International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1160
Artificial Intelligence Attitude Scale for Primary School Students
Статья научная
Based on this gap in the literature, the problem situation identified was deemed worth investigating in terms of contributing to the accumulation of knowledge on the subject area. In addition, it is thought that this study will contribute to future studies on artificial intelligence in primary school education. The aim of this study is to create a Likert-type attitude scale that can be used to determine primary school students’ attitudes towards artificial intelligence. In this study, exploratory sequential design, one of the mixed research method types, was used. A 32-item draft scale form was prepared in the light of the literature review, student opinions collected through a structured interview form and data obtained from field experts. In order to examine the validity of the scale, exploratory and confirmatory factor analyses, item-factor total correlations and item discriminations were evaluated. The goodness of fit values obtained in confirmatory factor analysis were [CMIN=245,020, df=159 (CMIN/df= 1.541), RMSEA= 0.45, RMR= 0.035, GFI= 0.916, AGFI= 0.889, CFI= 0.903, NFI= 0.773, IFI= 0.906]. To evaluate the reliability of the scale, internal consistency coefficient was calculated, and test-retest analysis was performed. Cronbach’s Alpha reliability coefficient for the overall scale was 0.807 and McDonald’s Omega coefficient was 0.816. As a result, it was determined that the Artificial Intelligence Attitude Scale, which consists of 4 factors and 20 items, is an appropriate, valid and reliable tool for evaluating primary school students’ attitudes towards artificial intelligence.
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Artificial Intelligence and Rhetorical Art: Argumentative Debate with ChatGPT
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This study delves into the interface between Rhetoric and Artificial Intelligence, with a specific focus on ChatGPT's ability to engage in argumentative dialogues and its potential educational applications. Specifically, the study aims to investigate the feasibility of conducting argumentative dialogues in English between users and ChatGPT, identify suitable instructions that facilitate a flowing debate, and assess the tool's ability to judge and determine the debate's winner. The study's findings indicate that ChatGPT can effectively participate in rhetorical competitions with the provision of specific instructions. While the tool demonstrates proficiency in generating relevant and logical arguments and counterarguments, it faces challenges in sustaining the topic's relevance throughout extended debates unless it assumes a judging role. Moreover, despite occasional violations of the rules of debate, its potential in pedagogical argumentation competitions remains promising. The results of the present research show that ChatGPT can participate in debates with specific rules. This finding suggests that ChatGPT can be used during training sessions in rhetoric educational clubs.
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Artificial Neural Network Training Criterion Formulation Using Error Continuous Domain
Статья научная
One of the trends in information technologies is implementing neural networks in modern software packages [1]. The fact that neural networks cannot be directly programmed (but trained) is their distinctive feature. In this regard, the urgent task is to ensure sufficient speed and quality of neural network training procedures. The process of neural network training can differ significantly depending on the problem. There are verification methods that correspond to the task’s constraints; they are used to assess the training results. Verification methods provide an estimate of the entire cardinal set of examples but do not allow to estimate which subset of those causes a significant error. This fact leads to neural networks’ failure to perform with the given set of hyperparameters, making training a new one time-consuming. On the other hand, existing empirical assessment methods of neural networks training use discrete sets of examples. With this approach, it is impossible to say that the network is suitable for classification on the whole cardinal set of examples. This paper proposes a criterion for assessing the quality of classification results. The criterion is formed by describing the training states of the neural network. Each state is specified by the correspondence of the set of errors to the function range representing a cardinal set of test examples. The criterion usage allows tracking the network’s classification defects and marking them as safe or unsafe. As a result, it is possible to formally assess how the training and validation data sets must be altered to improve the network’s performance, while existing verification methods do not provide any information on which part of the dataset causes the network to underperform.
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Artificial Neural Network in Prognosticating Human Personality from Social Networks
Статья научная
The analysis of text in the form of tweets, chat or posts can be an interesting as well as challenging area of research. In this paper, such an analysis provides information about the human behavior as positive, negative or neutral. For simplicity, tweets from social networking site, Twitter, are extracted for analyzing human personality. Various concepts from natural language processing, text mining and neural networks are used to establish the final outcome of the application. For analyzing text, Neural Networks are implemented which are so modeled that they predict the Human behavior as positive, negative or neutral based on extracted and preprocessed data. Using Neural Networks, the particular pattern is identified and weights are provided to words based on the extracted pattern.Neural networks have an added advantage of adaptive learning. This application can be immensely useful for politics, medical science, sports, matrimonial purposes etc.The results so obtained are quite promising.
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Aspectual Analysis of Legacy Systems: Code Smells and Transformations in C
Статья научная
This paper explores the various code smells or the so called bad code symptoms present in procedural C software. The code smells are analyzed in the light of aspect oriented programming. The intention is to handle the code smells with aspect oriented constructs as it offers more versatile decomposition techniques than the traditional modularization techniques, for software evolution and understandability. The code smells are described at the function and program level. The code smells are followed by the aspect oriented transformations that may be required in order to improve the code quality.
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Assessing Student Academic Performance with Fuzzy Expert System
Статья научная
Nowadays, higher education institutions and universities are facing a competitive environment for enhancing the quality of students to achieve extensive knowledge with critical thinking skills and a good personality for better employment in the industry. Universities and other higher education establishments ensure that students overcome the obstacles in these cutthroat environments. In order to do this, it is necessary to analyze the academic performance of each student by determining their strengths and weaknesses. A fuzzy expert system (FES) is used in this study to evaluate student’s academic performance. This FES uses fuzzy logic to classify each student’s performance based on a variety of linguistic factors. It classifies each student’s performance by considering various linguistic factors using fuzzy logic. For this purpose, seven significant input factors have been taken into account which is Stress, Motivation, Confidence, Parent’s support & Availability, Self study hours, Punctuality, and Friend circle. Several defuzzification techniques are applied in order to examine student performance using the FES & generate more precise and measurable results. These findings could aid colleges and other educational establishments in determining the right variables that influence student’s academic performance. Additionally, a comparison of various Mamdani fuzzy defuzzification techniques, including the centroid, bisector, and mean of maxima methods, is provided in this study. After comparing all three techniques by taking different scenarios of all the external factors, it has been concluded that all of them are performing equally.
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Статья научная
The integration of artificial intelligence (AI) in education is a promising transformation. Drawing on advanced technologies, AI enriches the learning experience through intelligent systems capable of analyzing, adapting and personalizing teaching. Despite a growing volume of scientific publications, there remains a lack of critical synthesis on the real impact of AI on the role of teachers, student learning and the transmission of knowledge. To fill this gap, this article proposes a systematic literature review, conducted using the PRISMA method, to identify the opportunities and limitations of AI in educational environments. From 1,248 publications extracted from the Scopus database between 2018 and 2024, 20 relevant studies were selected and analyzed after applying inclusion and exclusion criteria. The results show significant growth in research in this field, and demonstrate that AI enables teachers to automate certain tasks, personalize teaching and better meet learners' individual needs. However, significant obstacles remain, including lack of digital skills, resistance to change, and ethical concerns. The study also points out that AI enhances learners' skills, promoting the personalization of pathways, the identification of struggling students, the adaptation of materials, as well as real-time engagement and monitoring. It also makes it possible to model and transmit knowledge through the creation and adaptation of digital educational resources. However, AI also presents certain limitations in the educational context, such as excessive dependence on technology, inequalities of access, automatic generation of answers without real learning, as well as issues relating to the confidentiality of personal data. AI is a powerful but complex lever in the field of education. Its effective integration requires targeted training for teachers, critical reflection on its uses, and a rigorous ethical framework. This review thus provides a solid basis for guiding future research towards complementary empirical studies, while accompanying practitioners in a reasoned and beneficial adoption of AI in educational contexts.
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Assessment and Feedback as Predictors for Student Satisfaction in UK Higher Education
Статья научная
Assessment and feedback mechanisms are essential components towards effective teaching in higher education and are continuously monitored. The annual student satisfaction survey in UK higher education collects students’ perception on those dimensions and issues results to assist institutions identify their weaknesses and amend their strategies and improve their teaching effectiveness. This study explores assessment and feedback as predictors for overall student satisfaction. It focuses on business schools mainly and uses the officially published dataset. Following a regression analysis approach, it can be concluded that there is evidence to support the claim that assessment and marking can be used as predictors for overall student satisfaction in this subdomain. The significance of the study lies in the fact that universities consider assessment and feedback as of key importance for improving student experience. It is thus critical for the institutions to gain a better understanding on whether those factors can be safely used as predictors of overall student satisfaction, something that is related to university ranking tables. Results in the study, demonstrate some important aspects of this and indicate that improved quality in marking and feedback can have a positive effect in student satisfaction. A more comprehensive study can unfold additional dimensions of the survey and shed light on how students perceive marking, assessment and feedback in higher education in general.
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Assessment and attainment of program educational objectives for post graduate courses
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As per the guidelines issued by NBA (National Board of Accreditation) of All India Council of Technical Education (AICTE) Outcome Based Education is implemented in engineering colleges of India. The outcome based evaluation model measures the performance of UG and PG programs. The performance is based on calculating attainments of Program Educational Objectives (PEOs) and Program Outcomes (PO).In this paper we will discuss the process for the attainments of POs and PEOs for Post Graduate program approved by AICTE, India. The attainments are calculated by applying direct and indirect tools. The attainments summaries are generated Batch wise and a comparison of different Batches were made. The attained PEOs and POs would help in accomplishing Vision and Mission of the department.
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Статья научная
The study aimed to assess whether the students from mathematical science-based undergraduate degree programmes in Kaduna State University perform academically better when either the Computer-Based Test (CBT) or the Paper-Pencil Test (PPT) is used to write the Unified Tertiary Matriculation Examination (UTME), which is conducted annually by the Joint Admissions Matriculation Board (JAMB). The study adopted a quantitative approach to research. A purposive sample of one thousand and twenty-three (1023) first-year students constituted the population for the study. This population was drawn from Computer Science, Mathematics and Physics undergraduate degree programmes in the Kaduna State University who were admitted from the 2010/2011 to 2012/2013 and 2015/2016 to 2016/2017 academic sessions respectively. The instruments used for data collection were the UTME scores and the academic standing of first-year Cumulative Grade Point Average (CGPA) results, which were coded and analysed with the aid of Computational Statistical Package for Social Sciences (SPSS) version 23. Descriptive statistics and Analysis of Variance (ANOVA) were the statistical tools used to answer the four (4) research questions raised. The results revealed a majority of the students who performed academically better were those who used the PPT as their test medium in writing the UTME. It concluded that the majority of the students who wrote the UTME using PPT performed better in their academics. The study thereby recommended that there is a need for the Joint Admissions Matriculation Board (JAMB) to review its examination policies in mathematics-based subjects to enable students to pass such subjects with flying colours, thereby encouraging them to perform better academically in the undergraduate studies.
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Статья научная
Various agile software development methodologies, since their commencement, encouraged the development of high quality software product. Quality of a product is the compelling trait that plays a vital role in any product's success. Usability engineering and User centered design are user-centered approaches, covering the customer's concerns. The way these approaches are understood and carried out with agile practices is not properly understood and adopted till now. For software applications to be usable and valuable it is necessary to understand the correct user requirements in order to develop the interface that is usable and valuable to the customer. In this research work, we are discussing the scrum approach of agile development and integrate this with the usability engineering and user centered design approaches which helps the agile development team to understand usability demand of users and develop a product according to their expectations.
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Automata-Theoretic Framework for Modeling and Optimizing Library Resource Allocation
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The efficient allocation of finite resources to a dynamic patron base represents a core challenge in modern library management. Traditional heuristic approaches often lack the formal rigor needed for verifiable optimization and proactive planning. This paper introduces a novel formal framework grounded in automata theory to model library operations, patron behavior, and resource allocation strategies. We define a Library Resource Automaton (LRA), a deterministic finite automaton whose states represent distinct configurations of resource availability, whose input alphabet encapsulates patron interactions, and whose transition function formally encodes allocation policies. By interpreting sequences of patron actions as strings in a formal language, the LRA provides a computationally tractable and analytically powerful model for simulating library states, predicting bottlenecks, and synthesizing optimal allocation strategies. We elaborate on the theoretical foundations of the model, present a detailed multi-layer automata architecture for handling complex, multi-resource scenarios, and discuss algorithms for state space analysis and policy optimization. Furthermore, we explore the integration of temporal logic for specifying and verifying critical system properties such as fairness and liveness. This work establishes a rigorous bridge between theoretical computer science and library information science, offering a new paradigm for building predictable, efficient, and patron-centric library management systems.
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Automated Cardiac Beat Classification Using RBF Neural Networks
Статья научная
This paper proposes a four stage, denoising, feature extraction, optimization and classification method for detection of premature ventricular contractions. In the first stage, we investigate the application of wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In this stage, the Stationary Wavelet Transform is used. Feature extraction module extracts ten ECG morphological features and one timing interval feature. Then a number of radial basis function (RBF) neural networks with different value of spread parameter are designed and compared their ability for classification of three different classes of ECG signals. Genetic Algorithm is used to find best value of RBF parameters. A classification accuracy of 100% for training dataset and 95.66% for testing dataset and an overall accuracy of detection of 95.83% were achieved over seven files from the MIT/BIH arrhythmia database.
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Статья научная
This paper deals with one of our research directions on software tools enhancing self-learning in computer science disciplines. In this study, we discuss an experiment on relational data bases learning using a tool for the edition and automated evaluation of learners’ solutions given as relational algebra trees. Indeed, in addition to the interest of the graphic languages for any training, the evaluation of our precedent works on modeling and evaluating solutions as algebraic expressions showed us some problems: first, there are various languages for the algebraic expressions. Second, among the detected errors by the prototype, developed in our precedent works for algebraic expressions, the form errors about the algebraic language have to be corrected before starting the semantic analysis. Third, in some cases, errors in the form have led to other non-committed errors which can cause inconsistencies in the errors’ diagnosis process. Starting from these problems, the two principal objectives of the work presented in this article concern the algebraic trees construction and the evaluation assisted by a graphic tool which essentially consists in a semantic analysis as recommended in ODALA (ontology driven auto-evaluation learning approach) that we have already proposed. The tool was evaluated by a set of tests and experimented with second year LMD license students. These experiments results were interesting and showed that the tool is particularly helpful for novice students and their teachers.
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Automatic Cyberstalking Detection on Twitter in Real-Time using Hybrid Approach
Статья научная
Many people are using Twitter for thought expression and information sharing in real-time. Twitter is one of the trendiest social media applications that cybercriminals also widely use to harass the victim in the form of cyberstalking. Cyberstalkers target the victim through sexism, racism, offensive language, hate language, trolling, and fake accounts on Twitter. This paper proposed a framework for automatic cyberstalking detection on Twitter in real-time using the hybrid approach. Initially, experimental works were performed on recent unlabeled tweets collected through Twitter API using three different methods: lexicon-based, machine learning, and hybrid approach. The TF-IDF feature extraction method was used with all the applied methods to obtain the feature vectors from the tweets. The lexicon-based process produced maximum accuracy of 91.1%, and the machine learning approach achieved maximum accuracy of 92.4%. In comparison, the hybrid approach achieved the highest accuracy of 95.8% for classifying unlabeled tweets fetched through Twitter API. The machine learning approach performed better than the lexicon-based, while the performance of the proposed hybrid approach was outstanding. The hybrid method with a different approach was again applied to classify and label the live tweets collected by Twitter Streaming in real-time. Once again, the hybrid approach provided the outstanding result as expected, with an accuracy of 94.2%, recall of 94.1%, the precision of 94.6%, f-score of 94.1%, and the best AUC of 98%. The performance of machine learning classifiers was measured in each dataset labeled by all three methods. Experimental results in this study show that the proposed hybrid approach performed better than other implemented approaches in both recent and live tweets classification. The performance of SVM was better than other machine learning algorithms with all applied approaches.
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Automatic Environmental Sound Recognition (AESR) Using Convolutional Neural Network
Статья научная
Automatic Environmental Sound Recognition (AESR) is an essential topic in modern research in the field of pattern recognition. We can convert a short audio file of a sound event into a spectrogram image and feed that image to the Convolutional Neural Network (CNN) for processing. Features generated from that image are used for the classification of various environmental sound events such as sea waves, fire cracking, dog barking, lightning, raining, and many more. We have used the log-mel spectrogram auditory feature for training our six-layer stack CNN model. We evaluated the accuracy of our model for classifying the environmental sounds in three publicly available datasets and achieved an accuracy of 92.9% in the urbansound8k dataset, 91.7% accuracy in the ESC-10 dataset, and 65.8% accuracy in the ESC-50 dataset. These results show remarkable improvement in precise environmental sound recognition using only stack CNN compared to multiple previous works, and also show the efficiency of the log-mel spectrogram feature in sound recognition compared to Mel Frequency Cepstral Coefficients (MFCC), Wavelet Transformation, and raw waveform. We have also experimented with the newly published Rectified Adam (RAdam) as the optimizer. Our study also shows a comparative analysis between the Adaptive Learning Rate Optimizer (Adam) and RAdam optimizer used in training the model to correctly classifying the environmental sounds from image recognition architecture.
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Статья научная
Some of the most common and typical issues in the field of intelligent tutoring systems (ITS) are (i) the correct identification of learners’ difficulties in the learning process, (ii) the adaptation of content or presentation of the system according to the difficulties encountered, and (iii) the ability to adapt without initial data (cold-start). In some cases, the system tolerates modifications after the realization and assessment of competences. Other systems require complicated real-time adaptation since only a limited number of data can be captured. In that case, it must be analyzed properly and with a certain precision in order to obtain the appropriate adaptations. Generally, for the adaptation step, the ITS gathers common learners together and adapts their training similarly. Another type of adaptation is more personalized, but requires acquired or estimated information about each learner (previous grades, probability of success, etc.). Some of these parameters may be difficult to obtain, and others are imprecise and can lead to misleading adaptations. The adaptation using machine learning requires prior training with a lot of data. This article presents a model for the real time automatic adaptation of a predetermined session inside an ITS called AI-VT. This adaptation process is part of a case-based reasoning global model. The characteristics of the model proposed in this paper (i) require a limited number of data in order to generate a personalized adaptation, (ii) do not require training, (iii) are based on the correlation to complexity levels, and (iv) are able to adapt even at the cold-start stage. The proposed model is presented with two different configurations, deterministic and stochastic. The model has been tested with a database of 1000 learners, corresponding to different knowledge levels in three different scenarios. The results show the dynamic adaptation of the proposed model in both versions, with the adaptations obtained helping the system to evolve more rapidly and identify learner weaknesses in the different levels of complexity as well as the generation of pertinent recommendations in specific cases for each learner capacity.
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Статья научная
This paper presents a boon and amend technique for eradicating the artifacts from the Electroencephalogram (EEG) signals. The abolition of artifacts from scalp EEGs is of considerable implication for both the computerized and visual investigation of fundamental brainwave activities. These noise sources increase the difficulty in analyzing the EEG and procurement clinical information related to pathology. Hence it is critical to design a procedure for diminution of such artifacts in EEG archives. This paper uses a blind extraction algorithm, appropriate for the generality of complex-valued sources and both complex noncircular and circular, is introduced. This is achieved based on higher order statistics of dormant sources, and using the de?ation approach Spatially-Constrained Independent Component Analysis (SCICA) to separate the Independent Components (ICs) from the initial EEG signal. As the next phase, level-4 daubechies wavelet db-4 is applied to extract the brain activity from purged artifacts, and lastly the artifacts are projected back and detracted from EEG signals to get clean EEG data. Here, thresholding plays an imperative role in delineating the artifacts and hence an improved thresholding technique called Otsu’s thresholding is applied. Experimental consequences show that the proposed technique results in better removal of artifacts.
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Automatic Robust Segmentation Scheme for Pathological Problems in Mango Crop
Статья научная
Machine vision and soft computing techniques have been promising in the field of agriculture and horticulture to remove the barriers of conventional methods for detecting the plant diseases using different plant parts. Image segmentation technique is first and primary step in all the related researches such as fruit grading, leaf lesion region detection etc. In this paper, a robust technique for Mango crop using different plant parts such as Fruit, Flower and Leaf has been proposed in order to detect the disease more accurately. The captured real time images are pre-processed for illumination normalization and color space conversion before segmentation. The standard K-Means clustering scheme has been made adaptive and edge detection transforms have been applied to improve the segmentation results. Here, the objective function of K-Means clustering technique has been modified and cluster centers also have been updated to segment the diseased parts from images. The results obtained are better in the terms of both general human observation and in computational time.
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Статья научная
This paper presents an information system developed to automate the systems analysis of the quality of technical students’ training using correlation and regression methods. The article considers key problems of quality assessment and outlines the theoretical foundations of correlation and regression analysis in the context of educational data. The structure and algorithm of an information system designed for automated analysis of educational datasets are presented. The system allows to determine pairs of courses for which prediction of grades by means of regression analysis is performed with minimal error. In this study, grades from courses for the previous period were considered as known parameters x, and grades from courses for the next period were considered as predicted results y. The correlation analysis of educational data involved calculating the Pearson correlation coefficient Corr, which quantitatively describes the linear relationship between two parameters, x and y, in the educational dataset. The correlation coefficient Corr allows for a targeted investigation of relationships with high Corr values. The regression analysis of the data involved constructing a regression equation approximated by a polynomial of degree p to establish the relationship between the x and y parameters of the educational dataset. The accuracy of the approximation was evaluated using the root mean square error (Rmse) for the training set and RmseV for the validation set. The automatic selection of the polynomial degree pA, was performed according to the criterion of minimizing the approximation error RmseV on the validation dataset, while also ensuring the monotonicity of the regression equation. Developed in Python, the software performs correlation and regression analysis, prediction, outlier detection, and result visualization. This approach was applied to analyze the semester grades of students in the 'Computer Science' program, covering 12 courses over the first four semesters. Using the constructed regression equations, were forecasted students’ grades in six courses for the 3rd and 4th semesters based on their performance in the same courses during the 1st and 2nd semesters. The developed regression model also allows for evaluating students’ academic achievements through the outlier detection. The proposed correlation and regression analysis models are highly scalable, enabling the processing of educational data for large size. Integrating correlation and regression methods into the systems analysis of technical education quality allows for automated analysis of educational monitoring data, forecasting of student performance, outlier detection, and the recommendation of elective courses to optimize students’ educational trajectories.
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