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 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.

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>

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.

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.

An Exploratory Study on the Use of Machine Learning to Predict Student Academic Performance

2018 ◽  
Vol 8 (4) ◽  
pp. 67-79 ◽  
Author(s):  
Patrick Kenekayoro
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Academic Performance ◽  
Support Vector Machines ◽  
Student Performance ◽  
Higher Education Institutions ◽  
Classification Model ◽  
Support Vector ◽  
Student Academic Performance ◽  
Vector Machines

Optimal student performance is integral for successful higher education institutions. The consensus is that big data analytics can be used to identify ways for achieving better student academic performance. This article used support vector machines to predict future student performance in computing and mathematics disciplines based on past scores in computing, mathematics and statistics subjects. Past subjects passed by students were ranked with state of art feature selection techniques in an attempt to identify any connection between good performance in a particular discipline and past subject knowledge. Up to 80% classification accuracy was achieved with support vector machines, demonstrating that this method can be developed to produce recommender or guidance systems for students, however the classification model will still benefit from more training examples. The results from this research reemphasizes the possibility and benefits of using machine learning techniques to improve teaching and learning in higher education institutions.

The University Experience Course: A Longitudinal Study of Student Performance, Retention, and Graduation

2001 ◽  
Vol 2 (4) ◽  
pp. 327-340 ◽  
Author(s):  
A. Michael Williford ◽  
Laura Cross Chapman ◽  
Tammy Kahrig
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Graduation Rates ◽  
Retention Rates ◽  
Orientation Course ◽  
Student Academic Performance ◽  
First Year Retention ◽  
Student Graduation ◽  
University Experience ◽  
The University

The purpose of this study is to investigate the relationship between participation in an extended orientation course and student academic performance, student retention, and student graduation. Ten years of participants in Ohio University's freshman “University Experience” course were compared with comparable nonparticipants. In the comparison of student academic performance, the effects of students' prior academic achievement and students' measured academic aptitude were controlled. First-year retention and four-, five-, and six-year graduation rates were compared. In most years of the study, participating students' year-end GPAs were higher than nonparticipants‘, retention rates were higher, and four-, five-, and six-year graduation rates were higher. The purpose of the course is to help students adjust to the demands of the university environment and develop long-term academic skills, which these results support. Student motivational effects are also discussed.

Predicting student academic performance using multi-model heterogeneous ensemble approach

2018 ◽  
Vol 10 (1) ◽  
pp. 61-75 ◽  
Author(s):  
Olugbenga Wilson Adejo ◽  
Thomas Connolly
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Data Sources ◽  
Content Type ◽  
Multiple Data Sources ◽  
Ensemble Techniques ◽  
Student Academic Performance ◽  
Multiple Data ◽  
Predicting Student Performance ◽  
Heterogeneous Ensemble

Purpose The purpose of this paper is to empirically investigate and compare the use of multiple data sources, different classifiers and ensembles of classifiers technique in predicting student academic performance. The study will compare the performance and efficiency of ensemble techniques that make use of different combination of data sources with that of base classifiers with single data source. Design/methodology/approach Using a quantitative research methodology, data samples of 141 learners enrolled in the University of the West of Scotland were extracted from the institution’s databases and also collected through survey questionnaire. The research focused on three data sources: student record system, learning management system and survey, and also used three state-of-art data mining classifiers, namely, decision tree, artificial neural network and support vector machine for the modeling. In addition, the ensembles of these base classifiers were used in the student performance prediction and the performances of the seven different models developed were compared using six different evaluation metrics. Findings The results show that the approach of using multiple data sources along with heterogeneous ensemble techniques is very efficient and accurate in prediction of student performance as well as help in proper identification of student at risk of attrition. Practical implications The approach proposed in this study will help the educational administrators and policy makers working within educational sector in the development of new policies and curriculum on higher education that are relevant to student retention. In addition, the general implications of this research to practice is its ability to accurately help in early identification of students at risk of dropping out of HE from the combination of data sources so that necessary support and intervention can be provided. Originality/value The research empirically investigated and compared the performance accuracy and efficiency of single classifiers and ensemble of classifiers that make use of single and multiple data sources. The study has developed a novel hybrid model that can be used for predicting student performance that is high in accuracy and efficient in performance. Generally, this research study advances the understanding of the application of ensemble techniques to predicting student performance using learner data and has successfully addressed these fundamental questions: What combination of variables will accurately predict student academic performance? What is the potential of the use of stacking ensemble techniques in accurately predicting student academic performance?

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