A Survey on Pre-Processing Educational Data

Romero, Cristóbal; Romero, José Raúl; Ventura, Sebastián

doi:10.1007/978-3-319-02738-8_2

Cited by 80 publications

(58 citation statements)

References 53 publications

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“…Data preprocessing is the step before applying data mining algorithm, it transforms the original data into a suitable shape to be used by a particular mining algorithm. Data preprocessing includes different tasks as data cleaning, feature selection and data transformation [23].…”

Section: Data Preprocessingmentioning

confidence: 99%

Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods

Amrieh¹,

Hamtini

Aljarah

2016

IJDTA

242

104

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Section: Data Preprocessingmentioning

confidence: 99%

Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods

Amrieh¹,

Hamtini

Aljarah

2016

IJDTA

242

104

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“…Attribute selection is "to identify and remove as much irrelevant and redundant information as possible" [9]. This subsection using the term attribute selection to distinguish with feature selection that will be used at the end of this section.…”

Section: Attribute Selectionmentioning

confidence: 99%

“…There are various techniques for normalization like binarizer, normalizer and standardscaler [12]. For e-learning variables, common technique like min-max can be used [9]. The technique converts all the data into values with range for example, 0 to 1.0.…”

Section: Data Transformationmentioning

confidence: 99%

Data pre-processing: a case study in predicting student’s retention in MOOC

Mohamad¹,

Ahmad²,

Sulaiman³

2018

J. Fundam and Appl Sci.

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Data pre-processing is a crucial phase prior to analytic task and yet rarely been discussed especially for e-learning data which has multilevel data. Providing a reliable data pre-processing is important to provide quality dataset. Therefore, this study investigates the problems arise in data pre-processing and in this case, for identifying the implement prediction task. A MOOC dataset is selected for the data pre process in generating the summary of dataset is explained and the ultimate aim is to produce a dataset with features that are ready for data model and suggestions, which can be applied to support more comprehensible tools for educational domain who is the end user. Subsequently, the data pre efficient for predicting student's

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“…In paper [6] the author used future extraction approach to know the grades of candidate. And used different classifiers to compare their performance and stated that Genetic algorithm is significant helpful in identifying the risk of the students early, particularly in very large classes, and allow the instructor to provide appropriate advice in a timely manner to the student.…”

Section: Literature Surveymentioning

confidence: 99%

Applying Data Mining Techniques on Acadamic Institutional System Using Weka

2018

IJRTER

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Abstract-These days, educational institutions and organizations are generating huge amount of data, more than the people can read in their lifetime. It is not possible for a person to learn, understand, decode, and interpret to find valuable information. Data mining is one of the most popular method which can be used to identify hidden patterns from large databases. User can extract historical, hidden details, and previously unknown information, from large repositories by applying required mining techniques. There are two algorithms which can be used to classify and predict, such as supervised learning and unsupervised learning. Classification is a technique which performs an induction on current data (existing data) and predicts future class. The main objective of classification is to make an unknown class to known class by consulting its neighbor class. therefore it is called as supervised learning, it builds the classifier by consulting with the known class labels such as k-nearest neighbor algorithm (k-NN), Naïve Bayes (NB), support vector machine (SVM), decision tree. Clustering is an unsupervised learning that builds a model to group similar objects into categories without consulting a class label. The main objective of clustering is find the distance between objects like nearby and faraway based on their similarities and dissimilarities it groups the objects and detects outliers. In this paper Weka tool is used to analyze by applying preprocessing, classification on institutional academic result of under graduate students of computer science & engineering.

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A Survey on Pre-Processing Educational Data

Cited by 80 publications

References 53 publications

Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods

Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods

Data pre-processing: a case study in predicting student’s retention in MOOC

Applying Data Mining Techniques on Acadamic Institutional System Using Weka

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