021

Last updated: 9 June 2025

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Harco, Leslie Hendric Spits Warnars, Fahrudin Arif, and Herry Utomo Wiranto. "Student performance prediction using simple additive weighting method." International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 630–37. https://doi.org/10.11591/ijai.v9.i4.pp630-637.

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In the world of student education is an important component where the role of students is as someone who is psychologically ready to receive lessons or other input from the school. However, each student has different performance and development, therefore it is important to do monitoring so that student performance will always be monitored by the school for improving student quality maintenance. Also, in the process of valuing education for students needs to be done by giving an appreciation in the form of giving gifts or just giving words and motivation so that students can perform better in learning and participating in other activities at school. In terms of selecting students with good performance or those who have a very declining development using the school method not only assess students by one criterion but with several criteria to produce a decision that can be accepted by many people. Performance Students must also be monitored by the school or the related rights. In this paper, the student performance prediction was assessed with 5 criteria components and the result shows there are 10 very satisfy students, 10 satisfying students, 10 well students, and 10 Enough students from sample 40 students.

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Kale, Radhika. "Student Performance Prediction for Education." International Journal of Scientific Engineering and Research 6, no. 4 (April 27, 2018): 90–91. https://doi.org/10.70729/ijser172461.

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Suscano, Shaine, Ivie Mahilum, Joshua Quipse, Liana Jeanne Andung, Reynalyn Aya-ay, Riyana Marani, Clark Jenrey Palomares, Jr. Romulo Bais, and Hernani Esperat. "The Role of Student Engagement in Shaping Academic Performance." Psychology and Education: A Multidisciplinary Journal 31, no. 5 (January 30, 2025): 520–25. https://doi.org/10.5281/zenodo.14772893.

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This study investigated the relationship between student engagement and academic performance among 119 Grade 11 students at Buenavista Integrated School in Zamboanga City, Philippines, for the school year 2024-2025. Employing a descriptive-correlational research design, the study utilized adapted survey questionnaires and school records to assess engagement and academic performance, respectively. Results revealed high levels of student engagement, particularly in areas related to family support and perceived educational value. Academic performance was categorized as "Satisfactory" with a mean score of 84.13. Surprisingly, statistical analysis showed no significant relationship between student engagement and academic performance (p-value = .179, R value = .124). These findings challenge conventional understanding of the engagement-performance relationship, suggesting that other factors such as teaching methods, student motivation, home environment, and individual study habits may play more substantial roles in determining academic outcomes. The study emphasizes the complexity of academic success and underscores the need for a multifaceted approach to improving student achievement that considers various influencing factors beyond engagement alone. Future research should explore additional variables that may influence academic performance and investigate how these interact with student engagement in different educational contexts, contributing to a more comprehensive understanding of the factors shaping academic success.

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Sugiarto, Meilani. "ANALISIS PERCEIVED ENJOYMENT SEBAGAI VARIABEL ANTISEDEN TECHNOLOGY ACCEPTANCE MODEL." Performance 24, no. 1 (October 2, 2017): 58. http://dx.doi.org/10.20884/1.performance.2017.24.1.315.

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Young people was active users of the information technology, especially internet. Even some of them tend to make the internet as a medium of the main support daily activities. These study indicate that internet use among young people, especially college student tend to have a model of its own behavior, making it attractive for further investigation. These study focus on the influence of perceived enjoyment as variables anticedent in the Technology Acceptance Model (TAM). The respondents of these study were college student as internet users in Yogyakarta, the province is considering a student city. These study conducted on 130 respondents. According to the structural analysis with SEM method, shows that perceive enjoyment has a role as anticedent variable on TAM, because those variable has significant effects on variables in technology acceptance model.

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Maturan, John Carlo, Mathena Pauline Comandante, Nova Jane Jabines, Maria Kolonas, Hyacinth Payosing, Ceikan Rosalejos, Michel Galavia, et al. "Assessing Student Satisfaction with Student Leaders' Performance." Journal of Educational Research and Practice 2, no. 3 (November 4, 2024): 1–20. http://dx.doi.org/10.70376/jerp.v2i3.204.

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This study investigates the satisfaction levels of Grade 9 students at St. Paul University of Surigao regarding the performance of their student council. Recognizing the vital role that student leaders play in fostering a supportive educational environment, the research aims to assess key performance areas, including leadership skills, approachability, communication skills, and contributions to school activities. Utilizing a quantitative descriptive design, data were collected through a researcher-made questionnaire administered to a randomly selected sample of 134 students. The findings reveal that students express general satisfaction with their leaders (M=3.09, SD=0.61). Among the assessed variables, the highest satisfaction is noted in the area of contributions made by student leaders, which received a mean score of 3.20 (SD=0.57). This study highlights the importance of student councils as representative bodies that enhance student engagement and community involvement within educational institutions. By understanding student perceptions, the research provides valuable insights for current and future student leaders to improve their effectiveness and better meet the needs of their peers during the 2024-2025 academic year. Specifically, leaders may actively seek feedback from their peers through regular informal meetings or suggestion boxes, ensuring that all students feel comfortable voicing their concerns and ideas. Ultimately, fostering strong leadership within schools contributes to a more inclusive and enriching educational experience for all students.

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Abdullah, AL HUSSAIN. "The Effect of WhatsApp Application on Saudi Electronic University Students' and the Role of Student Engagement on Academic Performance." International Journal of Novel Research in Computer Science and Software Engineering 10, no. 1 (January 28, 2023): 12–17. https://doi.org/10.5281/zenodo.7578904.

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Abstract: Digital applications have enhanced the educational experience and students performing. This paper discussed the effect of WhatsApp application on students engagement and its role on academic performance. The research addressed five hypotheses to determine the role of WhatsApp usage on student engagement. The paper also considered some influential factors that are affected using WhatsApp, including major. The intended research method the study would take was the quantitative approach using the survey to collect the data from the students. It aimed to understand the effect of major on the use of WhatsApp on student engagement and then students’ academic performance. The findings of this study would specifically participate in enhancing students engagement and their educational experiences. It would also provide educators with the importance use of technology to implement it in their teaching styles and learning process.   Keywords: Student Engagement; Academic Performance; WhatsApp; Saudi Students; Saudi Electronic University. Title: The Effect of WhatsApp Application on Saudi Electronic University Students' and the Role of Student Engagement on Academic Performance Author: Abdullah AL HUSSAIN International Journal of Novel Research in Computer Science and Software Engineering ISSN 2394-7314 Vol. 10, Issue 1, January 2023 - April 2023 Page No: 12-17 Novelty Journals Website: www.noveltyjournals.com Published Date: 28-January-2023 DOI: https://doi.org/10.5281/zenodo.7578904 Paper Download Link (Source) https://www.noveltyjournals.com/upload/paper/The%20Effect%20of%20WhatsApp%20Application-28012023-2.pdf

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Johargy, Dr Ayman, Dr Pushpamala Ramaiah, and Dr Ibtesam Nomani Lamia Ahmed Elsayed Grace Lindsey. "Student Nurses Academic Performance- Multidimensional Constructs." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 818–22. http://dx.doi.org/10.31142/ijtsrd23144.

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Laxmi, A. Sindura, and Sandeep Patil. "The Student Performance Monitoring Using ARM9." International Journal of Scientific Research 1, no. 7 (June 1, 2012): 66–69. http://dx.doi.org/10.15373/22778179/dec2012/27.

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Aishwarya, T. G., O. D. Vijay Raju Goud, Lakkireddy Vinay, and Lakshme Arthee C. "Student Performance Prediction Using Machine Learning." International Journal of Research Publication and Reviews 6, no. 5 (May 2025): 11040–44. https://doi.org/10.55248/gengpi.6.0525.1889.

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Shetty, Isha D., Dipshi Shetty, and Sneha Roundhal. "Student Performance Prediction." International Journal of Computer Applications Technology and Research 8, no. 5 (April 24, 2019): 157–60. http://dx.doi.org/10.7753/ijcatr0805.1003.

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Zeluff, Karen A. "Improving Student Performance." Academic Therapy 24, no. 1 (September 1988): 95–98. http://dx.doi.org/10.1177/105345128802400116.

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Sawalkar, Prof M. S., Shubham Bhore, Santosh Doiphode, Nilesh Sonawane, and Vinayak Sunkewar. "Student Performance Prediction." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 3855–62. http://dx.doi.org/10.22214/ijraset.2024.62370.

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Abstract: Academic integration is now defined by how students are able to access, and effectively interact with, and are supported by engineering faculty and staff; Academic integration, which is a very important construct that contributes to a student’s persistence and ultimate graduation. But, several factors, either numeric or non-numeric can have varying degrees of influence on academic performance of students. Rankings of aforementioned factors, hopefully, will assist the management in order to initiate corrective action with a view to students' academic performance improvement. In an academic environment, teachers are frequently in close contact with students and therefore their belief about the degree of influence of factors on students’ academic performance could be of vital significance. The case study presented in this paper uses fuzzy relational calculus and the Dempster Shafer theory of evidence to examine and suggest a feasible and realistic ranking approach of only chosen factors!

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Truong, The Doan, and Xiem Thuy Cao. "UNDERGRADUATE STUDENT PERFORMANCE." International Journal of Social Science & Economic Research 08, no. 09 (2023): 2593–611. http://dx.doi.org/10.46609/ijsser.2023.v08i09.006.

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Thorat, Prof Madhuri, Nilesh Sonawane, Shubham Bhore, Santosh Doiphode, and Vinayak Sunkewar. "Student Performance Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 1789–92. http://dx.doi.org/10.22214/ijraset.2023.56313.

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Abstract: Assessing student’s learning performance is a fundamental aspect of evaluating educational systems, playing a pivotal role in addressing challenges within the learning process and measuring learning outcomes. The emergence of educational data mining (EDM) as a research field has harnessed the power of data and knowledge to enhance education systems. EDM involves the development of techniques to analyze data collected from educational environments, offering a more comprehensive understanding of students and facilitating improvements in educational outcomes. The integration of machine learning (ML) technology has witnessed substantial growth, enabling researchers and educators to leverage data mining insights to predict and simulate educational processes, including success rates, dropouts, and more. This research paper explores the analysis of students' performance through data mining methods. It employs classification technique to discern the early-stage impact on GPA. In the classification methodology, various machine learning models are experimented with to predict student performance in the early stages, incorporating diverse features such as course grades and admission test scores. The paper employs different assessment metrics to evaluate model performance. The findings underscore the potential of educational systems to proactively address the risk of student failures during their initial stages of education

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Hardi, Tambunan, Sinaga Bornok, and Widada Wahyu. "Analysis of teacher performance to build student interest and motivation towards mathematics achievement." International Journal of Evaluation and Research in Education (IJERE) 10, no. 1 (March 1, 2021): 42–47. https://doi.org/10.11591/ijere.v10i1.20711.

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The purpose of this study was to determine the impact, and categories of teacher performance in building student interest and motivation on mathematics achievement. The population in this study was students in the eighth grade of junior high schools from six public and two private schools, and a sample of 277 students was taken by cluster sampling. Data collection instruments used a questionnaire, and data analysis was done by using by descriptive and path analysis. The results of data analysis showed that partially, teacher performance significantly affected student interest and motivation excel at mathematics. Simultaneously, teacher performance is very significant in influencing student interest and motivation to be excelent in mathematics. Partially, teacher performance builds interest and student motivation for mathematics achievement is low category. Simultaneously, teacher performance builds student interest and motivation to excel at mathematics is low category. Both of these can be caused by the ability of teachers to build motivation and interest is not good, so students are also less interested and motivated to learn mathematics.

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Wu, Yongshun, and Yudi Fernando. "Student Innovation Performance: Underpinning Theories." International Journal of Innovation Research in Education, Technology and Management 1, no. 1 (February 27, 2024): 23–32. http://dx.doi.org/10.61098/ijiretm.v1i1.111.

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This study explores the relationship between extracurricular activities and student innovation performance, drawing on various theoretical frameworks and empirical evidence. Through a comprehensive literature review, the study examines the impact of extracurricular activities on students' development of critical thinking, creativity, communication, and collaboration skills—essential components of innovation. The TRIZ innovation theory and social cognitive career theory provide theoretical underpinnings for understanding how participation in extracurricular activities fosters innovation skills among students. Additionally, the study presents a literature review matrix summarizing key findings from previous research on extracurricular activities and their effects on student development. The findings suggest that participation in extracurricular activities significantly enhances students' capacity for innovation by cultivating higher-order thinking skills and providing opportunities for real-world problem-solving. Moreover, the study discusses the implications of extracurricular engagement for students' future career success and organizational innovation. The study concludes with recommendations for future research, emphasizing the need for comparative studies across diverse student populations, in-depth analyses of specific types of extracurricular activities, and exploration of cultural differences in attitudes toward extracurricular involvement. Overall, this study contributes to a better understanding of how extracurricular activities influence student innovation performance and provides insights for educators and policymakers seeking to enhance innovation capabilities among young professionals

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Setemen, K., and I. K. Purnamawan. "Student performance assessment strategies by involving peer students." Journal of Physics: Conference Series 1810, no. 1 (March 1, 2021): 012064. http://dx.doi.org/10.1088/1742-6596/1810/1/012064.

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Pervin, Mst Maleka, Nafiza Ferdowsh, and Israt Jahan Munni. "Teacher-student interactions and academic performance of students." Dhaka University Journal of Biological Sciences 30, no. 1 (February 3, 2021): 87–93. http://dx.doi.org/10.3329/dujbs.v30i1.51812.

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The main goal of this study was to investigate the influence of teacherstudent interaction on academic performance of the students. Hundred participants (50 teachers and 50 students) were selected through purposive sampling technique to conduct this research. Teacher and student feedback were measured by using the Questionnaire on Teacher Interaction (QTI) as well as students’ academic performance was calculated by using last year grade point average (GPA) of each student. Pearson product moment correlation was used which indicated a positive significant correlation (r = 0.70, p < 0.05) between teacher-student interaction and academic performance of the students. The findings indicated the importance of teacher-student relationships to attain academic success of the students. The results suggest providing appropriate academic resources and aids for the teachers to meet the individual needs of their students. Dhaka Univ. J. Biol. Sci. 30(1): 87-93, 2021 (January)

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Albattat Ahmad, GuoXinru,. "Student Factors Affecting Performance Anxiety." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (October 16, 2023): 606–10. http://dx.doi.org/10.52783/tjjpt.v44.i4.896.

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Performance anxiety among university students has a serious impact on their artistic development. Performances are usually conducted in the form of examinations, competitions, and performances, which put higher demands on performers, but "performance anxiety" is a common phenomenon, which is encountered by amateurs, students at professional colleges and universities, as well as mature artists. College students may suffer from insomnia, anxiety, fear, and loss of appetite before going on stage. Appropriate performance anxiety helps to play the music, while excessive performance anxiety may cause stage fright, nervousness, blankness, stiffness, etc., and may even lead to the fear of performance and the idea of jumping ship. Performance anxiety is a widespread phenomenon among people engaged in performing arts activities, which has a great impact on people's learning, performance effect and quality, and an in-depth analysis of its causes and influencing factors can help to alleviate this situation and carry out related academic research.

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More, Manasi Sunil. "Academic Performance Analyzer." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (March 31, 2025): 1136–44. https://doi.org/10.22214/ijraset.2025.67490.

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An "Academic Performance Analyzer" is a system designed to collect, analyze, and visualize student academic data, utilizing algorithms to identify trends, strengths, and weaknesses in performance, thereby providing actionable insights to educators and students to improve learning outcomes ;it typically includes features like grade calculation, comparison analysis, customized reporting, and potential predictive modeling to identify at-risk students and areas needing targeted intervention. The Academic Performance Analyzer is an innovative software tool designed to streamline and enhance the evaluation of student performance, particularly in the context of the Maharashtra State Board of Technical Education (MSBTE) exams .The system provides a detailed performance analysis by generating accurate reports and insightful visualizations, allowing educators to track student progress over time and identify areas for improvement.

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M, Rajeena, and Dr Abdul Haleem Quraishi. "Leveraging Artificial Intelligence for Student Performance Monitoring." International Journal of Research Publication and Reviews 5, no. 5 (May 17, 2024): 9642–45. http://dx.doi.org/10.55248/gengpi.5.0524.1364.

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PURCARU, Monica A. P. "ASPECTS REGARDING THE LEVEL OF PERFORMANCE IN MATHEMATICS." Review of the Air Force Academy 18, no. 2 (January 22, 2021): 57–62. http://dx.doi.org/10.19062/1842-9238.2020.18.2.6.

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In the present paper addressing the issue of advanced performance in mathematics, we seek to answer to the following study questions: “What are the main factors that lead to the achievement of advanced performance?”; “Which is the profile of the teacher able to train the Olympic students in Mathematics?”; “Why do teachers want for their students to achieve advanced performance in mathematics?”; “Why do the students want to reach a high degree of performance in Mathematics?”; “What difficulties could significantly impede on student progress?”; “What is the connection between enthusiasm and advanced performance in Mathematics?”; “ Why does an Olympic student participate in competitions?”.

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Zillmer, Devon, and Thomas Mussmann. "Improving Student Performance with ILS Based Student-Teacher Pairings." International Journal on Studies in Education 5, no. 3 (April 11, 2023): 317–31. http://dx.doi.org/10.46328/ijonse.133.

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Students are often assigned to instructors by student schedule or request. We present findings that assigning students to instructors based on learning style similarity can improve learning. We assume that instructors teach consistent with their learning style, and use the Felder and Silverman Index of Learning Styles (ILS) to describe learning style as a vector in R4. We examine the effect on grade difference a as a function of the learning style “distance” between students and their instructors . X2 tests show that the students (N=300) who were more similar (nearer) to their instructor performed better (p-value: 0.000453), and linear regression indicates that student performance could improve by up to 2% by being assigned to a more similar instructor. Our study shows that, with our three novel approaches (standardized learning style measurement, differenced grades, and the consideration of different norms), our data indicate a mild support for using the idea behind the meshing hypothesis to provide recommendations for student-teacher pairing.

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Foos, Paul W. "Effects of Student-Written Questions on Student Test Performance." Teaching of Psychology 16, no. 2 (April 1989): 77–78. http://dx.doi.org/10.1207/s15328023top1602_10.

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Effects of student-written test questions on student test performance were examined in an Introductory Psychology class. Before each of three tests, randomly assigned students wrote essay questions, multiple-choice questions, or no questions. All tests contained essay and multiple-choice items but no questions written by students. Question writers performed significantly better than nonwriters on the first two tests; the difference on the third test was marginally significant. No differences were found between students who wrote essay and those who wrote multiple-choice questions. Question writing appears to be an effective study technique.

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Martin, Andre Anthony. "Exploring the impact of teacher quality on student academic achievement in primary schools." American Journal of Social Sciences and Humanities 8, no. 1 (February 1, 2023): 35–45. http://dx.doi.org/10.55284/ajssh.v8i1.845.

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There has been debate among scholars, policymakers, and practitioners regarding the effects of teacher quality on students' academic performance. This quantitative descriptive study seeks to determine the impact of teacher quality on students' performance and the relationship between teacher quality and student performance. Student performance was measured using regional assessments in mathematics and language arts. A survey was conducted to collect primary data from teachers, and both sets of data were analyzed using the t-test, ANOVA, and Pearson’s correlation coefficient (r) to test for performance differences and relationships between student performance and teacher quality. The findings revealed no significant differences in the academic performances among students taught by trained and untrained teachers, degree and non-degree teachers, and teachers with varying years of experience and hours of professional development. The interactions of the teacher quality variables had no practical effect on student performance, and there were no significant relationships between student performance and the teacher quality variables of experience and professional development. There are implications for reorganizing or advancing teacher professional development, training, certification and licensing. The study adds to the scarce literature in the region and can guide policy development in teacher workforce planning, retention, and hiring.

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Ronelo B., Butaslac, Niño, Chatto Michael E, Damiles, Kurt Archer M., Delos Santos, Marie Faz L., Echavez, Shanen Jane M., Mabaga, Neil John O., Pontejos Marlou N., et al. "A Descriptive Quantitative Study between Teachers’ Performance and Student Learning Outcomes among Grade 11 Students." International Journal of Research and Innovation in Social Science VIII, no. IV (2024): 2895–901. http://dx.doi.org/10.47772/ijriss.2024.804272.

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This study dealt with the influence of teachers’ performance and student learning outcomes among Grade 11 students in Lorenzo S. Sarmiento Sr. National High School. The primary goal of this study was to determine the level of teachers’ performance and student learning outcomes in terms of their indicators, the significant difference between teachers’ performance and student learning outcomes, and what domains of teachers’ performance substantially influence student learning outcomes. Also, this study utilized a quantitative-correlational design with 202 respondents among Grade 11 students in Lorenzo S. Sarmiento Sr. National High School. The average weighted mean, Pearson R, and multiple regression analysis were the statistical tools used in this study. Along with this, the results showed a high level of teachers’ performance among Grade 11 students in terms of lesson planning, delivery, and evaluation of pupils’ performance. Likewise, the results showed a high level of student learning outcomes in the Grade 11 students regarding teaching, assessment, and generic skills and learning experience. In addition, there was a high correlation and a significant relationship between teachers’ performance and student learning outcomes. Hence, this led to the rejection of the null hypothesis. Also, there are two domains of teachers’ performance that showed a high level of ratings as perceived by the students. Thus, the teacher could continue with their performance for teaching to see good student learning outcomes by providing their needs with new activities to develop their listening skills, public speaking, and writing inside the classroom, and the students may also engage and participate in the teachers’ discussion to make better progress and improve their academic performances.

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Bazán-Ramírez, Aldo, Homero Ango-Aguilar, Víctor Cárdenas-López, Roberta B. Anaya-González, Walter Capa-Luque, and Miguel A. Bazán-Ramírez. "Self-Reporting of Teacher–Student Performance in Virtual Class Interactions in Biological Sciences during the SARS-CoV-2/COVID-19 Pandemic." Sustainability 15, no. 23 (November 22, 2023): 16198. http://dx.doi.org/10.3390/su152316198.

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We used an interbehavioral model of teacher–student didactic performance with six pairs of criteria, as follows. Exploration of competencies and precurrent for learning, explicitness of teacher criteria and identification of student criteria, teacher illustration and illustration—student participation, supervision of the practice by the teacher and pertinent student practice, teacher feedback and feedback—student improvement, and teacher evaluation and evaluation—student application. The objective was to determine the level of covariation, divergence and convergence between the constructs of teacher didactic performance and student didactic performance in virtual classes as a result of the SARS-CoV-2 pandemic, in November 2020. Three hundred-thirty undergraduate students of biological sciences from a public university in Peru completed two self-report scales in virtual modality through Google forms, one on the performance of their teachers and the other on their own performance. By means of confirmatory factor analysis and an analysis of the covariance of teacher–student performance constructs, three models were obtained with good fits for the convergent and divergent validities of six constructs of the two teaching performance scales. Adequate models of functional correspondence in each pair of the six teacher–student didactic performance criteria were obtained. Likewise, didactic performances related to the identification of teaching–learning criteria and to the evaluation—application criteria were the most frequent during teaching–learning, according to the students’ self-report.

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Liu, Chen-Chung, Gwo-Dong Chen, Chin-Yeh Wang, and Ching-Fang Lu. "Student Performance Assessment Using Bayesian Network and Web Portfolios." Journal of Educational Computing Research 27, no. 4 (December 2002): 437–69. http://dx.doi.org/10.2190/el2a-tqf1-pyvm-n51v.

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Web-based curriculum development allows students to develop their learning portfolio and interact with peers on a Web learning system. This system contains Web portfolios that record in detail students' learning activities, peer interaction, and knowledge progress. However, teachers cannot easily diagnose students' learning processes and regulate effective strategies according to student activity performance without the model of activity performance on Web learning systems. This study proposes a novel methodology that employs Bayesian network software to assist teachers in efficiently deriving and utilizing the student model of activity performance from Web portfolios in an online manner. Teachers can assess and diagnose performances with the model of learning activity on Web learning systems. The model of activity performance also allows teachers to manage various activity performances in Web learning systems so that desired strategies can be achieved to promote learning effectiveness.

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Halim Purnomo, Mahpudin Mahpudin, Cahyo Setiadi Ramadhan, Abdul Karim, and Irfan Fauzi Rahmat. "Prıncıpal Leadershıp And Teacher Performance On Student Success." Jurnal Elementaria Edukasia 7, no. 2 (June 30, 2024): 2637–56. http://dx.doi.org/10.31949/jee.v7i2.9145.

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The determinants of student achievement in schools are widely studied, including factors at school. Research on principal leadership, teacher performance, and student success has been carried out by many other researchers with varied results. This study aims to explain these two factors on student achievement based on teacher and student assessments. The research method used is quantitative through survey techniques. Statistical analysis used is non-parametric statistic Mean-Withney test. This research compared perceptions of teachers and students, male and female, from two school institutions. The comparison results show that both teachers and students agree that the principal has an effect on student achievement (U = 364,000; p = 0.229). In addition, teachers and students agreed that teacher support affected student achievement (U = 369,000; p = 0.266). These results indicate that both students and teachers see a relationship between the leadership role of school principals and teacher performance with student success. These findings can contribute to the development of psychology and education.

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Amalia, Hilda, Ari Puspita, Ade Fitria Lestari, and Frieyadie Frieyadie. "APPLICATION OF DECISION TREE AND NAIVE BAYES ON STUDENT PERFORMANCE DATASET." Jurnal Pilar Nusa Mandiri 18, no. 1 (March 9, 2022): 53–58. http://dx.doi.org/10.33480/pilar.v18i1.2714.

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Student performance is the ability of students to deal with the entire academic series taken during school. Student performance produces two labels, namely successful and unsuccessful students. Successful students can graduate with excellent, excellent, and suitable performance labels. At the same time, students who have a label on average are students who get poor performance. Measurement of student performance is needed for every educational institution to take strategic steps to improve student performance. This study aimed to obtain a data mining method that worked well on student performance datasets. In this study, student performance datasets were processed, which had 11 indicators with one result label. Student performance datasets are processed using data mining methods, namely decision tree and nave Bayes, while the tool used for dataset processing is WEKA. The research results from processing student performance datasets obtained that the accuracy value for the decision tree method was 94.3132%, and the accuracy produced by the naive Bayes method was 84.8052%.

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Kim, Kyong-Jee, Yeon Ji Lee, Mi Jin Lee, and Young Hyo Kim. "e-Learning for enhancement of medical student performance at the Objective Structured Clinical Examination (OSCE)." PLOS ONE 16, no. 7 (July 1, 2021): e0253860. http://dx.doi.org/10.1371/journal.pone.0253860.

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This study aimed to investigate the impact of student e-learning on the development of clinical competencies. The study participants were 3rd year students (n = 43) at a private mid-sized medical school located in a South Korean suburb on a four-year medical program. Educational intervention was implemented to enhance student clinical performance. Students engaged in learning activities that intended to promote their self-directed learning abilities and clinical performances using e-learning resources. Intervention was conducted for the duration of six months during the 3rd year and its effectiveness was investigated by comparing student performances in OSCEs in a pre- and post- comparison format and also by comparing them with national scores. In addition, student perceptions of the impact of e-learning on their OSCE performances were assessed using a questionnaire, which included 36 items that elicited student perceptions of their experiences of e-learning and readiness for e-learning. Student OSCE scores improved significantly after educational intervention in all domains of clinical competencies assessed and for total scores (p < 0.001). Furthermore, students achieved higher OSCE scores than national average scores in the post-test, whereas they had performed lower than national average scores in the pre-test. Students showed neutral or slightly positive responses to the effectiveness of e-learning, and their perceptions of e-learning were not associated with their e-learning readiness scores. The study shows student OSCE performance improved significantly after educational intervention, which indicate the effectiveness of e-learning to support student learning of clinical performance. Despite significant improvements in student OSCE scores after e-learning, their perceptions of its effectiveness were neutral. Furthermore, student perceptions of e-learning were not associated with their readiness for it. Suggestions are made to help students use e-learning more effectively to enhance their clinical competencies.

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Tutania, Lawrence, and Eddie Manzano. "Student Attitude and Academic Performance." International Journal of Open-access, Interdisciplinary and New Educational Discoveries of ETCOR Educational Research Center (iJOINED ETCOR) 4, no. 2 (April 6, 2025): 91–97. https://doi.org/10.63498/nxz2st258.

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Abstract Aim: This study explored the relationship between student attitudes, focusing on smartphone use, promptness, and academic performance among the Grade 10 students in the Second District of Eastern Samar. Methodology: This study used a descriptive-correlational design using surveys to determine the relationship between the variables. This study was conducted in the Second District of Eastern Samar, and 349 respondents participated. Results: With a correlation coefficient (r) of 0.068, there is no strong or meaningful connection between the level of students’ smartphone use and their academic performance. However, there is a low correlation between academic procrastination and academic performance, as indicated by the correlation coefficient of (r) 0.384, which suggests that lack of promptness, manifested as academic procrastination, influences students' performance, but other factors also contribute to their academic success or difficulties. Conclusion: The study reveals that while Grade 10 students in Eastern Samar demonstrate satisfactory academic performance despite average to excessive smartphone usage and moderate promptness driven by internal anxieties, it negatively impacts their outcomes. Interventions like time management workshops, counseling, and parental involvement are crucial to mitigate procrastination and optimize academic performance, even though problematic smartphone use did not directly correlate with academic results. Keywords: student attitude, academic performance, smartphone use, promptness, academic procrastination

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Ziyi (Kevin), Wang. "CLASSIFICATION FOR STUDENT PERFORMANCE." Young Scholars Journal, no. 3-4 (2022): 10–14. http://dx.doi.org/10.29013/ysj-22-3.4-10-14.

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Castolo L, Carmencita, and Rosemariebeth R. Dizon. "Evaluating Student Teacher Performance." i-manager's Journal on School Educational Technology 2, no. 4 (May 15, 2007): 31–34. http://dx.doi.org/10.26634/jsch.2.4.834.

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McDaniel Mohr, Tanga. "Iclickers and student performance." International Review of Economics Education 14 (September 2013): 16–23. http://dx.doi.org/10.1016/j.iree.2013.10.006.

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Aslam, M. M. Haris, Ahmed F. Siddiqi, Khuram Shahzad, and Sami Ullah Bajwa. "Predicting Student Academic Performance." International Journal of Knowledge Management 10, no. 3 (July 2014): 18–35. http://dx.doi.org/10.4018/ijkm.2014070102.

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The biggest challenge in nurturing an academic community is encouraging knowledge sharing among its members. Literature on communities, however, has paid less attention on the role of outcome expectations in encouraging the knowledge sharing behaviors. This study examines the effects of Personal Outcome Expectations (POE) and Community-related Outcome Expectations (COE) on the knowledge sharing behaviors of students and its consequent impact on their academic performance. In order to study these relationships a survey of university students was conducted. Based on structural equation modeling approach, it was found that COE have significant impact on knowledge sharing among the students.

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Jalomo Jr., Romero. "Assessing Minority Student Performance." New Directions for Community Colleges 2000, no. 112 (2000): 7–18. http://dx.doi.org/10.1002/cc.11201.

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Muzaffar, Farwa, Aqsa Qandeel, Sadaf Mahmood, and Uzma Niaz. "Nexus between Work-Family life Balance and Health of Professional Women: A Case study of The Women University Multan." Journal of Social & Organizational Matters 3, no. 2 (June 30, 2024): 481–500. http://dx.doi.org/10.56976/jsom.v3i2.101.

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This study examined the impact of technologies and co-curricular activities on students’ academic achievement at the under graduate level. The data collection includes 200 enrolled college and university undergraduate students, male and female, class of 2018 who participated in this research study in Karachi, Pakistan. Universities can measure student success through the grade point average (GPA) attained in their exams, whereas colleges measure students’ performance through the highest percentage, which determines student academic success. While evidence exists that extracurricular activities benefit student achievement, the relationship between such participation and student learning, as measured by GPA, has not been quantified. This study sought to understand how technologies and co-curricular activities affect students’ academic performance. The findings depict a significant positive relationship between co-curricular activities and students’ academic performances. Furthermore, the results showed a weak impact of technologies on students’ academic performances. Colleges and universities may contribute to increased student learning through co-curricular activities. To improve student learning, universities should introduce, understand, and implement technologies and co-curricular activities to enhance their students’ knowledge, skills, and abilities.

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Zaidi, Syeda Sana, Muhammad Imran, Farzana Jabeen Khoso, and Zehara Sultana. "Impact of Technologies & Co-Curricular Activities on Students’ Academic Achievement at the Undergraduate Level." Journal of Social & Organizational Matters 3, no. 3 (September 15, 2024): 75–90. http://dx.doi.org/10.56976/jsom.v3i3.99.

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This study examined the impact of technologies and co-curricular activities on students’ academic achievement at the Undergraduate Level. Data on approximately 200 enrolled college and university students from the undergraduate level male and female Class of 2018 who participated in this research study in Karachi, Pakistan. Universities can measure student success through their highest GPA in their exams, whereas colleges measure students’ performance through the highest percentage to measure student academic success. While evidence exists that co-curricular activities benefit student achievement, the relationship between such participation and student learning, as measured by GPA, has not been quantified. This study sought to understand how technologies and co-curricular activities affect students’ academic performance. The findings depict significant positive relationship between co-curricular activities and students’ academic performances and also showed weak impact of technologies on students’ academic performances. Colleges and Universities may contribute to increased student learning through co-curricular activities. To improve student learning, universities should introduce, understand, and implement technologies and co-curricular activities to enhance their students’ knowledge, skills, and abilities.

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Davis, Summer, Xihe Zhu, and Justin Haegele. "High School Student Fitness Test Attributions: Does BMI or Performance Matter?" Journal of Teaching in Physical Education 40, no. 1 (January 1, 2021): 49–57. http://dx.doi.org/10.1123/jtpe.2019-0123.

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Purpose: To examine differences in fitness test performance and the attributions made for the performances between high school students of different weight status. Methods: High school students (n = 185) completed the Progressive Aerobic Cardiovascular Endurance Run and the push-up fitness tests and then completed the modified Causal Dimension Scale to assess their attributions for their performances. Results: Students of a healthy weight performed higher than overweight/obese students on both fitness tests. There were no significant differences in attribution dimension scores for either test between healthy weight and overweight/obese students. Student performance played a significant role on the attribution dimension scores for both tests. Students primarily attributed their push-up and Progressive Aerobic Cardiovascular Endurance Run performance to ability. Conclusion: As student performance is significantly associated with attribution dimension scores, improving fitness test performance should subsequently foster adaptive attributions, creating a high expectancy for future success.

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Lavanya, K., G. Swathi, T. Aparna, and P. Viswa Teja. "Students Exam Performance Prediction." Shanlax International Journal of Arts, Science and Humanities 11, S3-July (July 8, 2024): 14–19. http://dx.doi.org/10.34293/sijash.v11is3-july.7913.

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Predicting student performance is crucial for understanding their progress and intervening effectively. This research aims to identify current student status and forecast future results to enable teachers to provide timely guidance and support. By analyzing dependencies for final examinations, we can recommend suitable courses for upcoming semesters, serving as advisor’s to students. Many students struggle due to a lack of proper guidance and monitoring, as teachers can’t monitor everyone simultaneously. An AI system can assist by identifying which students require specific types of support. Ultimately, the goal is to empower students to avoid predicted poor results through proactive intervention, potentially achieving accuracy rates as high as 94.88%.

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N, Patel Karan. "Attendance and Performance Monitoring System Implementation." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3088–94. http://dx.doi.org/10.22214/ijraset.2022.45671.

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Abstract: Attendance Management is very important for every organization. Taking attendance of lot student is very time consuming process. It is difficult to analyze attendance of students how frequently one is skipping classes. And also there will be some proxy attendance of student in traditional system. The possible solution to this problem is to use an automatic attendance system which uses face recognition techniques. This system will mark attendance electronically and recorded attendance will be stored in a database. The preparation of a question paper for the Internal Assessment Exam can be automatically generated with the help of teachers according to RBT levels. The Internal Assessment is very important for students. This system will give individual student reports based on the performance of student in internal assessment, exactly in which type of RBT level the student need to improve themselves.

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Rosalita, Glenn D., and William A. Buquia. "Students' Proficiency and Performance in English in Relation to Academic Performance." International Multidisciplinary Journal of Research for Innovation, Sustainability and Excellence (IMJRISE) 2, no. 6 (June 6, 2025): 123–33. https://doi.org/10.5281/zenodo.15605648.

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Abstract:   This study investigated students' proficiency levels in the K-12 Education Program within a small division during the 2023-2024 academic year. It examined student demographics and proficiency in English across reading, writing, speaking, and listening domains. Findings revealed that many students with older age, fewer siblings, and lower-income backgrounds exhibit proficiency levels below planning standards across all assessed areas. The study underscored the need for targeted interventions tailored to the linguistic needs of students within identified demographic profiles. Specifically, most respondents were seniors with fewer siblings and belonged to lower-income families. Despite comparable proficiency levels across demographic groups, a statistically significant difference in writing proficiency based on age was observed. Recommendations included implementing targeted intervention programs, developing structured curricula, introducing interactive exercises, and fostering a supportive learning environment to enhance English proficiency. Additionally, further investigation into factors influencing writing proficiency and the continuous adaptation of teaching methods was advised to promote academic success in English education.   Keywords: Academic performance, student’s performance, student’s proficiency.

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Ariani, Mohsen Ghasemi, and Fatemeh Mirdad. "The Effect of School Design on Student Performance." International Education Studies 9, no. 1 (December 29, 2015): 175. http://dx.doi.org/10.5539/ies.v9n1p175.

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The present study aims at exploring the influence of school design on student performance. The participants consisted of 150 students who studied at two Iranian public school and private school in Mashhad City. School Design and Planning Laboratory (SDPL) model of Georgia University (and Tanner (2009)) was used as an appraisal indicator of school design and some of its design characteristics such as unrestricted and functional views, indoor and outdoor spaces, and natural life were explained to students. In addition, student performance, defined by the average of their final achievement scores, was compared to measures of school environmental characteristics. To this end, design characteristics of these schools and students’ performances have been studied. Quantitative data, subjected to a set of descriptive statistics, showed that school design was a strong factor for students of private school. In fact, the finding indicated that characteristics of physical learning space and deign of private school had fundamental effects on student performance. That is, learning space as a strong component can motivate students to study and progress.

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Warnars, Harco Leslie Hendric Spits, Arif Fahrudin, and Wiranto Herry Utomo. "Student performance prediction using simple additive weighting method." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 630. http://dx.doi.org/10.11591/ijai.v9.i4.pp630-637.

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Hadi, Cholichul, Hung Chiao, Suen Mein-Woei, Dimas Agung Trisliatanto, and Muh Fitrah Ramadhan Umar. "What Predicts Students’ Academic Performance." International Journal of Social Service and Research 2, no. 4 (April 29, 2022): 277–90. http://dx.doi.org/10.46799/ijssr.v2i4.100.

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The Performance of a higher education institution was one of the important factors determining success in producing quality graduates. Academic achievement obtained by students was considered to the success of a student and the learning system at the institution. The academic quality was also inseparable from the background of the student itself. Besides, the system and climate of teaching and learning are created in the educational environment. This study aimed to describe students' academic Performance and the factors that influence the improvement in academic performance of undergraduate students majoring in Psychology at Airlangga University. This research used a quantitative approach with a descriptive survey research design. Based on the results of research conducted, it concluded that student performance, the program of study performance, and university performance on student's academic performance, both simultaneously and partially for undergraduate students at the Faculty of Psychology, Airlangga University.

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Qadri, Nurmuslimah, Nurdevi Bte Abdul, and Maharida Maharida. "ANALYSIS OF STUDENTS' ANXIETY IN SPEAKING PERFORMANCE IN MUHAMMADIYAH UNIVERSITY OF MAKASSAR." Indonesian Journal of Psycholinguistics 2, no. 1 (March 15, 2023): 9–18. http://dx.doi.org/10.56983/ijp.v2i1.470.

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This study aims to determine the factors that cause students' anxiety in speaking performances and to determine the level of student anxiety in speaking performances in 3rd semester students, especially in speaking courses for the intermediate level at Muhammadiyah University of Makkassar.This research is a descriptive qualitative with a case study conducted on 8 students in semester 3. It consists of interviews and checklist observations. Each interview was used to find out the factors that cause students' anxiety in speaking performances. While, the observation checklist was carried out to determine the level of students‟ anxiety in speaking performance.The results of the study indicate that there are 2 factors that cause student anxiety, namely internal factors and external factors. Internal factors and external factors are very influential on students' anxiety on speaking performance. This can be seen from the results of data analysis at the student level in speaking performance. Therefore, it can be concluded that the factors that cause students‟ anxiety in speaking performance were internal and external factors. While at the level of students‟ anxiety there are three types, namely: low level of anxiety, medium level of anxiety and high level of anxiety.

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More, Sachin K., Yashawant M. Patil, and Popat D. Kumbhar. "Improving Student Performance through Interactive Webinars." Journal of Engineering Education Transformations 37, IS2 (January 1, 2024): 339–42. http://dx.doi.org/10.16920/jeet/2024/v37is2/24059.

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Abstract— In the rapidly evolving landscape of education, the integration of technology has proven to be instrumental in reshaping traditional learning paradigms. This paper delves into the realm of improving student’s performance by harnessing the potential of interactive webinars as on innovative educational tool. The paper aims to investigate the impact of interactive webinars on student engagement, knowledge retention and overall academic achievement in the course. The findings of the study provide empirical evidence that interactive webinars positively influence student performance. The analysis demonstrates that students who actively participate in interactive webinars exhibit higher levels of comprehension, engagement and retention compared to conventional classroom instructions. The students’ engagements in the webinars are monitored by MOODLE activities, question answer sessions and their quick notes. The comparative graph of pre-test and post-test clearly describe that, there is improvement and shifting of students from CD, DD & FF grade to AA, AB, BB & BC grade. Employing a mixed-methods approach, the research draws upon quantitative data from pre and post webinar assessment, as well as qualitative insights obtained from student surveys and interviews. These methods enable a comprehensive evaluation of the effectiveness of interactive webinars in comparison to traditional instruction methods. In addition to that incorporating industry resource persons into educational activities like interactive webinars can significantly enhance the learning experience of the students. Industry professionals bring real-worlds insights, practical knowledge and helping bridge the gap between academic theory and practical application. Keywords— Interactive Webinar, Industry Resource Person, Student Performance, Industry Practices.

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Sun, Lizhi, Danhui Chen, and Qingping Yang. "Key Influencing Factors Affecting the Student Academic Performance and Student Satisfactions Ratings: Evidence from Undergraduate Students in China." Global Research in Higher Education 5, no. 3 (September 5, 2022): p19. http://dx.doi.org/10.22158/grhe.v5n3p19.

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This paper has developed a sound and practical method to evaluate the key teaching quality including the student academic performance and student satisfaction ratings. The method makes use of the existing data already readily available in a Chinese university, focusing on the identification of key influencing factors affecting the student academic performance and student satisfactions ratings. The data analyses have shown the university student academic performance is significantly affected student gender, age, previous academic performance, settlements and occupations of parents. There is significant difference in the student ratings for different genders and academic positions of teaching staff. The student performance and satisfaction ratings also significantly vary in different years of intakes and different Schools/programs. The student’s university academic performance can be accurately predicted using artificial neural networks with a prediction error of about 7%. This approach can help the university to improve the student academic performance and student satisfactions.

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S. Satheesbabu, P. Gokulakrishnan, R. Karthikeyan,. "MACHINE LEARNING BASED STUDENT PERFORMANCE ANALYSIS SYSTEM." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 17, 2021): 1174–81. http://dx.doi.org/10.17762/itii.v9i1.251.

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The academic output of the student is normally stored in various formats in the student administration system (files, documents, records, photographs and other formats). These data can be collected for valuable knowledge from the students. However, it is difficult to analyze the increasing amount of data of students through conventional statistical techniques and database management tools. For universities to gather valuable information, a tool is therefore required. This helpful knowledge can be used to predict the success of students. Leistungs analyze learning results is a framework that aims for success in the areas of student interest at various levels and dimensions. This paper proposes a complete structure as a rule-based recommendation method not only for analyzing and forecasting the success of students but also for presenting their reasons. The proposed system analyzes demographic details for pupils, studies and psychological features so that students, teachers and parents can collect all possible knowledge. To seek maximum accuracy in academic predictions across a range of powerful techniques of data mining. The system successfully recognizes the limitations of the student and makes adequate recommendations. The practical case study on 200 students indicates the excellent performance of the proposed system compared to the current framework.

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