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Author(s):
Dionie E. Arban.
Page No : 1-9
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Mathematics Performance of Grade 7 Learners through Catch-Up Numeracy Intervention Program
Abstract
This study investigated the mathematics performance of Grade 7 learners at Ajuy National High School during School Year 2023–2024 in relation to the implementation of the Catch-Up Numeracy Intervention Program. The primary objective was to determine whether systematic numeracy support could significantly enhance students’ mathematical achievement across grading periods. Using a descriptive–quantitative approach, the researchers employed convenience sampling to obtain the academic records of 198 Grade 7 students enrolled in the program. Data were processed and analyzed using the Statistical Package for the Social Sciences (SPSS) to generate descriptive statistics and to examine differences in performance across quarters, gender, and class sections. Findings revealed that participation in the Numeracy Intervention Program was associated with a consistent and significant improvement in mathematics performance. Mean ranks increased steadily from the first to the fourth grading period, indicating progressive gains in mathematical skills over the school year. Positive effects were evident among both male and female learners, suggesting that the program benefited students regardless of gender. When analyzed by section, results similarly demonstrated upward trends in achievement, confirming that the intervention had a broad and sustained impact across classes. Overall, the study supports the conclusion that the Catch-Up Numeracy Intervention Program effectively enhanced the mathematics competencies of Grade 7 learners and contributed to improved academic outcomes. The findings underscore the importance of structured numeracy initiatives in addressing learning gaps and promoting equitable achievement in mathematics. Recommendations for future research include examining long-term retention of skills, exploring qualitative aspects of student engagement, and identifying best practices to further strengthen numeracy interventions in secondary schools. Keywords: Mathematics Performance, Grade 7 Learners, and Catch-up Numeracy Intervention Program
| 2 |
Author(s):
Danica G. Liboon, Lester John E. Anuran , Kiylyn M. Balonsit, Vergie A. Berog, Rica Faith D.Destacamento, Frecy S.Naquita.
Page No : 10-22
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The Future of Science Education: Artificial Integration (AI) Integration and Student Achievement
Abstract
This study was conducted to determine the impact of adopting Artificial Intelligence (AI) educational tools on the Academic performance of science major students enrolled in Bachelor of Secondary Education major in science in the second semester, 2023-2024, at Northern Iloilo State University – Ajuy Campus. This study used Descriptive-Correlational Design to determine the relationship of Artificial Intelligence (AI) Educational tools and academic performance of science major students. The research instrument used to gather the data from the respondents was a researchers-made questionnaire, it was validated and undergo a reliability testing. The study's findings show that 94.8% of the Science major students are using Artificial Intelligence (AI) Educational tools. The top three Artificial Intelligence (AI) educational tools that science major students used are Quillbot, ChatGPT, and Grammarly. The level of utilization of Artificial Intelligence (AI) Educational tools by science majors was occasional. Artificial Intelligence (AI) Educational Tools has moderate impact on the academic performance of science major students. It shows no significant relationship between the Artificial Intelligence (AI) Educational tools and Academic performance of science major students when classified according to sex, year level, and socio-economic status. Therefore, researchers concluded that the Science major students of NISU Ajuy-Campus actively use Artificial Intelligence (AI) Educational tools. However, despite their engagement, these tools are only used occasionally and moderately impact their academic performance. There was no significant relationship between Artificial Intelligence (AI) Educational tools and the academic achievement of the Science major students, suggesting the need for better integration and promotion of these technologies.
| 3 |
Author(s):
Ronhick E. Sanchez, Nikko T. Ederio.
Page No : 23-40
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Predictors of Research Self-Efficacy of STEM Graduates in a Science Investigatory Project Class: Toward a Research Self-Efficacy Promotion Model
Abstract
This study determined the predictors of research self-efficacy among STEM graduates in a science investigatory project class and examined the levels at which these predictors occur, their influence on STEM graduates’ research self-efficacy, to develop a model for facilitating research self-efficacy. Utilizing a descriptive-correlational research design, the study employed Partial Least Squares - Structural Equation Modeling (PLS-SEM) with 241 STEM graduates of Surigao del Norte National High School who responded to adapted and researcher-made survey questionnaires via Google Forms. The findings revealed that the highest level of research self-efficacy was in Research Ethics and Integrity (M=3.55), while the lowest was in Data Analysis (M=3.38). Regarding the extent of factors that predict research self-efficacy, Research Mentoring Experience recorded the highest mean (M=3.63), while Research Training Environment scored the lowest (M=3.31). Significant intercorrelations were observed between all factors of research self-efficacy, with correlation coefficients ranging from moderate to weak (r = 0.16 to 0.78). Crucially, although the level of research self-efficacy in science investigatory project were all positively correlated with all the factors that predict research self-efficacy, multiple linear regression analysis only identified Investigative Interest (β=0.41, p<0.001) and Research Training Environment (β=0.12, p=0.019) as the only significant predictors of research self-efficacy, while Research Anxiety, Working Alliance, and Research Mentoring Experience were non-significant predictors. Further, this was translated to the findings of the structural equation modeling and validated Investigative Interest and Research Training Environment as the only factors that significantly and positively influence research self-efficacy. The study contributes to the SIRSE (Social Cognitive Processes, Investigative Interest, Research Training Environment, Self-Efficacy) Model, providing an empirically derived research self-efficacy model mediated by Social Cognitive Theory. Thus, the study recommends the implementation of hands-on data analysis workshops and establishing a dedicated research budget to provide students with research materials. Ultimately, the adoption of the SIRSE Model is proposed to systematically recalibrate DepEd’s research curriculum for fostering confident researchers.
