Data mining approach to predicting the performance of first year
2018. 12. 3.· CART). By applying knowledge discovery techniques using data mining, the study by (Burgos et al. 2018) evaluated student records from e-learning platforms for students taking distance learning courses using predictive models. After model implementation, a teaching plan was developed and deployed, and the teaching plan was able to reduceData mining approach to predicting the performance of,2018. 12. 3.· Educational data mining is a data driven process for identifying student learning issues and performance trends in institutions of learning (Bucos and Drăgulescu 2018 ). Machine learning has found application in studying the academic behaviour and performanceA Data Mining Approach for Predicting Academic Success –,2019. 1. 29.· Abstract. The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the,Student’s Employability Prediction Using Data Mining,2018. 4. 21.· Predicting student employability can help identify the students who are at risk of unemployment and, Data mining techniques are effective for implementation on, D. Kabakchieva“Predicting student performance by using data mining , methods for classification, ” Cybernatics and Information Technologies,,Evaluating Performance and Dropouts of Undergraduates,This paper presents an architecture that uses educational data mining techniques to predict and identify those who face the risk of dropping out. The approach may assist educational managers in supervising the development of students at the end of each academic term, identifying the ones with difficulties to fulfill their requirements.Evaluating the effectiveness of educational data mining,2017. 8. 1.· In is proposed an approach for predicting students' performance based on three EDM techniques: instance-based learning Classifier, Decision Tree and Naive Bayes. The experiment was performed in a distance education course and it was performed in three steps, which correspond to different stages in a semester.
Predicting students’ academic performance using
Different approaches have been applied to predicting student academic performance, including traditional mathematical models and modern data mining techniques. In these approaches, a set of mathematical formulas was used to describe the quantitative relationships between outputs and inputs ( i.e. , predictor variables).PREDICTING STUDENTS ACADEMIC PERFORMANCE USING,Different approaches have been applied to predicting student academic performance, including traditional mathematical models and modern data mining techniques. In these approaches, a set of mathematical formulas was used to describe the quantitative relationships between outputs and inputs (i.e., predictor variables).The impact of engineering students' performance in the,2019. 2. 1.· Educational data mining is the use of data mining techniques to extract vital information from a dataset generated within the, Predicting student performance by using data mining methods for classification. Cybern, data on academic performances of engineering undergraduates in Nigerian private university. Data,Study of general education diploma students’ performance,2018. 10. 24.· Data mining is “the process of finding patterns from a large amount of data by applying some techniques.” 4 Data mining places great scientific attention to detail in large volumes of data. Its use of arithmetic calculations makes it robust and it precisely reveals information patterns from row data. Data mining results could add incredible value to educational organizations.Data mining approach to predicting the performance of first,2018. 12. 3.· Data mining approach to predicting the performance of first year student in a university using the admission requirements Aderibigbe Israel Adekitan1 & Etinosa Noma-Osaghae1 Received: 17 September 2018/Accepted: 13 November 2018/ # Springer Science+Business Media, LLC, part of Springer Nature 2018Data mining approach to predicting the performance of,2018. 12. 3.· Data mining: A prediction for Student's performance using classification method. World Journal of Computer Application and Technology, 2, 43–47. Google Scholar Ahuja, R. & Kankane, Y. (2018). Predicting the probability of student's degree completion by using different data mining techniques. 474–477.
Student’s Employability Prediction Using Data Mining
2018. 4. 21.· Predicting student employability can help identify the students who are at risk of unemployment and, Data mining techniques are effective for implementation on, D. Kabakchieva“Predicting student performance by using data mining , methods for classification, ” Cybernatics and Information Technologies,,Educational Data Mining Survey for Predicting Student’s,2019. 8. 1.· Data Mining is the most suitable technique to analyze the student’s performance. Lots of work is already done in this direction, but still there are many parameters to be considered. This paper presents the survey on Educational Data Mining. Also presents the finding of that research on student performance. Provided information is very,PREDICTING STUDENTS ACADEMIC PERFORMANCE,Different approaches have been applied to predicting student academic performance, including traditional mathematical models and modern data mining techniques. In these approaches, a set of mathematical formulas was used to describe the quantitative relationships between outputs and inputs (i.e., predictor variables).PREDICTING STUDENTS ACADEMIC PERFORMANCE,Different approaches have been applied to predicting student academic performance, including traditional mathematical models and modern data mining techniques. In these approaches, a set of mathematical formulas was used to describe the quantitative relationships between outputs and inputs ( i.e. , predictor variables).(PDF) PREDICTING ACADEMIC SUCCESS OF,PREDICTING ACADEMIC SUCCESS OF ARCHITECTURE UNDERGRADUATES AT KADUNA STATE, indicates the lowest predictor of academic performance is occurrence of not succeeding is in 400L with likewise supported by several studies across a single, Predict Prospective Mathematics students using data mining techniques.Study of general education diploma students’,2018. 10. 24.· Data mining is “the process of finding patterns from a large amount of data by applying some techniques.” 4 Data mining places great scientific attention to detail in large volumes of data. Its use of arithmetic calculations makes it robust and it precisely reveals information patterns from row data. Data mining results could add incredible value to educational organizations.
Predicting Students Academic Performance Using
2018. 1. 15.· PREDICTING STUDENTS ACADEMIC PERFORMANCE USING ARTIFICIAL NEURAL NETWORK. CHAPTER ONE. INTRODUCTION. 1.1 BACKGROUND TO THE STUDY. 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 tertiaryPREDICTING STUDENTS ACADEMIC PERFORMANCE,PREDICTING STUDENTS ACADEMIC PERFORMANCE USING ARTIFICIAL NEURAL NETWORK. CHAPTER ONE. INTRODUCTION. 1.1 BACKGROUND TO THE STUDY. 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) remainData mining approach to predicting the performance of,2018. 12. 3.· Data mining: A prediction for Student's performance using classification method. World Journal of Computer Application and Technology, 2, 43–47. Google Scholar Ahuja, R. & Kankane, Y. (2018). Predicting the probability of student's degree completion by using different data mining techniques. 474–477.Predicting Students’ Performance and Problem Solving Behavior from iList Log Data,2020. 9. 28.· Log files of an ITS generally contain information related to users’ performance and problem solving behavior while using the system. Data mining techniques can be applied to information extracted from log files to build models that can predict future performance of users (Cetintas, Si, Xin, & Hord, 2010; Romero, Ventura, Espejo, & Hervás, 2008).PREDICTING STUDENTS ACADEMIC PERFORMANCE,Different approaches have been applied to predicting student academic performance, including traditional mathematical models and modern data mining techniques. In these approaches, a set of mathematical formulas was used to describe the quantitative relationships between outputs and inputs ( i.e. , predictor variables).Student performance analysis and prediction in classroom,2020. 7. 1.· Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of e ducational datasets further catalyse this interestingness, especially in online learning.
Survey on Predicting Educational Trends by Analyzing the
2019. 3. 22.· Industries and Institutions use complex computational techniques to improve and identify their growth trend by using... Driving decisions using data is being followed in most of the business units., Survey on Predicting Educational Trends by Analyzing the Academic PerformancePredicting academic performance: a systematic,2018. 7. 2.· Amirah Mohamed Shahiri, Wahidah Husain, et al. 2015. A review on predicting student’s performance using data mining techniques. Procedia Computer Science 72 (2015), 414–422. Google Scholar Cross Ref; Ashkan Sharabiani, Fazle Karim, Anooshiravan Sharabiani, Mariya Atanasov, and Houshang Darabi. 2014.Study of general education diploma students’,2018. 10. 24.· Data mining is “the process of finding patterns from a large amount of data by applying some techniques.” 4 Data mining places great scientific attention to detail in large volumes of data. Its use of arithmetic calculations makes it robust and it precisely reveals information patterns from row data. Data mining results could add incredible value to educational organizations.Predicting Students Academic Performance Using,2018. 1. 15.· PREDICTING STUDENTS ACADEMIC PERFORMANCE USING ARTIFICIAL NEURAL NETWORK. CHAPTER ONE. INTRODUCTION. 1.1 BACKGROUND TO THE STUDY. 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 tertiaryPREDICTING STUDENTS ACADEMIC PERFORMANCE,PREDICTING STUDENTS ACADEMIC PERFORMANCE USING ARTIFICIAL NEURAL NETWORK. CHAPTER ONE. INTRODUCTION. 1.1 BACKGROUND TO THE STUDY. 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) remainEvaluating the effectiveness of educational data mining,bib6 H. Bydzovska, Acomparative analysis of techniques for predicting stu- dent performance, International Educational Data Mining Society, 2016. Google Scholar bib7 R. Caruana, A. Niculescu-Mizil, An empirical comparison of supervised learning algorithms, in: Proceedings of the 23rd international conference on machine learning ICML 06, ACM, New York, NY, USA, 2006, pp. 161-168.
