With growing technological advancements, demands for the industry with skilled and equipped workforce are proportionately rising. While this match between curricular offerings in academia and needs in the industry has been addressed in many countries across the globe through initiatives such as the K-12 educational system, some countries like the Philippines have only started its adoption. In the Philippines’ early adoption of the K-12 educational system, several concerns have been raised regarding its implementation, mainly, the mismatch between coursework offered in Philippine K-12 educational institutions with industry demands. With such outcomes, it is necessary to determine the status of the K-12 educational system in the Philippines. This paper attempts to shed light on such concerns by evaluating the performance of the K-12 students using a standardized approach. The Scholastic Abilities Test for Adults, a standardized test for measuring the academic competence of adults, is used in this study to measure scholastic abilities. The descriptive analyses made in this paper may aid in the development of more robust strategy frameworks for positioning the current K-12 educational system to global and industry demands. Moreover, the results obtained in this study would aid stakeholders in overseeing strategies that would address current gaps in the K-12 educational system of the country.

/pdf/evaluating-the-academic-performance-of-k-12-students-in-the-xbf98u5o50.pdf

Evaluating the Academic Performance of K-12 Students in the Philippines: A Standardized Evaluation Approach

https://doi.org/10.1016/j.evalprogplan.2022.102100

Preference analysis on the online learning attributes among senior high school students during the COVID-19 pandemic: A conjoint analysis approach

Recent years have seen a surge in scientific research examining the several ways in which racism may have a detrimental effect on health. This emphasis has been highlighted by the remarkable willpower of racial inequities in health, and observable data also suggests that social factors by themselves do not adequately explain racial oppressions in health and well-being. Racism is believed to be a primary cause of ill health outcomes for racial minorities and racial inequities in health. Racial bias towards black people, citizens, and people of color persist in the health care system. This results in significant harm to those who utilize and work in these environments. This research study provided an overview of institutional racism, cultural racism, individual-level prejudice, and its associated mental and physical health repercussions. We describe significant search is for and define significant problems for future investigation for each instrument. Additionally, we review the evidence for anti-racism interventions and highlight the research required to achieve more informed competency in a particular area.

Racism and Health: Evidence and Needed Research

This paper aims at reviewing the School-Based Management recent studies. As the research method, researcher follows the systematic procedures for literature review article such as collecting data, reviewing, categorizing, and presentation. This paper answers the three questions: (A) definition of SBM, (B) factors considered to implement SBM, and (C) the roles of practitioners. Data is based mainly on secondary data, published papers in the international journals in particular. In the findings, the researcher presents a certain definition of SBM defined by prior researchers, authority sharing, facility and infrastructure management, budget management, autonomy, transparency as well as the roles of school players, in-depth and breadth. Eventually, the recommendations are also appointed for further studies to promote the School-Based Management literature.

http://pedagogi.ppj.unp.ac.id/index.php/pedagogi/article/download/897/289

Revisiting the School-Based Management Recent Studies

Business forecasting is the act of evaluating previous performance in order to use the knowledge gathered to forecast future business situations so that business strategies can be created to achieve goals. Recent computing and technological advancements have eased the routine capture and storing of corporate data that may be utilised to support business decisions. In recent years, no corporate function has grown as quickly as forecasting. Business forecasting is a planning tool that is used to aid in decision making and planning within a business. It empowers people to identify critical parameters and variables that may be controlled in advance to provide management-oriented results in the future. In layman's terms, business forecasting is the practise of estimating or projecting future patterns based on company data. Forecasting is becoming increasingly crucial in today's business world as companies strive to increase customer happiness while lowering the cost of providing goods and services. When a man takes on the job of operating a business, he automatically takes on the burden of attempting to anticipate the future, and his success or failure is heavily reliant on his ability to properly forecast the future course of events. Forecasting tries to reduce the uncertainty that has surrounded management decision making in terms of cost, profit, sales, production, pricing, and so on. [1][2]
Machine learning is one of the most active research topics in artificial intelligence today, involving the study and development of computational models of learning processes. A lot of intriguing work has recently been done in the field of implementing machine learning algorithms. Machine learning is the most basic method of making a machine intelligent.[3][4]
The goal of machine learning is to acquire new knowledge or skills, arrange knowledge structures, and gradually enhance its own performance. Machine learning is a critical component of artificial intelligence [5]. Learning and intellect are inextricably intertwined. Learning is always about self-improvement of future behaviour based on previous experiences. In circumstances where we cannot immediately inscribe a computer code to answer a given, but instead require example data or experience, we require learning.
Machine learning is a highly interdisciplinary field that draws and expands on ideas from statistics, computer science, engineering, cognitive psychology, optimization theory, and many other scientific and mathematical disciplines. We may build a learn model using example data or past experiences by merging all of these fields. This model could be predictive in order to make future forecasts, descriptive in order to gather knowledge from data, or both. Machine learning based on data is a critical component of modern intelligent techniques; it primarily studies how to obtain rules that cannot be obtained through theoretical analysis from observed samples, and then how to apply these rules to recognise objects and predict future data or unobserved data. In a nutshell, machine learning is an efficient method for recognising new samples by learning from previous samples. [6][7]
This article discusses how machine learning techniques can be used to forecast business outcomes.

Machine learning techniques in business forecasting - a performance evaluation

In industrialized nations, heart disease affects around 5% of persons under the age of 35 and more than 20% of those over the age of 75 [1] [2]. Around 3 to 5 percent of hospital admissions are due to heart collapse. Heart failure is the most prevalent reason for doctors to admit patients to the hospital in the course of their professional practice. Affluent countries spend up to 20% of their entire healthcare budget on these expenses.
Computer algorithms that use machine learning can extract new information in the form of patterns from a database's history. Using data mining techniques, it is possible to predict illnesses at an early stage. Costly tests are necessary for patients to examine symptoms and reasons for an accurate disease diagnosis, and these tests can be rather expensive for individuals. Data mining and machine learning algorithms can reduce the number of tests performed on patients.[3]
As a result, heart disease prediction is crucial since it allows healthcare practitioners to examine the qualities needed for diagnosis such as blood pressure and diabetes. Despite the fact that many data mining algorithms are already in use in the medical business, more study has to be done on the performance evaluation of such classification approaches in order to enhance and adjust the degree of accuracy. The goal of the project is to solve the problems of developing the prediction models to forecast heart disease by giving prompt response among the best candidates. Aiming to improve the accuracy of cardiac disease prediction, this research aims to address and overcome the problems of the field. [4][5]
Primary goal of this project is to create a prediction system that is more accurate by improving the categorization algorithm. In order to improve the accuracy of heart disease prediction, it is required to change the necessary components. In addition, it illustrates that data mining may be used to healthcare datasets in order to forecast or categorize data with realistic accuracy.
A disease prediction framework is shown in Figure 1. These data are fed into the system via a set of student performance metrics. To decrease noise and make the input data set consistent, this student data set has been preprocessed. A variety of machine learning algorithms are used on the input data, including SVM, ID3, and C4.5. This involves the categorization of data. There is a comparison of the categorization results of different techniques. There is a comparison between the categorization results of different techniques.



 


 
 



 
 
 



 


 
 



 
 
 
 
 
 
 
 
 
 
 
 
 
Fig.1. Framework for disease prediction
 
This research relies on the UCI heart disease data gathering equipment [6]. ID3, C4.5, SVM uses the 303-record Cleveland database as input. Attained precision may be seen in Figure 2.
 
 
 
 
Fig.2. Disease Data Classification Results

A Machine Learning Based Framework for Heart Disease Detection

An E-learning outreach program for public schools: Findings and lessons learned based on a pilot program in Makati City and Cabuyao City, Laguna, Philippines

This correlation study  aimed to determine the association between the level of self-esteem and romantic jealousy among selected undergraduate university students from Metro Manila, Philippines. The researchers conducted an online survey with the use of self-administered questionnaires. Purposive non-probability sampling was used to identify the 40 respondents for this study. to gather respondents. Descriptive and inferential statistics.  Majority of the respondents were females (23 out of 40) and in their early stage of relationship (1-4 months). Results show that the respondents have a moderate level of self-esteem (median of 3) and high level of romantic jealousy (median of 5). A statistically significant inverse correlation was found between their level of self-esteem and romantic jealousy (r =-0.185, p<.05). The result suggests that the higher their level of self-esteem, the lower the romantic jealousy, i.e. romantic jealousy has no space if one is confident in one's romantic relationship.

/pdf/a-correlation-study-between-self-esteem-and-romantic-4i28ztcf3o.pdf

A Correlation Study between Self-esteem and Romantic Jealousy among University Students

Introduction: The quality of life (QOL) of the caregivers who attend to children in need of special protection (CNSP) influences their effectiveness in rendering care to their care recipients. This study aimed to determine the QOL of caregivers with their levels of role overload (RO) and role distress (RD). Methods: A total of 130 caregivers, identified through a convenience sampling technique, from 17 child-caring institutions from the Philippines' National Capital Region (NCR) participated in this descriptive correlational study. The survey method was used to gather the data using a structured self-administered questionnaire. Data were processed and analyzed using Statistica 13.0 and Spearman's rho test. Results: The study found a significant inverse moderate correlation between the level of QOL of the caregivers and their level of RO. A significant inverse strong correlation was also found between the level of their QOL and their level of RD. Conclusion: The findings confirm that as the levels of RO and RD of the caregivers decrease, their level of QOL increases. Conversely, their level of QOL decreases as their levels of RO and RD increase. Recommendations were provided to improve the QOL of the caregivers of CNSP in the Philippines.

https://jcs.tbzmed.ac.ir/PDF/jcs-9-173.pdf

Myla M. Arcinas

Papers

A Machine Learning Based Framework for Heart Disease Detection

An E-learning outreach program for public schools: Findings and lessons learned based on a pilot program in Makati City and Cabuyao City, Laguna, Philippines

A Correlation Study between Self-esteem and Romantic Jealousy among University Students

Quality of Life of Filipino Caregivers of Children in Need of Special Protection: Correlations with their Role Overload and Role Distress.

Machine learning techniques in business forecasting - a performance evaluation