International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1080
Статья научная
Synchronous and asynchronous e-learning are two popular e-learning modes that are commonly used in distant learning education. The study investigates how synchronous and asynchronous e-learning affect the academic performance of students. A questionnaire was used to collect data for this study from some students of the National Open University of Nigeria. The findings showed that students' attitude to synchronous and asynchronous e-learning affect their academic performance. The results demonstrated that only 60% of the respondents understand what asynchronous and synchronous e-learning means. Also, only 55% of the respondents believed that asynchronous and synchronous e-learning mode has a positive impact on their academic performance. Moreover, only 52% of the respondents are of the opinion that the curriculum in use at National Open University needs to be updated to increase the impact of the e-learning mode on the learners.
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Статья научная
Currently, the online discussion forums have become the focal point for e-learning in many Higher Learning Institutions (HLIs); this is due to the ubiquitous of Information and Communication Technology (ICT) tools, significant rate and fast growing technology adoption and use in many fields including education. However, developing countries, such as Tanzania, are experiencing technical adoption difficulties, such as limited access to computers, problems with Internet connections as well as the technological reliance gap between tutors and learners; these affect the use of technology in Teaching and Learning (T/L). This study aims to use an Online Discussion Platform (onlineDP) to bridge the technological reliance gap between the tutors and learners in HLIs in Tanzania. In this study, the literature review and qualitative research methods were conducted to develop the prototype of the platform. The UMBC semantic similarity service was used to develop the contents filter used to reduce the number of duplicate discussion questions. The application was mainly developed using Laravel Pre-processor (PHP) framework and My Structured Query Language (MySQL) database. The result is the web-based application prototype that enhances the collaborative learning environment in HLIs in Tanzania. The technologies to be used for T/L, should consider both sides of tutors and learners as well as the theoretical framework for their implementations.
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An optimized approach towards reversible adder/subtractor design on QCA
Статья научная
In the present era of miniaturization, higher power dissipation in form of heat has become a very critical issue for the digital Circuits. This excessive heat may result in the lower chip reliability and even destroy it. Due to this reason a substitute is required for the traditional CMOS technology, Reversible logic is a paradigm in this direction. This paper encompasses of the newly proposed SA reversible logic and basic combinational implementations using a single SA building block only resulting in lower circuit level complexity as well as hardware requirement. The output responses and energy dissipation of proposed SA reversible logic are verified and calculated with the help of QCADesigner and QCADesigner-E simulation tools respectively.
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Analyses of impacting factors of ICT in education management: case study
Статья научная
Research studies and attitudes towards ICT use in education management are shifting, and often significantly. This is likely to have a major impact upon ICT and education management. However, the real significance of the impact for educational management has yet to be seen within this research study. The focus of the research study is to investigate and analyses the ICT usage in Education Management. ICT in teaching has an important role and its impact on the advancement of educational processes related to effective teaching and learning, and modern research in this field is almost irreplaceable. Another important element is the use of different software platforms that facilitate learning visible and make it more concrete, more practical and applicable to everyday life. In order to analyze the data a combination of qualitative and quantitative methodology has been used. In order to analyze this, Case study analysis of high schools in city of Skopje, Macedonia is realized. Insights and recommendations are provided.
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Analysis of Experimental Research Results Focused on Improving Student Psychological Health
Статья научная
The students' psychological health in high education is still being a problematic issue. This article describes the importance of ensuring students' psychological health in higher education today and related empirical research results. The aim of this paper is exploring and analysis research results focused on students' psychological health. To determine the students' psychological health there were used special psychodiagnostic methods. The components of the psychological health were divided as 1) satisfaction level; 2) perceptions of a healthy lifestyle; 3) emotional stability; 4) psychoemotional state; and 5) attitude towards themselves. Obtained results on students' psychological health indicate specific conclusions about various psychological health indicators and the relationship between behaviour and internal health, including in emotional, cognitive and behavioural areas. The results of this research work showed that one of the important indicators of students' psychological health is a decrease in the level of emotional distress and emotional instability (neuroticism, nervousness), a positive change in students' internal state, and an increase in students' satisfaction with their educational environment. The results could be used in the high education system, especially measuring and monitoring students' psychological health.
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Статья научная
The development of the Internet in Indonesia is quite rapid, it is marked by the increasing use of social networks, especially Twitter. Not only to share status or stories, Twitter has become become a means of promotion and campaign for elections. The Twitter data can be used to find out the political polarization in Indonesia that is needed in the 2019 presidential election. The method used in this research is sentiment analysis using naïve bayes classifier and social network analysis using the calculation of network attribute values and centrality values. 8.814 Twitter data was collected using data crawling method. The data are divided into three subsets consisting of jokowi’s sentiments, prabowo’s sentiments, and pilpres’s sentiments. Final result of the sentiment analysis is classified sentiments into positives, negatives, and neutral. The average value of the classification results was 91.27% positive sentiment, 7.56% negatives sentiment, and 1.17% neutral sentiment. This classification yielded the average accuracy of 69.2% for jokowi’s sentiments and 100% for prabowo sentiments. The classification accuracy calculation uses ROCs method. Final results of the social network analysis based on the calculation of network attributes yielded 277 nodes, 7.950 edges, 57,401 average degree, 56.44 average weighted degree, network diameter is 4, 1.853 average path length, 0.201 density, and 5 of number communities. Centrality values generates the 5 most influential actors in social network interactions are jokowi’s of first rank, 2nd SBYudhoyono’s, 3rd detikcom, 4th yjuniardi, 5th mohmahfudmd.
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Analysis of Large Set of Images Using MapReduce Framework
Статья научная
Due to the limitations of a physical memory, it is quite difficult to analyze and process big datasets. The Hadoop MapReduce algorithm has been widely used to process and mine such large sets of data using the Map and Reduce functions. The main contribution of this paper is to implement MapReduce programming algorithm to analyze large set of fingerprint images which cannot be normally processed due to a limited physical memory in order to find the features of these images at once. At first, the images are maintained in an image data store in order to be preprocessed and to extract the features for the biometric trait of each user, and then store them in a database. The algorithm preprocesses and extracts the features (ridges and bifurcation) from multiple fingerprint images at the same time. The extracted points are detected using the Crossing Number (CN) concept based on the proposed algorithm. It is validated using data taken from the National Institute of Standards and Technology’s (NIST) Special Database 4. The data consist of fingerprint images for many users. Our experiments on these large set of fingerprint images shows a significant reducing in the processing time to a nearly half when extracting the features of these images using our proposed MapReduce approach.
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Analysis of PV-FC Hybrid System Operation Considering Sale Electricity
Статья научная
This paper presents a hybrid power generation system modeling and simulation with the objective of electricity sale to distribution network (DN) which consists of photovoltaic (PV) module, proton exchange membrane (PEM) fuel cell (FC), hydrogen storage tank (HST) and electrolyzer (EL).Since last researches in optimal FC and PV application aimed in power electronic approach, In this paper the application between FC and PV is considered with the aim of maximizing profit gained due to electricity sale revenue to DN. The revenue from electricity sale to DN considering electricity price in low load, shoulder load and peak load hours is considered as the system profit. Also in a sensitivity analysis the impact of technical parameters of hybrid system components is investigated on system profit. The results showed that the system saves the electricity by hydrogen storage in HST in low load hours and sale it with more prices in shoulder load hours to DN. Also the obtained results show that several technical parameters of PV and PEM FC have considerable impact on system operation and profit.
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Analysis of Students' Performance by Using Different Data Mining Classifiers
Статья научная
Data mining is the analysis of a large dataset to discover patterns and use those patterns to predict the likelihood of the future events. Data mining is becoming a very important field in educational sectors and it holds great potential for the schools and universities. There are many data mining classification techniques with different levels of accuracy. The objective of this paper is to analyze and evaluate the university students' performance by applying different data mining classification techniques by using WEKA tool. The highest accuracy of classifier algorithms depends on the size and nature of the data. Five classifiers are used NaiveBayes, Bayesian Network, ID3, J48 and Neural Network Different performance measures are used to compare the results between these classifiers. The results shows that Bayesian Network classifier has the highest accuracy among the other classifiers.
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Статья научная
The foundational tenet of any nation's prosperity, character, and progress is education. Thus, a lot of emphasis is laid on quality of education and education delivery system in India with current financial year (2022-23) education budget outlay of Rs. 1,04,277.72 crores. This research contributes in analyzing how students perform in academics depending upon the time spent on their extracurricular activities with the help of three Machine Learning prediction algorithms namely Decision Tree, Random Forest and KNN. Additionally, in order to comprehend the underlying causes of the shortcomings in each machine learning technique, comparisons of the prediction outcomes obtained by these various techniques are made. On our dataset, the Decision Tree outscored all other algorithms, achieving F1 84 and an accuracy of 85%. The research, which is at an introductory level, is meant to open the door for more complexes, specialised, and in-depth studies in the area of predicting the performance in academics.
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Статья научная
The study was aimed at analyzing the effectiveness of blended education for economics students using information and communication technology (ICT). The research methods consisted of literature analysis, case method, comparative analysis, mathematical statistics, and statistical experiment. The article describes the following results. Three Russian universities (Vladivostok State University of Economics and Service [VSUES], Kemerovo State University [KemSU], and Ryazan State Radio Engineering University [RSREU]) have introduced ICT to implement a blended model for teaching economic disciplines. This made it possible to use the strengths of traditional classroom and distance electronic education, as well as to quickly correct the problems that arise at the initial stage of ICT implementation, especially when training systems are integrated into international educational projects. The field study enrolled 236 economics students from the above-mentioned universities. The obtained empirical data confirmed some hypotheses regarding the effectiveness of ICT in teaching economics students. The practical significance of the article lies in the possibilities of applying leading ICT technologies to improve the professional competences of future businesspeople in blended economic learning. The results obtained can help universities to shape a rational economic blended learning course to maximize the business impact for future careers in this field. Future researchers may pay attention to the effectiveness of using Massive Open Online Courses (MOOCs) in the context of improving the economic education of future entrepreneurs with the possibility of involving real business cases in their educational process.
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Статья научная
Pedagogical scientists often need to process the results of a pedagogical experiment. However, not every scientist (especially in humanitarianism) has appropriate mathematical training, so statistical data processing is a problem for him. Scientists-pedagogues in Ukraine use various statistical methods to process the results of a pedagogical experiment and face the problem of cumbersome calculations and the accuracy of assessments. Therefore, we developed a method that is based on the correct mathematical apparatus, simplifies the processing of empirical data, and allows us to draw qualitative conclusions without the explicit use of mathematical apparatus. To simplify the statistical analysis of the results of the pedagogical experiment and the interpretation of the obtained data, the authors suggest using a spreadsheet and analyzing the data according to Student's and Fisher's criteria (comparing the average sample and its variance) and controlling intermediate indicators of the results of the pedagogical experiment. The method developed by the authors has an advantage compared to other methods: it is enough to analyze the pair "mean and variance" for the sample to conclude the significance of the differences in the control and experimental groups. The method has a simple implementation since almost every researcher has a spreadsheet processor on his computer. The method does not require a thorough knowledge of the statistics course. The method guarantees more reasonable conclusions (two criteria are used at once), which is important when conducting a pedagogical experiment.
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Analysis on Energy Optimized Data Collection in Tree Based Ad-Hoc Sensor Network
Статья
Fast and energy efficient data collection in an energy constraint ad-hoc sensor network is always a challenging issue. The network topology and interferences causes significant effects on data collection and hence on sensors’ energy usage. Various approaches using single channel, multichannel and convergecasting had already been proposed. Here in this paper we have shown data collection performance using multi-frequency in channel assignment, and effect of network topology, for moderate size networks of about 50-100 nodes. For the study we have used some realistic simulation models under many-to-one communication paradigm called convergecast, a single frequency channel and TDMA technique to have minimum time slots for convergecasting.
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Analyzing Sentiments on Twitter Using Deep Learning Techniques
Статья научная
In today’s digital age dominated by social media, understanding public sentiment through Twitter analysis has become imperative. With a staggering 100 million active users on platforms like Twitter and an influx of 572,000 new accounts daily, the vast reservoir of user-generated content underscores the necessity for advanced sentiment analysis tools. This study delves into the realm of sentiment analysis techniques on Twitter, with a particular emphasis on employing Machine Learning (ML) methods. The proposed framework harnesses the power of Natural Language Processing (NLP) and Deep Learning architectures, specifically advocating for a synergistic blend of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Additionally, it explores the efficacy of traditional ML algorithms such as Support Vector Machines (SVM), Random Forest, and Multi-Layer Perceptron (MLP) in this context. The study’s findings illuminate diverse performance metrics across the employed models. While SVM exhibits moderate accuracy, it grapples with challenges in recall and F1-score for sentiment class 1. Conversely, the CNN-LSTM model emerges as a standout performer, boasting impressive accuracy rates of 97% and 98% respectively. Notably, this model excels in sentiment classification across all classes, underscoring its efficacy in discerning nuanced sentiment nuances within tweets. Furthermore, the study underscores the critical importance of judiciously selecting ML algorithms tailored to the intricacies of Twitter sentiment analysis. By leveraging advanced NLP techniques and deep learning architectures, researchers and practitioners can glean deeper insights into the dynamic landscape of public sentiment on social media platforms like Twitter. Such insights hold significant implications for diverse domains, including marketing, brand management, and public opinion analysis.
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Analyzing Student Evaluations of Teaching in a Completely Online Environment
Статья научная
Almost all educational institutions have shifted their academic activities to digital platforms due to the recent COVID-19 epidemic. Because of this, it is very important to assess how well teachers are performing with this new way of online teaching. Educational Data Mining (EDM) is a new field that emerged from using data mining techniques to analyze educational data and making decision based on findings. EDM can be utilized to gain better understanding about students and their learning processes, assist teachers do their academic tasks, and make judgments about how to manage educational system. The primary objective of this study is to uncover the key factors that influence the quality of teaching in a virtual classroom environment. Data is gathered from the students’ evaluation of teaching from computer science students of three online semesters at X University. In total, 27622 students participated in these survey. Weka, sentimental analysis, and word cloud generator are applied in the process of carrying out the research. The decision tree classifies the factors affecting the performance of the teachers, and we find that student-faculty relation is the most prominent factor for improving the teaching quality. The sentimental analysis reveals that around 78% of opinions are positive and “good” is the most frequently used word in the opinions. If the education system is moved online in the future, this research will help figure out what needs to be changed to improve teachers’ overall performance and the quality of their teaching.
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Analyzing Students’ Performance Using Fuzzy Logic and Hierarchical Linear Regression
Статья научная
Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.
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Статья научная
A well designed website using various search engine optimization (SEO) techniques can help to survive in the competition. Thus, for the students who are likely to be web developer in future; learning the theoretical concept of SEO is not enough. The way in which SEO strategy is being drafted varies as per the purpose of website. Hence along with the concept assimilation, instructor needs to make the student think critically to identify the problem and solve it in best possible way. Hence to explore the board panorama of SEO techniques, experiential and collaborative leaning techniques are used. The main objective of the study is to analyses the impact of these modern techniques on depth of concept assimilation by students. To ascertain the effect of these learning techniques, analytical data of the entire website is analyzed. Also feedback is taken from student to know their perception about the whole process. It has been found that students enjoyed the whole learning process. The analytical data proves that the website performed really well which in turn proves that student got in depth understanding of the concept and they were able to implement it commendably in real world scenario.
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Analyzing the Influencing Factors of Group Learning: A Mixed Approach
Статья научная
The purpose of this paper is to explore which factors influence group learning content, and content analysis is chosen as the research method. The sample for this study is the literature of group learning. 35 books and 1 paper was examined. The coding system for the content analysis is an opened and a self-expanded system in this study, which means that the original coding system can be updated if the new coding item is developed during the data collection. A total of 62 influencing factors are identified in terms of the content analysis. In order to organise them systematically, we categorised them into four aggregations according to one model of the group learning processes:planning, organising, learning process, and evaluation. The result of this study may be used to design a questionnaire and to model group learning process in our further research.
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Analyzing the Performance of SVM for Polarity Detection with Different Datasets
Статья научная
Social media and micro-blogging websites have become the popular platforms where anyone can express his/her thoughts about any particular news, event or product etc. The problem of analyzing this massive amount of user-generated data is one of the hot topics today. The term sentiment analysis includes the classification of a particular text as positive, negative or neutral, is known as polarity detection. Support Vector Machine (SVM) is one of the widely used machine learning algorithms for sentiment analysis. In this research, we have proposed a Sentiment Analysis Framework and by using this framework, analyzed the performance of SVM for textual polarity detection. We have used three datasets for experiment, two from twitter and one from IMDB reviews. For performance evaluation of SVM, we have used three different ratios of training data and test data, 70:30, 50:50 and 30:70. Performance is measured in terms of precision, recall and f-measure for each dataset.
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Analyzing the performance of various clustering algorithms
Статья научная
Clustering is one of the extensively used techniques in data mining to analyze a large dataset in order to discover useful and interesting patterns. It partitions a dataset into mutually disjoint groups of data in such a manner that the data points belonging to the same cluster are highly similar and those lying in different clusters are very dissimilar. Furthermore, among a large number of clustering algorithms, it becomes difficult for researchers to select a suitable clustering algorithm for their purpose. Keeping this in mind, this paper aims to perform a comparative analysis of various clustering algorithms such as k-means, expectation maximization, hierarchical clustering and make density-based clustering with respect to different parameters such as time taken to build a model, use of different dataset, size of dataset, normalized and un-normalized data in order to find the suitability of one over other.
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