predicting students' academic performance

To improve on the quality of education, there is a need to be able to predict academic performance of the students. 44 0 obj <> endobj The relative influence of different independent variables on students' academic performance in pre-university science program was examined. International Science Community Association Mining Student Academic Performance on ITE subjects using Descriptive. Academic performance of students in schools and colleges is an important factor in determining their overall success and sustainability. startxref The ability to predict student performance is very important in educational environments. Among the issues of education system, questions concerning admissions into academic institutions (secondary and tertiary level) remain important (Ting, 2008). If educational institutions can predict students' academic performance early before their final examination, then extra improve retention rates, such programs need prior knowledge of students performance (Yadav et al., 2012). Webber, Henry Y. Zheng, Ying Zhou 0000010792 00000 n This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts. The book is conceptually divided in seven parts. Abstract. To study the factor affecting academic grades of student. Predicting Students' Academic Performance by Their Online Learning Patterns in a Blended Course: To What Extent Is a Theory-driven Approach and a Data-driven Approach Consistent? Predicting student academic performance has long been an important research topic. 0000000796 00000 n Two different approaches were used. Predicting students' academic performance based on school and socio-demographic characteristics Tamara Thielea*, Alexander Singletonb, Daniel Popec and Debbi Stanistreetc aDepartment of Psychological Science, University of Liverpool, Eleanor Rathbone Building, Bedford Street South, Liverpool L69 7ZA, UK; bDepartment of Geography and Planning, University of Liverpool, Jane Herdman Building . CS students predict students' academic performance because of the huge bulks of data stored in the environments of educational databases. One of the most important functions of EDM has been to predict student performance based on past activity. The usage of machine learning to predict either the student performance or the student dropout is a commonly found subject in academic literature. While there is a plethora of success stories in the literature . The data were collected from 8 year period intakes from July 2006/2007 until July 2013/2014 that contains the students' demographics, previous academic records, and family background information. This project aims to develop a prediction model for students' academic performance based on machine learning techniques. Increasing student success is a long term goal in all academic institutions. Predicting Students' Academic Performance (SAP) is one of the important research areas in Higher Learning Institutions. in prediction of student academic performance. xref The predicting academic performance of students. 0000001379 00000 n This book constitutes the refereed proceedings of the First International Conference on Intelligent Cloud Computing, ICC 2019, held in Riyadh, Saudi Arabia, in December 2019. Most higher learning institutions have systems to store student's information and these databases contain useful knowledge that can be extracted. 0000006926 00000 n Regression Models of Predicting Student Academic Performance in an Engineering Dynamics Course. Predicting Students Academic Performance Using Education Data Mining . This study will educate on the design and implementation of Artificial Neural Network. Objectives The following are the objective of this study. In the previous work, thereare many methodsproposed topredictthe performanceof students such as Scholastic Aptitude Test (SAT) or American College Test (ACT), Intelligent Test, Fuzzy Set Theory, Neural Network, Decision Tree and Naïve ... In recent years , research evolution in domain of education focus on analytics and which provides insights on students academic performance. The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. By Mia Villarica. In machine learning field, predicting students' academic performance is considered as supervised learning. Intuitively one expects the performance of a student to be a function of some number of 0000003452 00000 n 0000004206 00000 n after students go through tests, exams, etc and are assigned grades based on their performance. The present research study was design to investigate the factors affecting academic performance of graduate students of Islamia University of Bahawalpur Rahim Yar Khan Campus. Gender also made significant impact on the students' academic performance. Student Performance Prediction Preface. Predicting student academic performance has long been an important research topic in many academic disciplines. Data mining in the field of education (Educational Data Mining - EDM), as a new field of research, has developed in the last decade as a special area . V.O. Performance of Classification Algorithms on Students' Data - A Comparative Study. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. Educational Data Mining (EDM) is a developing research field that involves many techniques to explore data relating to educational background. Data mining techniques are implemented to predict students . The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Predicting students academic performance using artificial neural network: A case study of an engineering course The Pacific Journal of Science and Technology , 9 ( 1 ) ( 2008 ) , pp. This book highlights recent research on intelligent systems design and applications. The scope of this paper is to predict the student marks and what are the factors that influence the performance of the students. Devoted entirely to the comparison of rates and proportions, this book presents methods for the design and analysis of surveys, studies and experiments when the data are qualitative and categorical. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics - a high-enrollment, high-impact, and core course that many engineering undergraduates are required to take. Our paper is a step towards detecting the best amalgamations of feature section algorithms and classification algorithms on student datasets. 0000003722 00000 n 0000002952 00000 n Most of the previous studies used either the student's background check or one semester academic performance as the variable for predicting the student's next semester academic performance. Identifying the factors that influence academic performance is an essential part of educational research. scholarly and exemplary practices in engineering education endstream endobj 45 0 obj<> endobj 47 0 obj<> endobj 48 0 obj<>/Font<>/ProcSet[/PDF/Text]/ExtGState<>>> endobj 49 0 obj<> endobj 50 0 obj<> endobj 51 0 obj<> endobj 52 0 obj<> endobj 53 0 obj[/ICCBased 67 0 R] endobj 54 0 obj<> endobj 55 0 obj<> endobj 56 0 obj<> endobj 57 0 obj<> endobj 58 0 obj<> endobj 59 0 obj<>stream and prediction of academic performance is widely researched. 0000001200 00000 n The tremendous growth of instructional institutions' electronic information provides the chance to extract info which will be wont to predict students' overall success, predict students' dropout rate, appraise the performance of academics and . H�|T�n�0��+�(֚�(R��q����KӃ"ӱ[*�������]ʱ�$�| ei��3��~ZIx�� �&�8�@��o��T�5���3Pv������BHyļ� ̷�EYm�n����4�(���$3I��DZ�\j|)���ե��^C��m����\K��PJTij�0B3�`�o!D�Ŕ��g�S���^\����A�T��hI�F�m����K�*�J\�0)�Khձ��9*�ٛc����l|�@�&p�p��?���(6���p����"��Ož�����ڮ�GR�,H�L8t�egh��U��~���>�`B h����#[l8;����K/y��2:E&�K"����>^\_�ݰw�G3�2�\K^�ђ��%�ő�o���TqV��UϨ��mU?�G�Hd���2�XA����~g}x��[WeOX�ꇵ�����b��4 K�n�v_�l�czG��H�1�4 C�8��1�h�����K�lشOW#嘚�:�X�Lϰ�kF�9,���Q�aͺ��A3Jc7�cU;ǒ����s�fV|ڽ6~����%��)�b��c��S�1,�N���,c)T�4:'A�f���vź�0��i�'���G���3�Q�0��M�4�Z���9�f�T�ů��52��s���=��f׸*q�?Xwx�����u�݁��)��͇�����0��������jK��hH?_�O� �wQ8 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7. Having spent the past few months studying quite a bit about machine learning and statistical inference, I wanted a more serious and challenging task than simply working and re-working the examples that many books and blogs make use of. The CS major focuses on the theory of computational applications, i.e., understanding the "why" behind computer pro-grams. The main objective of the admission system is to determine the candidates who would likely perform well . x�b```�V�6 ��2�0p�t`h`��V���(ֳLe��Sn;�U�,�!����չ��,&�Lt��J�d� ��̍����З*�c��Y�.�����S�U��6�D�tt4�XH$DN0 Eʸ�A@A9��r -��`�J@_Ld�e���Θ�x (r��2�Y��ZL��e�>0�2=`����i�wK��[F!�x�%+.1�1�1pB}������fbk� rG Various factors like Socioeconomic, Psychological, Cognitive, and Lifestyle are considered in analyzing the performance of students and predictions will be made based on their Semester GPA. Predicting students' performance has been an issue studied previously in educational data mining research in the con-text of student attrition (Zafra & Ventura,2009;Zimmer-mann et al.,2011). Student success plays a vital role in educational institutions, as it is often used as a metric for the institution's performance. This could include looking at past CGPA scores or internal assessments, student . This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems This book presents the proceedings of the 6th International Conference on Frontier Computing, held in Kuala Lumpur, Malaysia on July 3–6, 2018, and provides comprehensive coverage of the latest advances and trends in information ... %%EOF in trying to predict student's academic performance [4]. To identify other approaches of predicting students academic performance. If educational institutions can predict students' academic performance early before their final examination, then extra Cognition of these features contributes to control their impact on student . Predicting students' academic performance based on school and socio-demographic characteristics Tamara Thielea*, Alexander Singletonb, Daniel Popec and Debbi Stanistreetc aDepartment of Psychological Science, University of Liverpool, Eleanor Rathbone Building, Bedford Street South, Liverpool L69 7ZA, UK; bDepartment of Geography and Planning, University of Liverpool, Jane Herdman Building . predict student's academic performance at an early stage and thus provide them with timely assistance. 0000003967 00000 n Improve student understanding of course material. Assess and attend to the needs of struggling learners. Improve accuracy in grading. Allow instructors to assess and develop their own strengths. This book explores the problem within the context of social, historical, cultural, and biological factors. examination of student performance by attempting to predict students' overall level of academic performance with variables from both theories. 0000007733 00000 n Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) ... This book explores successful transition strategies to, within and from university for students from around the globe, with Macquarie University, a large Australian university, studied in depth. The resultant model can be used to identify any student's performance . The ability to predict student performance is very important in educational environments. Predicting the academic performance of students is very challenging due to large volume of data in the educational institutions database. Highlighting a range of topics such as augmented reality, ethics, and online learning environments, this book is ideal for educators, instructional designers, higher education faculty, school administrators, academicians, researchers, and ... This book includes high-quality research papers presented at the Second International Conference on Innovative Computing and Communication (ICICC 2019), which is held at the VŠB - Technical University of Ostrava, Czech Republic, on 21–22 ... 0000009269 00000 n But still a lot of attention is required to construct student performance prediction models with the help of feature selection algorithms. It was observed that Naïve Bayes classifier outperforms other two classifiers by achieving overall prediction accuracy of 86%. Information is widely available and accessible, but frequently leads to information overload and overexposure, while the effort for coding, storing, hiding, securing, transmitting and retrieving it may be excessive Intelligence is required ... models for predicting the academic performance of the students [4]. predicting students' academic performance of first year bachelor students in Computer Science course. 0000001120 00000 n The main objectives of Prediction methods in EDM are to study the features of model that are essential for predicting SAP and to provide information about the underlying . A. Mueen, B. Zafar, and U. Manzoor, "Modeling and Predicting Students'Academic Performance Usin Data Mining Techniques," International Journal of Modern Education and Computer Science, vol. 0000008503 00000 n Predicting student academic performance has long been an important research topic. Three predictive models had been developed namely Artificial Neural Network, Decision Tree and Linear regression. By Lakshmiprabha Murali. To develop a model predicting academic performance of students based on identified factors. Student academic performance prediction plays a major role in the current educational systems to improve the quality of education. Bydzovska (2016) proposed an approach to predict the students' performance using course characteristics and previous grades. It is to certify that the manuscript "Student-Performulator: Predicting Students' Academic Performance at Secondary and Intermediate Level Using Machine Learning" fulfilled the following: 1) This material is the authors' own original work. Among the issues of education system, questions concerning admissions into academic institutions (secondary and tertiary level) remain important (Ting, 2008). Abstract: There have been voluminous studies upon the potential predictive power of students' non-cognitive attributes such as self-confidence, motivation and student's interaction with institutions on their collegiate performance. Feifei Han1, 2* and Robert A. Ellis1 1Office of Pro-Vice-Chancellor (Arts, Education and Law), Griffith University, Australia // 2Griffith Institute for The IBM Statistical Package for Social Studies (SPSS) is used to apply the Chi-Square . methods for prediction of academic performance of university students. student academic performance takes advantage of artificial neural network. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics--a high-enrollment, high-impact, and core course that many engineering undergraduates are required to take. endstream endobj 241 0 obj <. The conference title is belonging 100 in the area of IEEE Computer Society This event would be a wonderful gathering between IEEE members in the area of South Pacific, Australia and the rest of the world to share the latest development in ... with student academic performance. To achieve this goal, an intelligent decision support system (IDSS) is essential to predict students . First ambition of using data mining techniques is to develop a prediction model the extracted model is then used to predict student's educational performance the overall student . Student academic performance measurement has . The experiments on a large-scale real-world dataset show the effectiveness of our methods for predicting academic performance and the effectiveness of proposed behavioral factors. Almost every mechanical or civil engineering student is required to take the Engineering Dynamics course a high-enrollment, high-impact, and core engineering course. Predicting student academic performance has long been an important research topic.Among the issues of education system, questions concerning admissions into academic institutions (secondary and tertiary level) remain important (Ting, 2008). Learning analytics is a methodology for helping students to succeed in the classroom; the principle is to predict student's academic performance at an early stage and thus provide them with timely assistance. Predicting Students Academic Performance Using Artificial Neural Network CHAPTER ONE INTRODUCTION 1.1 BACKGROUND TO THE STUDY. Now, activity can mean a lot of things and over the years many researchers have used different indicators to estimate the performance of the student. 46 0 obj<>stream model to predict the performance of a student before admitting the student. It affects the modification of the existing programs and the creation of new ones. This book presents peer-reviewed articles from the 6th International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS 2020), held at Fez, Morocco. Traditionally, schools and colleges have measured this after the fact i.e. Predicting Students' Academic Performance and Main Behavioral Features using Data Mining Techniques Suad Almutairi 1, Hadil Shaiba , and Marija Bezbradica2 1Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia {sfalmutairi,hashaiba}@pnu.edu.sa Lately, machine learning techniques have been extensively used for prediction purpose. Findings showed that Interpersonal EI was the highest predictor of academic achievement followed by Intrapersonal EI. 0 0000006140 00000 n The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. 0000010058 00000 n It will also educate on how Artificial Neural Network can be used in predicting students academic performance. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Using Data Mining to Predict Secondary School Student Performance. This book presents the proceedings of the 4th International Conference of Reliable Information and Communication Technology 2019 (IRICT 2019), which was held in Pulai Springs Resort, Johor, Malaysia, on September 22–23, 2019. Predicting student's academic performance is one of the most important steps towards efficient education and university's profitability, especially for private ones which are fully funded by tuition fees. Students' performance can be predicted with the help of various available techniques. 0000004433 00000 n Increasing student success is a long term goal in all academic institutions. Minaei-Bidgoli (Minaei-Bidgoli,2003) used a combination of multiple classifiers to predict their final grade based on features extracted from logged . This paper designed an application to assist higher education institutions to predict their students‟ academic performance at an early stage before graduation and decrease students‟ dropout. Early detection of students at risk, along with preventive measures, can drastically improve their success. Data Mining is the most prevalent family of techniques to predict students' performance and is extensively Predicting students‟ academic performance is very crucial especially for higher educational institutions. Predicting students' academic performance in advance is of great importance for parents, management of higher education institutions and the student itself. Predicting Student Academic Performance . 0000002555 00000 n In particular, students' lack of achievement could be predicted by monitoring their first-year GPA. Prediction of student's performance became an urgent desire in most of educational entities and institutes. Including innovative studies on learning environments, self-regulation, and classroom management, this multi-volume book is an ideal source for educators, professionals, school administrators, researchers, and practitioners in the field of ... The study aimed to determine if any of the entry requirements such as Ordinary Level (OL) results, Unified Tertiary Matriculation Examination (UTME) scores or Post-UTME (PUTME) scores could predict an outstanding academic performance of first-year undergraduate students admitted into the Faculty of Science in the Kaduna State University, Kaduna. Predicting Students' Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course. With accelerated IT development and lower prices . The main objective of the admission system is to determine the candidates who would likely perform well . 44 25 Neural Network, Bayesian network etc. Oladokun, Ph.D. . In order to achieve a decisional database, many steps need to be taken which are explained in this thesis. This work investigates the efficiency, scalability, maintenance and interoperability of data mining techniques. Intelligent computing Google Scholar Although predicting students' performance is widely studied, it still a challenge and complex process because students' performance influenced by different features such as demographic, social, academic, economic, and other environmental features [5, 6]. This book will equip you with the tools you'll need to face the frustrations you're sure to encounter as an educator, while enabling to you find renewed purpose and meaning as you influence your students to be the best they can be. %PDF-1.4 %���� to predict student performance. Fourth International Conference on Information and Communication Technology for Competitive Strategies targets state-of-the-art as well as emerging topics pertaining to information and communication technologies (ICTs) and effective ... Underpinned by research indicating that students are more likely to continue with higher education if they are engaged in their studies and have developed networks and relationships with their fellow students, this book presents best ... Other than that, article by Baijam & Lenin (2019), a total of 79 observation were selected consist of 11 different attributes. The research. Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, ... This book examines standards-based education reform and reviews the research on student assessment, focusing on the needs of disadvantaged students covered by Title I. With examples of states and districts that have track records in new ... Selection of a right academic program at right time can save time, efforts and resources of both parents and educational institutions. mining classification methods to predict SAP (student academic performance). recommender system to predict the academic performance of students at the early stage by using classification algorithms. The two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th International Conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 International Conference, AIAI 2011, held ... Dropout prediction in That is where performance prediction becomes important. Predicting students academic performance from wellness status markers using machine learning techniques Rabiu Muazu Musa 1* , Muhammad Zuhaili Suhaimi 1 , Azlina Musa 1 ,Mohamad Razali Abdullah 2 , Anwar P. P. Abdul Majeed 3 ,Ahmad Bisyri Husin Musawi Maliki 4

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