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Place-based knowledge, a legacy from our ancestors, is inherently sustainable. However, modern lifestyles have eclipsed this wisdom, leading to environmental issues such as land and water pollution. Similarly, the current pedagogical practices often fail to connect place-based knowledge. It resulted in a deviation of students’ contextual learning. Thus, this paper explores the integration of place-based knowledge into pedagogy, fostering students’ contextual engagement and promoting sustainable living. The guiding research questions for this study are how can integration of place-based knowledge in the pedagogical practice lead to contextualized learning and sustainability? and how can researchers, teachers, and community members incorporate place-based knowledge into pedagogical practices for contextualized learning and sustainability? This article identifies pertinent issues by subscribing to participatory action research and devises collaborative solutions with teachers, co-researchers, and the community. The study involved four teachers from a seventh-grade class in a school in Lalitpur, Nepal. The findings revealed that integrating place-based pedagogical practices helped students connect with their ancestors’ knowledge about their local and its relationship with nature, thereby facilitating contextual knowledge construction. This study asserts that integrating place-based knowledge into pedagogical practices equips learners to effectively address present and future environmental issues.

Engaging students in their learning depends on the teachers’ pedagogical practices. The first author is a STEAM scholar and preschool educator who realized engagement is necessary for meaningful learning. For this, teachers and students must have exposure to innovative and arts-integrated pedagogy (e.g., storytelling). However, the teachers in the first author’s school practiced a conventional teaching approach, such as lecture and monotonous activity-based instruction, that compelled a disengaged learning culture. Similarly, storytelling was limited to reading aloud from storybooks. Thus, this paper investigated integrating storytelling pedagogy to engage preschoolers in learning by centralizing on the research question: How do teachers integrate storytelling pedagogy to enhance meaningful learning? This study used an action research method to intervene in the current pedagogical practice with storytelling as an innovative pedagogy. The teachers and students of Upper Kindergarten were involved in practicing storytelling approaches to conceptualize content knowledge and learn meaningfully. The findings of this study revealed that storytelling as an innovative pedagogy enhances the students’ engagement in their learning process. Similarly, it also helps to enrich students’ imaginative and creative skills, which are crucial soft skills in the 21st century. Moreover, storytelling enhances a deeper understanding of the content knowledge if the stories are developed based on the content knowledge of the curriculum. Therefore, storytelling is an innovative and arts-integrated pedagogy to foster preschoolers’ knowledge and skills through engagement and motivation. This research is applicable for educators and schoolteachers to improve and innovate their teaching-learning practices for students’ meaningful learning experiences.

This paper examines different layers of participation while conducting Participatory Action Research (PAR). In the journey of three years of fieldwork with teachers, many realizations were made about becoming co-researchers and engaging in a collaborative knowledge-building process for developing an engaged pedagogical approach. The paper had two purposes: a) exploring the different layers of participation in PAR, and b) documenting the lead researcher’s continuous professional learning in understanding PAR. The lead researcher proposed “ignoring” to “autonomous” participation as levels. The lead researcher also changed from overly influencing roles on PAR to accepting co-researchers' voices and respecting their efforts for sustainable change.

The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020–June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. This study suggests that, as more newly-infected individuals become asymptomatically-infectious, the overall level of positive behavior change can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

The pneumonia-like illness that emerged late in 2019, caused by SARS-CoV-2 (and coined COVID-19), became the greatest public health challenge humans have faced since the 1918/1919 influenza pandemic, causing over 670 million confirmed cases and 7 million fatalities globally. This dissertation contributes in providing deep qualitative insights and understanding on the transmission dynamics and control of the pandemic, using mathematical modeling approaches together with data analytics and computation. Specifically, it addresses some of the pertinent challenges associated with modeling the dynamics of the disease, notably the disproportionate effect of the disease on certain (risk and demographic) populations (inducing various heterogeneities) and behavior changes with respect to adherence or lack thereof to interventions. An m-group model, which monitors the temporal dynamics of the disease in m heterogeneous populations, was designed and used to study the impact of age heterogeneity and vaccination on the spread of the disease in the United States. For instance, the disease-free equilibrium for the case of the model with m = 1 (i.e., the model with a homogeneous population) was shown to be globally-asymptotically stable for two special cases (when vaccine is perfect or when disease-induced mortality is negligible) whenever the associated reproduction number of the homogeneous model is less than one. The homogeneous model has a unique endemic equilibrium whenever the reproduction threshold exceeds unity (this equilibrium was shown to be globally-asymptotically stable for a special case, using a nonlinear Lyapunov function of Goh-Volterra type). The homogeneous model was fitted to the observed cumulative mortality data for the SARS-CoV-2 pandemic in the United States during the period from January to May of 2022 (when Omicron was the predominant variant). It was shown that vaccine-derived herd immunity (needed to eliminate the disease) cannot be attained using the homogeneous model regardless of the proportion of individuals fully vaccinated. Such vaccine-derived immunity can, however, be achieved using the m-group heterogeneous model, with m = 2 (where the total population is split into two groups: those under 65 years of age, and those 65 years and older), if at least 61\\% of the susceptible population is fully vaccinated. Thus, this dissertation shows that heterogeneity reduces the level of vaccine coverage needed to eliminate the pandemic (and models that do not account for heterogeneity may be over-estimating the vaccination coverage needed to achieve herd immunity in the community). To quantify the impact of human behavior changes on the spread and control of the pandemic, we designed a novel behavior-epidemiology model which considers numerous metrics for inducing human behavior changes (such as current level of disease burden and intervention adherence fatigue). Unlike the equivalent model without human behavior explicitly incorporated, the behavior-epidemiology model fits the observed cumulative mortality and predicts the observed daily mortality data very well. It was also shown that the behavior metrics related to the level of SARS-CoV-2 mortality and symptomatic transmission were more influential in inducing positive behavior changes than all other behavior metrics considered. Finally, a model was developed to assess the utility of wastewater surveillance to study the transmission dynamics and control of SARS-CoV-2 in a community. Specifically, we developed and calibrated a wastewater-based epidemiology model using wastewater data from Miami-Dade county, Florida, during the third wave of the SARS-CoV-2 pandemic. The model showed a strong correlation between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model's prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matched the time when the maximum observed/reported cases were recorded (August 14, 2021). Furthermore, the model's projection of the maximum number of cases for the week of August 14, 2021 was about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). In addition to being in line with other modeling studies, this result is consistent with the CDC estimate that the reported confirmed case data may be 10 times lower than the actual (since the confirmed data did not account for asymptomatic and presymptomatic transmission). Furthermore, the model accurately predicted a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function (namely, the cumulative viral load in the wastewater). This analysis identified key parameters that significantly affect the value of the response function (hence, they should be targeted for intervention). This dissertation conclusively showed that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.

This study presents a wastewater-based mathematical model for assessing the transmission dynamics of the SARS-CoV-2 pandemic in Miami-Dade County, Florida. The model, which takes the form of a deterministic system of nonlinear differential equations, monitors the temporal dynamics of the disease, as well as changes in viral RNA concentration in the county’s wastewater system (which consists of three sewage treatment plants). The model was calibrated using the wastewater data during the third wave of the SARS-CoV-2 pandemic in Miami-Dade (specifically, the time period from July 3, 2021 to October 9, 2021). The calibrated model was used to predict SARS-CoV-2 case and hospitalization trends in the county during the aforementioned time period, showing a strong correlation between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model’s prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matches the time when the maximum observed/reported cases were recorded (which was August 14, 2021). Furthermore, the model’s projection of the maximum number of cases for the week of August 14, 2021 is about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). This result is consistent with the result of numerous SARS-CoV-2 modeling studies (including other wastewater-based modeling, as well as statistical models) in the literature. Furthermore, the model accurately predicts a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function—the cumulative viral load in the wastewater. This analysis revealed that the transmission rate of infectious individuals, shedding rate of infectious individuals, recovery rate of infectious individuals, average fecal load per person per unit time and the proportion of shed viral RNA that is not lost in sewage before measurement at the wastewater treatment plant were most influential to the response function during the entire time period of the study. This study shows, conclusively, that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.

This study presents a wastewater-based mathematical model for assessing the transmission dynamics of the SARS-CoV-2 pandemic in Miami-Dade County, Florida. The model, which takes the form of a deterministic system of nonlinear differential equations, monitors the temporal dynamics of the disease, as well as changes in viral RNA concentration in the county's wastewater system (which consists of three sewage treatment plants). The model was calibrated using the wastewater data during the third wave of the SARS-CoV-2 pandemic in Miami-Dade (specifically, the time period from July 3, 2021 to October 9, 2021). The calibrated model was used to predict SARS-CoV-2 case and hospitalization trends in the county during the aforementioned time period, showing a strong correlation between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model's prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matches the time when the maximum observed/reported cases were recorded (which was August 14, 2021). Furthermore, the model's projection of the maximum number of cases for the week of August 14, 2021 is about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). This result is consistent with the result of numerous SARS-CoV-2 modeling studies (including other wastewater-based modeling, as well as statistical models) in the literature. Furthermore, the model accurately predicts a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function-the cumulative viral load in the wastewater. This analysis revealed that the transmission rate of infectious individuals, shedding rate of infectious individuals, recovery rate of infectious individuals, average fecal load per person per unit time and the proportion of shed viral RNA that is not lost in sewage before measurement at the wastewater treatment plant were most influential to the response function during the entire time period of the study. This study shows, conclusively, that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.

The educational sector has significantly advanced through the application of technology and cutting-edge tools. Global education has been further complicated by the COVID-19 pandemic, which has not been seen in decades. Several schools and institutions in nearly every region of the world have been closed in 2020 or switched to online or remote learning or blended learning because of the worldwide surge of COVID-19 cases, which will have various consequences for student learning. Over time, behaviorism and constructivism have developed into two major, contradictory aspects of blended learning. Blended learning is defined as the total mix of pedagogical methods that employ a combination of different learning strategies with and without technology utilized at KUSOED, Nepal. The evaluation is based on a blended learning model aligned with behaviorism and constructivism approaches following four dimensions: structured/unstructured, individual/group, face-to-face/distance, and self/teacher directed. In this paper, authors discuss their experiences working in blended learning environments to teach and learn to create an engaged pedagogy in various semesters at Kathmandu University School of Education, Nepal, that involves their (PGD, Master, MPhil, and PhD) students of the 2019-2022 batches in techno-pedagogy and its trend in learning. This paper describes the ongoing learning from research that was accomplished in the context of the authors' teaching methods. The authors give examples of some of their PGD, Master, MPhil, and PhD students' blended learning experiences that they conducted for students in the 20192022 batches. These experiences were designed for on-campus, online, and distance learning to ensure better learning outcomes.

A learning management system (LMS) is a digital learning platform for developing, delivering, and managing courses, learning resources, activities, assessments, etc. Traditional classroom-based, online, blended, and distance learning are all possible learning methods that could be executed in LMSs. The use of learning management systems and their associated tools brought significant benefits to higher education institutions worldwide, including improvements in content deliverability, accessibility, and retrievability. This is also valid in the case of Kathmandu University School of Education (KUSOED), Nepal. In 2011, KUSOED launched LMS and continued online and blended learning practices. The LMS follows a social constructivist approach to education, which allows educational stakeholders (parents, students, leaders, facilitators, etc.) to engage in learning activities to scaffold the learning experiences. However, some studies criticized LMS as a teacher-centric platform, which limits opportunities for social and informal learning. So, this paper aims to discuss the success and failure aspects of LMS in the context of the KUSOED. The discussion will cover various perspectives on LMS as an emerging technology for learning and will draw conclusions based on our experiences at KUSOED. For the success aspects of LMS, we discovered four factors: sign-in, resources and learning management, content management, and integration. But, for the failure aspects, we found content creation and sharing, communicative features, course structures, learning engagement, and assessment. Overall, this research has implications for educational institutions, instructors, developers, and system providers. These stakeholders can make more informed decisions about implementing and using these systems to their fullest potential in learning.

Insect pathogenic fungi offer a promising alternative to chemical insecticides for controlling insecticide resistant mosquitoes. One proposed method involves releasing male Anopheles mosquitoes contaminated with transgenic Metarhizium pingshaense (Met-Hybrid), to lethally infecting females during mating. This study presents a novel deterministic mathematical model to evaluate the impact of this control approach in malaria-endemic areas. The model incorporates two fungus transmission pathways: mating-based transmission and indirect transmission through contact with fungus-colonized mosquito cadavers. We found the fungus cannot establish in the mosquito population without transmission from infected cadavers (in this scenario, the reproduction number of the model is zero). However, if transmission from colonized cadavers is possible, the fungus can persist in the local mosquito population when the reproduction number exceeds one. Simulations of periodic releases of infected male mosquitoes, parameterized using Met-Hybrid-exposed mosquito data from Burkina Faso, show that an 86% reduction in the local female mosquito population can be achieved by releasing 10 Met-Hybrid-exposed male mosquitoes per wild mosquito every three days over six months. This matches the efficiency of some genetic mosquito control approaches. However, a 90% reduction in the wild mosquito population requires, for instance, daily releases of the fungal-treated mosquitoes in a 6:1 ratio for about 5 months, proving less efficient than some genetic approaches. This study concludes that fungal programs with periodic releases of infected males may complement other methods or serve as an alternative to genetic-based mosquito control methods, where regulatory, ethical, or public acceptance concerns restrict genetically-modified mosquito releases.