scholarly journals Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

2021 ◽  
Vol 13 (17) ◽  
pp. 9775
Author(s):  
Bashir Khan Yousafzai ◽  
Sher Afzal ◽  
Taj Rahman ◽  
Inayat Khan ◽  
Inam Ullah ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Student Performance ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Educational Data Mining ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Superior Performance

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

scholarly journals Tinjauan Pustaka Sistematis: Implementasi Metode Deep Learning pada Prediksi Kinerja Murid

2021 ◽  
Vol 10 (2) ◽  
pp. 131-138
Author(s):  
Muhammad Haris Diponegoro ◽  
Sri Suning Kusumawardani ◽  
Indriana Hidayah
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
State Of The Art ◽  
Educational Data Mining ◽  
Short Term ◽  
Long Short Term Memory

Pemanfaatan machine learning yang merupakan salah satu implementasi dalam bidang artificial intelligence telah merambah ke berbagai bidang, salah satunya adalah bidang pendidikan. Dengan menggunakan kombinasi teknik machine learning, statistik, dan basis data, dapat dilakukan educational data mining untuk mengetahui pola yang ada dalam suatu dataset tertentu. Salah satu penggunaan educational data mining adalah untuk melakukan prediksi kinerja murid. Hasil dari prediksi kinerja murid dapat digunakan sebagai salah satu instrumen untuk melakukan monitoring dan evaluasi terhadap proses pembelajaran sehingga dapat membantu menentukan langkah-langkah lanjutan dalam rangka meningkatkan proses pembelajaran. Makalah ini bertujuan untuk mengetahui state of the art implementasi deep learning yang merupakan bagian dari machine learning pada konteks educational data mining, khususnya mengenai prediksi kinerja murid. Pada makalah ini disajikan systematic literature review untuk mengetahui variasi teknik atau algoritme deep learning yang digunakan beserta kinerja yang dicapai. Dari dua puluh publikasi ilmiah yang ditelusuri, rata-rata kinerja yang dicapai dalam melakukan prediksi adalah 89,85%. Mayoritas teknik yang digunakan adalah Deep Neural Network (DNN), Recurrent Neural Network (RNN), dan Long Short-Term Memory (LSTM) dengan fitur data demografis, perilaku, dan akademis.

scholarly journals Role of FCBF Feature Selection in Educational Data Mining

2020 ◽  
Vol 39 (4) ◽  
pp. 772-778
Author(s):  
Maryam Zaffar ◽  
Manzoor Ahmad Hashmani ◽  
K.S. Savita ◽  
Syed Sajjad Hussain Rizvi ◽  
Mubashar Rehman
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Prediction Models ◽  
Educational Data Mining ◽  
Action Plans ◽  
Factors Affecting ◽  
Academic Organization

The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.

scholarly journals An Improved Model for Analyzing Textual Sentiment Based on a Deep Neural Network Using Multi-Head Attention Mechanism

2021 ◽  
Vol 4 (4) ◽  
pp. 85
Author(s):  
Hashem Saleh Sharaf Al-deen ◽  
Zhiwen Zeng ◽  
Raeed Al-sabri ◽  
Arash Hekmat
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Short Term ◽  
Arbitrary Length ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Improved Model

Due to the increasing growth of social media content on websites such as Twitter and Facebook, analyzing textual sentiment has become a challenging task. Therefore, many studies have focused on textual sentiment analysis. Recently, deep learning models, such as convolutional neural networks and long short-term memory, have achieved promising performance in sentiment analysis. These models have proven their ability to cope with the arbitrary length of sequences. However, when they are used in the feature extraction layer, the feature distance is highly dimensional, the text data are sparse, and they assign equal importance to various features. To address these issues, we propose a hybrid model that combines a deep neural network with a multi-head attention mechanism (DNN–MHAT). In the DNN–MHAT model, we first design an improved deep neural network to capture the text's actual context and extract the local features of position invariants by combining recurrent bidirectional long short-term memory units (Bi-LSTM) with a convolutional neural network (CNN). Second, we present a multi-head attention mechanism to capture the words in the text that are significantly related to long space and encoding dependencies, which adds a different focus to the information outputted from the hidden layers of BiLSTM. Finally, a global average pooling is applied for transforming the vector into a high-level sentiment representation to avoid model overfitting, and a sigmoid classifier is applied to carry out the sentiment polarity classification of texts. The DNN–MHAT model is tested on four reviews and two Twitter datasets. The results of the experiments illustrate the effectiveness of the DNN–MHAT model, which achieved excellent performance compared to the state-of-the-art baseline methods based on short tweets and long reviews.

scholarly journals Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs

2019 ◽  
Vol 9 (24) ◽  
pp. 5539 ◽  
Author(s):  
Shaojie Qu ◽  
Kan Li ◽  
Bo Wu ◽  
Shuhui Zhang ◽  
Yongchao Wang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Student Achievement ◽  
Short Term Memory ◽  
Online Courses ◽  
Educational Data Mining ◽  
Prediction Method ◽  
Learning Processes ◽  
Achievement Prediction ◽  
Temporal Learning

With the development of data mining technology, educational data mining (EDM) has gained increasing amounts of attention. Research on massive open online courses (MOOCs) is an important area of EDM. Previous studies found that assignment-related behaviors in MOOCs (such as the completed number of assignments) can affect student achievement. However, these methods cannot fully reflect students’ learning processes and affect the accuracy of prediction. In the present paper, we consider the temporal learning behaviors of students to propose a student achievement prediction method for MOOCs. First, a multi-layer long short-term memory (LSTM) neural network is employed to reflect students’ learning processes. Second, a discriminative sequential pattern (DSP) mining-based pattern adapter is proposed to obtain the behavior patterns of students and enhance the significance of critical information. Third, a framework is constructed with an attention mechanism that includes data pre-processing, pattern adaptation, and the LSTM neural network to predict student achievement. In the experiments, we collect data from a C programming course from the year 2012 and extract assignment-related features. The experimental results reveal that this method achieves an accuracy rate of 91% and a recall of 94%.

scholarly journals Dynamic Malware Analysis with Feature Engineering and Feature Learning

2020 ◽  
Vol 34 (01) ◽  
pp. 1210-1217
Author(s):  
Zhaoqi Zhang ◽  
Panpan Qi ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Expert Knowledge ◽  
Malware Detection ◽  
Feature Representation ◽  
Feature Engineering ◽  
Malware Analysis ◽  
Neural Network Architecture

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released (“zero-day”) malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.

scholarly journals Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Yupei Zhang ◽  
Yue Yun ◽  
Rui An ◽  
Jiaqi Cui ◽  
Huan Dai ◽  
...  
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Performance Prediction ◽  
Educational Data Mining ◽  
Prediction Method ◽  
Data Set ◽  
Public Data ◽  
Personalized Education ◽  
Higher Education In China ◽  
Problem Formalization

Student performance prediction (SPP) aims to evaluate the grade that a student will reach before enrolling in a course or taking an exam. This prediction problem is a kernel task toward personalized education and has attracted increasing attention in the field of artificial intelligence and educational data mining (EDM). This paper provides a systematic review of the SPP study from the perspective of machine learning and data mining. This review partitions SPP into five stages, i.e., data collection, problem formalization, model, prediction, and application. To have an intuition on these involved methods, we conducted experiments on a data set from our institute and a public data set. Our educational dataset composed of 1,325 students, and 832 courses was collected from the information system, which represents a typical higher education in China. With the experimental results, discussions on current shortcomings and interesting future works are finally summarized from data collections to practices. This work provides developments and challenges in the study task of SPP and facilitates the progress of personalized education.

scholarly journals Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 392
Author(s):  
Nizar Ahmed ◽  
Fatih Dilmaç ◽  
Adil Alpkocak
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Multiclass Classification ◽  
Feature Representation ◽  
Classification Model ◽  
Biomedical Texts

This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical weighting schemes to combine with each element of WE vectors, which were term frequency (TF), inverse document frequency (IDF) and class probability (CP) methods. Thus, we built a multiclass classification model using a bidirectional long short-term memory (BLSTM) with deep neural networks for all investigated operations of feature vector combinations. We used MIMIC III and the PubMed dataset for the developing language model. To evaluate the performance of our weighted feature representation approaches, we conducted a set of experiments for examining multiclass classification performance with the deep neural network model and other state-of-the-art machine learning (ML) approaches. In all experiments, we used the OHSUMED-400 dataset, which includes PubMed abstracts related with specifically one class over 23 cardiovascular disease categories. Afterwards, we presented the results obtained from experiments and provided a comparison with related research in the literature. The results of the experiment showed that our BLSTM model with the weighting techniques outperformed the baseline and other machine learning approaches in terms of validation accuracy. Finally, our model outperformed the scores of related studies in the literature. This study shows that weighted feature representation improves the performance of the multiclass classification.

scholarly journals Study Of Students’ Performance Prediction Models Using Machine Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 3085-3091
Author(s):  
Mr. S. Viswanathan, Et. al.
Keyword(s):  
Data Mining ◽  
Student Success ◽  
Student Performance ◽  
Performance Prediction ◽  
Prediction Models ◽  
Educational Data Mining ◽  
Success Prediction ◽  
Novel Concept ◽  
The Given ◽  
Improving Student Performance

Educational Data Mining (EDM) is a novel concept associated with developing methods for exploring the specific types of data produced by educational settings and using those approaches to effectively understand students and the environments in which they learn. Prediction attempts to shape trends that will allow it to predict results or learning outcomes based on available data. Predicting student success has become an appealing challenge for researchers. They develop an understandable and efficient model using supervised and unsupervised EDM techniques. This assists decision-makers in improving student performance. The task of deciding the best model leads to the emergence of various techniques from both EDM techniques. The numerous research models used to solve the problem of student success prediction using educational data mining are discussed in this paper. The primary purpose of this paper is to explain the methodology for implementing the proposed solution for student performance prediction, as well as to present the findings of a study aimed at evaluating the performance of various data mining classification algorithms on the given dataset in order to assess their potential usefulness for achieving the goal and objectives.

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