The effectiveness of using deep learning algorithms in predicting students achievements

Akour, Mohammed; Sghaier, Hiba Al; Qasem, Osama Al

doi:10.11591/ijeecs.v19.i1.pp388-394

Cited by 43 publications

(40 citation statements)

References 16 publications

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“…The experiments applied to eight open-source projects show that the proposed model outperforms state-of-the-art defect prediction approaches. Deep learning algorithms utilization in the prediction areas reveals promising results; some of the most recent published articles used CNN and MLP without concentrate on addressing the effectiveness of manipulating the main factors that might have direct influence of the performance of prediction [46], [47].…”

Section: Deep Learningmentioning

confidence: 99%

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

2020

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The discovery of software faults at early stages plays an important role in improving software quality; reduce the costs, time, and effort that should be spent on software development. Machine learning (ML) have been widely used in the software faults prediction (SFP), ML algorithms provide varying results in terms of predicting software fault. Deep learning achieves remarkable performance in various areas such as computer vision, natural language processing, speech recognition, and other fields. In this study, two deep learning algorithms are studied, Multi-layer perceptron's (MLPs) and Convolutional Neural Network (CNN) to address the factors that might have an influence on the performance of both algorithms. The experiment results show how modifying parameters is directly affecting the resulting improvement, these parameters are manipulated until the optimal number for each of them is reached. Moreover, the experiments show that the effect of modifying parameters had an important role in prediction performance, which reached a high rate in comparison with the traditional ML algorithm. To validate our assumptions, the experiments are conducted on four common NASA datasets. The result shows how the addressed factors might increase or decrease the fault detection rate measurement. The improvement rate was as follows up to 43.5% for PC1, 8% for KC1, 18% for KC2 and 76.5% for CM1.

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Section: Deep Learningmentioning

confidence: 99%

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

2020

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“…For example, in the study [15], the authors proved that the Deep Dense neural network improve the accuracy of failure-prone student prediction. The convolutional neural network was investigated for the same researching area in [16]. These deep learning techniques were utilized for predicting student final performance in [8] and for predicting students' future development in [17].…”

Section: Related Workmentioning

confidence: 99%

“…The authors announced the good performance of Deep Dense network and also compared the proposed models with other algorithms such as the decision tree algorithm C4.5, random forest, logistic regression and support vector machine. Deep learning models for SAPP problem based on the Long shortterm memory network and convolutional neural network were introduced in [5,20,21]. The results showed that the proposed deep learning models have superior results compared to other tested algorithms.…”

Section: Related Workmentioning

confidence: 99%

Optimize the Combination of Categorical Variable Encoding and Deep Learning Technique for the Problem of Prediction of Vietnamese Student Academic Performance

Hien¹,

Thi²,

Kim³

et al. 2020

IJACSA

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Deep learning techniques have been successfully applied in many technical fields such as computer vision and natural language processing, and recently researchers have paid much attention to the application of this technology in socioeconomic problems including the student academic performance prediction (SAPP) problem. In this specialization, this study focusses on both designing an appropriate Deep learning model and handling categorical input variables. In fact, categorical data variables are quite popular in student academic performance prediction problem, and deep learning technique in particular or artificial neural network in general only work well with numerical data variables. Therefore, this study investigates the performance of the combination categorical encoding methods including label encoding, one-hot encoding and "learned" embedding encoding with deep learning techniques including Deep Dense neural network and Long short-term memory neural network for SAPP problem. In experiment, this study compared these proposed models with each other and with some prediction methods based on other machine learning algorithms at the same time. The results showed that the categorical data transformation method using the "learned" embedding encoding improved performance of the deep learning models, and its combination with long short-term memory network gave an outstanding result for the researched problem.

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“…Different methodologies are designed and built to help developers in the refactoring process such as code smells detection strategies [12], logic meta-programming [13], invariant mining [14] and search-based [15,16]. Moreover, machine learning is harnessed in the area of prediction and shows noticeable performance in terms of prediction in various fields as computer vision, defect prediction, natural language processing, code comprehension, bioinformatics, speech recognition, and finance [17][18][19][20][21][22][23][24]. Several machine learning algorithms are utilized in code refactoring prediction at class and method level as well [25,26].…”

Section: Introductionmentioning

confidence: 99%

Harnessing deep learning algorithms to predict software refactoring

2020

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During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying the targeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers' skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring prediction at the class level. The effectiveness of the proposed model is evaluated using a very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring prediction.

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The effectiveness of using deep learning algorithms in predicting students achievements

Cited by 43 publications

References 16 publications

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

Optimize the Combination of Categorical Variable Encoding and Deep Learning Technique for the Problem of Prediction of Vietnamese Student Academic Performance

Harnessing deep learning algorithms to predict software refactoring

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