Undergraduate Students’ Effectiveness in an Institution With High Dropout Index (original) (raw)
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Procedia Computer Science, 2016
Educational data mining is a growing field that uses the data obtained from educational information systems to discover knowledge and find answers to questions and problems concerning the education system. High dropout rates and poor academic performance among students are examples of the most common issues that affect the reputation of an educational institution. Students' academic records can be analyzed to explore the factors behind these phenomena. This paper discusses the building of a model to predict the performance of students in a programming course based on their grades in courses in other subjects. A classification based on an association rules algorithm is used to build a classifier to help evaluate the student's performance in the programming course. This model aims to reduce dropout levels by helping student predict their likelihood of success in a course before they enroll in it. In addition, course instructors will be able to enhance student performance in the course by better estimating their abilities to learn the subject matter and adjusting their teaching strategies and methods.
Analysis of Students Performance: Input to Program Enhancement of Students in Computing
International Journal of Information and Education Technology, 2019
The increase of students attending tertiary levels creates challenges for educational institutions to keep track on university status through student's academic performance. This study is a prospective investigation of the academic predictors of academic performance of Bachelor of Science in Information Technology students of selected university in the Philippines. It aims to analyze the student's performance using predictive data mining techniques, specifically, on classification. The results of the study shows that students' failure or success in passing their enrolled professional course has nothing to do with their gender. Moreover, consideration in the number of units in designing the curriculum should also be considered since data shows that a very high passing rate for summer class was evident, the period when the students have less course load.
Analyzing Students’ Academic Performance through Educational Data Mining
3C Tecnología_Glosas de innovación aplicadas a la pyme
Predicting students' performance is a very important task in any educational system. Therefore, to predict the learner's behavior towards studies many data mining techniques are used like clustering, classification, regression. In this paper, new student's performance prediction model and new features are introduced that have a great influence on student's academic achievement i.e. student absence days in class and parents' involvement in the learning process. In this paper, considerable attention is on the punctuality of students and the effect of participation of parents in the learning process. This category of features is concerned with the learner's interaction with the e-learning management system. Three different classifiers such as Naive Bayes, Decision Tree, and Artificial Neural Network are used to examine the effect of these features on students' educational performance. The accuracy of the proposed model achieved up to 10% to 15% and is much improved as compared to the results when such features are removed.
A Brief Review about Educational Data Mining applied to Predict Student’s Dropout
Anais da V Escola Regional de Sistemas de Informação do Rio de Janeiro, 2018
Educational Data Mining (EDM) may be a very useful technique as much to understand student behavior as to plan and manage government investments in education. EDM helps to analyzes and to expose the hidden information of educational data. Particularly, an important application of EDM is to predict or analyze the students' dropout. This problem affects several educational institutions in Brazil and the world, and identify its origin has been a relevant research motivator. This paper presents a brief introduction about EDM applied to predict students' dropout and analyzes some important articles during the period from 2013 to 2018.
Students’ Academic Performance and Dropout Prediction
MALAYSIAN JOURNAL OF COMPUTING, 2019
Students' Academic Performance (SAP) is an important metric in determining the status of students in any academic institution. It allows the instructors and other education managers to get an accurate evaluation of the students in different courses in a particular semester and also serve as an indicator to the students to review their strategies for better performance in the subsequent semesters. Predicting SAP is therefore important to help learners in obtaining the best from their studies. A number of researches in Educational Psychology (EP), Learning Analytics (LA) and Educational Data Mining (EDM) has been carried out to study and predict SAP, most especially in determining failures or dropouts with the goal of preventing the occurrence of the negative final outcome. This paper presents a comprehensive review of related studies that deal with SAP and dropout predictions. To group the studies, this review proposes taxonomy of the methods and features used in the literature for SAP and dropout prediction. The paper identifies some key issues and challenges for SAP and dropout predictions that require substantial research efforts. Limitations of the existing approaches for SAP and dropout prediction are identified. Finally, the paper exposes the current research directions in the area.
Contributions from Data Mining to Study Academic Performance of Students of a Tertiary Institute
American Journal of Educational Research, 2014
Education-oriented data mining allows to predict determined type of factor or characteristic of a case, phenomenon or situation. In this article the mining models used are described and the main results are discussed. Mining models of clustering, classification and association are considered especially. In all cases seeks to determine patterns of academic success and failure for students, thus predicting the likelihood of dropping them or having poor academic performance, with the advantage of being able to do it early, allowing addressing action to reverse this situation. This work was done in 2013 with information on the years 2009 to 2013, students of the subject Operating Systems tertiary career Superior Technical Analyst (TSAP) Higher Institute of Curuzú Cuatiá (ISCC), Corrientes, Argentina.
Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program
European Journal of Open, Distance and E-Learning, 2014
This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The collected data included 10 variables, which were gender, age, educational level, previous online experience, occupation, self efficacy, readiness, prior knowledge, locus of control, and the dropout status as the class label (dropout/not). In order to classify dropout students, four data mining approaches were applied based on k-Nearest Neighbour (k-NN), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN). These methods were trained and tested using 10-fold cross validation. The detection sensitivities of...
Predicting GPA and academic dismissal in LMS using educational data mining: A case mining
6Th National And 3Rd International Conference Of E -Learning And E -Teaching, 2012
In this paper, we describe an educational data mining (EDM) case study based on the data collected from learning management system (LMS) of elearning center and electronic education system of Iran University of Science and Technology (IUST). Our main goal is to illustrate the applications of EDM in the domain of e-learning and online courses by implementing a model to predict academic dismissal and also GPA of graduated students. The monitoring and support of freshmen and first year students are considered very significant in many educational institutions. Consequently, if there are some ways to estimate probability of dismissal, drop out and other challenges within the process of the graduation, and also capable tools to predict GPA or even semester by semester grades, the university officials can design and improve more efficient strategies for education systems especially for e-learning ones which include less known and more complicated problems. To achieve the mentioned goal, a common methodology of data mining has been utilized which is called CRISP. Our results show that there can be confident models for predicting educational attributes. Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community.
IEEE Access, 2020
This paper presents a web-based software tool for tutoring support of engineering students without any need of data scientist background for usage. This tool is focused on the analysis of students' performance, in terms of the observable scores and of the completion of their studies. For that purpose, it uses a data set that only contains features typically gathered by university administrations about the students, degrees and subjects. The web-based tool provides access to results from different analyses. Clustering and visualization in a low-dimensional representation of students' data help an analyst to discover patterns. The coordinated visualization of aggregated students' performance into histograms, which are automatically updated subject to custom filters set interactively by an analyst, can be used to facilitate the validation of hypotheses about a set of students. Classification of students already graduated over three performance levels using exploratory variables and early performance information is used to understand the degree of course-dependency of students' behavior at different degrees. The analysis of the impact of the student's explanatory variables and early performance in the graduation probability can lead to a better understanding of the causes of dropout. Preliminary experiments on data of the engineering students from the 6 institutions associated to this project were used to define the final implementation of the web-based tool. Preliminary results for classification and drop-out were acceptable since accuracies were higher than 90% in some cases. The usefulness of the tool is discussed with respect to the stated goals, showing its potential for the support of early profiling of students. Real data from engineering degrees of EU Higher Education institutions show the potential of the tool for managing high education and validate its applicability on real scenarios. INDEX TERMS Drop-out prediction, educational data mining, performance prediction, visual analytics.