Analysis of web-based learning systems by data mining

Access to agricultural credit is one of the key factors that boost the adoption of technologies to improve agricultural production. Information and Communication Technology (ICT) tools have been referred to as essential channels in the dissemination of agricultural extension information. However, it has been observed that the majority of peasants were not using them to access the information. The aim of this study was to delineate the level of agricultural credit access, sources of the credit, amount of the credit accessed, and the correlation between access to the credit and the use of ICT tools in the extension services among peasants. A correlation research design was utilized in this study at Rangwe Sub-County, Kenya. Data were collected with the help of pretested structured questionnaire from 106 peasants who grow cassava in the Sub-County. The data obtained were analyzed using Spearman’s correlation and descriptive statistics with the aid of Statistical Package for Social Science (SPSS) Version 25. Descriptive results revealed that 68% of the peasants interviewed had no access to the credit, while 32% had access. The majority (70%) of those who had the access received it from Saving and Credit Co-Operative (SACCOs). The majority (68%) received the lowest amount of credit. Spearman’s correlation revealed that there was a moderate, positive correlation between access to credit and the use of the ICT tools. The correlation was statistically significant at 1% level of significance (R = +.646**, P = .000, R2 =0.417). Access to credit appears to provide a positive and moderate correlation with the use of the ICT tools as it predicts 42% of the use of the tools in cassava production. The positive correlation coefficient indicates that an increase in access to agricultural credits among the peasants translates to an increase in the adoption of ICT tools in agricultural extension.

Aims: This study sought to determine selected demographic characteristics, the extent of Information and Communication Technology (ICT) tools’ usage, opportunities in ICT, challenges in the use, strategies to improve the use, and the correlation between the selected demographic factors and ICT tools’ usage among peasants who produce cassava in Rangwe Sub-County, Kenya. Study Design: A correlation research design was used in this study conducted at Rangwe Sub-County, Kenya from 8th December 2021 to 14th January 2022.
 Methodology: The study gathered data with the aid of pretested structured questionnaire from 106 Cassava Peasants (CPs) who used or not used ICT tools in agricultural extension.
 Results: The response rate achieved was 100%. The majority of the CPs were female (62%), middle-aged (36-50 years), attained primary education (52%), and earned the lowest average annual income (X ≤ KES160, 000). The majority of ICT tools users in extension were males, elite, higher-income earners, and youths. The largest percentage of ICT tools users mentioned, ICT tools availability as one of the opportunities in ICT, expensiveness as the main challenge in the use, ICT services subsidies as one of the improvement strategies. Spearman’s correlation analysis showed that a correlation between the selected demographic factors and ICT tools’ usage was statistically significant at a 1% level of significance (P=.000).
 Conclusion: Adoption of ICT tools in agricultural extension services is directly proportional to income, gender equality, and education, while it is inversely proportional to age. The provision of supporting policies for the selected demographic factors, availability of training centers, and subsidized credit interest rate would increase ICT tools usage in extension.

T his study sought to investigate EFL faculty members’ perceptions, competencies, current practice and preparation in integrating information and communication technology (ICT) tools in their teaching and learning activities in relation to different variables (gender, age, teaching experience, academic level differences, training); as well as barriers that prevent them from making effective use of such tools. A total of one hundred and seventy two EFL faculty members from 14 public universities in Egypt participated in this study. They completed a five-point Likert scale questionnaire that was later analyzed using quantitative methods. Subsequently, follow-up interviews were conducted with 12respondents. Overall, the results revealed that while EFL staff members had positive perceptions of the usefulness of ICT tools in language teaching and learning and they are familiar with ICT use mostly in their personal lives and, to a lesser degree, in teaching practices, they were less likely to integrate them into their language teaching. Findings suggested continuous professional training should be seen as a main solution in assisting staff members strengthens their ICT skills and encouraging them to keep up to date with recent technologies in ELT.

Abstract: This project can benefit from the use of data mining techniques in a variety of fields, including science, medicine, and education. Because of laws and computer accessibility, a lot of data is now available in the medical and health care sectors. It is no longer necessary to handle such a large amount of data at once because of the significant advancements in computer technology made by modern technology. Evaluating data mining methods for clinical and healthcare applications is one of the paper's main goals, which aims to facilitate informed decision-making. In the world of computers, this well-known and potent technology is highly sought after. It is a branch of computer science that creates new research and findings from data that is already available in various databases. It extracts new patterns from massive data sets and the knowledge associated with these patterns by utilizing Artificial Intelligence, machine learning, and database management techniques. This method can be used to extract data automatically or semi-automatically. Clustering, forecasting, path analysis, and predictive analysis are some of the various parameters used in data mining.

This research was conducted in Karangtengah Village, Imogiri District, Bantul Regency, Special Region of Yogyakarta. The landslide incident that occurred in the Karangtengah Village area on November 28, 2017 then prompted several groups to take the initiative to install an early warning system and several other supporting tools at the scene. This study aims to identify what Information and Communication Technology tools already exist in the Karangtengah Village area that can support landslide hazard monitoring activities, and see how residents manage these tools. This research was conducted with a qualitative method using a Participatory Rural Appraisal tool. The results of this study show that the presence of Information and Communication Technology tools requires the community to have the capacity to synergize with existing tools and manage them independently so that there is no failure of the role of Information and Communication Technology in supporting the work of Community-Based Disaster Risk Management in Karangtengah Village. Access to data generated by existing Information and Communication Technology tools will also be useful for residents to monitor phenomena or conditions around them.

The use of educational data mining approaches presents numerous data from a variety of educational databases. This data is mined to give useful knowledge and information that can assist in the prediction of student performance perceptions. Considering the use of supervised learning approaches, decision trees can be obtained to assist with the perception predictions. A decision tree is a data classification technique that is used in displaying a result after the data mining process. The use of data mining techniques on data related to student performance assists in extracting valuable information from large datasets available within the institutions. This paper discusses the factors that influence the student learning process in an internet mediated environment. Additionally, this paper uses a decision tree to show which factors affect student performance in these environments. This will enable us determine whether a student is likely to perform well or not to perform well while using the technology in the learning process.

The study was conducted in Nuapada District of Odisha to know the knowledge about information and communication technology tools by Paddy growers the in Nuapada district of Odisha. A total number of 120 respondents were selected randomly from six villages under Nuapada block because productivity, production and area under rice cultivation were found to be maximum. The data were collected by personnel interview method by using pre structured interview schedule and later appropriate statistical analysis was done to find out the meaningful results. The findings of the study revealed that 54.16 per cent of the respondents belonged to the middle- aged group, majority of the respondents 59.16 per cent belong to the OBC caste and majority of the respondents belongs to medium level of annual income i.e. 50,000 - 1 lakh. Majority of the respondents i.e. 45.83 per cent had medium level knowledge about rice cultivation practices. The findings also reveled that 48.34 per cent of respondents had medium level knowledge about information and communication technology tools followed by 30 per cent and 21.66 per cent of the respondents with high and low levels of knowledge respectively. It was found that independent variables like age, caste, economic motivation were positively and significantly correlated with knowledge about ICT tools by the respondents.

This chapter examines the influence of Facebook (FB) in formal and informal learning in the language classroom. The theoretical framework of Web-based language teaching is first introduced. A selection of studies concerning the use of Social Networking Sites in the teaching of English (ELT) and conclusions drawn from the literature follow suit. The studies have been selected on the grounds of discussing students' engagement, motivation, collaboration, and communication through the use of SNS and, for the most part, Facebook in ELT. The studies also focus on the attitudes of higher education, foreign language learners towards FB, as well as the potential of FB as an academic teaching medium that may support other Information and Communication Technology (ICT) tools like Learning Management Systems. The review of the studies indicates that Facebook may reinforce communication skills and engagement in the learning process and support participation and collaborative learning in formal learning environments.

In the context of the Philippine educational system, the integration of Information and Communication Technology (ICT) in various subjects is a growing trend that has the potential to enhance both teaching and learning processes. This study assessed the influence of the teachers’ attitudes and perceptions toward the integration of Information and Communication Technology tools in the selected upland elementary schools of Tabogon District, Tabogon, Cebu, for the school year 2024–2025 as the basis for a targeted intervention plan. The research design utilized a descriptive correlational research design. The weighted mean and the Pearson product-moment correlation r were also employed. The study revealed that there was no significant relationship between most of the respondents’ profile variables such as age, gender, highest educational attainment, number of years in teaching, learners’ general academic performance, and availability of ICT tools and their perceptions in all three aspects (challenges, benefits, and ethical considerations). In addition, it also concluded that variables such as age, gender, highest educational attainment, number of years in teaching, participation in ICT tools, learners’ general academic performance, and the availability of ICT tools do not significantly influence respondents’ attitudes in these three areas. Therefore, the researcher highly recommends applying the output of this study entitled “Intervention Plan on Enriching Attitudes and Perceptions Toward the Integration of Information and Communication Technology Tools and Academic Performance in EPP”.

The Place of Feedback in the Pedagogical System of the Lesson

Data Mining refers to a set of methods applicable to large and complex databases to eliminate the randomness and discover the hidden pattern. Datamining (DM), also known as knowledge discovery from databases (KDD), is the extraction of new knowledge from huge databases. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. Data mining tools can forecast the future trends and activities to support the decision of people. The scope of datamining is associated with Uncovering trends and patterns are a great power for the businesses of all sectors and industries. Modern intrusion detection applications are confronted with a variety of issues. These applications must be reliable, extensible, manageable, and minimal in maintenance costs. Data mining-based intrusion detection systems (IDSs) have shown high accuracy, good generalisation to novel types of intrusion, and stable behaviour in a changing environment in recent years. The number of hidden layers in various neural network topologies is evaluated in order to discover the best neural network. The technique of attempting to discover instances of network attacks by comparing current behaviour to the expected actions of an intruder is known as misuse detection. Artificial neural networks have the ability to detect and classify network activity using data that is limited, incomplete, and nonlinear. The main purpose of this work is to identify privacy and security concerns among cloud computing participants and consumers in a distributed environment. Techniques like Machine Learning, Natural Language Processing (NLP), and Data Mining are combined to automatically identify and uncover patterns from many sorts of materials. Predictive analytics is capable of dealing with both continuous and discontinuous changes. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics. The contemporary study in text or document mining is focusing on syntactic components and the semantic environment. In order to accomplish this, and with the motivation gained from our previous research contributions, we investigated a mining model to classify documents based on the Order of Context, Concept, and Semantic Relations (OCCSR). The use of data mining techniques based on Cloud computing will enable users to retrieve meaningful information from virtually integrated data warehouses, lowering infrastructure and storage costs. Data mining can extract useful and potentially useful information from the cloud. Big Data is typically defined by three characteristics known as the 3Vs (Volume, Velocity and Variety). The surveys approaches, environments, and technologies in key areas for Big Data analytics capabilities and discusses how they aid in the development of analytics solutions for Clouds. The clustering technique belongs to an unsupervised learning and it is used to discover a new set of categories. Grid-based clustering has the shortest processing time, which is typically determined by the size of the grid rather than the data. We compare the performance of three clustering algorithms: hierarchical clustering, density-based clustering, and K Means clustering. The majority of current approaches to detecting misuse involve the use of rule-based expert systems to identify indicators of known attacks. We provide a brief overview of the use of various Artificial Intelligence techniques and their advancements in the design, development, and application of Intrusion Detection Systems (IDS) for protecting computer and communication networks from intruders. The goal of Knowledge Discovery in Data (KDD) is to extract information that is not obvious by using careful and detailed analysis and interpretation. To drive decisions and actions, analytics employs KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation techniques.

Free and Open Source Software (FOSS)/Open Educational Systems development projects abound in higher education today. Many universities worldwide have adopted open source software like ATutor and Moodle as an alternative to commercial or homegrown systems. The move to open source learning management systems entails many special considerations, including usage analysis facilities. The tracking of users and their activities poses major technical and analytical challenges within web-based systems. This paper examines how user activity tracking challenges are met with data mining techniques, particularly web usage mining methods, in four different open learning management systems: ATutor, LON-CAPA, Moodle, and Sakai. As examples of data mining technologies adapted within widely used systems, they represent important first steps for moving educational data mining outside the research laboratory. Moreover, as examples of different open source development contexts, exemplify the potential for programmatic integration of data mining technology processes in the future. As open systems mature in the use of educational data mining, they move closer to the long-sought goal of achieving more interactive, personalized, adaptive learning environments online on a broad scale.

Computer-based technologies have changed our daily lives, such as the way we live, interact, learn, and play. Data collected through these technologies are now being used to support the second round of revolution in all of these areas. Data mining (DM) research has progressed rapidly in recent years, and the information design of educational management systems has also progressed significantly. Using big data technologies, several educational management systems have collected a great deal of information. However, the DM implementation in educational management systems is still in its initial stages. The use of DM techniques in educational management systems is theoretically important and practically useful. With the advancement of information technology (IT) in the field of college education, the present ideological and political theory courses in colleges and universities have gradually developed into information-based teaching, which has increased teaching efficiency and quality. This paper investigates the “work assessment quantitative table” of ideological and political education (IAPE) administration in universities using data mining and clustering technology techniques. In addition, this study utilizes the k-means clustering algorithms to analyze the data from the “work assessment quantitative table” of IAPE of university administration, which successfully overcomes the limitations and inadequacies of standard analysis approaches.

Discover the next generation of data-mining tools and technologyThis book brings together an international team of eighty experts to present readers with the next generation of data-mining applications. Unlike other publications that take a strictly academic and theoretical approach, this book features authors who have successfully developed data-mining solutions for a variety of customer types. Presenting their state-of-the-art methodologies and techniques, the authors show readers how they can analyze enormous quantities of data and make new discoveries by connecting key pieces of data that may be spread across several different databases and file servers. The latest data-mining techniques that will revolutionize research across a wide variety of fields including business, science, healthcare, and industry are all presented. Organized by application, the twenty-five chapters cover applications in:. Industry and business . Science and engineering . Bioinformatics and biotechnology . Medicine and pharmaceuticals. Web and text-mining . Security. New trends in data-mining technology. And much more . . .Readers from a variety of disciplines will learn how the next generation of data-mining applications can radically enhance their ability to analyze data and open the doors to new opportunities. Readers will discover:. New data-mining tools to automate the evaluation and qualification of sales opportunities. The latest tools needed for gene mapping and proteomic data analysis. Sophisticated techniques that can be engaged in crime fighting and preventionWith its coverage of the most advanced applications, Next Generation of Data-Mining Applications is essential reading for all researchers working in data mining or who are tasked with making sense of an ever-growing quantity of data. The publication also serves as an excellent textbook for upper-level undergraduate and graduate courses in computer science, information management, and statistics

Audit of financial statements is an activity that can usually be carried out to evaluate the fairness of a company's financial statements carried out by an independent party. Assessment of the fairness of financial statements is very important because of the conflict of interest between the preparers of financial reports and stakeholders. The audit usually uses sampling to obtain data related to the fairness assessment. The auditor uses sampling because of time and cost constraints. Sampling also has a risk that can lead to errors in assessing the fairness of financial statements. Therefore we need a method that can improve accuracy in data collection and processing, for example data mining. Data mining is a method that can be used to collect and process data more quickly and accurately. The use of data mining techniques that may develop in the future can have an impact on the audit process. Data mining in audits will be both an opportunity and a challenge for auditors in the future.