scholarly journals Using Machine Learning for Prediction Students Failure in Morocco: an Application of the CRISP-DM Methodology

2021 ◽  
Vol 15 ◽  
pp. 344-352
Author(s):  
Nada Lebkiri ◽  
Mohamed Daoudi ◽  
Zakaria Abidli ◽  
Joumana Elturk ◽  
Abdelmajid Soulaymani ◽  
...  
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Data Mining ◽  
Learning Process ◽  
Teaching And Learning ◽  
Higher Education Institutions ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Student Failure ◽  
University Course

Student failure prediction is one of the main topics in university learning contexts, as it helps to avoid failure in higher education institutions and provides a basis to make the teaching and learning process more effective, efficient and reliable. The overall aim of this study is to identify students who are susceptible to fail a given university course. This research paper reports the implementation of an Educational Data Mining project based on the CRISP-DM methodology. The data was collected from the APOGEE system of Ibn Tofail University, a form and specifications of the tested courses. The business goal of this paper is to develop a model that can identify students who are susceptible to failure in a given academic course. Such a model helps prevent failure in higher education institutions and provides a basis for making the teaching and learning process more effective, efficient and reliable. Most common machine learning algorithms in the field of Educational Data Mining were used. The results of our research showed that the proposed method was able to achieve an overall accuracy of 97% in predicting students at potential failure.

scholarly journals Predicting Student Failure in University Examination using Machine Learning Algorithms

2020 ◽  
Vol 9 (5) ◽  
pp. 956-959
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Performance Management ◽  
Student Performance ◽  
Learning Algorithms ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Social Characteristics ◽  
Student Failure

Student Performance Management is one of the key pillars of the higher education institutions since it directly impacts the student’s career prospects and college rankings. This paper follows the path of learning analytics and educational data mining by applying machine learning techniques in student data for identifying students who are at the more likely to fail in the university examinations and thus providing needed interventions for improved student performance. The Paper uses data mining approach with 10 fold cross validation to classify students based on predictors which are demographic and social characteristics of the students. This paper compares five popular machine learning algorithms Rep Tree, Jrip, Random Forest, Random Tree, Naive Bayes algorithms based on overall classifier accuracy as well as other class specific indicators i.e. precision, recall, f-measure. Results proved that Rep tree algorithm outperformed other machine learning algorithms in classifying students who are at more likely to fail in the examinations.

scholarly journals Educational data mining for student placement prediction using machine learning algorithms

2017 ◽  
Vol 7 (1.2) ◽  
pp. 43 ◽  
Author(s):  
K. Sreenivasa Rao ◽  
N. Swapna ◽  
P. Praveen Kumar
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Data Mining ◽  
Recursive Partitioning ◽  
Learning Algorithms ◽  
Educational Data Mining ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Conditional Inference ◽  
Higher Education Organizations

Data Mining is the process of extracting useful information from large sets of data. Data mining enablesthe users to have insights into the data and make useful decisions out of the knowledge mined from databases. The purpose of higher education organizations is to offer superior opportunities to its students. As with data mining, now-a-days Education Data Mining (EDM) also is considered as a powerful tool in the field of education. It portrays an effective method for mining the student’s performance based on various parameters to predict and analyze whether a student (he/she) will be recruited or not in the campus placement. Predictions are made using the machine learning algorithms J48, Naïve Bayes, Random Forest, and Random Tree in weka tool and Multiple Linear Regression, binomial logistic regression, Recursive Partitioning and Regression Tree (rpart), conditional inference tree (ctree) and Neural Network (nnet) algorithms in R studio. The results obtained from each approaches are then compared with respect to their performance and accuracy levels by graphical analysis. Based on the result, higher education organizations can offer superior training to its students.

Pharmacy Education: Moving Early and Responding Strongly

2020 ◽  
Vol 35 (11) ◽  
pp. 482-483
Author(s):  
Bhojraj Suresh
Keyword(s):  
Higher Education ◽  
Learning Process ◽  
Teaching And Learning ◽  
Higher Education Institutions ◽  
Pharmacy Education ◽  
Single Solution

In a country with 1.3 billion population and about 1,000 universities and more than 50,000 higher education institutions and enrollment of 30 million students and a diverse demography, to find a single solution that can keep the teaching and learning process continued was a mammoth task. The focus was on engaging the students and completing the academic sessions. Having crossed the phase of teaching and learning, the institutions are now grappling as to how the examinations and progression of these students be considered. Learning from global experiences and a bit of innovation, this too has been overcome with methodologies that would have been unacceptable earlier. The challenge presently is the preparedness of higher education in the post pandemic period.

scholarly journals Prediction and Analysis of Student Performance in Secondary Education Based on Data Mining and Machine Learning Techniques

2020 ◽  
pp. 294-301
Author(s):  
Meenal Joshi ◽  
Shiv Kumar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Secondary School ◽  
Student Performance ◽  
Learning Algorithms ◽  
Research Work ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Student’S Performance

<p>According to modern era education is the key to achieve success in the future; it develops a human personality, thoughts, and social skills. The purpose of this research work is to focus on educational data mining (EDM) through machine learning algorithms. EDM means to discover hidden knowledge and pattern about student's performance. Machine learning can be useful to predict the learning outcomes of students. From last few years, several tools have been used to judge the student's performance from different points of view like the student's level, objectives, techniques, algorithms, and different methods. In this paper, predicting and analyzing student performance in secondary school is conducted using data mining techniques and machine learning algorithms such as Naive Bayes, Decision Tree algorithm J48, and Logistic Regression. For this the collection of dataset from "Secondary School" and then filtration is applying on desired values using WEKA, tool.</p>

Machine Learning in Higher Education

2020 ◽  
pp. 27-46
Author(s):  
Garima Jaiswal ◽  
Arun Sharma ◽  
Reeti Sarup
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Data Mining ◽  
At Risk ◽  
Educational Data Mining ◽  
Training Data ◽  
Pedagogical Practices ◽  
Dropping Out ◽  
Support Vector ◽  
Class Labels

Machine learning aims to give computers the ability to automatically learn from data. It can enable computers to make intelligent decisions by recognizing complex patterns from data. Through data mining, humongous amounts of data can be explored and analyzed to extract useful information and find interesting patterns. Classification, a supervised learning technique, can be beneficial in predicting class labels for test data by referring the already labeled classes from available training data set. In this chapter, educational data mining techniques are applied over a student dataset to analyze the multifarious factors causing alarmingly high number of dropouts. This work focuses on predicting students at risk of dropping out using five classification algorithms, namely, K-NN, naive Bayes, decision tree, random forest, and support vector machine. This can assist in improving pedagogical practices in order to enhance the performance of students predicted at risk of dropping out, thus reducing the dropout rates in higher education.

scholarly journals The uses of educational data mining in academic performance analysis at higher education institutions (case study at UNJANI)

2021 ◽  
Vol 11 (1) ◽  
pp. 26-35
Author(s):  
Yulison Herry Chrisnanto ◽  
◽  
Gunawan Abdullah ◽  
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Higher Education Institutions ◽  
Educational Data Mining ◽  
Strategic Decision ◽  
Data Set ◽  
Study Results ◽  
Mining Model

Education is an important thing in a person's life, because by having adequate education, one's life will be better. Education can be obtained formally through formal institutions that constructively provide a person's abilities academically. This study aims to determine student performance in terms of academic and non-academic domains at a certain time during their education using techniques in data mining (DM) which are directed towards academic data analysis. Academic performance is delivered through the Educational Data Mining (EDM) integrated data mining model, in which the techniques used include classification (ID3, SVM), clustering (k-Means, k-Medoids), association rules (Apriori) and anomaly detection (DBSCAN). The data set used is academic data in the form of study results over a certain period of time. The results of EDM can be used for analysis related to academic performance which can be used for strategic decision making in aca-demic management at higher education institutions. The results of this study indicate that the use of several techniques in data mining together can maximize the ability to analyze academic performance with the same data source and produce different analysis patterns.

scholarly journals Implementing AutoML in Educational Data Mining for Prediction Tasks

2019 ◽  
Vol 10 (1) ◽  
pp. 90 ◽  
Author(s):  
Maria Tsiakmaki ◽  
Georgios Kostopoulos ◽  
Sotiris Kotsiantis ◽  
Omiros Ragos
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Outcomes ◽  
Learning Strategies ◽  
Data Science ◽  
Learning Algorithms ◽  
Educational Data Mining ◽  
Search Space ◽  
Specific Model ◽  
Machine Learning Algorithms

Educational Data Mining (EDM) has emerged over the last two decades, concerning with the development and implementation of data mining methods in order to facilitate the analysis of vast amounts of data originating from a wide variety of educational contexts. Predicting students’ progression and learning outcomes, such as dropout, performance and course grades, is regarded among the most important tasks of the EDM field. Therefore, applying appropriate machine learning algorithms for building accurate predictive models is of outmost importance for both educators and data scientists. Considering the high-dimensional input space and the complexity of machine learning algorithms, the process of building accurate and robust learning models requires advanced data science skills, while is time-consuming and error-prone in most cases. In addition, choosing the proper method for a given problem formulation and configuring the optimal parameters’ values for a specific model is a demanding task, whilst it is often very difficult to understand and explain the produced results. In this context, the main purpose of the present study is to examine the potential use of advanced machine learning strategies on educational settings from the perspective of hyperparameter optimization. More specifically, we investigate the effectiveness of automated Machine Learning (autoML) for the task of predicting students’ learning outcomes based on their participation in online learning platforms. At the same time, we limit the search space to tree-based and rule-based models in order to achieving transparent and interpretable results. To this end, a plethora of experiments were carried out, revealing that autoML tools achieve consistently superior results. Hopefully our work will help nonexpert users (e.g., educators and instructors) in the field of EDM to conduct experiments with appropriate automated parameter configurations, thus achieving highly accurate and comprehensible results.

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