Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy

Text classification which is an integral part of text mining has caught much attention in various industries and fields recently. The ability is in assigning text documents to one or more pre-defined categories based on content similarity. While most of application of text classification focuses on document level, question classification works at much granular level such as sentence and phrase. There have been numerous studies on question classification in accordance to Bloom taxonomy in assessments to measure cognitive level of learners in higher learning institutions. But it has not been effective yet to resolve overlapping issue of Bloom taxonomy verb keywords being assigned to more than one category of Bloom taxonomy. The presence of this poses a problem in respect of classifying a particular question into a right category of Bloom taxonomy. And feature extraction plays an important role in improving the accuracy of classifier such as Support Vector Machine in question classification. Much of the past related research work focus on feature extraction methods such as bag of word (BOW) and syntactic analysis to classify questions and to address the issue, an improvement in feature extraction is needed. In view of this, this study proposes an integrated approach in feature extraction involving semantic aspect in classifying questions in accordance to Bloom taxonomy. Support Vector Machine classifier is used as it is well known for its high accuracy in text classification. With all this in place, an improved accuracy in classifying questions in accordance to Bloom taxonomy can be expected.

<p>This study aimed to evaluate chemistry questions from the General Secondary Examination<br />in Jordan (Altawjihi) for the years 2010 – 2015. Criteria for evaluation included type of<br />questions (objective vs. essay), inclusion of Bloom's Taxonomy of Cognitive Domain, and<br />the availability of chemistry processes. This study was accomplished by following the style<br />of content analysis that considers the sub-question as the unit of analysis and then determines<br />which standards are included in the questions. The number of questions that were analyzed<br />reached to 540 sub-questions. The researcher used frequencies, percentages, chi-square<br />distribution, and Holsti's method in the statistical treatment to calculate the stability of the<br />question analysis. The results exhibited the domination of objective questions over essay<br />questions and questions of completion. Objective questions dominated the findings (57.6%).<br />Essay questions were second most frequently used (36.7%), with completion questions used<br />least frequently (5.7%). Questions that measure lower cognitive levels also outperformed the<br />other types, reaching 85.7%. However, the questions that measure higher cognitive levels (as<br />based on Bloom’s Taxonomy of the Cognitive Domain— analysis, synthesis, and evaluation)<br />were only sparingly used, at 7.8%, 3.0%, and 3.5%, respectively. Chemical calculation and<br />recognition occupied center stage in the educational processes for achieving chemical<br />knowledge and chemical expression, whereas the assumption process has been used almost<br />not at all (0.5%).</p>

Objective For the development and growth in conceptual understanding of education, evaluation is one of the key factors of it. Improving a student's cognitive level is highly dependent upon the questions being asked in exams. The primary aim of this study is to analyze the cognitive level of physiotherapy exam papers using Bloom's taxonomy. Material and Methods The study was performed in a Private Medical University, Doctor of Physical Therapy Program in all 5 years of mid-term examination of 2019. One thousand and eighty multiple-choice questions were evaluated on revised Bloom's taxonomy of cognitive domain. Results It was found that most lower order cognitive questions were asked from first- and second-year students, whereas third- to fifth-year students were asked higher order cognitive questions ranging from 27.5 to 38%. Conclusion The examination analyzed the efficacy of education being provided. It helped in finding the subject content that needs greater emphasis and clarification. The faculty should give consideration on higher order cognitive level questions to encourage critical thinking among students and the medical colleges should develop the policy on construction of question papers according to the goal of each study year.

The study aimed at analyzing English questions of the Jordanian Secondary Certificate Examinations via Blooms' cognitive levels. An analysis sheet was prepared by the researcher for the purpose of the study, which was ensured to be valid and reliable. The whole questions of the general secondary examinations for English course in both levels (level three and level four) during 2010-2013 composed the sample of the study. Frequencies and percentages were tabulated to facilitate the analysis of the results. The result of the study revealed that the total percentage of the first three levels (comprehension, knowledge, and analysis) is (69.6) but the total percentage of the last three levels (application, synthesis, and evaluation) is (30.4) so it indicated that the English questions included in general secondary examinations emphasize low order thinking levels. The researcher recommended that the questions designers should improve their questioning techniques in writing questions of exams.

One of the student's abilities that must be measured in the learning process is cognitive ability. This measurement is essential to establish the level of students' understanding of the material that has been taught. Giving questions with the appropriate difficulty level is an attempt to measure these cognitive abilities. According to Bloom's taxonomy, there are six levels of cognitive abilities, remembering, understanding, applying, analyzing, evaluating, and creating. An automatic question classifier is helpful for teachers to develop a high-quality question and comply with different cognitive levels. This paper proposed automation question classification using the classical classification method (Support Vector Machine and Multinomial Naïve Bayes) and the pre-trained language model, IndoBERT. We conducted an experiment based on 670 mathematics and Indonesian language questions in primary and high school. The evaluation results show that the best classification accuracy was achieved by the IndoBERT method, which is 82% for classifying mathematics questions and 63% for classifying Indonesian Language questions. We analyzed that the small number of datasets and unbalanced at each taxonomy level affected the classification results.

The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

Activity classification have been used in different fields such as energy expenditure measurement or health monitoring. Many combinations of different sensors and machine learning techniques have been proposed in order to do this kind of classification. The aim of this paper is to introduce an activity classification approach for Climbing/Descending stairs detection divided in two phases. In the first phase the signals from accelerometer and gyroscope are filtered, then implementing step detection allows us to extract the relevant features from these signals. The second phase consists of a principal component analysis (PCA) for reducing dimensionality, and a support vector machines (SVM) classifier to identify the motion. Using this methodology, an accuracy of 98.76% is achieved. The data used for classification were taken from an inertial measurement unit carried by three users in their ankles, which was provided by a database from the UCI machine learning repository.

This paper presents a new application of data mining techniques, particularly text mining, to analyze educational questions asked by teachers in classrooms. More specifically, it reports on the performance of four machine learning techniques and four feature selection approaches on the classification of teacher's questions into different cognitive levels identified in Bloom's taxonomy. In doing so, a dataset of questions has been collected and classified manually into Bloom's cognitive levels. Preprocessing steps have been applied to convert questions into a suitable representation. Using the dataset, the performance of machine learning techniques under feature selection approaches has been evaluated. The results show that Rocchio Algorithm performs the best regardless of the used feature selection approach. Moreover the best RA performance can be obtained when Information Gain is used for feature selection.

The authors provide a transformative solution to address prevalent challenges in educational assessment in this research work. These challenges include aligning examination papers with syllabi, framing question papers to cover all Bloom’s Levels, and upgrading Bloom’s Levels by re-framing questions. Bloom’s Taxonomy (BT) is a cognitive level-based framework for understanding students’ educational progress through the assessment. The educational assessment method is the foundation for our chatbot, Bloomify. It is designed to revolutionize the assessment and help the keen educationists in three key scenarios. In the first scenario, mostly the educationists are not able to reflect the all the course outcomes of the subject. Bloomify will help them to design the question paper to cover all the course outcomes mapped with Bloom’s levels (BL). Second, often in a hurry, juggling between students’ assessment and institution’s accreditation tasks, the educationists fail to balance the question papers with all BLs covered. Bloomify will overcome the human fatigue and exhaustion associated with the manual creation of balanced question papers. In the third scenario, it is observed that many of the universities use of taxonomies and hence fail to design the assessment for the cognitive development of the students. Bloomify helps the universities that lack taxonomy or use alternative taxonomies, and offers a Bloom’s approach for assessing student development. Further, authors define a Bloom’s Score (BS) to find the average cognitive level of a question paper. BS will help to focus on specific cognitive level of an assessment. For example, the assessment for the graduate students can be defined with BS of 5 or 6 indicating higher Bloom’s levels: evaluate and create. Bloomify automates the calculation of Bloom’s scores ensuring coverage of all the cognitive levels. This will help the educationists in question paper creation process, saving time and effort. Bloomify includes three features: 1. Question classification 2. Suggestion of BL, and 3. Generation of entire question paper based on specific criteria like the syllabus, marking scheme, and desired average Bloom’s score. Bloomify provides a user-friendly interface, ensuring seamless integration with the assessment creation process. Bloomify enhances educational assessment by addressing all cognitive levels, ensuring critical thinking in students. It supports the educationists’ efforts to design an assessment focusing on knowledge application, analysis, evaluation, and creation, aligning with the real-world demands. The effectiveness of Bloomify is gauged by allowing different educationists to use this chatbot and calculating the Mean Opinion Score (MoS). Bloomify received scores of 9, 9.5, and 8 for classification, suggestion, and generation features, respectively from the educators of the authors’ institute.

In order to improve the performance of Support Vector Machine (SVM) classifier for imbalanced data, this paper proposes dynamic cost-sensitive SVM classifier based on chaos particle swarm optimization (CPDC_SVM). Firstly, this paper introduces dynamic cost-sensitive thought to SVM classifier, and gives the method for structuring dynamic cost and cost-sensitive SVM model. Secondly, we propose the evaluation methodology performance for classifier, and adopts decimal base to code the particles. At last, chaos thought is introduced in particle swarm optimization algorithm, and the Algorithm of the dynamic cost-sensitive SVM classifier is given, which improves convergent speed and accuracy of particle swarm optimization, and can optimize dynamic cost-sensitive SVM well, so CPDC_SVM adds effectively the convergence speed and accuracy for the particle swarm optimization algorithm. Experimental results show CPDC_SVM has higher precision than traditional SVM classifier, and dynamic cost and chaos particle swarm optimization can improve the performance for classifier.

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

BackgroundThis study aimed to screen the feature modules and characteristic genes related to ulcerative colitis (UC) and construct a support vector machine (SVM) classifier to distinguish UC patients.MethodsFour datasets that contained UC and control samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) with consistency were screened via the MetaDE method. The weighted gene coexpression network (WGCNA) was used to distinguish significant modules based on the four datasets. The protein–protein interaction network was established based on intersection genes. Enrichment analysis of Gene Ontology (GO) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were established based on DAVID. An SVM combined with recursive feature elimination was also applied to construct a disease classifier for the disease diagnosis of UC patients. The efficacy of the SVM classifier was evaluated through receiver operating characteristic curves.ResultsTwelve highly preserved modules were obtained using the WGCNA, and 2009 DEGs with significant consistency were selected using the MetaDE method. Sixteen significantly related GO BPs and 12 KEGG pathways were obtained, such as cytokine-cytokine receptor interaction, cell adhesion molecules, and leukocyte transendothelial migration. Subsequently, 41 genes were used to construct an SVM classifier, such as CXCL1, CCR2, IL1B, and IL1A. The area under the curve (AUC) was 0.999 in the training dataset, whereas the AUC was 0.886, 0.790, and 0.819 in the validation set (GSE65114, GSE37283, and GSE36807, respectively).ConclusionsAn SVM classifier based on feature genes might correctly identify healthy people or UC patients.

Epilepsy is a persistent neurological condition of the brain in which the activity of the brain goes out of normal state. Classification and Analysis of EEG signal is the early approach for epilepsy diagnosis. During this paper, we have a tendency to propose an EEG signal classification approach based on Support Vector Machine (SVM) classifier. In extracting features from the raw EEG data we applied the Hilbert Transform method and used its coefficients. Then, after the PCA dimension reduction a two-class SVM classifier is used for EEG signals automatic classification, one class for healthy subjects and another for subjects with epilepsy. In SVM classifier we need to divide the EEG signals into a training dataset and testing dataset for classification. We have used five sets of EEG signals which are publicly accessible on EEG time series database. The evaluation and comparison of SVM based classifier with two other classification methods such as KNN and LVQ based classifier was done. The average classification accuracy of our study is 95.33%.

This study investigated the effect of a new approach, the support vector machine, as a classifier tool to identify the weeds in corn fields at early growth stage. Image segmentation was done by transforming original color images to gray level images according to the statistical values of red, green, blue components. The Gray Level Co-occurrence Matrix (GLCM) and statistical properties of the histogram from the gray level images were further used to obtain the texture features of the weeds and corn seedlings. These texture features were used in the classification procedure. Principle component analysis was used to select the texture features according to their better contributions to reduce space dimensions. A Support Vector Machine (SVM) classifier was employed to recognize the weeds and the corn seedlings. The results indicated that the SVM classifiers with different feature selections could identify successfully weed-corn with a higher accuracy ranged from 92.31 to 100%. A comparison study of the recognition capabilities of SVM and back-propagation (BP) neural-network classifier using the same data set was conducted. It was found that the SVM classifier provided the best recognition performance with an accuracy of 100%, which exceeded the accuracy of 80% given by the BP classifier. Key words: Corn seedling, weeds, texture, support vector machine, recognize.