Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1064
Edge-balanced Index Sets of the Nested Graph with Power-cycle C5mxPm5 (I)
Статья научная
Based on the power-cycle nested graph brought before, using the research methods and techniques of graph theory and combinatorial mathematics, through studying the new design idea about the basic graph,nested-cycle subgraph with gear and five-vertex sector subgraph-group, the edge-balanced index sets of the power-cycle nested graph n=5 are provided here, for m≡1(mod3) and m ≥4, and the proofs of the computational of formulas and the construction of the corresponding graphs also give out.
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Educational Background and High School Maths Teachers’ Specialism
Статья научная
Teachers’ Specialism is a world development trend and fashion, but also the needs and the direction of teacher education reform. After the latest curriculum reform, the educational reform and development of math,teachers have become universally concentrated and thoughtful in the field of mathematical education. The study adopting questionnaires and telephone interviews carried out a sample survey to 59 common high school math teachers from 3 provinces, and analyzed the connection between math teachers’ specialism and educational background by the statistical analysis tool SPSS quantitatively and qualitatively. The study shows that both mathematical science knowledge and mathematical educational skills have an obvious connection with the educational background, while there’s little connection between mathematical educational knowledge and the educational background. The study points out a relevant strategy which high school math teachers should attach the same important to pre-job training and post-job training
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Educational Data Mining: Classification Techniques for Recruitment Analysis
Статья научная
Data Mining is a dominant tool for academic and educational field. Mining data in education atmosphere is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational/academic database and can be used for decision making in educational/academic systems. This work demonstrates an effective mining of students performance data in accordance with placement/recruitment process. The mining result predicts weather a student will be recruited or not based on academic and other performance during the entire course. To mine the students' performance data, the data mining classification techniques such as – Decision tree- Random Tree and J48 classification models were built with 10 cross validation fold using WEKA. The constructed classification models are tested for predicting class label for new instances. The performance of the classification models used are tested and compared. Also the misclassification rates for the classification experiment are analyzed.
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Educational Suport for Hypermedia Design
Статья научная
This paper presents a project directed towards the production of a book in two versions, printed and digital, with the provisional title of "Design, Education and Technology: support for teaching Hypermedia Design, which constitutes a didactic material to support teaching and research activities for the Design area. The starting point was the work by Dr. Cristina Portugal, funded by National Council for Scientific and Technological Development – CNPq, as a scholarship researcher in a Post-PhD internship in the Graduate Studies in Design Program. Besides the theoretical content itself, the project for the aforementioned book includes studies on information architecture, layout, programming language and navigability, among other aspects. The book will gather issues about Design, Education and Hypermedia aimed at offering resources to enhance the use of multiple languages that converge in hypermedia environments, their applicability, techniques and methods in light of Design in Situations of Teaching-Learning. The content for producing didactic material, the object of this project, is partially concluded.
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Educational performance analytics of undergraduate business students
Статья научная
Educational data mining (EDM) is an emerging interdisciplinary research area concerned with analyzing and studying data from academic databases to better understand the students and the educational settings. In most of the Asian countries, it is a challenging task to perform EDM due to the diverse characteristics of the educational data. In this study, we have performed students’ educational performance prediction, pattern analysis and proposed a generalized framework to perform rigorous educational analytics. To validate our proposed framework, we have also conducted extensive experiments on a real-world dataset that has been prepared by the transcript data of the students from the Marketing department of a renowned university in Bangladesh. We have applied six state-of-the-art classification algorithms on our dataset for the prediction task where the Random Forest model outperforms the other models with accuracy 94.1%. For pattern analysis, a tree diagram has been generated from the Decision Tree model.
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Effect of Friction on Ball-On-Sphere System Modelled by Bond Graph
Статья научная
This paper presents the model analysis of ball-on-sphere system by considering the effect of friction. The ball-on-sphere system is modelled using bond graph technique. In the bond graph modelling procedures of the system, the various subsystems, storage elements, junction structures, transformer elements, dissipating element with appropriate causality assignments and energy exchange that make up the ball-on-sphere system were identified and modelled. In the model analysis of the ball-on-sphere system, the developed model with effect of friction had time of angular position response of the ball (1504237026699027.png) achieved at 0.5253s while in the system model without effect of friction, time of 0.5408s was achieved for the angular position response of the ball (1504237026699027.png). This shows 2.9% improvement of the angular position response of the ball considering frictional effect in the developed bond graph model of the system.
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Effect of GameMaker on Student Attitudes and Perceptions of Instructors
Статья научная
Computational thinking including the ability to think critically and solve problems provides benefits for every career path. A positive attitude toward computer science can increase the possibility of students selecting courses that increase computational thinking or pursuing computer science (CS) as a major. This research examined the effect of using GameMaker on the attitudes of students toward computer science (CS) and CS instructors in an introductory CS course. The research consisted of an initial study and a two year longitudinal study. The data was collected using student surveys, qualitative student perceptions, and anonymous teaching evaluations. We hypothesized that students who used GameMaker in their class would show improved attitudes toward CS and would evaluate the instructor more favorably. Our research provides evidence that the incorporation of GameMaker into computer science courses may improve students' short-term attitudes toward computer science and both long-term and short-term perceptions of the class instructor.
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Effective Training Data Improved Ensemble Approaches for Urinalysis Model
Статья научная
Urinalysis remains one of the most commonly performed tests in clinical practice. Laboratory work can be greatly relieved by automated analyzing techniques. However, noisy and imbalanced urine samples make automatically identifying and classifying urine-related diseases become very difficult. This paper proposed hybrid sampling-based ensemble learning strategies by improving training data and classification performance. Having compared the effectiveness of several learning classifiers and data processing techniques, the experiments showed that the suggesting methods provided better classification accuracy than other approaches.
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Effectiveness of English Online Learning Based on Dual Channel Based Capsnet
Статья научная
Web-based learning systems have quickly developed, by giving students a broader access to wide range of courses. However, when presented with a huge number of courses, it might be difficult for users to rapidly discover the ones they are interested in, from a large amount of online educational resources. As a result, a course recommendation system is crucial to increase users' learning benefit. Presently, numerous online learning platforms have developed a variety of recommender systems using conventional data mining techniques. Still, these methods have several shortcomings, like adaptability and sparsity. To solve this problem, this study provides a deep learning based English course recommendation system with the extraction of features using a dual channel based capsule network (CapsNet). This network extracts all the important features about the courses and learners and suggests suitable courses for the learners. To evaluate the proposed model’s performance, several investigations are performed on a real-world dataset (XuetangX) and outperforms existing recommendation approaches with an average of 91% precision, 45% recall, 55% f1-score, 0.798 RMSE, and 0.671 MSE. According to the experimental findings, the proposed model provides better and more reliable recommendation performance than the conventional approaches. According to the experimental findings, the proposed model provides better and more reliable recommendation performance than the conventional approaches.
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Effectiveness of MOODLE in Education System in Sri Lankan University
Статья научная
This study examines students' overview of the students' online capabilities of course that we have implemented in the MOODLE platform in a developing country and underlying information technology principles that are critical for an in-depth understanding of e-learning. A structured multiple choice questionnaire was distributed among students' who were enrolled in the certificate of teaching in higher education course at the General Sir John Kotelawela Defence University, Sri Lanka. A total of 31 students participated in this study and completed written and online multiple choice questionnaire on MOODLE. The outcome of this study shows that there is a strong positive response on e-learning on MOODLE platform. Almost 61% of them were able to get extreme good results in the online examination and observed late submission in both printed and online examination. Although the outcome is preliminary in nature, the results provide cause for concern over the status of e-learning education in MOODLE platform in Sri Lanka which is highly satisfactory.
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Effects of Natural Dust on the Performance of PV Panels in Bangladesh
Статья научная
Energy is considered a prime agent in the generation of wealth and a significant factor in economic development. Limited fossil resources and environmental problems associated with them have emphasized the need for new sustainable energy supply options that use renewable energies. Among available technologies for energy production from solar source, photovoltaic system could give a significant contribution to develop a more sustainable energy system. Solar Panel has its wide use starting from a simple 5W diode lamp to a few kW ac drives. A solar panel with a battery and a charge controller and other auxiliary devices like dc to ac converters constitute a Solar Home System (SHS).Solar home system (SHS) is becoming popular day by day and even poor households are now becoming interested to purchase solar home system due to its various advantages. Solar home systems (SHS) have a major problem that is low efficiency. It also decreases output day by day because of improper maintenances, effect of dust and shadow. Accumulation of dust on solar panel of solar photovoltaic (PV) system is a natural process. It was found from the study that the accumulated dust on the surface of photovoltaic solar panel can reduce the system’s efficiency by up to 35% in one month .In this paper we show that the effect of dust accumulation on the solar panel naturally and how it is possible to overcome this problem.
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Статья научная
VANETs is the open model which stimulate in academia and industry oriented researches. However, the model is open and there are many violations in a communication of vehicle to vehicle (V2V) and Vehicle to Infrastructure (V2I). Any anonymous user may extract the useful information. Researchers have proposed many research proposal and solved issues related to VANET. The security is the major concern and to avoid mishappening in driving the vehicle. We proposed the authentication system that provides safety of the driver during travel on the roads. The proposed results deliver the following features: 1) Reliability of VANET model 2) Road Safety 3) Privacy of the vehicles 4) Authentication of message delivery to adjacent nodes. Finally, we provide a view point of how to detect the attacks and withdraw malicious node more efficiently.
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Efficient feature extraction in sentiment classification for contrastive sentences
Статья научная
Sentiment Classification is a special task of Sentiments Analysis in which a text document is assigned into some category like positive, negative, and neutral on the basis of some subjective information contained in documents. This subjective information called as sentiment features are highly responsible for efficient sentiment classification. Thus, Feature extraction is essentially an important task for sentiment classification at any level. This study explores most relevant and crucial features for sentiment classification and groups them into seven categories, named as, Basic features, Seed word features, TF-IDF, Punctuation based features, Sentence based features, N-grams, and POS lexicons. This paper proposes two new sentence based features which are helpful in assigning the overall sentiment of contrastive sentences and on the basis of proposed features; two algorithms are developed to find the sentiment of contrastive sentences. The dataset of TripAdvisor is used to evaluate our proposed features. Obtained results are compared with several state-of-the-art studies using various features on the same dataset and achieve superior performance.
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Element-Based Computational Model
Статья научная
A variation on the data-flow model is proposed to use for developing parallel architectures. While the model is a data driven model it has significant differences to the data- flow model. The proposed model has an evaluation cycle of processing elements (encapsulated data) that is similar to the instruction cycle of the von Neumann model. The elements contain the information required to process them. The model is inherently parallel. An emulation of the model has been implemented. The objective of this paper is to motivate support for taking the research further. Using matrix multiplication as a case study, the element/data-flow based model is compared with the instruction-based model. This is done using complexity analysis followed by empirical testing to verify this analysis. The positive results are given as motivation for the research to be taken to the next stage - that is, implementing the model using FPGAs.
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Статья научная
The increasing use of e-mail in the world because of its simplicity and low cost, has led many Internet users are interested in developing their work in the context of the Internet. In the meantime, many of the natural or legal persons, to sending e-mails unrelated to mass. Hence, classification and identification of spam emails is very important. In this paper, the combined Particle Swarm Optimization algorithms and Artificial Neural Network for feature selection and Support Vector Machine to classify and separate spam used have and finally, we compared the proposed method with other methods such as data classification Self Organizing Map and K-Means based on criteria Area Under Curve. The results indicate that the Area Under Curve in the proposed method is better than other methods.
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Emotional Design in Multimedia Learning: How Emotional Intelligence Moderates Learning Outcomes
Статья научная
This study is designed as a preliminary study to explore the effects of emotional intelligence (EI) on achievement, perceived intrinsic motivation and perceived satisfaction when expose to an emotional designed Multimedia Learning Environment (MLE) that was designed to induce either positive, neutral or negative emotions. All three designs had similar content and narration but differed in visual element such as colour, font size, font style and images. Based on the findings, it was reported that students performed better in the design used to induce negative emotion (NegD design) followed by the positive (PosD) and Neutral (NeuD). There is no significant difference in levels of emotional intelligence towards these learning outcomes; however, students with Low EI performed better overall. EI only qualified perceived satisfaction when using a MLE designed to induce emotions and it was found that students with Low EI preferred the design that induces positive emotions. In addition, High EI students favored designs with emotionality (positive or negative) compared to neutral design.
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Empirical Analysis of Bagged SVM Classifier for Data Mining Applications
Статья научная
Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The feasibility and the benefits of the proposed approaches are demonstrated by the means of data mining applications like intrusion detection, direct marketing, and signature verification. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on real and benchmark data sets of intrusion detection, direct marketing, and signature verification in conjunction with as the base learner. The proposed is superior to individual approach for data mining applications in terms of classification accuracy.
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Empirical Analysis of HPC Using Different Programming Models
Статья научная
During the last decade, Heterogeneous systems are emerging for high performance computing [1]. In order to achieve high performance computing (HPC), existing technologies and programming models aims to see rapid growth toward intra-node parallelism [2]. The current high computational system and applications demand for a massive level of computation power. In last few years, Graphical processing unit (GPU) has been introduced an alternative of conventional CPU for highly parallel computing applications both for general purpose and graphic processing. Rather than using the traditional way of coding algorithms in serial by single CPU, many multithreading programming models has been introduced such as CUDA, OpenMP, and MPI to make parallel processing by using multicores. These parallel programming models are supportive to data driven multithreading (DDM) principle [3]. In this paper, we have presented performance based preliminary evaluation of these programming models and compared with the conventional single CPU serial processing system. We have implemented a massive computational operation for performance evaluation such as complex matrix multiplication operation. We used data driven multithreaded HPC system for performance evaluation and presented the results with a comprehensive analysis of these parallel programming models for HPC parallelism.
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Статья научная
The main aim of this paper is to avoid hot-spot problem in wireless sensor network with uniform energy dissipation among cluster heads in the network. It proposes an energy efficient unequal clustering mechanism to form limited and equivalent number of clusters across different levels of wireless sensor network to enable invariable energy consumption among them. Concentrated cluster formation near base station ensures minimum relay burden on cluster heads to avoid hot-spot problem in multi-hop data forwarding model. Equivalent number of clusters at each level ensures in-common network load on each cluster head among different data forwarding routes. In addition, a simple disjoint multi-hop routing technique is proposed for smooth data forwarding process. Simulation results evidence that the proposed unequal clustering algorithm overcomes hot-spot problem with invariable energy dissipation among cluster heads across the network and elevates sensor network lifetime.
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Energy saving VM placement in cloud
Статья научная
The tremendous gain owing to the ubiquitous acceptance of the cloud services across the globe results in more complexity for the cloud providers by way of resource maintenance. This has a direct effect on the cost economy for them if the resources are not efficiently utilized. Most of the allocation strategies follow mechanisms involving direct allotment of VMs onto the servers based on their capabilities. This paper presents a VM allocation strategy that looks at VM placement by allowing server capacity to be partitioned into different classes. The classes are mainly based on the RAM and processing abilities which would be matched with VMs need. When the match is found the servers from this category are provisioned for the task executions. Based on the experimentation for various datacenter scenarios, it has been found that the proposed mechanism results in significant energy savings with reduced response time compared to the traditional VM allocation policies.
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