International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1080
Predicting Student Program Completion Using Naïve Bayes Classification Algorithm
Статья научная
Data mining approaches provide different educational institutions opportunities to find hidden patterns from the data stored in the database. Many researchers have used these data to develop a model that would assist the institution administrators in decision-making. This study was performed to predict student program completion using the Naïve Bayes classifier technique. The dataset utilized in this study was obtained from Bulacan State University – Sarmiento Campus in the Philippines under BS Information Technology program from five-year graduates’ data for Academic Year 2012-2016. This dataset was pre-processed, cleansed, transformed, and balanced before constructing the model. Ten predictors were used for predicting student completion. The feature selection technique was used to filter and evaluate the significance of each factor. The significant variables assessed by the feature selection technique (Weight by Correlation) were the final parameters in creating the model. The Naïve Bayes classifier was applied to predict the students’ completion using the 70:30 ratios for training and testing dataset distribution. Correlation analysis identified the weight of individual attributes to the label attribute. From 10 possible predictor variables, only four (4) predictor variables were selected after correlation analysis. The identified significant attributes affecting program completion, namely (in order of significance): parents' monthly income, mother and father's educational attainment, and High School GPA attributes. The significant attributes identified in correlation analysis splitted into 70% training data or 447 records and 30% testing data or 191 records. There were 84 out of 191 data samples, or 44% of students were predicted to complete the program. On the other hand, 107 out of 191 data samples, or 56%, were predicted as not completing the program. The accuracy values performed an 84% rating with 80.46% class precision, and 83.33% class recall in the testing dataset (n=191). The outcomes of this study have a significant impact on HEIs, particularly on college completion rates. This study shall be highly significant and beneficial specifically to university administrators as this be a tool for them to identify students who will complete college based on variables included in the model.
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Статья научная
This study compared two neural network models (Multilayer Perceptron and Generalized Regression Neural Network) with a view to identifying the best model for predicting students’ academic performance based on single performance factor. Only academic factor (students’ results) was considered as the single performance factor of the study. One cohort of graduated students’ academic data was collected from the Computer Science and Engineering Department of Obafemi Awolowo University, Nigeria using documents and records technique. The models were simulated using MATLAB version 2015a and evaluated using mean square error, receiver operating characteristics and accuracy as the performance metrics. The results obtained show that although Multilayer Perceptron had prediction accuracy of 75%, Generalized Regression Neural Network had a better accuracy. The response time of Generalized Regression Neural Network (0.016sec) was faster than Multilayer Perceptron (0.03sec) and its memory consumption size (5kb) lower than that of Multilayer Perceptron (8kb). The simulated models were further compared with t-test method using a confidence interval of 95%. The attained t-test result from p-value (0.6854) suggests acceptance of null hypothesis, which shows that there is no significant difference between the predicted Grade Point Average and the actual Grade Point Average. The findings therefore reveal that the overall performance of Generalized Regression Neural Network outperforms the Multilayer Perceptron model with an accuracy of 95%. The study concluded that Generalized Regression Neural Network model which was simulated and with 95 % accuracy could be deployed by educationists to predict students’ academic performance using single performance factor.
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Predictive Model for Academic Training Course Recommendations Based on Machine Learning Algorithms
Статья научная
Given the significance of online education, a recommendation system provides a good opportunity to advise the most suitable courses according to their interest and preferences. This study proposes an academic training course recommendation that applies machine learning algorithms to provide the most appropriate 21st century learning based on individual preferences. To address the issue of imbalanced classification, the eight development skills are grouped into three skill categories during the preprocessing stage. In the classification step, several machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, and Backpropagation Neural Network, are used to create a predictive model, which is then compared to the results of Logistic Regression. These machine learning algorithms predict the skill group based on the teacher preference data, which results in the suggestion of training courses that are customized to the teacher's profile. According to the experimental results, all machine learning algorithms showed superior prediction performance than Logistic Regression. The Backpropagation Neural Network exhibits high precision, reaching up to 78%, and demonstrates the best performance for the testing data. This research demonstrates that machine learning algorithms significantly improve the accuracy and efficiency of the training course recommendation. On this basis, this training course recommendation system will be advantageous to both the teachers looking for up- and reskilling training courses for 21st century learning. Additionally, it will be appropriate for training course designers to establish training courses that develop 21st-century learning in accordance with participants’ interests and professional development.
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Predictive analytic game-based model for Yoruba language learning evaluation
Статья научная
Be it indigenous or foreign language, languages are core for communicating messages from one person to another or group of persons. Primarily, it is learnt at home, schools, through the media like television and radio programmes. However, most of these language-teaching approaches do not measure the percentage growth of people who have gained the knowledge of the language over the years; they also lack the capacity to foretell the range of people that will acquire the knowledge of the language in the latest future. This is because several of the language teaching aids do not have the required dataset to describe and effectively predict the state of the language (a category of people who can speak and write the language) now, and against the future. Here, the research proposed an analytic game based model for Yoruba language evaluation. The essence is first to ascertain the user’s initial knowledge of a language, train users through difference fun filled game stages and levels, evaluate the user at the end of every level and analyse the clustered dataset of users game points to describe and predict the state of the language by using a dual level predictive analytics technique.
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Статья научная
Literature show that there are limited factors for existing models in e-learning systems’ adoption. This has raised an increasing sensible debate about factors affecting successful adoption of e-leaning systems in universities in developing world particularly in Tanzania. This preliminary study aimed at exploring multiple factors for successful adoption of e-learning systems in universities in learner perspective, using DeLone and McLean (2003) IS success model as a base model. This study was conducted by collecting data randomly, using the questionnaire from students of Open Universities of Tanzania (OUT) with response rate of 0.83 in a cross-sectional study and later analyzed through content validity, reliability, and criterion-based predictive validity. The preliminary analysis shows that there are twelve distinctive factors affecting e-learning systems’ adoption in universities in Tanzania. This finding suggests more empirical research studies to follow it up, to cement and generalize this case and validate the proposed model in large scale. The novelty of this research lies on the number and uniqueness of factors found.
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Prioritization of Test Cases in Software Testing Using M2 H2 Optimization
Статья научная
By and large, software testing can be well thought-out as a adept technique of achieving improved software quality as well as reliability. On the other hand, the eminence of the test cases had significant effect on the fault enlightening competence of testing activity. Prioritization of Test case (PTC) remnants one challenging issue, as prioritizing test cases remains not up in the direction of abrasion by means of respect to Faults Detected Average Percentage (FDAP) and time execution results. The PTC is predominantly anticipated to scheme assortment of test cases in accomplishing timely optimization by means of preferred properties. Earlier readings have been presented for place in order the accessible test cases in upsurge speed the fault uncovering rate in testing. In this phase, this learning schemes a Modern modified Harris Hawks Optimization centered PTC (M2H2O-PTC) method for testing. The anticipated M2H2O-PTC method aims to exhaust the possibilities the FDAP and curtail the complete execution time. Besides, the M2H2O algorithm is considered for boosting the examination and taking advantage abilities of the conservative H2O algorithm. For validating the enhanced efficiency of the M2H2O-PTC method, an extensive variety of simulations occur on contradictory standard programs and the outcomes are inspected underneath numerous characteristics. The investigational results emphasized enhanced proficiency of the M2H2O-PTC method in excess of the modern methodologies in standings of dissimilar measures.
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Privacy in the age of Pervasive Internet and Big Data Analytics – Challenges and Opportunities
Статья научная
In the age of pervasive internet where people are communicating, networking, buying, paying bills, managing their health and finances over the internet, where sensors and machines are tracking real-time information and communicating with each other, it is but natural that big data will be generated and analyzed for the purpose of "smart business" and "personalization". Today storage is no longer a bottleneck and the benefit of analysis outweighs the cost of making user profiling omnipresent. However, this brings with it several privacy challenges – risk of privacy disclosure without consent, unsolicited advertising, unwanted exposure of sensitive information and unwarranted attention by malicious interests. We survey privacy risks associated with personalization in Web Search, Social Networking, Healthcare, Mobility, Wearable Technology and Internet of Things. The article reviews current privacy challenges, existing privacy preserving solutions and their limitations. We conclude with a discussion on future work in user controlled privacy preservation and selective personalization, particularly in the domain of search engines.
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Procedure for Processing Biometric Parameters Based on Wavelet Transformations
Статья научная
The problem of the article is related to the improvement of means of covert monitoring of the face and emotions of operators of information and control systems on the basis of biometric parameters that correlate with two-dimensional monochrome and color images. The difficulty in developing such tools has been shown to be largely due to the cleaning of images associated with biometric parameters from typical non-stationary interference caused by uneven lighting and foreign objects that interfere with video recording. The possibility of overcoming these difficulties by using wavelet transform technology, which is used to filter images by combining several identical, but differently noisy monochrome and color images, is substantiated. It is determined that the development of technology for the use of wavelet transforms is primarily associated with the choice of the type of basic wavelet, the parameters of which must be adapted to the conditions of use in a particular system of covert monitoring of personality and emotions. An approach to choosing the type of basic wavelet that is most effective in filtering images from non-stationary interference is proposed. The approach is based on a number of the proposed provisions and efficiency criteria that allow to ensure when choosing the type of basic wavelet taking into account the significant requirements of the task. A filtering procedure has been developed, which, due to the application of the specified video image filtering technology and the proposed approach to the choice of the basic wavelet type, allows to effectively clean the images associated with biometric parameters from typical non-stationary interference. The conducted experimental studies have shown the feasibility of using the developed procedure for filtering images of the face and iris of operators of information and control systems.
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Professional Courses for Computer Engineering Education
Статья научная
A sequence of professional courses of study in Computer Engineering at the authors’ university was initiated. These included Digital Fundamentals, Programmable Logic Design, Computer Hardware and Digital Systems Design. This paper presents a study on how the problem based learning has been used for these courses. It also describes how CDIO (Conceive, Design, Implement, Operate) concepts have been applied with an overview of all the hardware resources necessary to support the degree.
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Статья научная
With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.
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Project-based Learning in Vocational Education: A Bibliometric Approach
Статья научная
The project-based learning (PjBL) paradigm is often considered the most advanced in vocational education. The increasing use of the PjBL paradigm in vocational education is an intriguing topic of study. In line with the rapid growth of information technology, it enables PjBL in vocational education to help students develop problem-solving, critical thinking, and teamwork skills. In this study, a bibliometric method is used to provide insight into the structure of the subject, social networks, research trends, and issues reflecting project-based learning in vocational education. On November 27, 2022, the Scopus database was searched using project-based learning terms in the title. The second search field appears in the title, abstract, and keywords vocational education or TVET, restricted to journal articles or proceedings and in English to keep them current. This analysis revealed 60 articles in Scopus-indexed journals and proceedings between 2010 and 2022. Dwi Agus Sudjimat from Malang State University, Indonesia, was the most prolific author, having authored four articles on the subject. Indonesia is the nation investing the most in developing PjBL models. According to the thematic data, project-based learning is located in the first quadrant, has high centrality and density, and has well-developed questions related to the study topic. The results of this study show that the project-based learning model that is evolving in vocational education is likely to continue to be an important teaching approach in this field.
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Статья научная
There were a lot of methods that introduced in the information search field; one of those methods is the wireless sensor networks; and one of the most famous protocols in WSNs is LEACH protocol. And because of that protocol suffering from some defects like sometimes the node attaching to C.H. near from it, but that C.H. far from the B.S. even the node itself near to the B.S. than its C.H.; to solve that problem a new method will introduce in this research which basing on: Allocation of 5 meters (0-5) and prevent the election of any C.H. on it. Division of the Network area into four parts (near, mid, far, and very far) according to the node`s distance from B.S. Restriction of the attachment between the nodes and the C.Hs. in the same part. If a particular part is empty from the C.H. so the nodes will attach to C.H. from the upper parts, But with a condition (the distance between the C.H. and the node <= the distance between node and B.S. /2) Through these improvements, good results were gotten in the simulation, which showed that the improved LEACH was more efficient than the original LEACH.
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Proposal for a mutual conversion relational database-ontology approach
Статья научная
Whereas ontologies are formal knowledge representations, conveying a shared understanding of a given domain, databases are a mature technology that describes specifications for the storage, retrieval, organization, and processing of data in information systems to ensure data integrity. Ontologies offer the functionality of conceptual modeling while complying with the web constraints regarding publication, querying and annotation, as well as the capacity of formality and reasoning to enable data consistency and checking. Ontologies converted to databases could exploit the maturity of database technologies, and databases converted to ontologies could utilize ontology technologies to be more used in the context of the semantic web. This work aims to propose a generic approach that enables converting a relational database into an ontology and vice versa. A tool based on this approach has been implemented as a proof of a concept.
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Статья научная
The work in this article focuses on the modelling of an intelligent digital ecosystem for educational and career guidance for students and young people seeking their first job or retraining. To do so, the multi-expert system paradigm was used to aggregate the different expertises required for a good guidance, the multi-agent system principle was used to have a modular and easily scalable ecosystem. Indeed, the agents of the system communicate with each other using the FIPA-ACL language, in a collaborative vision, throughout the orientation assistance process to perform tasks such as proposing business sectors, occupations, training, and training paths. The ontologies of the Semantic Web have been used to have a complete semantic description of the shared information and to promote communication between the different software agents of the ecosystem. Big Data principles have also been deployed to manage and exploit structured and unstructured data from different data sources related to the guidance ecosystem. The ecosystem modeled in this way has several innovative and powerful technological and scientific aspects. Thus, in terms of design and modelling, the proposed ecosystem considers all the actors and factors involved in the guidance process, including labor market trends. In technological/scientific terms, it is based on methods that allow it to be modular and scalable.
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Proposal to Teach Software Development Using Gaming Technique
Статья научная
The world today has witnessed the evolution imposes on researchers in the field of education to review the methods and strategies of teaching, since the teaching and learning system is not a collection of information and knowledge that stuffed in mind. It is a development of cognitive performance and modes of thinking in addition to the use of innovative ways and methods to help the student to adapt to its environment and to solve the problems that he/ she faces to make learning meaningful. One of the recent trends is the use of educational teaching games. Games increase the motivation of the learner and ensure the interaction with educational material which, in turn, offers fun and enjoyable manner in order to achieve the desired objectives. This paper attempts to address the need to utilize gaming to improve learning in active ways and to raise level of the learning process in an interactive environment for the students and the teachers. To evaluate the proposed solution, this paper used survey research methodology and the results are highly encouraging by the professionals working in academia.
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Proposed Automated Framework to Select Suitable Design Pattern
Статья научная
Many design patterns are available in the existing literature. Due to the availability of the enormous quantity of design patterns, it is extremely hard for a developer to find the suitable design pattern to address the problem. An experienced developer can even face problem to select the appropriate pattern for a specific problem and it is no man's land for junior developers. This paper proposes a novel framework that will generate problem-related questions to a developer to find suitable design pattern using a repository. The answers to these questions can guide developers to select the suitable design patterns. This paper uses the questionnaire as a data collection instrument to conclude the results. The results are found supportive indicating that the proposed framework will solve the problem in hand.
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Proposed framework to manage software requirements and reuse
Статья научная
Requirement elicitation and analysis form the focal point in the initial stages of the software development process. Unfortunately, in many software development projects, developers and end-users speak different languages. On one hand, end-users prefer to use natural languages while software developers who are technically perceptive, tend to use conceptual models. This difference in technical knowledge creates a communication gap, a potential cause of poor quality software products or project conflicts. The aim of this paper is to investigate the feasibility of a novel technique that seeks to foster effective elicitation of software requirements and support the implementation of structures that match particular requirements. By combining requirement elicitation and re-usable parts, the proposed solution envisages improvements in the overall software design process leading to enhanced requirement specifications. The novel idea is to incorporate an intermediate step for mapping Unified Modeling Language (UML) to Web Ontology Language (OWL) to enable the addition of ontology languages. The proposed model is validated through a survey. The validation results show that the proposed solution allows software developers to elicit software requirements and implement structures that match certain requirements.
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Provisioning Remote Lab Support for IT Programs in Distance Education
Статья научная
In the recent past Internet has become the de-facto communication network. It is being prominently used by Telecommunication, Television and other such networks as a carrier network. The current Internet technology has matured enough to support both Non-Real Time and Real-Time Streaming applications. Recently, even the speed of the access network through which an end user accesses the Internet has also increased substantially. All these have given way to newer Applications for being ported on to the Internet. A similar attempt has been made here to extend the Networking Lab infrastructure to students who have enrolled for their higher education through Distance mode. These students who are spread across the country are able to access the Network Lab to perform their Lab Exercises Live on Network devices as part of their Practical Course.
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Proximity Measurement Technique for Gene Expression Data
Статья научная
Data Mining is an analytical process intended to explore the data in search of consistent patterns. Due to its wide spread applications in biomedical industry and publicly available genomic data, data mining has become upcoming topic in the analysis of gene expression data. Clustering is the first step in understanding the complicated biological systems. The objective of clustering is to organize the samples into intrinsic clusters such that samples with high similarity belong to same cluster. The significance of clustering gene profiles is two-fold. Firstly, it assists in diagnosis of the disease condition and secondly it discloses the effect of certain treatment on genes. In this paper, we propose a new method to cluster gene expression data that is solely based on the concept of hierarchical clustering with a different method to compute the similarity between datasets and merge the pairs. The experimental results on two microarray data show the correctness and competence of proposed technique.
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Psychometric Analysis Using Computational Intelligence for Smart Choices
Статья научная
Currently with world's industries providing endless job varieties, it is getting difficult for the students to choose optimum career lines. Ranging from 16-24 years old, these age groups find themselves unable to recognize their future endeavors. Hence, psychometric tests provide a solution, helping them to recognize their interests, aptitude and personality traits to produce better results. The research process was facilitated by questionnaires involving verbal, spatial, logical, critical and numerical aptitude. The responses were analyzed using statistical techniques, and machine learning algorithms. A number of graphs were plotted for better understanding of the technical details. The proposed psychometric and aptitude analysis model entails accuracy calculation assigning K-means, KNN, confusion matrices and SVM plots. The results of the psychometric analysis gave broad spectra of career choices by studying the pattern of the choices selected by the people. Respondents were supposed to give information about their interests and perceptions in their day to day activities, which in turn reflect information about their inner humanly traits, unknowingly providing an ideal career path.
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