Predicting Student Performance from Combined Data Sources

Wolff, Annika; Zdráhal, Zdeněk; Herrmannova, Drahomira; Knoth, Petr

doi:10.1007/978-3-319-02738-8_7

Cited by 28 publications

(26 citation statements)

References 9 publications

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“…However, in the last 10 years, Educational Data Mining (EDM) has emerged as a new application area concerned with developing, researching and applying computerized methods to detect patterns in large collections of educational data that would otherwise be hard or impossible to analyse because of the enormous volume of data within which they exist (Baker & Yacef, 2009;. One of the oldest and well-known applications of EDM is predicting student performance in which the goal is to estimate the unknown value of a student's performance, knowledge, score or mark (Romero & Ventura, 2007;Romero & Ventura, 2010;Wolff et al, 2014;Yoo & Kim, 2014). Classification is the most commonly employed technique for resolving this problem by discovering predictive models of student performance based on historical data of the students (Hämäläine & Vinni, 2011;Vialardi et al, 2011;.…”

Section: Introductionmentioning

confidence: 99%

Early dropout prediction using data mining: a case study with high school students

et al. 2015

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Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well‐known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4–6 weeks of the course and trustworthy enough to be used in an early warning system.

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Section: Introductionmentioning

confidence: 99%

Early dropout prediction using data mining: a case study with high school students

et al. 2015

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“…Annika Wolff et al [4] have applied decision tree classifier and SVM method for prediction of student performance. The purpose of prediction model was to identify the student who will benefit the tutor interventions.…”

Section: Literature Surveymentioning

confidence: 99%

“…The knowledge obtained through the application of Educational Data Mining (EDM) [2][3][4][5] can be used for various things such as offer suggestions to academic planners in higher education institutes to enhance their decision-making process, take actions for underperforming students [6][7]. Because of benefit provided to learners i.e.…”

Section: Introductionmentioning

confidence: 99%

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Student Performance Prediction Model Based on Lion-Wolf Neural Network

Lakshmanan¹,

Geetha²,

Khalid³

et al. 2017

IJIES

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Early prediction of student performance helps to take action for better achievements of students. To achieve the better education standard, several attempts have been made to predict the performance of the student, but the prediction accuracy is not acceptable. To accomplish the enhanced prediction, neural network (NN) based method is proposed. In this paper, an approach to predict student academic performance in college education based on Lion-Wolf artificial neural network is proposed. Lion algorithm and Grey Wolf optimizer is integrated to develop a Lion-wolf training algorithm to find the optimal weight for every neuron in NN. The proposed prediction model is validated based on Mean Squared Error (MSE) & Root Mean Square Error (RMSE) with the existing NN based prediction model. The experimental results show that performance of proposed prediction model is improved compared to existing prediction model with MSE of 5.25 and RMSE of 2.3.

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“…Background Predicting a student's performance has been studied previously in educational data mining research in the context of student attrition. Wolff et al (2014) explored the effectiveness of predictive modelling methods for identifying students who will benefit most from tutor interventions in distance learning. The students and tutor will not meet face to face in the case of distance learning.…”

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confidence: 99%

Predicting Students’ Academic Performance in the University Using Meta Decision Tree Classifiers

Shanthini

Vinodhini

Chandrasekaran

2018

Journal of Computer Science

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Student performance prediction is an area of concern for educational institutions. At the University level learning system, the method or rule adopted to identify the candidates who pass or fail differs depending on various factors such as the course, the department of study and so on. Predicting the result of a student in a course is an issue that has recently been addressed using machine learning techniques. The focus of this work is to find a way to predict a student's academic performance in the University using the machine learning approach. This is done by using the previous records of the student rather than applying course dependent formulae to predict the student's final grade. In this work, meta decision tree classifier techniques based on four representative learning algorithms, namely Adaboost, Bagging, Dagging and Grading are used to construct different decision trees. REPTree is used as the decision tree method for meta learning. These four meta learning methods have been compared separately with respect to the training and test sets. Adaboost is found to be the best meta decision classifier for predicting the student's result based on the marks obtained in the semester.

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Predicting Student Performance from Combined Data Sources

Cited by 28 publications

References 9 publications

Early dropout prediction using data mining: a case study with high school students

Early dropout prediction using data mining: a case study with high school students

Student Performance Prediction Model Based on Lion-Wolf Neural Network

Predicting Students’ Academic Performance in the University Using Meta Decision Tree Classifiers

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