scholarly journals A Review on Predictive Modeling Technique for Student Academic Performance Monitoring

2019 ◽  
Vol 255 ◽  
pp. 03004 ◽  
Author(s):  
Mat Asiah ◽  
Khidzir Nik Zulkarnaen ◽  
Deris Safaai ◽  
Mat Yaacob Nik Nurul Hafzan ◽  
Mohamad Mohd Saberi ◽  
...  
Keyword(s):  
Academic Performance ◽  
Prediction Model ◽  
Predictive Model ◽  
Student Performance ◽  
Predictive Modeling ◽  
Performance Monitoring ◽  
Predictive Analytics ◽  
Performance Model ◽  
Research Activities ◽  
Student Academic Performance

Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous researchers to develop the best performance model using variety of students data, techniques, algorithms and tools. Predictive modeling used in predicting student performance are related to several learning tasks such as classification, regression and clustering. To achieve best prediction model, a lot of variables have been chosen and tested to find most influential attributes to perform prediction. Accurate performance prediction will be helpful in order to provide guidance in learning process that will benefit to students in avoiding poor scores. The predictive model furthermore can help instructor to forecast course completion including student final grade which are directly correlated to student performance success. To harvest an effective predictive model, it requires a good input data and variables, suitable predictive method as well as powerful and robust prediction model.

scholarly journals Local socioeconomic status and paramedic students’ academic performance

2019 ◽  
Author(s):  
Lydia Hamel ◽  
Ashley Procum ◽  
Justin Hunter ◽  
Donna Gridley ◽  
Kathleen O’Connor ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Academic Performance ◽  
Food Insecurity ◽  
Student Performance ◽  
Graduation Rate ◽  
The United States ◽  
Individual Student ◽  
Income Poverty ◽  
Linear Regression Models ◽  
Student Academic Performance

AbstractResearch indicates students of lower socioeconomic status (SES) are educationally disadvantaged. We sought to examine differences in paramedic student academic performance from counties with varying SES in the United States. Student performance data and SES data were combined for counties within the states of California, Mississippi, Louisiana, Texas and Virginia. Linear multiple regression modelling was performed to determine the relationship between income, high school graduation rate, poverty and food insecurity with first-attempt scores on the Fisdap Paramedic Readiness Exam (PRE) versions 3 and 4. Linear regression models indicated that there was a significant relationship between county-level income, poverty, graduation rate, food insecurity, and paramedic student academic performance. It remains unclear what type of relationship exists between individual SES and individual academic performance of paramedic students. These findings support the future collection of individual student level SES data in order to identify issues and mitigate impact on academic performance.

scholarly journals A new proposal Classification method based on Fuzzy Association Rule Mining for Student Academic Performance Prediction

2017 ◽  
Vol 33 (2) ◽  
Author(s):  
Giap Cu
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Association Rule ◽  
Tree Structure ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Student Academic Performance ◽  
Fixed Set ◽  
Fuzzy Association Rule ◽  
Fuzzy Association Rule Mining

Predicting student academic performance (SAPP) is an important task in moderneducation system. Proper prediction of student performance improves construction of educationprinciples in universities and helps students select and pursue suitable occupations. Theprediction approaching fuzzy association rules (FAR) give advantages in this circumstancebecause it gives the clear data-driven rules for prediction outcome. Applying fuzzy conceptbrings the linguistic terms that are close to people thought over a quantitative dataset, howeveran efficient mining mechanism of FAR requires a high computing effort normally. The existingFAR-based algorithms for SAPP often use Apriori-based method for extracting fuzzy associationrules, consequently they generate a huge number of candidates of fuzzy frequent itemsets andvarious redundant rules. This paper presents a new proposal model of predictor using FAR toelevating prediction performance and avoids extraction of the fixed set of FAR beforepredictions progress. Indeed, a modification tree structure of a FP-growth tree is used in fuzzyfrequent itemset mining, when a new requirement rises, the proposed algorithm mines directly inthe tree structure for the best prediction results. The proposal model does not require to predeterminethe antecedents of prediction problem before the training phrase. It avoids searchingfor non-relative rules and prunes the conflict rules easily by using a new rule relatednessestimation.

scholarly journals Impact of grading system on student academic performance during the COVID-19 pandemic

2021 ◽  
Vol 52 (4) ◽  
pp. 80-93
Author(s):  
Alexander K. Pogrebnikov ◽  
◽  
Vyacheslav N. Shestakov ◽  
Yuri Yu. Yakunin ◽  
◽  
...  
Keyword(s):  
Academic Performance ◽  
Distance Learning ◽  
Learning Outcomes ◽  
Student Performance ◽  
Educational Process ◽  
Wilcoxon Test ◽  
Grading System ◽  
Chi Square ◽  
Student Academic Performance ◽  
Grading Systems

The grading systems used for assessment students' knowledge in traditional and distance learning differ and give different assessments of learning outcomes, which affects the indicators of student academic performance. The trends of modern education are aimed at the increasing involvement of distance forms in the educational process of universities, which requires a certain synchronization of grading systems at the level of learning outcomes. The research is aimed at identification and confirmation of the existence of differences between the results of assessment in traditional and distance learning forms, which are reflected in the indicators of student performance. The study used methods of nonparametric analysis using the STATISTICA 10.0 program. The Mann-Whitney U-test was used to compare independent populations in cases where there were no signs of normal data distribution, and Pearson's Chi-square test was used to compare nominal values. To compare related (paired) quantitative samples, the Wilcoxon test was used. The research results showed a statistically significant increase in student academic performance during the COVID-19 pandemic during distance learning. Therefore, when comparing the indicators of student performance in the spring semesters of 2018/2019 and 2019/2020 academic years, significant differences were found in the U-criterion and Chi-square with different levels (p <0.05, p <0.01, p <0.001) depending on the course of study and the performance indicator. The only exceptions are the senior students of Master's programmes who have shown a decline in academic performance. According to the results of the study, it can be concluded that the distance grading system makes lower requirements for learning outcomes in terms of student academic performance indicators, which overestimates them relative to the traditional form of education.

Competitiveness vis-à-vis motivation and personality as drivers of academic performance

2018 ◽  
Vol 32 (1) ◽  
pp. 185-202 ◽  
Author(s):  
Chris Baumann ◽  
Marina Harvey
Keyword(s):  
Academic Performance ◽  
Extrinsic Motivation ◽  
Performance Model ◽  
Stepwise Multiple Regression ◽  
Content Type ◽  
Student Academic Performance ◽  
Intrinsic And Extrinsic Motivation ◽  
Micro Level ◽  
And Performance ◽  
Reported Data

Purpose The purpose of this paper is to build on the foundational theories of personality and motivation to explore the role of competitiveness and of ethnicity in relation to student academic performance. Survey data from 328 students across four sites (Australia, Denmark, Hong Kong and Korea) provided self-rated responses to items measuring personality, motivation, competitiveness and ethnicity. Design/methodology/approach Stepwise multiple regression was used to identify the variables that predicated student academic performance, including testing for interaction effect of ethnicity. Both student self-reported data and independently assessed performance measures were used to avoid common method variance. Findings This study affirmed that both intrinsic and extrinsic motivation are significantly associated with academic performance. The personality traits of conscientiousness, agreeableness, extraversion and neuroticism are significantly associated with a student’s competitiveness. The interaction of competitiveness and ethnicity is significantly and positively associated with performance. Research limitations/implications The variable of student competitiveness requires further research to better understand its role in academic performance. Researching ethnicity at the micro level allows the acknowledgement and investigation of “intra-national diversity” (Tung, 2008; Tung and Baumann, 2009). Originality/value This study is original in its approach in that it combines the concepts of motivation, personality, competitiveness and ethnicity in relation to student academic performance. While previous studies have explored these concepts individually and often at the macro level, a crucial contribution of this study is that competitiveness and ethnicity (as opposed to national culture) are examined at a micro level. The authors demonstrate the combined importance of intrinsic and extrinsic motivation (carrot and stick) in driving performance and introduce the new motivation, competitiveness and performance model which recognises that competitiveness, as a driver of performance, is moderated by the learners’ ethnicity.

Does Teacher Empowerment Affect the Classroom? The Implications of Teacher Empowerment for Instructional Practice and Student Academic Performance

1997 ◽  
Vol 19 (3) ◽  
pp. 245-275 ◽  
Author(s):  
Helen M. Marks ◽  
Karen Seashore Louis
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Hierarchical Linear Modeling ◽  
Classroom Practice ◽  
School Organization ◽  
Teacher Empowerment ◽  
Linear Modeling ◽  
Pedagogical Quality ◽  
Student Academic Performance ◽  
Quality Assessments

Findings from recent research about the relationship of teacher empowerment to other school reform objectives of interest, such as classroom practices or student academic performance, are mixed. This study investigates teacher empowerment in schools that have at least four years of experience with some form of decentralized or school-based management. Based on the assumption that participation in school decisionmaking can enhance teachers’ commitment, expertise, and, ultimately, student achievement, we hypothesize a positive relationship between empowerment and student performance through the linkages of school organization for instruction and pedagogical quality. The data we use to examine empowerment are drawn from a sample of 24 restructuring elementary, middle, and high schools—8 schools at each grade level. Most of the schools are urban, representing 16 states and 22 school districts. Data sources include teacher surveys, ratings of pedagogical quality, assessments of student academic performance, and case studies based on interviews and observations; the primary method of analysis is hierarchical linear modeling (HLM). The results suggest: (1) Overall, empowerment appears to be an important but not sufficient condition of obtaining real changes in teachers’ ways of working and their instructional practices; (2) The effects of empowerment on classroom practice vary depending on the domain in which teacher influence is focused; (3) Teacher empowerment affects pedagogical quality and student academic performance indirectly through school organization for instruction.

scholarly journals Factors Impacting Students Academic Performance Using R Code Programming Language for Analysing the Data

2020 ◽  
pp. 404-409
Author(s):  
Adesina Fatimat. O ◽  
Akande Ademola ◽  
Ajala Abiodun. L ◽  
Kolawole Tolulope ◽  
Ogundeji Tajudeen. O
Keyword(s):  
High School ◽  
Academic Performance ◽  
Student Performance ◽  
Programming Language ◽  
Policy Makers ◽  
Student Academic Performance ◽  
Ex Post ◽  
Ex Post Facto ◽  
Instrument Measuring ◽  
Management Students

<p>This work intends to investigate and propose a model that can be used by students, parents, teachers and education policy makers to understand and predict high school student academic performance based on pre-defined factors identified as capable of impacting students’ academic performance. This study uses ex post facto research design. An instrument measuring students’ academic performance has been used to collect data from the management students and R-Code programming language was used to analyse the data collected and there was a positive and statistically significant impact of learning facilities, age, romance and proper guidance from parent on student performance.</p>

Bifactor Models of Personality and College Student Performance: A Broad versus Narrow View

2014 ◽  
Vol 28 (6) ◽  
pp. 604-619 ◽  
Author(s):  
Samuel T. McAbee ◽  
Frederick L. Oswald ◽  
Brian S. Connelly
Keyword(s):  
Academic Performance ◽  
College Student ◽  
Student Performance ◽  
Latent Variable ◽  
Performance Model ◽  
Personality Factors ◽  
Personality Psychology ◽  
Grade Point ◽  
Personality Facets ◽  
Meaningful Relationships

Research in the area of personality traits and academic performance has been supported by consistent meta–analytic evidence demonstrating positive relationships between Conscientiousness and grade point average (GPA). However, academic performance is not solely a function of GPA but also a number of other important intellectual, interpersonal and intrapersonal behaviours. This wider criterion space opens up the possibility for many personality factors and their underlying facets to relate to academic performance. Using bifactor latent variable modelling, the current study investigates the six–factor HEXACO model of personality, along with their 24 underlying facets, for predicting students’ academic performance. Model results reveal interpretable and meaningful relationships between both broad factors and narrow personality facets in predicting college student outcomes. Implications for measurement, modelling and prediction are discussed. Copyright © 2014 European Association of Personality Psychology

scholarly journals Analysis of Student Academic Performance Using Human-in-the-Loop Cyber-Physical Systems

Telecom ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 18-31
Author(s):  
Soraya Sinche ◽  
Pablo Hidalgo ◽  
José Fernandes ◽  
Duarte Raposo ◽  
Jorge Silva ◽  
...  
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Student Behavior ◽  
Data Gathering ◽  
Cyber Physical Systems ◽  
University Campus ◽  
Physical Systems ◽  
Human In The Loop ◽  
Student Academic Performance ◽  
The University

Is it possible to analyze student academic performance using Human-in-the-Loop Cyber-Physical Systems (HiLCPS) and offering personalized learning methodologies? Taking advantage of the Internet of Things (IoT) and mobile phone sensors, this article presents a system that can be used to adapt pedagogical methodologies and to improve academic performance. Thus, in this domain, the present work shows a system capable of analyzing student behavior and the correlation with their academic performance. Our system is composed of an IoT application named ISABELA and a set of open-source technologies provided by the FIWARE Project. The analysis of student performance was done through the collection of data, during 30 days, from a group of Ecuadorian university students at “Escuela Politécnica Nacional” in Quito, Ecuador. Data gathering was carried out during the first period of classes using the students’ smartphones. In this analysis, we found a significant correlation between the students’ lifestyle and their academic performance according to certain parameters, such as the time spent on the university campus, the students’ sociability, and physical activity, etc.

Sign in / Sign up

Export Citation Format

Share Document