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.

Artificial Immune System Ais -A Soft Computing Based Approach for Electroencephalography Eeg Signal Classification

Human immune system is characterized as a group of cells, molecules and organs which is capable of performing several tasks, like pattern recognition, learning from stored data in memory, detection of diseases and optimize response against diseases. Development of immunological principles inspired computational techniques are being taken up by the researchers. These techniques are being used to solve engineering problems in the field of artificial intelligence. Extensive research has been undertaken to develop and derive algorithms which are inspired by human immune system. These algorithms use computationally intelligent techniques to model the human system and are known as Artificial Immune Systems (AIS). This research focusses on development of a classification system based on Negative Selection Algorithm (NSA) which uses non-invasive brain electroencephalogram (EEG) recorded with the help of electrodes placed on brain motor cortex. Multi-domain features, time domain and frequency domain, were considered to ascertain the classification accuracy. Mel frequency cepstral coefficients (MFCC) are commonly used as features for audio signal and speech identification. In this research use of MFCC for EEG signal classification demonstrated the highest classification accuracy and selected as the best feature for EEG signals under consideration. Dimensionality reduction is an important aspect of data preprocessing for improving the computational complexity. Stacked auto-encoder, with two pre-trained hidden layers, has been used for EEG data dimensionality reduction. The multivariate motor imagery EEG signals have been classified by set of detectors (artificial lymphocytes) which are trained and optimized using Genetic Algorithm (GA). The underlying rule for training is the negative selection algorithm (NSA), which is developed after taking inspiration from human negative selection principle for maturation of lymphocytes inside thymus.These detector sets are trained and optimized for each class of motor movement for detection of non-self pattern based on a threshold and detector radius. The radius of detector is optimized using GA such that it does not mis-classify the sample of EEG signal. Finally, a comprehensive Negative Selection Classification Algorithm (NSCA) is proposed in this research for classification of brain EEG signals. The AIS based NSCA exhibits improved performance of multivariate classification as compared to the recent techniques used by researchers.