Pub Date : 2024-08-09DOI: 10.38124/ijisrt/ijisrt24jul300
Yashoda Upreti
Aim: This study aimed to understand the driving factors that lead to commercial sexual exploitation of children (CSEC), the conditions they face in their workplaces, and their compulsions to work in entertainment sector of Kathmandu Valley. Methods: A mixed method of quantitative survey design (N=78 girls) supported by qualitative techniques was applied. A survey included 87 CSEC girls and conducted two focus group discussions. The quantitative and quantitative data were cleaned, analyzed, and presented. Results: The study outcome has revealed that economic factors, lack of education, and inadequate skills were factors for leaving their place of birth to associate with CSEC. Lack of awareness, financial hardships, and uneducated parents living in remote locations of Nepal were other factors making them prone to work as CSEC. Inadequate access to the resources to address the unmet financial and family needs remained other factors pushing the children into CSEC. The physical, psychological, and emotional impact of their association with CSEC is significantly prevalent without having proper redress mechanisms and structures to address the root causes. The safety mechanisms, protection measures, and prevention actions at the source are additional remedies to be in place. Conclusions: Over 90% of the CSEC (N= 78) girls wished for rehabilitation with adequate support for vocational training, livelihood, and education facilities in place. Study findings strongly recommend immediate actions to address these unmet needs.
{"title":"Assessing the Commercial Sexual Exploitation of Children in Kathmandu Valley's Entertainment Sector","authors":"Yashoda Upreti","doi":"10.38124/ijisrt/ijisrt24jul300","DOIUrl":"https://doi.org/10.38124/ijisrt/ijisrt24jul300","url":null,"abstract":"Aim: This study aimed to understand the driving factors that lead to commercial sexual exploitation of children (CSEC), the conditions they face in their workplaces, and their compulsions to work in entertainment sector of Kathmandu Valley. Methods: A mixed method of quantitative survey design (N=78 girls) supported by qualitative techniques was applied. A survey included 87 CSEC girls and conducted two focus group discussions. The quantitative and quantitative data were cleaned, analyzed, and presented. Results: The study outcome has revealed that economic factors, lack of education, and inadequate skills were factors for leaving their place of birth to associate with CSEC. Lack of awareness, financial hardships, and uneducated parents living in remote locations of Nepal were other factors making them prone to work as CSEC. Inadequate access to the resources to address the unmet financial and family needs remained other factors pushing the children into CSEC. The physical, psychological, and emotional impact of their association with CSEC is significantly prevalent without having proper redress mechanisms and structures to address the root causes. The safety mechanisms, protection measures, and prevention actions at the source are additional remedies to be in place. Conclusions: Over 90% of the CSEC (N= 78) girls wished for rehabilitation with adequate support for vocational training, livelihood, and education facilities in place. Study findings strongly recommend immediate actions to address these unmet needs.","PeriodicalId":517644,"journal":{"name":"International Journal of Innovative Science and Research Technology (IJISRT)","volume":"70 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.38124/ijisrt/ijisrt24jul1024
Okafor Godwin, Martha O. Musa
This study explores the challenges faced by public institutions in implementing and maintaining effective ICT security controls, focusing on the University of Port Harcourt. By examining the perceptions of various stakeholders, including ICT administrators, staff, and students, the research identifies key issues related to confidentiality, integrity, and availability within the institution's ICT systems. The findings highlight significant areas for improvement, such as policy enforcement, training, and risk management. The article provides practical recommendations and strategies for public institutions to enhance their ICT security measures, ensuring alignment with the CIA Triad model and addressing emerging security threats. These insights are crucial for policymakers and ICT professionals aiming to strengthen the security posture of educational institutions.
{"title":"Challenges and Strategies for Enhancing ICT Security in Public Institutions","authors":"Okafor Godwin, Martha O. Musa","doi":"10.38124/ijisrt/ijisrt24jul1024","DOIUrl":"https://doi.org/10.38124/ijisrt/ijisrt24jul1024","url":null,"abstract":"This study explores the challenges faced by public institutions in implementing and maintaining effective ICT security controls, focusing on the University of Port Harcourt. By examining the perceptions of various stakeholders, including ICT administrators, staff, and students, the research identifies key issues related to confidentiality, integrity, and availability within the institution's ICT systems. The findings highlight significant areas for improvement, such as policy enforcement, training, and risk management. The article provides practical recommendations and strategies for public institutions to enhance their ICT security measures, ensuring alignment with the CIA Triad model and addressing emerging security threats. These insights are crucial for policymakers and ICT professionals aiming to strengthen the security posture of educational institutions.","PeriodicalId":517644,"journal":{"name":"International Journal of Innovative Science and Research Technology (IJISRT)","volume":"30 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.38124/ijisrt/ijisrt24jul1459
Tsitsi Jester Mugejo, Weston Govere
Missing data cause the incompleteness of data sets and can lead to poor performance of models which also can result in poor decisions, despite using the best handling methods. When there is a presence of outliers in the data, using KNN algorithm for missing values imputation produce less accurate results. Outliers are anomalies from the observations and removing outliers is one of the most important pre-processing step in all data analysis models. KNN algorithms are able to adapt to missing value imputation even though they are sensitive to outliers, which might end up affecting the quality of the imputation results. KNN is mainly used among other machine learning algorithms because it is simple to implement and have a relatively high accuracy. In the literature, various studies have explored the application of KNN in different domains, however failing to address the issue of how sensitive it is to outliers. In the proposed model, outliers are identified using a combination of the Empirical- Cumulative-distribution-based Outlier Detection (ECOD), Local Outlier Factor (LOF) and isolation forest (IForest). The outliers are substituted using the median of the non- outlier data and the imputation of missing values is done using the k-nearest neighbors algorithm. For the evaluation of the model, different metrics were used such as the Root Mean Square Error (RMSE), (MSE), R2 squared (R2 ) and Mean Absolute Error (MAE). It clearly indicated that dealing with outliers first before imputing missing values produces better imputation results than just using the traditional KNN technique which is sensitive to outliers.
{"title":"Integrated ECOD-KNN Algorithm for Missing Values Imputation in Datasets: Outlier Removal","authors":"Tsitsi Jester Mugejo, Weston Govere","doi":"10.38124/ijisrt/ijisrt24jul1459","DOIUrl":"https://doi.org/10.38124/ijisrt/ijisrt24jul1459","url":null,"abstract":"Missing data cause the incompleteness of data sets and can lead to poor performance of models which also can result in poor decisions, despite using the best handling methods. When there is a presence of outliers in the data, using KNN algorithm for missing values imputation produce less accurate results. Outliers are anomalies from the observations and removing outliers is one of the most important pre-processing step in all data analysis models. KNN algorithms are able to adapt to missing value imputation even though they are sensitive to outliers, which might end up affecting the quality of the imputation results. KNN is mainly used among other machine learning algorithms because it is simple to implement and have a relatively high accuracy. In the literature, various studies have explored the application of KNN in different domains, however failing to address the issue of how sensitive it is to outliers. In the proposed model, outliers are identified using a combination of the Empirical- Cumulative-distribution-based Outlier Detection (ECOD), Local Outlier Factor (LOF) and isolation forest (IForest). The outliers are substituted using the median of the non- outlier data and the imputation of missing values is done using the k-nearest neighbors algorithm. For the evaluation of the model, different metrics were used such as the Root Mean Square Error (RMSE), (MSE), R2 squared (R2 ) and Mean Absolute Error (MAE). It clearly indicated that dealing with outliers first before imputing missing values produces better imputation results than just using the traditional KNN technique which is sensitive to outliers.","PeriodicalId":517644,"journal":{"name":"International Journal of Innovative Science and Research Technology (IJISRT)","volume":"27 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.38124/ijisrt/ijisrt24jul1421
T. Swathi, Bhargav Ram. M, Suriyamoorthi, J. M. Ismail Sait, T.Sam Pradeep Raj
This article introduces the creation and implementation of a real-time dashboard for forecasting groundwater levels using javascript and web technologies. The dashboard utilizes historical data and real-time sensor information to offer nearly instantaneous predictions of groundwater levels, aiding in water resource management. The groundwater government URL is a JavaScript program that establishes an interactive web-based platform for forecasting and interpreting groundwater levels visually. By combining machine learning models with geospatial data and continuous monitoring, GPD can anticipate changes in groundwater depth (such as flood risk) and local water table levels at any given moment. Information such as purity level (mg/l), water depth in meters, borewell location, and Ph Level is presented on this dashboard. Users can add parameters to forecast values, visualize predictions, and download data.
{"title":"Creating Dashboard for Groundwater Level Prediction","authors":"T. Swathi, Bhargav Ram. M, Suriyamoorthi, J. M. Ismail Sait, T.Sam Pradeep Raj","doi":"10.38124/ijisrt/ijisrt24jul1421","DOIUrl":"https://doi.org/10.38124/ijisrt/ijisrt24jul1421","url":null,"abstract":"This article introduces the creation and implementation of a real-time dashboard for forecasting groundwater levels using javascript and web technologies. The dashboard utilizes historical data and real-time sensor information to offer nearly instantaneous predictions of groundwater levels, aiding in water resource management. The groundwater government URL is a JavaScript program that establishes an interactive web-based platform for forecasting and interpreting groundwater levels visually. By combining machine learning models with geospatial data and continuous monitoring, GPD can anticipate changes in groundwater depth (such as flood risk) and local water table levels at any given moment. Information such as purity level (mg/l), water depth in meters, borewell location, and Ph Level is presented on this dashboard. Users can add parameters to forecast values, visualize predictions, and download data.","PeriodicalId":517644,"journal":{"name":"International Journal of Innovative Science and Research Technology (IJISRT)","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.38124/ijisrt/ijisrt24jul1490
Kausthubha K. P., S. N V, Anirudh C. K., Chandrahas A.
Tibial plateau fractures are the fractures that involve the articular surface of the tibial condyles. The Schatzker and OA classifications are the most commonly used to classify these fractures. The Schatzker classification system is a widely recognized method used by orthopedic surgeons to categorize tibial plateau fractures into six distinct types. This classification helps in assessing the initial injury, planning the appropriate management strategy, and predicting the prognosis. Each type represents a different pattern of fracture, which can guide treatment decisions and expectations for recovery. These fractures typically result from the external(valgus) or internal(varus) forces on the knee with axial loading. In younger individuals, tibial plateau fractures are most commonly caused by road traffic accidents due to the high-energy impact. However, in elderly patients with osteopenic bone, even a simple fall can lead to these fractures because their bones are more fragile and susceptible to injury. The tibial plateau fractures are intra-articular fractures of the knee joint and are often difficult to treat and have a high complication rate, including early-onset osteoarthritis. Surgery is the preferred modality of treatment for these fractures, along with bone void fillers to address bone defects caused by the injury. At present, there is no consensus on the optimal method of fixation or the void filling to treat such fractures. Techniques of operative management of tibial plateau fractures have become more successful in achieving and maintaining reduction of the fracture. Still, avoiding malalignment of the limb has been shown to be at least as important as articular congruity to long-term joint viability.
{"title":"Radiographic and Functional Outcome of Depressed Tibial Plateau Fractures Treated with Raft Plate with Bone Graft – A Prospective Study","authors":"Kausthubha K. P., S. N V, Anirudh C. K., Chandrahas A.","doi":"10.38124/ijisrt/ijisrt24jul1490","DOIUrl":"https://doi.org/10.38124/ijisrt/ijisrt24jul1490","url":null,"abstract":"Tibial plateau fractures are the fractures that involve the articular surface of the tibial condyles. The Schatzker and OA classifications are the most commonly used to classify these fractures. The Schatzker classification system is a widely recognized method used by orthopedic surgeons to categorize tibial plateau fractures into six distinct types. This classification helps in assessing the initial injury, planning the appropriate management strategy, and predicting the prognosis. Each type represents a different pattern of fracture, which can guide treatment decisions and expectations for recovery. These fractures typically result from the external(valgus) or internal(varus) forces on the knee with axial loading. In younger individuals, tibial plateau fractures are most commonly caused by road traffic accidents due to the high-energy impact. However, in elderly patients with osteopenic bone, even a simple fall can lead to these fractures because their bones are more fragile and susceptible to injury. The tibial plateau fractures are intra-articular fractures of the knee joint and are often difficult to treat and have a high complication rate, including early-onset osteoarthritis. Surgery is the preferred modality of treatment for these fractures, along with bone void fillers to address bone defects caused by the injury. At present, there is no consensus on the optimal method of fixation or the void filling to treat such fractures. Techniques of operative management of tibial plateau fractures have become more successful in achieving and maintaining reduction of the fracture. Still, avoiding malalignment of the limb has been shown to be at least as important as articular congruity to long-term joint viability.","PeriodicalId":517644,"journal":{"name":"International Journal of Innovative Science and Research Technology (IJISRT)","volume":"9 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.38124/ijisrt/ijisrt24jul1248
Nirmeet M Rao
Generative AI models have revolutionized various industries by enabling the creation of high- quality synthetic data, text, images, and more. However, these models face significant challenges in two critical areas: the inability to update information in real-time and inherent biases resulting from training data. The lack of real-time updates limits the applicability of generative AI in dynamic environments where information rapidly changes. Biases in generative AI models can lead to skewed outputs that reinforce existing prejudices, posing ethical and practical concerns. This research addresses these challenges by proposing a novel framework that integrates a built- in research engine and a verifier into generative AI models. The research engine dynamically retrieves and incorporates up-to-date information during the generation process, ensuring that outputs reflect the most current data available. The verifier cross-checks the retrieved information against trusted sources, enhancing the reliability and accuracy of the generated content. To mitigate bias, we introduce a comprehensive bias detection and correction strategy. This approach involves identifying biases in training data using advanced metrics and algorithms and applying corrective techniques to produce more balanced and fair outputs. Experimental results demonstrate significant improvements in both real-time relevance and bias mitigation. Our proposed solutions outperform traditional generative models in maintaining the currency and impartiality of generated content. These advancements have profound implications for the deployment of generative AI in various sectors, including news generation, personalized content creation, and decision support systems. This study highlights the importance of real-time adaptability and fairness in AI, offering a robust framework that can be further refined and expanded to meet the evolving needs of AI applications.
{"title":"Solving Real-Time Information Updates and Mitigating Bias in Generative AI Models","authors":"Nirmeet M Rao","doi":"10.38124/ijisrt/ijisrt24jul1248","DOIUrl":"https://doi.org/10.38124/ijisrt/ijisrt24jul1248","url":null,"abstract":"Generative AI models have revolutionized various industries by enabling the creation of high- quality synthetic data, text, images, and more. However, these models face significant challenges in two critical areas: the inability to update information in real-time and inherent biases resulting from training data. The lack of real-time updates limits the applicability of generative AI in dynamic environments where information rapidly changes. Biases in generative AI models can lead to skewed outputs that reinforce existing prejudices, posing ethical and practical concerns. This research addresses these challenges by proposing a novel framework that integrates a built- in research engine and a verifier into generative AI models. The research engine dynamically retrieves and incorporates up-to-date information during the generation process, ensuring that outputs reflect the most current data available. The verifier cross-checks the retrieved information against trusted sources, enhancing the reliability and accuracy of the generated content. To mitigate bias, we introduce a comprehensive bias detection and correction strategy. This approach involves identifying biases in training data using advanced metrics and algorithms and applying corrective techniques to produce more balanced and fair outputs. Experimental results demonstrate significant improvements in both real-time relevance and bias mitigation. Our proposed solutions outperform traditional generative models in maintaining the currency and impartiality of generated content. These advancements have profound implications for the deployment of generative AI in various sectors, including news generation, personalized content creation, and decision support systems. This study highlights the importance of real-time adaptability and fairness in AI, offering a robust framework that can be further refined and expanded to meet the evolving needs of AI applications.","PeriodicalId":517644,"journal":{"name":"International Journal of Innovative Science and Research Technology (IJISRT)","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.38124/ijisrt/ijisrt24jul727
Mrunal G. Vekhande, Sushant Sawant, Deepanjana Dass, Savitri Mandavi, Prakash N Khandelwal
Introduction: The incidence of adverse drug reactions (ADRs) has significant implications for patient safety and public health. In 1937, the introduction of sulphanilamide for streptococcal infections marked a pivotal moment in drug safety. Present study assessed the pattern, causality and severity of the ADRs reported from a tertiary referral centre. Materials and Methods: Study conducted at the Mahatma Gandhi Mission Medical College and Hospital Kamothe Navi Mumbai recorded the pattern of ADRs between January 2021 and December 2022. The data was collected from the ADR Monitoring Center using the suspected ADR reporting form, version 1.4 of IPC, Ghaziabad, India. The suspected ADR forms were assessed to understand the pattern of ADRs regarding the completeness score of the ADR form. Findings and Discussion: A total of 111 ADRs were recorded, with 43.24% of cases falling within the age range of 21 to 40. Antimicrobial medications were the main culprits behind the majority of ADRs reported by the departments of dermatology and general medicine. Rashes, edema, and urticaria are among the skin-related symptoms among the most frequently reported adverse drug reactions (ADRs). 72.9% of instances were classified as mild, according to severity assessment, whereas 57.65% of ADRs were found to be likely. Conclusions: Because ADRs represent a serious threat to public health, our study highlights the significance of pharmacovigilance in tracking and preventing them. Databases on a national and international level are enhanced by systematic, regular reporting and monitoring of ADRs. In order to raise awareness of ADRs among patients and healthcare professionals, spontaneous reporting is still essential.
{"title":"Adverse Medication Response Recorded in a Referral Health Facility: An Observational Study","authors":"Mrunal G. Vekhande, Sushant Sawant, Deepanjana Dass, Savitri Mandavi, Prakash N Khandelwal","doi":"10.38124/ijisrt/ijisrt24jul727","DOIUrl":"https://doi.org/10.38124/ijisrt/ijisrt24jul727","url":null,"abstract":"Introduction: The incidence of adverse drug reactions (ADRs) has significant implications for patient safety and public health. In 1937, the introduction of sulphanilamide for streptococcal infections marked a pivotal moment in drug safety. Present study assessed the pattern, causality and severity of the ADRs reported from a tertiary referral centre. Materials and Methods: Study conducted at the Mahatma Gandhi Mission Medical College and Hospital Kamothe Navi Mumbai recorded the pattern of ADRs between January 2021 and December 2022. The data was collected from the ADR Monitoring Center using the suspected ADR reporting form, version 1.4 of IPC, Ghaziabad, India. The suspected ADR forms were assessed to understand the pattern of ADRs regarding the completeness score of the ADR form. Findings and Discussion: A total of 111 ADRs were recorded, with 43.24% of cases falling within the age range of 21 to 40. Antimicrobial medications were the main culprits behind the majority of ADRs reported by the departments of dermatology and general medicine. Rashes, edema, and urticaria are among the skin-related symptoms among the most frequently reported adverse drug reactions (ADRs). 72.9% of instances were classified as mild, according to severity assessment, whereas 57.65% of ADRs were found to be likely. Conclusions: Because ADRs represent a serious threat to public health, our study highlights the significance of pharmacovigilance in tracking and preventing them. Databases on a national and international level are enhanced by systematic, regular reporting and monitoring of ADRs. In order to raise awareness of ADRs among patients and healthcare professionals, spontaneous reporting is still essential.","PeriodicalId":517644,"journal":{"name":"International Journal of Innovative Science and Research Technology (IJISRT)","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.38124/ijisrt/ijisrt24jul1270
Maricel G. PEREZ
Fundamentally, effective classroom strategies are a measure expected to improve self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte. In this study, the researcher selected 185 elementary school teachers as the respondents of the study. Stratified random sampling technique was utilized in the selection of the respondents. Non-experimental quantitative research design using descriptive-correlational method was employed. The data collected were subjected to the following statistical tools: Mean, Pearson Moment Product Correlation and multiple linear regression analysis. Findings revealed that effective classroom strategies and self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte were described as extensive. Further, correlation analysis demonstrated that there is a significant relationship between effective classroom strategies and self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte. Evidently, regression analysis proved that effective classroom strategies in terms of behavioral strategies were a significant predictor of self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte. In other words, effective classroom strategies have an influence on the process in self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte. The study, therefore, was conducted for further utilization of findings through publication in reputable research journal.
{"title":"Effective Classroom Strategies and Self-Regulated Learning Behavior among Learners in Davao Del Norte","authors":"Maricel G. PEREZ","doi":"10.38124/ijisrt/ijisrt24jul1270","DOIUrl":"https://doi.org/10.38124/ijisrt/ijisrt24jul1270","url":null,"abstract":"Fundamentally, effective classroom strategies are a measure expected to improve self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte. In this study, the researcher selected 185 elementary school teachers as the respondents of the study. Stratified random sampling technique was utilized in the selection of the respondents. Non-experimental quantitative research design using descriptive-correlational method was employed. The data collected were subjected to the following statistical tools: Mean, Pearson Moment Product Correlation and multiple linear regression analysis. Findings revealed that effective classroom strategies and self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte were described as extensive. Further, correlation analysis demonstrated that there is a significant relationship between effective classroom strategies and self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte. Evidently, regression analysis proved that effective classroom strategies in terms of behavioral strategies were a significant predictor of self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte. In other words, effective classroom strategies have an influence on the process in self-regulated learning behavior among learners in Braulio E. Dujali District, Davao del Norte. The study, therefore, was conducted for further utilization of findings through publication in reputable research journal.","PeriodicalId":517644,"journal":{"name":"International Journal of Innovative Science and Research Technology (IJISRT)","volume":"119 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.38124/ijisrt/ijisrt24jul1437
Roderick D. Swanson
STEM is the acronym for the fields of study in science, technology, engineering, and mathematics. This manuscript is to highlight the need to increase the number of females pursuing education and future employment opportunities in careers that requires study in STEM. Regarding STEM programs, education leaders must provide advantages that bridge the academic achievement gaps for females and other underrepresented minoritized (URM) student groups. Parents, teachers, and school administrators must fill the gaps often found in the academic areas of mathematics and science. The resolution is to introduce students at an early age to the American workforce in STEM-related fields. Students' early interventions include businesses, industries, and community mentorship programs. These mentorship programs are central to meeting every capable STEM worker's need to keep America in a global leadership position. At the forefront, educationalists, policymakers, and legislators are taking the initiative to establish a firm educational foundation that will increase the roles of women and minorities in STEM-related fields. STEM education must break traditional ethnic and gender roles. America must ensure that every gender, race, or ethnicity has a seat at the economic table. Minorities having a seat at the trade and industry table is essential for the nation to compete in a global economy. The educational systems must spark an interest in students pursuing a career in the various fields of STEM. Nevertheless, more people of color must sit at the economic negotiation table to decide their future.
{"title":"Literature Review in Educational Leadership, Policy, and Law within STEM Education","authors":"Roderick D. Swanson","doi":"10.38124/ijisrt/ijisrt24jul1437","DOIUrl":"https://doi.org/10.38124/ijisrt/ijisrt24jul1437","url":null,"abstract":"STEM is the acronym for the fields of study in science, technology, engineering, and mathematics. This manuscript is to highlight the need to increase the number of females pursuing education and future employment opportunities in careers that requires study in STEM. Regarding STEM programs, education leaders must provide advantages that bridge the academic achievement gaps for females and other underrepresented minoritized (URM) student groups. Parents, teachers, and school administrators must fill the gaps often found in the academic areas of mathematics and science. The resolution is to introduce students at an early age to the American workforce in STEM-related fields. Students' early interventions include businesses, industries, and community mentorship programs. These mentorship programs are central to meeting every capable STEM worker's need to keep America in a global leadership position. At the forefront, educationalists, policymakers, and legislators are taking the initiative to establish a firm educational foundation that will increase the roles of women and minorities in STEM-related fields. STEM education must break traditional ethnic and gender roles. America must ensure that every gender, race, or ethnicity has a seat at the economic table. Minorities having a seat at the trade and industry table is essential for the nation to compete in a global economy. The educational systems must spark an interest in students pursuing a career in the various fields of STEM. Nevertheless, more people of color must sit at the economic negotiation table to decide their future.","PeriodicalId":517644,"journal":{"name":"International Journal of Innovative Science and Research Technology (IJISRT)","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.38124/ijisrt/ijisrt24jul1108
Natalia C. Giordana Caffarone Klausen
This study re-examines the life and works of Leo Tolstoy, one of the most influential Russian writers, through the integrated lenses of Critical Race Theory (CRT) and feminist theory, employing a social constructivist approach. By analysing Tolstoy's significant works, including "War and Peace" and "Anna Karenina," this research explores how his narratives address the intersectionality of race, gender, and class. By critically examining characters such as Natasha Rostov, Anna Karenina, and various marginalised figures, the study uncovers Tolstoy's critique of his time's social constructs and power dynamics. This Analysis highlights Tolstoy's progressive empathy for the oppressed and the complexity of his portrayal of women and ethnic minorities. Furthermore, it discusses the contemporary relevance of his work in understanding and addressing ongoing social injustices. By situating Tolstoy's literary contributions within the frameworks of CRT and feminist theory, this research offers new insights into the enduring significance of his critique of systemic inequalities, emphasising the importance of intersectional perspectives in literary studies.
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