The Big Shift: Examining Practices, Challenges, and Coping Mechanisms of Teachers and Students in Transitioning to Modular Distance Learning

In response to the COVID-19 pandemic threat, the Department of Education (DepEd) established the Basic Education - Learning Continuity Plan (BE-LCP) to allow students to continue their education and teachers to conduct instruction in a safe working and learning environment. As a result, DepEd implemented the distance learning approach, including Modular Distance Learning (MDL), for the School Year 2020-2021. This paper investigated the practices, challenges, and coping mechanisms of teachers and students involved in the implementation of the MDL in Schools Division of Laoag City. This qualitative research utilized semi-structured interview guide to collect data from 20 teachers and 20 learners from elementary, junior high and senior high schools. Using the phenomenological study, data were analyzed and organized into themes. The study's major themes revealed that teachers and students began familiarizing themselves with the features of MDL but encountered challenges such as printing, distribution, and retrieval of modules, as well as monitoring of student progress on the part of the teacher and answering overloaded activities on the part of the students. They claimed, however, that they have unique coping mechanisms in dealing with the identified challenges by resolving issues independently and seeking help from family and colleagues. Finally, the Modular Distance Learning Adoption Framework (MDLAF) was developed and validated for teachers and students to effectively adopt MDL. The researchers recommended that relevant scaffolding such as capacity building, counseling and instructional support be provided to both teachers and students to effectively adopt different learning modalities such as MDL.

Modeling of High Strength and Wear Resistance Alumi

Modeling of high strength and wear resistance aluminum alloy based casting of composite material developed via conventional foundry method which is one of the most economical versatile and active research area and so for has not been thoroughly investigated. Due to complex nature of the composite materials and their related problems such as the nonlinear relationship between composition, processing parameters, heat treatment with the strength and abrasive wear, resistance can more efficiently be modeled by artificial neural networks. The artificial neural networks modeling requires sufficient data concerned with chemical composition , processing parameters and the resulting mechanical properties which were not available for such type of modeling. Therefore, a wide range of experimental work was conducted for the development of aluminum composites using conventional foundry method. Alloy containing Cu-Mg- Zn as matrix and reinforced with 1- 15 % Al 2 O 3 particles were prepared using stir casting method. The molten alloys composites were cast in metal mold. More than eighty standard samples were prepared for tensile tests and sixty samples were given solution treatment at 580 0 C for 1⁄2 hour and tempered at 120 0 C for 24 hours. Various characterization techniques apparatus such as X-ray Spectrometer, Scanning Electron Microscope, Optical Metallurgical Microscope, Universal Tensile Testing Machine, Vickers Hardness and Abrasive Wear Testing Machine were used to investigate the chemical composition, microstructural features, density, tensile strength, ductility (elongation), hardness and abrasive wear resistance. xixThese investigations including the material development and characterization were used for data generations as needed for modeling of high strength and abrasive wear résistance aluminum cast composites. For modeling purpose a multilayer perceptron (MLP) feedforward was developed and back propagation learning algorithm was used for training, testing and validation of the model. The modeling results shows that an architecture of 14 inputs with 9 hidden neurons and 4 outputs which include the tensile strength, elongation, hardness and abrasive wear resistance gives reasonably accurate results with an error within the range of 2-7 % in training, testing and validation. The modeling results shows that an alloy contents 2-3 % Cu, 2-3 % Mg, 3-5 % Zn reinforced with 10 % Al 2 O 3 can successfully be developed for highest strength (297 MPa) and highest abrasive wear résistance (0.4 gm weight loss /15 minutes using stir casting method. The modeling results also suggest that it is possible to develop the highest strength 466 MPa tensile strength and highest abrasive wear resistance aluminum alloy based casting composite materials having the matrix composition of 6 % Si, 2 % Mg with 3 % Zn reinforced with 2-5 % Al 2 O 3 particles.