Self-learning Module Research Articles

Evaluation of educational assessment module for flexible STEM education

<p>Modules are instructional materials used in flexible STEM education. With this, the Plan-Do-Study-Act action research model was employed to design, implement, and evaluate a self-learning module on Assessment in Learning (ASL) 2 for preservice teachers. Three education experts validated the ASL 2 module during the design phase. The Math and Science preservice teachers (N = 95) used and rated the validated module utilizing a pretest/posttest exam, survey questionnaire, and open-ended questions. The findings revealed that ASL 2 adhered to the prescribed module structure, format, and relevant outcomes-based education (OBE), technological, pedagogical, and content knowledge (TPACK), universal design for learning (UDL), and inclusivity frameworks. The preservice teachers had significant learning gains in assessment in four ASL chapters and provided positive perceptions and feedback regarding the module. Hence, the ASL module is a valid, practical, and effective instructional tool, particularly in flexible STEM education. The second cycle of the PDSA model, considering other learning outcomes and specializations, is recommended to be implemented in other teacher education institutions.</p>

Digitally Driven Short- and Intermediate-term Courses of Primary Care Psychiatry for Doctors: A Theme-based Analysis and Performance Audit.

The National Mental Health Survey of India 2015-2016 identified a substantial treatment gap in mental healthcare, emphasizing the urgent need for improved training of primary care doctors (PCDs) in managing psychiatric disorders. This study aimed to evaluate the effectiveness of three digitally delivered primary care psychiatry courses offered by the department of psychiatry in a tertiary neuropsychiatric center: the Certificate Course in Primary Mental Healthcare (CCPMH), the Diploma in Community Mental Health (DCMH), and the Diploma in Primary Care Psychiatry (DPCP). Based on the board of studies documents, including curriculum, delivery methods, accreditation criteria, and outcomes, a comparative and thematic analysis of the courses was conducted. The digital courses differed in duration, depth, and delivery methods. The DPCP, the most intensive and personalized program, was highly effective in translating knowledge into clinical practice. The CCPMH provided a foundational, short-term course for PCDs, while the DCMH covered a broader range of topics relevant to primary care psychiatry, supplemented with self-learning modules. All courses significantly contributed to reducing the mental health treatment gap by training a substantial number of PCDs. Digitally delivered courses are essential in addressing the mental health treatment gap in India. The DPCP, with its emphasis on practical training and personalized learning, demonstrates high effectiveness in equipping PCDs with the necessary skills to manage psychiatric disorders. However, further research is needed to evaluate long-term outcomes and to address the scalability and accessibility challenges of these.

Relationship between junior high students’ perceptions of self-learning modules and their academic performance

This study investigated junior high school students’ perceptions of self-learning module utilization in San Miguel, Bulacan, Philippines during the 2020-2021 school year and examined the relationship of these perceptions and their academic performance. The researcher surveyed 100 Grade 7-10 students, based on stratified purposing sampling, using a validated instrument and analyzed the results using SPSS. Key findings indicate that students generally viewed self-learning modules favorably, with feedback and communication systems rated highest, while self-directed learning and time management were identified as areas needing improvement. Perceptions of self-learning module utilization had a positive significant relationship with academic performance. Specifically, parental and community support (r = .33, p < .01), technological affordances and learning resources (r = .33, p < .01), and self-directed learning and time management (r = .31, p < .01) were significantly related to performance. Simple linear regression analysis revealed that students' perceptions of self-learning modules predict their academic performance (β = 2.86, p < .05), though perceptions accounted for only 7% of the variance. Recommendations include developing teacher training on effective module implementation, providing parent orientation on supporting at-home learning, and creating a systematic program for module implementation involving all stakeholders. Future research should explore additional variables influencing academic performance in this context.

SELF-LEARNING MODULE ASSESSMENT AND FEEDBACK IN MATHEMATICS

<p>The paper investigates self-learning module assessment and feedback in Mathematics among the respondents. It analyzes self-learning module assessment and feedback on improved competency knowledge of students in Mathematics in terms of self-learning assessment feedback, suggestions and comments, and advantages, as well as disadvantages of the self-learning process. Mixed methods are employed, which is a combination of quantitative and qualitative research approaches concentrated on focused group discussion (FGD). Likewise, purposive sampling is utilized to gather the study's sample size. Hence, one hundred seventy-two (172) comprised the research respondents. Results show that exploring the learning process through self-explanation, self-pace, more freedom, hands-on exposure, and detailed lessons to discuss the principles, rules, and laws of the integration learning process. However, it is complex and challenging to understand the self-learning assessment among students, show that there is a need to elaborate lessons with more examples, show detailed lesson where it gains knowledge, flexible, and enjoy learning, and show no interaction and guidance during the lesson process because the learning process is slow.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0770/a.php" alt="Hit counter" /></p>

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities.

In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Physics Teachers’ Level of Knowledge and Integration of the Deped Core Values

This descriptive-qualitative research determined the level of knowledge and integration of the DepEd Core Values of Physics teachers and the challenges which the teachers encounter during the integration of the DepEd Core Values in teaching Physics, as well as the DepEd Core Values reflected in the self-learning modules. The sixty-five (65) JHS Physics teachers from the Schools Division of Ilocos Norte, who were chosen via total enumeration, were the participants of the study. The main tools used in gathering data were the adapted survey questionnaire, a researcher-made tally sheet, and an interview guide. The data gathered were statistically analysed using mean, frequency, and rank. Results show that the teachers are highly knowledgeable in the application of the DepEd Core Values in teaching Physics. Meanwhile, results also show that the teachers are advanced in integrating the DepEd Core Values in teaching Physics. However, there is a limited frequency of 15 DepEd Core Values reflected in the SLMs of JHS Physics, which affect the teachers’ integration of the DepEd Core Values in their lesson. Moreover, the teachers face challenges in the integration of the DepEd Core Values it is because teaching Physics as the subject focuses on content and skills development. Values integration is difficult to implement and time consuming, and there is limited integration of the DepEd Core Values in the prescribed SLMs.

Flipped Classroom Learning Model Using a Digital Self-Learning Network Infrastructure Administration

The flipped classroom model has emerged as a significant pedagogical innovation, offering an alternative approach to traditional teaching methods. This model is particularly relevant for vocational education, where practical skills and active learning are critical. Network Infrastructure Administration, a core subject in vocational high schools (SMKs), presents unique teaching challenges that can be addressed through this model. This study aims to develop and evaluate a flipped classroom learning model integrated with a digital self-learning and diagnostic system for vocational high schools' Network Infrastructure Administration course. A mixed-method research design was employed, involving quantitative and qualitative data collection. The study was conducted in several vocational high schools, with participants including teachers and students of the Network Infrastructure Administration course. Tools used included digital self-learning modules and a diagnostic system to continuously assess student performance and provide feedback. The implementation of the flipped classroom model with digital tools resulted in improved student engagement and learning outcomes. Quantitative data showed a significant increase in test scores and practical skill proficiency. Qualitative feedback from both teachers and students indicated higher satisfaction and motivation levels. The developed flipped classroom model, supported by a digital self-learning and diagnostic system, enhances the learning experience and effectiveness of teaching Network Infrastructure Administration in vocational high schools. These findings suggest that integrating technology with innovative pedagogical approaches can significantly improve vocational education. Future research should explore the long-term impact of this model and its applicability to other vocational subjects.

Trends and Practices of Araling Panlipunan Instructions in the Schools Amidst Covid-19 Pandemic: Input for Technical Assistance

This action research determined the implementation of Araling Panlipunan Instructions in the schools amidst the COVID-19 pandemic. The study aimed to discover the profile of the participants; the trends and practices of teachers in the new normal; the challenges and issues encountered in the absence of face-to-face classes; and solutions utilized by teachers in addressing the situations affecting the instructions. The participants in the study are the teachers of elementary and secondary schools. The researcher used the descriptive survey method and frequency for the interpretation and analysis of the data. The survey tool was validated by twenty non-respondent teachers from select schools before it was administered via Google link to one hundred- six respondents. An interview and focus group discussion also was conducted to collect more information and validate the gathered data. Based on the results, the findings revealed that the majority of respondents were female, teachers utilized modular distance learning, attended the training in lesson planning, modular and online distance learning, and joined learning action cells. The teachers utilized learning modules, worksheets, videos, and manuals as learning resources. The respondents utilized the formative and summative tests, actual performance and skills tests, and portfolios, as assessments. The use of films and photographs, pictures, globes and maps, and problem-solving were the most common approaches in handling their classes. The most common problems encountered were the distance of learners’ houses, parents answering self-learning modules, poor internet connection, late submission of learners’ output, assessment of learners’ performance, and non-completion of activities. Although there were problems, the teachers strategically applied the best practices such as strong collaboration, parents’ orientation, open communication, access to free wifi providers, simplified activities for learners, giving feedback, and conducting a home visitation to help the learners during the pandemic Keywords: instructions, trends and practices, challenges, strategies, specialization, pandemic, new normal, assessment, modular, online kumustahan, learning delivery

Challenges and Alternatives of Parents as Home Learning Partners in the Implementation of Printed Modular Distance Learning

Because of the risks posed by COVID-19, most public educational institutions have implemented printed modular distance learning instruction. The purpose of this study was to determine the challenges and alternatives faced by parents as they performed their role as home learning partners during the last two quarters of the school year 2020-2021 using the printed modular distance learning modality. It also sought to investigate the suggestions of parents as home learning partners. The study used an exploratory descriptive qualitative (EDQ) approach. It included fourteen parents from a public elementary school who were purposively chosen. In-depth interviews (IDI) were used to gather information. The different themes were identified through inductive coding. The study found that parents face the following challenges: lack of electronic gadgets, difficulty explaining/teaching, insufficient time, non-conforming modules, and an inconsistent schedule for SLM distribution. To fulfill their responsibilities, parents have employed a variety of coping strategies, such as effective time management, the use of ICT, approaching MKOs, motivation through a reward system, and spoon-feeding. Although parents considered various options for fulfilling their role as home learning partners, most participants preferred to unburden themselves by returning to in-person classes. To reduce their workload, parents suggest the following: (1) a free mobile phone for parents who do not already have one, (2) a free Wi-Fi zone, (3) module updates, and (4) monthly distribution of self-learning modules. Being a home learning partner imposes an additional burden on parents. It is suggested that the government or school work with other government agencies or non-governmental organizations to implement parents’ suggestions. Keywords: challenges, alternatives, home learning partner, modular distance learning

A self-learning multi-population evolutionary algorithm for flexible job shop scheduling under time-of-use pricing

Due to the energy crisis and environmental downgrade, manufacturing companies face rising costs. The pressure on manufacturers to reduce costs and increase efficiency is increasing. The flexible job shop scheduling problem considering time-of-use pricing (FJSPTOUP) is studied to minimize the maximum completion time and total electricity cost. A new self-learning multi-population evolutionary algorithm (SLMPEA) is proposed to address the static scheduling problem. A self-learning module is designed to acquire knowledge from elite individuals in subpopulations. The knowledge discovered can guide the evolution of subpopulations. To improve the convergence of the algorithm and maintain the diversity of the population, we present an elite retention and enhancement strategy. Two enhancement operators are developed to elevate the quality of elite individuals, and a selection strategy is designed for the enhancement operators. A time-of-use-based adjustment strategy is proposed to reduce the total electricity costs without increasing the maximum completion time. Experimental results show that the proposed SLMPEA outperforms the comparative algorithms in solving FJSPTOUP.

Involvement of Working Parents in Students’ Academic Achievement: Basis of Support Programs

The study aimed to determine the level of involvement of working parents and the level of seriousness of the problems they encountered in modular distance learning. It sought to test whether there is a significant relationship between the academic achievement of the students and their parents’ level of involvement. This study utilized a descriptive-correlational research design with a questionnaire as a data-gathering tool. Results show that working parents are highly engaged in the new learning modality while their children manifested a satisfactory level of academic performance. The relationship between the overall level of parents’ involvement and level of academic achievement was found insignificant, but coaching and tutoring, and monitoring students’ compliance flagged a significant relationship with academic achievement. A set of programs was proposed to address problems experienced by parents such as poor internet connection, insufficient resources at home, unclear instructions in the self-learning modules, and distraction from gadgets.

Utilization of Self-Learning Module in the Development of Reading Comprehension of Grade Four Amethyst

Utilization of Self-Learning Module in the Development of Reading Comprehension of Grade Four Amethyst

Managing Modular Instruction and Students' Learning Outcomes

This study was conducted to determine the level of management of modular instruction in relation to the student’s learning outcomes. The study was conducted among 31 teachers, 12 school heads, 55 parents, and 191 Grade 10 students in Paquito S. Yu Memorial National High School of Lower Timonan, Dulop National High School of Dulop, Dumingag National High School, all of Dumingag Zamboanga del Sur during the School Year 2021-2022. This study used the quantitative-correlational research design. The descriptive statistics included the use of weighted average mean, standard deviation, frequency and percentage distribution; and inferential statistic were done using Kruskal-Wallis test, Spearman-rho correlation and t-test of r using SPSS and online data analysis calculators. The findings revealed that the respondent schools are highly equipped with educational technology to support modular instruction, have modules downloadable through the DepEd websites. The schools have sufficient financial support and material resources and it is properly utilized to sustain the massive reproduction of Self-Learning Modules (SLMs). Teachers were highly trained to manage modular instruction including the preparation of the contents, printing, distribution and assessment. They were also provided with adequate trainings to enrich their content knowledge with integration of educational technology. The contents of the self-learning modules were not only limited to the minimum essential learning competencies, rather it is enriched with in-depth discussions of concepts, visual illustrations, links for demonstrations and short videos. Moreover, the study concluded that the four groups of participants have similar perceptions on the level of management of modular instruction. Most of the student participants have satisfactorily achieved the minimum essential learning competencies in their English subject during the second quarter. The empirical evidence pointed out that even if the schools have highly evident provisions of technology and highly trained teachers, the students’ learning outcomes may not be very impressive. Finally, the study recommends that the Department of Education should allocate an additional budget for modular distance learning modalities; provide teachers with laptops, ensure reliable internet connectivity, provide assistance to teachers, design enhancement activities to improve students' learning outcomes; and encourage parents to support their children. Future researches should focus on the effect of modular instruction on higher-order thinking skills.

GENDER STEREOTYPING IN SELF-LEARNING MODULES (SLMs) IN THE PHILIPPINES: A CRITICAL DISCOURSE ANALYSIS

This study aimed to determine the processes employed in embedding male and female characters in the Self-Learning Modules (SLMs) in the Philippines, how these processes reflect gender stereotype, and the underlying ideologies embedded in them. This study utilized a qualitative research method employing Critical Discourse Analysis. In the conduct of this study, 51 SLMs from the Department of Education Central Office which served as the corpora of the study were examined. The results showed six processes employed in embedding male and female characters which include material, mental, relational, behavioral, verbal, and existential. In addition, these processes reflect gender stereotypes as shown in: Males Doing, Females Behaving, Males Sensing, Females Saying, Males as Cognitive and Desiderative Characters, and Females as Perceptive and Emotive Characters. Lastly, the ideologies embedded in the SLMs are traditional and egalitarian. KEYWORDS: Education, Linguistics, Qualitative, Self-Learning Modules, processes, gender stereotyping, ideologies, critical discourse analysis, Philippines

Implementation and Evaluation of a Curricular Framework for Online Language Courses

Constraints caused by the Covid-19 pandemic led to a spike in courses originally designed for in-class instruction being delivered online. As the pandemic wanes many courses will return to in-class instruction while other programs will look to develop post-pandemic long-term online courses, indicating a clear need for an effective curriculum for online foreign language classes. This article describes the implementation and evaluation of a newly developed curricular framework for online language courses at university level. Primarily, this framework is designed to foster oral proficiency. The technology used to implement the framework was also developed specifically to facilitate speaking practice. We have used the framework at Purdue University to develop a curriculum for Japanese online courses Levels 1–4 (i.e., semesters 1–4). Japanese Level 1 is used in this article to illustrate the features and effectiveness of the framework. The framework has three major components: asynchronous self-learning modules, weekly small-group synchronous sessions, and performance-based assessments. Each component involves a pedagogical innovation, a technological innovation, or both. This study’s main research question was, “Do students in an online course designed using the new framework develop a degree of oral proficiency comparable to that of students in a classroom?” An oral test administered at the end of the course showed that the online students performed comparably to their classroom counterparts on question and answer and picture description tasks and outperformed them on an elicited imitation task. The results of a questionnaire also indicated participants’ positive perceptions of the technology used and the online course itself. These findings serve as further evidence for the effectiveness of the proposed curricular framework.

A Spatiotemporal Graph Neural Network with Graph Adaptive and Attention Mechanisms for Traffic Flow Prediction

This study addresses the complex challenges associated with road traffic flow prediction and congestion management through the enhancement of the attention-based spatiotemporal graph convolutional network (ASTGCN) algorithm. Leveraging toll data and real-time traffic flow information from Orange County, California, the algorithm undergoes refinement to adeptly capture abrupt changes in road traffic dynamics and identify instances of acute congestion. The optimization of the graph structure is approached from both macro and micro perspectives, incorporating key factors such as road toll information, node connectivity, and spatial distances. A novel graph self-learning module is introduced to facilitate real-time adjustments, while an attention mechanism is seamlessly integrated into the spatiotemporal graph convolution module. The resultant model, termed AASTGNet, exhibits superior predictive accuracy compared to existing methodologies, with MAE, RMSE, and MAPE values of 8.6204, 14.0779, and 0.2402, respectively. This study emphasizes the importance of incorporating tolling schemes in road traffic flow prediction, addresses static graph structure limitations, and adapts dynamically to temporal variations and unexpected road events. The findings contribute to advancing the field of traffic prediction and congestion management, providing valuable insights for future research and practical applications.

Challenges of Parents and Learners in the Primary Grades in Using Self-learning Modules

Challenges of Parents and Learners in the Primary Grades in Using Self-learning Modules

An Appeal for Help: The Struggles of Readers Using Self-Learning Modules

This study used self-learning modules to determine the learners’ experiences, challenges, and insights when using self-learning modules. The researcher gathered ten (10) informants who were interviewed and were the primary source of information. The data gathered in this qualitative narrative inquiry is based on the responses to three research questions. Results were grouped into themes. Findings revealed that learners struggled using the self-learning modules, such as learning content confusion, mental exhaustion, restricted learning experience, and quickly getting distracted as part of their lived experience. Moreover, they have faced challenges such as the inability to ask for clarifications, lack of guidance, unstructured content, and unfamiliar language use. Finally, the informants’ insights were the practice of active learning, continuous teacher monitoring, efficient time management, use of multiple resources, and review modules with fewer tasks. This implies that it is vital to consider the learners’ level of comprehension and balance in content, and the teachers’ availability should be considered in creating modules or self-learning modules. This research hopes to make strategies and methods to help learners complete and understand the self-learning modules. Additionally, this study focuses on the qualitative appeal to assist readers struggling with self-learning modules at Public Senior High School in Panabo City, Davao del Norte; the same methodology can be used to help students struggling with self-learning modules nationwide.

Effectiveness of self-learning module on pediatric nurse’s knowledge regarding protein energy malnutrition for children in Mosul Teaching Hospitals

Effectiveness of self-learning module on pediatric nurse’s knowledge regarding protein energy malnutrition for children in Mosul Teaching Hospitals