Abstract
Mathematics in the Modern World (MMW) is a core general education course that develops quantitative reasoning and applied problem solving, yet many first-year students struggle with its concepts and applications. Such challenges call for support designs that extend beyond remediation and function in blended learning environments. This study profiled students perceived challenges in MMW and examined their associations with examination performance to inform Math-RetoKiSS, a blended learning support system integrating asynchronous digital resources, self-assessment, and instructor-mediated remediation. Using a descriptive-correlational needs-assessment design, 371 first-year students from five campuses of a Philippine state university in 2022–2023 completed a validated questionnaire covering course content, instructional delivery, teaching strategies, and assessment methods. Midterm and final grades were obtained from course records. Descriptive statistics, one-way ANOVA, and Pearson correlations were used. Among the four domains, course content obtained the least favorable mean (M = 2.60, SD = 0.71) and was prioritized for support. Overall performance was very satisfactory (midterm M = 2.31, SD = 0.56; final M = 2.34, SD = 0.61; lower scores indicate better performance), although campus differences were significant. All domain-performance associations were statistically significant but small to moderate (|r| = 0.138–0.349). Findings informed a modular toolkit consisting of topic guides, worked examples, guided practice, self-check exercises, and answer keys deployable in tutorials, online review, and LMS integration. Math-RetoKiSS is positioned to strengthen formative assessment, immediate feedback, self-regulated learning, digital literacy, and critical thinking. The study contributes a multi-campus diagnostic profile and a scalable, practical blended mathematics support model.
1 Introduction
Mathematics remains central to higher education because it develops quantitative reasoning, logical thinking, and problem solving that students must apply in academic, professional, and civic contexts. Within general education curricula, Mathematics in the Modern World (MMW) is expected to help students connect mathematical ideas with real-life decisions and contemporary issues. These expectations also align with broader 21st-century competency agendas that emphasize critical thinking, collaboration, digital literacy, and student agency as transferable outcomes of higher education (National Research Council, 2012; OECD, 2019; 2023). For first-year students, however, success in MMW depends not only on topic mastery but also on how they navigate explanations, practice opportunities, feedback, and increasingly digital learning environments.
Blended learning offers one route to addressing such needs by purposefully combining face-to-face and online learning experiences rather than treating digital resources as add-ons (Garrison and Kanuka, 2004; Hrastinski, 2019). In mathematics education, this is especially relevant when learners need repeated exposure, scaffolded worked examples, low-stakes practice, and timely feedback both during class time and in out-of-class review. A support resource designed for blended use can extend teacher explanations beyond the classroom, support asynchronous revision, and provide a common structure that can be adapted across campuses or sections.
In the present study setting, MMW is a university-wide, one-semester general education course offered across multiple campuses and academic programs. Informal course monitoring suggested that many first-year students struggle not only with core topics such as logic, statistics, modular arithmetic, and graph concepts but also with applying these ideas to practical contexts. Before a support resource is rolled out across a multi-campus system, it is important to determine which aspects of the course students perceive as most difficult and whether performance patterns appear consistent across delivery contexts.
Existing studies in mathematics education have documented learning difficulties and, in some cases, evaluated intervention programs. However, fewer higher-education studies use local diagnostic evidence and performance data to inform the design of a scalable blended-learning support environment for a general-education mathematics course. The gap is therefore not only the limited linkage between perceived challenges and performance, but also the limited use of such evidence in designing blended learning innovations that intentionally support 21st-century mathematics learning.
The present study was guided by a multi-theoretical framework integrating constructive alignment, blended learning, self-regulated learning, cognitive load theory, and 21st-century competency perspectives. Constructive alignment positioned course outcomes, teaching-learning activities, and assessment opportunities as mutually reinforcing components of effective mathematics instruction (Biggs, 1996). Blended learning theory situated Math-RetoKiSS as a support environment that combines asynchronous digital resources with instructor-mediated remediation across classroom, tutorial, and learning-management-system (LMS)-supported contexts (Garrison and Kanuka, 2004; Hrastinski, 2019). Self-regulated learning theory explained how self-check exercises, answer keys, and guided review can support learners’ forethought, performance monitoring, and self-reflection (Zimmerman, 2002; Panadero, 2017). Cognitive load theory provided the rationale for modular topic segmentation, worked examples, and graduated practice in mathematical areas that impose substantial demands on working memory (Paas et al., 2010; Sweller et al., 2019). Finally, 21st-century competency frameworks oriented the toolkit toward critical thinking, digital literacy, self-regulated learning, collaboration, and student agency as relevant outcomes of support in Mathematics in the Modern World (National Research Council, 2012; OECD, 2019; 2023). Figure 1 summarizes the conceptual framework underpinning the study and the design logic of Math-RetoKiSS.
Figure 1
Math-RetoKiSS is therefore conceptualized not merely as a repository of materials but as a blended-learning support environment that integrates asynchronous digital resources with instructor-mediated remediation. It is designed for use across face-to-face tutorials, asynchronous online review, and LMS integration, allowing common core materials to be adapted to local teaching contexts while preserving a consistent logic of formative support.
Accordingly, this Brief Research Report sought to (1) describe students’ perceived challenges in MMW across four domains, (2) examine campus-level differences in examination performance, and (3) test the associations between perceived challenges and examination grades. A final practical aim was to translate the findings into design priorities for Math-RetoKiSS as a data-informed blended learning support system that promotes 21st-century competencies and assessment innovation. Guided by these objectives, the analysis tested two working hypotheses: H1, examination performance differs significantly across campuses; and H2, perceived challenge domains are significantly associated with midterm and final grades. This article reports only the needs assessment and design rationale; evaluation of toolkit implementation and effectiveness is left for future research.
2 Methods
2.1 Design and participants
This quantitative study used a descriptive-correlational needs-assessment design. Participants were 371 first-year students who completed the Mathematics in the Modern World (MMW) course during the first or second semester of the 2022–2023 academic year across five constituent campuses of Batangas State University. Stratified proportional sampling was employed to reflect campus enrolment patterns, and Table 1 shows the resulting distributions by campus and department. Although no a priori sample-size calculation was conducted, a post hoc sensitivity analysis based on N = 371 indicated 80% power at α = 0.05 to detect two-tailed correlations of approximately |r| = 0.145 and five-group one-way ANOVA effects of approximately f = 0.181. These estimates indicate adequate sensitivity to detect small associations and small-to-moderate omnibus group differences, although unequal subgroup sizes may have reduced statistical precision.
Table 1
| Category | Group | n/summary | % |
|---|---|---|---|
| Campus | Campus 1 (Alangilan) | 160 | 43.13 |
| Campus | Campus 2 (Pablo Borbon) | 122 | 32.88 |
| Campus | Campus 3 (ARASOF-Nasugbu) | 41 | 11.05 |
| Campus | Campus 4 (JPLPC-Malvar) | 24 | 6.47 |
| Campus | Campus 5 (Lipa) | 24 | 6.47 |
| Department | Dept A | 78 | 21.02 |
| Department | Dept B | 15 | 4.04 |
| Department | Dept C | 126 | 33.96 |
| Department | Dept D | 27 | 7.28 |
| Department | Dept E | 35 | 9.43 |
| Department | Dept F | 20 | 5.39 |
| Department | Dept G | 70 | 18.88 |
| Performance | Midterm grade (mean ± SD) | 2.31 ± 0.56 | |
| Performance | Final grade (mean ± SD) | 2.34 ± 0.61 |
Respondent distribution by campus and department, and overall examination performance (N = 371).
Department groupings are retained for descriptive profiling only. Lower numerical grades indicate better performance. Inferential group comparisons in this revised report focus on campuses because the verified ANOVA structure comprises five groups.
2.2 Instrument and measures
A researcher-developed questionnaire assessed perceived challenges in Mathematics in the Modern World (MMW) across four domains: course content, instructional delivery, teaching strategies, and assessment methods. Items were grouped by domain and rated on a 4-point scale. During instrument development, the item pool underwent face and content review and was subsequently refined for clarity, relevance, sequencing, and alignment with the study objectives. The instrument yielded an internal consistency coefficient of 0.971, interpreted here as Cronbach's alpha, indicating excellent reliability. As additional construct-validation statistics were unavailable, the questionnaire was treated as a domain-level needs-assessment instrument rather than a latent-variable scale. Composite means were interpreted comparatively across domains, with lower means indicating less favorable perceptions and therefore higher-priority areas for reinforcement.
Students’ midterm and final examination grades were obtained from course records. In the institutional numerical grading system, lower values indicate better performance. These grades provide ecologically valid indicators of classroom performance because they reflect authentic course assessments under ordinary teaching conditions. However, because they were drawn from regular course records across campuses and instructors, they are not standardized achievement measures and may reflect variation in grading practices, local test difficulty, and instructor expectations. Accordingly, they are interpreted as contextual performance indicators rather than exact cross-campus measures of mathematical ability.
Perceived challenge was likewise not treated as equivalent to actual conceptual difficulty. In this study, the questionnaire functioned as a diagnostic input for support design rather than as a direct measure of mastery. This distinction is important for interpreting the findings and framing subsequent evaluation of the toolkit.
2.3 Procedure and data analysis
Surveys were administered using minimal-risk educational research procedures with assurances of confidentiality and voluntary participation. Students were informed of the purpose of the study, and analytical files used de-identified survey and grade data. Data were summarized using frequencies, percentages, means, and standard deviations. One-way ANOVA was used to test H1 regarding campus-level differences in midterm and final grades, and Pearson product-moment correlation was used to test H2 regarding associations between the four perceived-challenge domains and the two examination grades. Statistical significance was set at alpha = 0.05.
3 Results
Table 1 summarizes the respondent distribution and overall examination performance. The largest shares of participants came from Campus 1 (Alangilan; 43.13%) and Campus 2 (Pablo Borbon; 32.88%), whereas Campuses 4 and 5 each contributed 6.47% of the sample. Department representation was likewise uneven, with Department C accounting for 33.96% and Department B for 4.04%. Overall performance was very satisfactory, with mean midterm and final grades of 2.31 (SD = 0.56) and 2.34 (SD = 0.61), respectively.
Table 2 presents the composite ratings of perceived challenges. Among the four domains, course content obtained the least favorable mean (M = 2.60, SD = 0.714), followed by instructional delivery (M = 2.94, SD = 0.570), teaching strategies (M = 3.15, SD = 0.579), and assessment methods (M = 3.22, SD = 0.579). Although all domain means remained in the upper half of the scale, their relative ordering indicates that course content was the highest-priority area for reinforcement.
Table 2
| Domain | Mean | SD |
|---|---|---|
| Course content | 2.60 | 0.714 |
| Delivery of instruction | 2.94 | 0.570 |
| Teaching strategies | 3.15 | 0.579 |
| Assessment methods | 3.22 | 0.579 |
Composite mean ratings of perceived challenges in MMW (4-point scale).
Lower means reflect less favorable perceptions and therefore higher reinforcement priority. Composite means are interpreted comparatively across domains.
Campus-level differences in examination performance were statistically significant (Table 3). Midterm grades differed across campuses, F(4, 366) = 17.290, p < 0.001, η2 = 0.159, and a similar pattern was observed for final grades, F(4, 366) = 7.791, p < 0.001, η2 = 0.078. These findings indicate that campus grouping captured nontrivial variation in course-record performance, although the study was not designed to identify the specific sources of this variation.
Table 3
| Outcome | df | F | p | η2 |
|---|---|---|---|---|
| Midterm examination grade | (4, 366) | 17.290 | < 0.001 | 0.159 |
| Final examination grade | (4, 366) | 7.791 | < 0.001 | 0.078 |
One-way ANOVA results for examination grades by campus.
eta2 values were derived from the reported ANOVA statistics. The reported degrees of freedom indicate a five-group comparison, corresponding to the five constituent campuses.
Supplementary Table S1 shows significant associations between all four domains of perceived challenge and both midterm and final grades. The coefficients were small to moderate in magnitude (|r| = 0.138–0.349). The positive coefficients for course content indicate that greater perceived challenge was associated with poorer performance, whereas the negative coefficients for instructional delivery, teaching strategies, and assessment methods indicate that more favorable perceptions in these areas were associated with better performance (i.e., lower numerical grades). The strongest association, between assessment methods and midterm grade (r = −0.349), accounted for only about 12% of shared variance, indicating that the relationships were informative but not determinative.
Supplementary Table S3 translates these findings into the design logic of Math-RetoKiSS as a blended learning support system. It shows how the toolkit components are intended to function across blended modalities, support 21st-century competencies, and strengthen assessment innovation through formative checking and immediate feedback.
4 Discussion
This study presents a university-based needs assessment and a theory-informed design rationale for Math-RetoKiSS as a blended-learning support system rather than merely a stand-alone repository or a validated intervention. Three main findings emerge. First, course content was identified as the primary area requiring reinforcement. Second, examination performance varied significantly across campuses. Third, the associations between perceived challenges and grades were statistically significant, although modest in magnitude.
The prominence of course content as a priority area is not unexpected in Mathematics in the Modern World (MMW). The course requires students to move across foundational concepts, symbolic representations, and real-world applications. For first-year students with uneven prior preparation, difficulty may arise not from a single topic but from the cumulative demands of interpreting concepts, translating contextualized situations into mathematical form, and selecting appropriate procedures or representations. Because the instrument generated domain-level rather than topic-level evidence, the findings do not identify a single lesson as the principal source of difficulty. Instead, they support a toolkit structure built around topic-based conceptual scaffolds and graduated practice. From a cognitive load perspective, modular topic segmentation, worked examples, and progressive problem sets are appropriate design responses because they can reduce unnecessary processing demands and support schema formation before learners engage in more complex applications.
At the same time, the significant negative correlations for instructional delivery, teaching strategies, and assessment methods indicate that reinforcement should not be limited to content review alone. Although these domains received more favorable mean ratings than course content, their associations with achievement suggest that the ways in which students encounter explanations, practice opportunities, and feedback remain educationally consequential. From a constructive alignment perspective (Biggs, 1996), student learning is strengthened when explanations, learning tasks, and assessment opportunities function coherently and mutually reinforce one another. Accordingly, Math-RetoKiSS was designed to include not only concise concept notes and worked examples but also self-check exercises, guided problem sets, and answer keys that can support both independent study and instructor-facilitated remediation.
These findings also clarify the study's connection to assessment innovation. Within the proposed design, self-check exercises function as a low-stakes formative assessment, answer keys provide immediate feedback, and guided practice tasks create feedback loops that students can use prior to high-stakes examinations. Assessment is therefore treated not only as an outcome domain but also as an integral component of the support design (Black and Wiliam, 2009; Hattie and Timperley, 2007; Suurtamm et al., 2016).
The significant campus-level differences do not, in themselves, explain why performance varied, and the present data do not justify attributing these differences to any single factor such as resources, demographics, or instructor quality. Prior achievement, instructor practices, and local implementation conditions were not measured. Rather than advancing unsupported causal claims, the study uses this finding pragmatically: when performance varies across campuses, a support resource should be modular and adaptable rather than rigidly uniform. From a blended learning perspective, Math-RetoKiSS is therefore designed for three interconnected modes of use: face-to-face tutorial remediation, asynchronous online review, and LMS-linked follow-up. This blended-use structure allows instructors to assign common core modules while selecting topics in response to local student needs.
The toolkit components also align with 21st-century competencies. Topic guides and worked examples support critical thinking by making mathematical reasoning and solution processes explicit. LMS-hosted digital resources and asynchronous review activities promote digital literacy by requiring students to navigate and use structured online materials. Self-check routines and answer keys foster self-regulated learning by helping students set goals, monitor progress, and reflect on errors. Guided practice tasks may also be implemented in pairs or small-group tutorial activities, thereby supporting collaboration in classroom and tutorial settings. Supplementary Tables S2, S3 make these links explicit.
The correlations reported in Supplementary Table S1 should nevertheless be interpreted with caution. Although statistically significant, the coefficients are small to moderate, with shared variance ranging from approximately 2% to 12%. These results indicate that perceived challenges are associated with performance but do not provide a comprehensive explanation of achievement. Their practical value lies not in predicting grades with precision, but in identifying priority areas for reinforcement and informing the design of appropriate support mechanisms.
Taken together, the study makes three contributions. Empirically, it provides a multi-campus diagnostic profile that combines perceived-challenge data with course-recorded performance in a general education mathematics course. In design terms, it offers a transparent, data-driven framework for translating local evidence into a modular blended learning toolkit. Practically, it proposes a scalable support system that can be implemented across campuses through tutorials, asynchronous online review, and LMS integration. The study does not claim that Math-RetoKiSS has already improved learning outcomes; that question remains to be answered by future implementation, usability, and impact studies.
4.1 Limitations
Several limitations should be acknowledged. First, the study relied heavily on self-reported data to identify perceived challenges in Mathematics in the Modern World (MMW). Perceived challenge should not be treated as equivalent to actual learning difficulty or demonstrated topic mastery. This construct validity limitation means that the findings are best interpreted as indicators for support design rather than as direct evidence of cognitive deficits. Future research should therefore triangulate questionnaire results with item-level difficulty analyses, common test-anchor items, student work and error analyses, learning analytics from the digital platform, and validated pretest–posttest measures.
Second, the sample was drawn from a single university system, and subgroup sizes were markedly unequal, which may have reduced the robustness of comparative analyses. Third, the outcome measures consisted of regular course-record grades collected across campuses and instructors rather than standardized achievement tests. This poses potential threats to measurement validity because grading standards, examination formats, and local implementation conditions may vary across settings. At the same time, these grades retain ecological validity because they reflect the authentic assessment conditions under which students experienced the MMW course. Future studies should therefore complement course-record grades with common parallel assessments or anchor items to improve comparability across instructors and campuses.
Fourth, the study did not include potentially relevant covariates such as prior mathematics achievement, academic background, or demographic characteristics. Fifth, department-level inferential statistics were not retained because the verified omnibus analysis corresponded to the five campus groups. Finally, the cross-sectional correlational design does not support causal inference, and the present study does not provide an empirical test of Math-RetoKiSS's effectiveness.
5 Conclusion
This Brief Research Report demonstrates that first-year students in Mathematics in the Modern World (MMW) perceived course content as the primary area requiring reinforcement and that these perceptions were significantly, albeit modestly, associated with examination performance. Campus-level variation further suggests that student support should be sufficiently flexible to accommodate differences in delivery contexts. The study's distinct contribution lies in translating a local needs assessment into an explicit, data-informed design rationale for a blended learning support system that responds to the demands of 21st-century mathematics learning.
Based on these findings, Math-RetoKiSS was developed as a modular package of topic guides, worked examples, practice tasks, self-check materials, and answer keys intended for use in face-to-face tutorials, asynchronous online review, and LMS-supported follow-up. As a support environment, the toolkit is positioned to strengthen formative assessment, immediate feedback, critical thinking, digital literacy, and self-regulated learning in MMW. Because the present evidence is correlational and pre-implementation, future research should pilot the toolkit, evaluate its usability and implementation fidelity, and examine its effects on achievement, confidence, and sustained engagement in mathematics using validated pretest-posttest measures aligned with MMW outcomes, rather than relying solely on unstandardized course-record grades.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
Ethics statement
The studies involving humans were approved by Batangas State University The National Engineering University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author contributions
IF: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing. RR: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing. IP: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the Research Office of Batangas State University, The National Engineering University.
Acknowledgments
The authors thank the participating students and MMW instructors across the constituent campuses for their cooperation and support during data collection.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2026.1821795/full#supplementary-material
A web-based version of the Math-RetoKiSS repository is hosted through Batangas State University Google Sites at https://sites.google.com/g.batstate-u.edu.ph/batstateu-mmw/home and is currently accessible only to users with an official Batangas State University Google account. For review purposes, the supplementary materials are also submitted separately with the manuscript. These include Supplementary Table 1, which presents the Pearson correlations between perceived challenge domains and examination grades, and Supplementary Table 2, which translates empirical findings into Math-RetoKiSS design priorities, blended-learning functions, 21st-century competencies, and assessment innovations
References
1
BiggsJ. (1996). Enhancing teaching through constructive alignment. High. Educ.32 (3), 347–364. 10.1007/BF00138871
2
BlackP.WiliamD. (2009). Developing the theory of formative assessment. Educ. Assess. Eval. Account.21 (1), 5–31. 10.1007/s11092-008-9068-5
3
GarrisonD. R.KanukaH. (2004). Blended learning: uncovering its transformative potential in higher education. Internet High. Educ.7 (2), 95–105. 10.1016/j.iheduc.2004.02.001
4
HattieJ.TimperleyH. (2007). The power of feedback. Rev. Educ. Res.77 (1), 81–112. 10.3102/003465430298487
5
HrastinskiS. (2019). What do we mean by blended learning?TechTrends63, 564–569. 10.1007/s11528-019-00375-5
6
National Research Council (2012). Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. Washington, DC: The National Academies Press. 10.17226/13398
7
OECD (2019). OECD Learning Compass 2030: A Series of Concept Notes. Paris: OECD Publishing.
8
OECD (2023). PISA 2022 Results (Volume I): The State of Learning and Equity in Education. Paris: OECD Publishing. 10.1787/53f23881-en
9
PanaderoE. (2017). A review of self-regulated learning: six models and four directions for research. Front. Psychol.8, 422. 10.3389/fpsyg.2017.00422
10
PaasF.van GogT.SwellerJ. (2010). Cognitive load theory: new conceptualizations, specifications, and integrated research perspectives. Educ. Psychol. Rev.22, 115–121. 10.1007/s10648-010-9133-8
11
SuurtammC.ThompsonD. R.KimR. Y.Diaz MorenoL.SayacN.SchukajlowS.et al (2016). “Assessment in mathematics education,” in Assessment in Mathematics Education: Large-Scale Assessment and Classroom Assessment (Cham: Springer), 1–38. 10.1007/978-3-319-32394-7_1
12
SwellerJ.van MerrienboerJ. J. G.PaasF. (2019). Cognitive architecture and instructional design: 20 years later. Educ. Psychol. Rev.31, 261–292. 10.1007/s10648-019-09465-5
13
ZimmermanB. J. (2002). Becoming a self-regulated learner: an overview. Theory. Pract.41 (2), 64–70. 10.1207/s15430421tip4102_2
Summary
Keywords
21st-century competencies, blended learning, formative assessment, higher education, mathematics education, Mathematics in the Modern World (MMW), Philippines, self-regulated learning
Citation
Flores IM, Robles RA and Peñero IP (2026) Profiling perceived challenges in Mathematics in the Modern World to inform Math-RetoKiSS: a data-informed blended learning support system for 21st-century mathematics learning in a Philippine university. Front. Educ. 11:1821795. doi: 10.3389/feduc.2026.1821795
Received
03 March 2026
Revised
14 April 2026
Accepted
20 April 2026
Published
04 May 2026
Volume
11 - 2026
Edited by
Brantina Chirinda, University of the Witwatersrand, South Africa
Reviewed by
Mark Donnel Viernes, Western Philippines University, Philippines
Jeremy R. Punzalan, University of Santo Tomas, Philippines
Updates
Copyright
© 2026 Flores, Robles and Peñero.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Imelda M. Flores imelda.flores@g.batstate-u.edu.ph
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.