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.

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 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>

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.

Machine Learning Techniques for Internet of Things

2019 ◽  
pp. 160-180
Author(s):  
P. Priakanth ◽  
S. Gopikrishnan
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Independent Learning ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Guided Learning ◽  
Learning Techniques ◽  
Learning Machine

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?

A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics

2021 ◽  
pp. 416-428
Author(s):  
R. Suganya ◽  
Rajaram S. ◽  
Kameswari M.
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Data Science ◽  
Learning Algorithms ◽  
Research Literature ◽  
Machine Learning Algorithms ◽  
Thyroid Disorder ◽  
Classification Models ◽  
Indian Women ◽  
Using Data

Currently, thyroid disorders are more common and widespread among women worldwide. In India, seven out of ten women are suffering from thyroid problems. Various research literature studies predict that about 35% of Indian women are examined with prevalent goiter. It is very necessary to take preventive measures at its early stages, otherwise it causes infertility problem among women. The recent review discusses various analytics models that are used to handle different types of thyroid problems in women. This chapter is planned to analyze and compare different classification models, both machine learning algorithms and deep leaning algorithms, to classify different thyroid problems. Literature from both machine learning and deep learning algorithms is considered. This literature review on thyroid problems will help to analyze the reason and characteristics of thyroid disorder. The dataset used to build and to validate the algorithms was provided by UCI machine learning repository.

Machine Learning Techniques for Internet of Things

2021 ◽  
pp. 1490-1506
Author(s):  
P. Priakanth ◽  
S. Gopikrishnan
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Independent Learning ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Guided Learning ◽  
Learning Techniques ◽  
Learning Machine

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?

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