scholarly journals PENGARUH TINGKAT PARTISIPASI KELAS TERHADAP KINERJA AKADEMIK MAHASISWA

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Anggita Langgeng Wijaya
Keyword(s):  
Academic Performance ◽  
Hypothesis Test ◽  
Accounting Education ◽  
Education Department ◽  
First Semester ◽  
Class Participation ◽  
Student Academic Performance ◽  
Simple Regression Analysis ◽  
Influence Degree ◽  
New Student

This research tests the influence degree of class participation on academic performance. Population of this research is all of new student at first semester from accounting education department at IKIP PGRI Madiun. In this study, sample consist 109 students from three introduction business class. Hypothesis test of this research uses simple regression analysis. The results show that class participation positively affects on student academic performance.

scholarly journals Professors' Expectations About Online Education and Its Relationship With Characteristics of University Entrance and Students' Academic Performance During the COVID-19 Pandemic

2021 ◽  
Vol 12 ◽  
Author(s):  
Karla Lobos Peña ◽  
Claudio Bustos-Navarrete ◽  
Rubia Cobo-Rendón ◽  
Carolyn Fernández Branada ◽  
Carola Bruna Jofré ◽  
...  
Keyword(s):  
Academic Performance ◽  
Online Education ◽  
Online Courses ◽  
Longitudinal Design ◽  
First Semester ◽  
Student Academic Performance ◽  
Academic Information ◽  
University Performance ◽  
Prospective Longitudinal ◽  
The Impact

Due to COVID-19, universities have been facing challenges in generating the best possible experience for students with online academic training programs. To analyze professors' expectations about online education and relate them to student academic performance during the COVID-19 pandemic, and considering the socio-demographic, entry, and prior university performance variables of students. A prospective longitudinal design was used to analyze the expectations of 546 professors (54.8% male) in T1. In T2, the impact of the expectations of 382 of these professors (57.6% men) was analyzed, who taught courses during the first semester to a total of 14,838 university students (44.6% men). Professors' expectations and their previous experience of online courses were obtained during T1, and the students' academic information was obtained in T2. A questionnaire examining the Expectations toward Virtual Education in Higher Education for Professors was used. 84.9% of the professors were considered to have moderate to high skills for online courses. Differences in expectations were found according to the professors' training level. The professors' self-efficacy for online education, institutional engagement, and academic planning had the highest scores. The expectations of professors did not directly change the academic performance of students; however, a moderating effect of professor's expectations was identified in the previous student academic performance relationship on their current academic performance.

Predictive Modeling for Student Grade Prediction Using Machine Learning and Visual Analytics

2020 ◽  
Author(s):  
Siti Dianah Abdul Bujang ◽  
Ali Selamat ◽  
Ondrej Krejcar
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Visual Analytics ◽  
Computational Models ◽  
Data Set ◽  
Final Grade ◽  
First Semester ◽  
Student Academic Performance ◽  
Interactive Visualizations ◽  
Tree Algorithms

Data-driven plays an important role in determining the quality of services in institutions of higher learning (HEIs). Increasingly data in education is encouraging institutions to find ways to improve student academic performance. By using machine learning with visual analytics, data can be predicted based on valuable information and presented with interactive visualizations for institutions to improve decision making. Therefore, predicting students’ academic performance is critical to identifying students at risk of failing a course. In this paper, we propose two approaches, such as (i) a prediction model for predicting students’ final grade based on machine learning that interacts with computational models; (ii) visual analytics to visualize predictive models and insightful data for educators. The data were tested using student achievement records collected from one of the Malaysian Polytechnic databases. The data set used in this study involved 489 first semester students in Computer System Architecture (CSA) course from 2016 to 2019. The decision tree algorithms (J48), Random Tree (RT), Random Forest (RF), and REPTree) was used on the student data set to produce the best predictions of the model. Experimental results show that J48 returns the highest accuracy with 99.8 %, among other algorithms. The findings of this study can help educators predict student success or failure for a particular course at the end of the semester and help educators make informed decisions to improve student academic performance at Polytechnic Malaysia.

College Student Academic Performance: Coping and Exercise as Mediators of Multiple Sources of Stress

2008 ◽  
Author(s):  
Joseph R. Scotti ◽  
Brittany Joseph ◽  
Christa Haines ◽  
Courtney Lanham ◽  
Vanessa Jacoby
Keyword(s):  
Academic Performance ◽  
College Student ◽  
Multiple Sources ◽  
Student Academic Performance

Teacher Professional Training and Student Academic Performance at an University from Lima, 2016 [Formación Profesional Docente y Rendimiento Académico en Estudiantes de una Universidad de Lima, 2016]

2019 ◽  
Vol 1 (2) ◽  
pp. 116
Author(s):  
Jorge Luis Torres Ugaz
Keyword(s):  
Veterinary Medicine ◽  
Academic Performance ◽  
Educational System ◽  
Professional Training ◽  
Student Academic Performance ◽  
Teacher Professional ◽  
Study Methodology ◽  
The Relationship

This work emphasizes the teaching work in the progress of the educational system. The objective was to determine the relationship between the Teacher Professional Training and the Academic Performance of the students of Veterinary Medicine and Zootechnics of an University of Lima, Perú. The study methodology was correlational, the sample was 6 teachers and 72 students. The teachers were surveyed and the students were evaluated through the minutes. A mean and direct correlation of 44.05% was obtained between the variables studied.

scholarly journals Web-based formative assessment through clinical cases: role in pathophysiology teaching

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nerea Fernández Ros ◽  
Felipe Lucena ◽  
Mercedes Iñarrairaegui ◽  
Manuel F. Landecho ◽  
Patricia Sunsundegui ◽  
...  
Keyword(s):  
Academic Performance ◽  
Formative Assessment ◽  
Learning Strategies ◽  
Web Based ◽  
First Semester ◽  
Intermediate Group ◽  
Active Learning Strategies ◽  
Online Formative Assessment ◽  
Intermediate Performance ◽  
Clinical Cases

Abstract Background Active learning strategies such as formative assessment through clinical cases may help to get a deeper learning. We have studied the effect of this kind of online formative assessment in pathophysiology teaching. Methods Seven brief clinical cases were used to give formative assessment in the first semester of a pathophysiology course. To evaluate its effect on learning, we analyzed the proportion of students that passed the end of semester exam with a score above 60 over 100. We also analyzed the effect of the intervention according to the students’ previous academic performance. Results Ninety-six students participated in the study and sat the exam. Sixty-five of them passed it. Students that passed the exam had a higher previous academic performance and had done a higher number of exercises of formative assessment, both in univariate and multivariate analysis. The participants were divided in three groups, according to their previous academic performance. In the intermediate group, the number of cases done by the students who passed the exam was significantly higher than in those who did not pass it (median: 4 versus 0; P = 0.009). Conclusion Formative assessment through web-based clinical cases was followed by an improvement of the academic results in pathophysiology, mainly in students with intermediate performance.

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