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Educational Data Mining (EDM) holds promise in uncovering insights from educational data to predict and enhance students' performance. This paper presents an advanced EDM system tailored for classifying and improving tertiary students' programming skills. Our approach emphasizes effective feature engineering, appropriate classification techniques, and the integration of Explainable Artificial Intelligence (XAI) to elucidate model decisions. Through rigorous experimentation, including an ablation study and evaluation of six machine learning algorithms, we introduce a novel ensemble method, Stacking-SRDA, which outperforms others in accuracy, precision, recall, f1-score, ROC curve, and McNemar test. Leveraging XAI tools, we provide insights into model interpretability. Additionally, we propose a system for identifying skill gaps in programming among weaker students, offering tailored recommendations for skill enhancement. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSI) contain a wide range of information, the most prominent technology for observing the earth. However, using an original HSI high-dimensional datacube, the classification task faces significant challenges since it has a high computational cost. As a result, dimensionality reduction is indispensable. A dimension reduction method has been introduced in this paper, including feature extraction and feature selection to obtain feature subsets. Minimum Noise Fraction (MNF) is a popular feature extraction method for HSI, requiring a high computational capability. We propose a segmented MNF that divides the complete HSI into groups utilising normalised cross-cumulative residual entropy (nCCRE). An nCCRE-based feature selection is also employed to improve the quality of the chosen features using the max-relevancy min-redundancy measure. The support vector machine (SVM) classifier is used on two real HSI to evaluate the efficiency of the extracted subsets. [ABSTRACT FROM AUTHOR]

The use of hyperspectral imaging sensors has greatly improved the classification of remotely sensed data because of the abundant spectral information they offer. However, the numerous contiguous, tiny wavelength bands captured in hyperspectral images (HSIs) often hinder the classification process. To overcome the aforementioned problem, various feature reduction techniques, including feature extraction (FE) and feature selection (FS), are commonly employed to improve classification performance. Linear discriminant analysis (LDA) is a well-established approach that has been utilized for the FE of HSI. LDA's consideration of global characteristics and the variance accumulator for FS can lead to the poor reduction of HIS's intrinsic characteristics. Furthermore, LDA's limited ability to select a very low number of features, i.e. the number of classes minus one, restricts its effectiveness in HSI classification. Therefore, we introduce an FS method based on a non-linear information-theoretic measure, normalized mutual information (nMI) combined with minimum redundancy maximum relevance (mRMR). This approach is used to identify inherent features from the transformed space of our proposed correlation-based segmented-LDA (SLDA) and spectral region-based segmented-LDA (SSLDA) FE methods. We thoroughly compare the performance of the SLDA-mRMR and SSLDA-mRMR methods with the existing linear and non-linear state-of-the-art techniques, including unsupervised principal component analysis (PCA), PCA-based methods, supervised LDA, and LDA-based methods. Additionally, we explore the performance of nMI-based mRMR selection with all FE methods by incorporating a cumulative variance-based top features pick-up strategy. Based on the experimental results, we observe that SSLDA-mRMR and SLDA-mRMR achieve the highest classification accuracy, such as 91.91%, and 91.57% for agricultural Indian Pines, respectively, 97.66%, and 97.54%, respectively, for Kennedy Space Center, and 96.69%, and 96.57%, respectively, for Pavia University. In contrast, the classification accuracies using all original features of the HSIs are 71.39%, 70.01%, and 83.52%, respectively. Moreover, our proposed SSLDA-mRMR and SLDA-mRMR combinations outperformed all other FE and FS combinations that were examined using actual HSI datasets. [ABSTRACT FROM AUTHOR]

Educational Data Mining (EDM) has become a promising research field for improving the quality of students and the education system. Although EDM dates back to several years, there is still lack of works for measuring and enhancing the computer programming skills of tertiary students. As such, we, in this paper, propose an EDM system for evaluating and improving tertiary students' programming skills. The proposed EDM system comprises two key modules for (i) classification process and (ii) learning process,. The classification module predicts the current status of a student and the learning process module helps generate respective suggestions and feedback to enhance the student's quality. In particular, for the classification module, we prepare a real dataset related to this task and evaluate the dataset to investigate six key Machine Learning (ML) algorithms, Support Vector Machine (SVM), decision tree, artificial neural network, Random Forest (RF), k -nearest neighbor and naive Bayes classifier, using accuracy-related performance measure metrics and goodness of the fit. The experimental results manifest that RF and SVM can predict the students more accurately than the other models. In addition, critical factors analysis is accomplished to identify the critical features toward achieving high classification accuracy. At last, we design an improvement mechanism in the learning process module that helps the students enhance their programming skills. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) contain notable information of land objects by acquiring an immense set of narrow and contiguous spectral bands. Feature extraction (FE) and feature selection (FS) as dimensionality (band) reduction strategies are performed to enhance the classification result of HSI. Principal component analysis (PCA) is frequently exploited for the FE of HSI. However, it often possesses the inability to extract local and subtle HSI structures. As such, segmented-PCA (SPCA), spectrally segmented-PCA (SSPCA) and folded-PCA (FPCA) are presented for local and useful FE from the HSI. In this paper, we propose two FE methods called segmented-FPCA (SFPCA) and spectrally segmented-FPCA (SSFPCA). SFPCA exploits SPCA and FPCA while SSFPCA exploits SSPCA and FPCA together. In particular, SFPCA and SSFPCA apply FPCA on highly correlated and spectrally grouped HSI bands, respectively. We consider nonlinear methods Kernel-PCA (KPCA) and Kernel entropy component analysis (KECA) for extended comparison. For the experimented agricultural Indian Pine and urban Washington DC Mall HSIs, the results manifest that SFPCA (95.6262% for the agricultural HSI and 97.4782% for the urban HSI) and SSFPCA (96.3221% for the agricultural HSI and 98.0116% for the urban HSI) outperform the conventional methods. [ABSTRACT FROM AUTHOR]