Categorization of Exam Questions based on Bloom Taxonomy using Naïve Bayes and Laplace Smoothing

Setyaningsih, Eka Rahayu; Listiowarni, Indah

doi:10.1109/eiconcit50028.2021.9431862

Cited by 19 publications

(9 citation statements)

References 5 publications

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“…Karamustafaoglu et al have classified biology questions as per Bloom's taxonomy by Biology teachers [47]. Various approaches, including naïve bayes, laplace smoothing [48], artificial neural networks [49], CNN [50], pre-trained language model [51], and rule-base [52] have been employed for question classification. The various types of context-based and grammarbased questions generated through the AQG model are depicted in Figures 5 and 6.…”

Section: 3mapping Of Questions With Blooms Taxonomymentioning

confidence: 99%

Bloom’s Taxonomy based automatic Marathi question generation

2024

IJATEE

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In the current era of automation, various fields, including education, are undergoing transformations to enhance their existing processes. One crucial aspect in the field of education is examination management. Automatic question generation (AQG) for creating evaluation systems and question papers represents a significant transformation that schools, colleges, and universities are experiencing. Although significant research has been conducted in AQG for foreign languages, there is a scarcity of such work in Indian regional languages. Considering this, a novel working model for AQG for Marathi language texts was presented. The proposed research generates a diverse set of questions automatically through various natural language processing (NLP) pipeline activities, including tokenization, parts of speech (POS) tagging, stemming, named entity recognition (NER), shallow parsing, and dependency parsing. The generated questions fall into the categories of context-based and grammar-based questions, each elaborated in detail with scientific interpretation. This process contributes to the validation and refinement of our question generation methodology. A benchmarking approach using Bloom's Taxonomy was employed to validate the accuracy of the generated questions, ensuring they were aligned with educational objectives and targeted the desired levels of cognitive complexity. The empirical evaluation of the proposed methodology is conducted using the bilingual evaluation understudy (BLEU) score and manual scoring. The accuracy achieved using the BLEU score is 90.37% for the 'wh' questions, based on the corpus created from the sixth standard science textbook published by the Maharashtra State Board, Maharashtra, India. A diverse set of high-quality Marathi language questions has been successfully curated, suitable for compiling question papers aligned with Bloom's taxonomy levels.

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Section: 3mapping Of Questions With Blooms Taxonomymentioning

confidence: 99%

Bloom’s Taxonomy based automatic Marathi question generation

2024

IJATEE

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“…In the NB classifier, the crop type with the largest posterior probability is set as the classification type of the sample. To perform tuning for the NB classifier, the hyperparameter Laplace was considered [38].…”

Section: Machine Learning Classifiersmentioning

confidence: 99%

Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features

Peng

Chen

et al. 2022

Remote Sensing

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Machine learning (ML) classifiers have been widely used in the field of crop classification. However, having inputs that include a large number of complex features increases not only the difficulty of data collection but also reduces the accuracy of the classifiers. Feature selection (FS), which can availably reduce the number of features by selecting and reserving the most essential features for crop classification, is a good tool to solve this problem effectively. Different FS methods, however, have dissimilar effects on various classifiers, so how to achieve the optimal combination of FS methods and classifiers to meet the needs of high-precision recognition of multiple crops remains an open question. This paper intends to address this problem by coupling the analysis of three FS methods and six classifiers. Spectral, textual, and environmental features are firstly extracted as potential classification indexes from time-series remote sensing images from France. Then, three FS methods are used to obtain feature subsets and combined with six classifiers for coupling analysis. On this basis, 18 multi-crop classification models (FS–ML models) are constructed. Additionally, six classifiers without FS are constructed for comparison. The training set and the validation set for these models are constructed by using the Kennard-Stone algorithm with 70% and 30% of the samples, respectively. The performance of the classification model is evaluated by Kappa, F1-score, accuracy, and other indicators. The results show that different FS methods have dissimilar effects on various models. The best FS–ML model is RFAA+-RF, and its Kappa coefficient can reach 0.7968, which is 0.33–46.67% higher than that of other classification models. The classification results are highly dependent on the original classification index sets. Hence, the reasonability of combining spectral, textural, and environmental indexes is verified by comparing them with the single feature index set. The results also show that the classification strategy combining spectral, textual, and environmental indexes can effectively improve the ability of crop recognition, and the Kappa coefficient is 9.06–65.52% higher than that of the single unscreened feature set.

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“…Such an algorithm is basically could be used for supervised learning. Naïve Bayes [20] can solve classification problems. It could perform fast classification and quick predictions.…”

Section: Related Studiesmentioning

confidence: 99%

“…Naïve Bayes would predict the probability of different classes based on various attributes which have been identified. A Naïve Bayes algorithm could be used in classification problems that consist of several classes [15,20]. On the other hand, tree-based classification techniques such as decision trees and random forests [13,21] are simple and very popular methods in data mining problems.…”

Section: Related Studiesmentioning

confidence: 99%

An LSTM-Autoencoder Architecture for Anomaly Detection Applied on Compressors Audio Data

Mobtahej

Zhang

Hamidi

et al. 2022

Computational and Mathematical Methods

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The compressors used in today’s natural gas production industry have an essential role in maintaining the production line operational. Each of the compressors’ components has routine maintenance tasks to avoid sudden failures. Hence, the significant advantages and benefits of performing preventative maintenance tasks in time are decreasing equipment downtime, saving additional costs, and improving the safety and reliability of the whole system. In this paper, anomaly classification and detection methods based on a neural network hybrid model named Long Short-Term Memory (LSTM)-Autoencoder (AE) is proposed to detect anomalies in sequence pattern of audio data, collected by multiple sound sensors deployed at different components of each compressor system for predictive maintenance. In research methodology, this paper has conducted experiments that employed different RNN architectures such as GRU, LSTM, Stacked LSTM, and Stacked GRU with various functions to create a baseline for model evaluation. Each architecture used audio signals dataset received from the compressor system for experiments to consider each neural network model’s performance. According to performance results, an optimal model for anomaly detection with the best performance scores has been proposed in this research. Experiments combined one-dimensional raw audio signal features using SC and Mel spectrogram features were fed to deep learning models to evaluate performance. Hence, such hybrid methods can effectively detect normal and anomaly audio signals collected from a compressor system, increasing the compressor system’s reliability and the sustainability of the gas production line. The combination of multiple-resource features in the proposed hybrid model showed a 100% score in all four-evaluation metrics such as accuracy, precision, recall, and F1 in LSTM-based autoencoder in both test and train results.

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Categorization of Exam Questions based on Bloom Taxonomy using Naïve Bayes and Laplace Smoothing

Cited by 19 publications

References 5 publications

Bloom’s Taxonomy based automatic Marathi question generation

Bloom’s Taxonomy based automatic Marathi question generation

Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features

An LSTM-Autoencoder Architecture for Anomaly Detection Applied on Compressors Audio Data

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