scholarly journals ACADEMIC PERFORMANCE PREDICTION APPLICATION (APPA)

YMER Digital ◽  
2021 ◽  
Vol 20 (12) ◽  
pp. 179-196
Author(s):  
Tejashree T Moharekar ◽  
◽  
Dr. Urmila R Pol ◽  
Keyword(s):  
Academic Success ◽  
Student Performance ◽  
Performance Prediction ◽  
Mobile Application ◽  
Research Study ◽  
Mobile App ◽  
Classification Model ◽  
Success Prediction ◽  
Proposed Model ◽  
Predicting Student Performance

The research study offers a thorough description of the process of deployment after training and testing of the classification model respectively. The performance of students is a crucial prerequisite to help students that don’t perform well in the examination and can impact the final semester result. To overcome the difficulties they come across while learning and assist them to achieve the best results. The researcher uses the advantages of the React-Native platform to build an "APPA" mobile application capable of delivering student performance prediction-related solutions. It also provides a proposed model of student academic success prediction. The further study highlights the further scope of the mobile App built for predicting student performance.

scholarly journals A THEORETICAL ANALYSIS OF STUDENTS’ ACADEMIC ACHIEVEMENT AND ASSOCIATED FACTORS

2021 ◽  
Vol 9 (47) ◽  
pp. 11543-11551
Author(s):  
Uma Sharma ◽  
Suraksha Bansal
Keyword(s):  
Academic Performance ◽  
Academic Success ◽  
Student Performance ◽  
School Environment ◽  
Economic Status ◽  
Educational Data Mining ◽  
Environment Pollution ◽  
Predicting Student Performance ◽  
Parental Family ◽  
And Behavior

The academic performance of student is influenced by several factors. Studies have been conducted in the field of educational data mining to find out what all the factors are that have an effect on a student's academic performance. There are many factors that can have an effect on a student's academic success, but our study aims to find the main factors that can have an effect on a student's academic performance. Predicting student performance becomes more difficult due to the large volume of information in academic databases. The responsibility of the teacher increases, she must be attentive to the activities and behavior of the student. To facilitate the work of a teacher, this study attempted to identify some factors that affect the academic performance of students. Our study could bring benefits and impacts to students, educators/teachers, and tutoring institutions. The factors investigated in this study are: student base knowledge, socio-economic status, college/school environment, environment pollution, teachers’ support, parental/family support, friend circle and health.

scholarly journals Multi-Relational and Social-Influence Model for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

2020 ◽  
Vol 9 (3) ◽  
pp. 2058-2066
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Matrix Factorization ◽  
Intelligent Tutoring Systems ◽  
Mean Squared Error ◽  
Mutual Influence ◽  
Intelligent Tutoring ◽  
Essential Elements ◽  
Tutoring Systems ◽  
Predicting Student Performance

Recent studies have shown that Matrix Factorization (MF) method, deriving from recommendation systems, can predict student performance as part of Intelligent Tutoring Systems (ITS). In order to improve the accuracy of this method, we hypothesize that taking into account the mutual influence effect in the relations of student groups would be a major asset. This criterion, coupled with those of the different relationships between the students, the tasks and the skills, would thus be essential elements for a better performance prediction in order to make personalized recommendations in the ITS. This paper proposes an approach for Predicting Student Performance (PSP) that integrates not only friendship relationships such as workgroup relationships, but also mutual influence values into the Weighted Multi-Relational Matrix Factorization method. By applying the Root Mean Squared Error (RMSE) metric to our model, experimental results from KDD Challenge 2010 database show that this approach allows to refine student performance prediction accuracy.

scholarly journals A detailed study on risk assessment of mobile app permissions

2017 ◽  
Vol 7 (1.1) ◽  
pp. 297
Author(s):  
D Naga Malleswari ◽  
A Dhavalya ◽  
V Divya Sai ◽  
K Srikanth
Keyword(s):  
Risk Assessment ◽  
Private Information ◽  
Mobile Applications ◽  
Mobile Application ◽  
Personal Information ◽  
Mobile App ◽  
Assessment Model ◽  
Privacy Concern ◽  
Proposed Model ◽  
Android Applications

Mobile phone have user’s personal and private information. When mobile applications have the permission to access to this information they may leak it to third parties without user’s consent for their own benefits. As users are not aware of how their personal information would be used once applications are installed and permissions are granted, this raises a potential privacy concern. Therefore, there is a need for a risk assessment model that can intimate the users about the threats the mobile application poses to the user's private information. We propose an approach that helps in increasing user’s awareness of the privacy risk involved with granting permissions to Android applications. The proposed model focuses on the requested permissions of the application and determines the risk based on the permission set asked and gives a risk score.

scholarly journals A Mobile Application for Early Prediction of Student Performance Using Fuzzy Logic and Artificial Neural Networks

2020 ◽  
Vol 14 (02) ◽  
pp. 4
Author(s):  
Ann Nosseir ◽  
Yahia Fathy
Keyword(s):  
Student Performance ◽  
Mobile Application ◽  
Learning Strategy ◽  
Early Stage ◽  
Fuzzy Model ◽  
Mobile App ◽  
Basic Knowledge ◽  
Students At Risk ◽  
Main Components ◽  
Efficient Learning

<p class="Abstract">Identifying students at risk or potentials excellent students is increasingly important for higher education institutions to meet the needs of the students and develop efficient learning strategy. Early stage prediction can give an indication of the students’ performance during their study years. This helps tailoring an appropriate learning strategy for different groups.</p><p class="Abstract">This work develops a novel framework for a mobile app to predict the students’ performance before starting the Universities’ education. The framework is built on a University’s students data from year 2009-2017. It has three main components, namely, a neural network model that predicts the GPA, a mobile App that tests basic knowledge in different domains, and a fuzzy model that estimates the future students’ performance. </p>

Predicting Community College and University Student Success

2016 ◽  
Vol 18 (4) ◽  
pp. 431-456 ◽  
Author(s):  
Monica L. Heller ◽  
Jerrell C. Cassady
Keyword(s):  
Community College ◽  
Academic Success ◽  
Student Success ◽  
Student Performance ◽  
Learning Strategies ◽  
Prediction Models ◽  
Performance Outcomes ◽  
College Level ◽  
Grade Point ◽  
Predicting Student Performance

The current study explored the differential influences that behavioral learning strategies (i.e., cognitive–metacognitive, resource management), motivational profiles, and academic anxiety appraisals have on college-level learners in two unique learning contexts. Using multivariate analysis of variance and discriminant analysis, the study first compared these variables across learners from a community college and traditional 4-year university located within the same regional area. The study also employed a series of multiple regression analyses to investigate the influence of these variables in predicting student performance outcomes (i.e., grade point average). The results illustrate that prior research on those factors most salient within student academic success prediction models within a social cognitive framework function as expected for the university population. However, the community college learner experience deviates significantly from this standard model. For the community college learner, it is the environmental factor that appears to be the most significant to predicting student success. These findings highlight those factors most influential in academic performance outcomes among diverse student populations.

Supporting Students' Mental Health and Academic Success Through Mobile App and IoT

2018 ◽  
Vol 9 (1) ◽  
pp. 50-64
Author(s):  
Karolina Baras ◽  
Luísa Soares ◽  
Carla Vale Lucas ◽  
Filipa Oliveira ◽  
Norberto Pinto Paulo ◽  
...  
Keyword(s):  
Mental Health ◽  
Academic Success ◽  
Young People ◽  
Interpersonal Relationships ◽  
Time Management ◽  
Mobile Application ◽  
Well Being ◽  
Mobile App ◽  
Management Guidelines ◽  
Health And Well Being

Smartphones have become devices of choice for running studies on health and well-being, especially among young people. When entering college, students often face many challenges, such as adaptation to new situations, establish new interpersonal relationships, heavier workload and shorter deadlines, teamwork assignments and others. In this paper, the results of four studies examining students' well-being and mental health as well as student's perception of challenges and obstacles they face during their academic journey are presented. In addition, a mobile application that acts as a complement to a successful tutoring project implemented at the authors' University is proposed. The application allows students to keep their schedules and deadlines in one place while incorporating virtual tutor features. By using both, the events from the student's calendar and his or her mood indicators, the application sends notifications accordingly. These notifications encompass motivational phrases, time management guidelines, as well as relaxation tips.

scholarly journals Performance Prediction for Students: A Multi-Strategy Approach

2017 ◽  
Vol 17 (2) ◽  
pp. 164-182 ◽  
Author(s):  
Thi-Oanh Tran ◽  
Hai-Trieu Dang ◽  
Viet-Thuong Dinh ◽  
Thi-Minh-Ngoc Truong ◽  
Thi-Phuong-Thao Vuong ◽  
...  
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Prediction Performance ◽  
Learning Systems ◽  
Prediction Problem ◽  
Regression Problem ◽  
National University ◽  
E Learning ◽  
Rating Prediction ◽  
Predicting Student Performance

Abstract This paper presents a study on Predicting Student Performance (PSP) in academic systems. In order to solve the task, we have proposed and investigated different strategies. Specifically, we consider this task as a regression problem and a rating prediction problem in recommender systems. To improve the performance of the former, we proposed the use of additional features based on course-related skills. Moreover, to effectively utilize the outputs of these two strategies, we also proposed a combination of the two methods to enhance the prediction performance. We evaluated the proposed methods on a dataset which was built using the mark data of students in information technology at Vietnam National University, Hanoi (VNU). The experimental results have demonstrated that unlike the PSP in e-Learning systems, the regression-based approach should give better performance than the recommender system-based approach. The integration of the proposed features also helps to enhance the performance of the regression-based systems. Overall, the proposed hybrid method achieved the best RMSE score of 1.668. These promising results are expected to provide students early feedbacks about their (predicted) performance on their future courses, and therefore saving times of students and their tutors in determining which courses are appropriate for students’ ability.

scholarly journals Learning Analytics: A Comprehensive Analysis of Methods for Student Performance Prediction

2019 ◽  
Vol 9 (2) ◽  
pp. 1509-1514
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Educational Institution ◽  
Comprehensive Analysis ◽  
Point Of View ◽  
Web Based ◽  
Business World ◽  
Regression Algorithms ◽  
Predicting Student Performance ◽  
Insight Into

Data Mining plays an important role in the Business world and it helps the educational institution to predict and make decisions related to the students’ academic status. From a large volume of data in educational databases it is difficult to predict student performance. In India currently the existing systems lack in monitoring and analyzing the students’ performance. The main reason is that the existing system has insufficient capabilities for identification of performance of the student and it also not considered all factors that affect the achievements of a student’s in the context of India. Therefore, a systematical literature review on predicting student performance by the proposed system is a web-based which makes use of the mining techniques for the extraction of useful information. This work is digging insight into the state-based and eventbased approaches for predicting student performance. A Comparative analysis is conducted to suggest regression-based algorithms of state-based framework lack accuracy and correlation-based algorithms under the event-driven approach outperform classical regression algorithms. It is also concluded from pedagogical a point of view, higher engagement with social media leads to higher final grades

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