Structured Knowledge Research Articles

Evaluate the effectiveness of Structured Teaching Programme on Knowledge Regarding Pubertal Changes and its Coping Strategies among Adolescent Girls

Puberty is the cornerstone of reproductive development. Therefore, the transition through puberty is a critical period of development that provides an important opportunity to build a healthy foundation for sexual and reproductive health.“Adolescence’’ comes from the Latin word meaning “to come to maturity”, a fitting description of this stage of life. The adolescent is maturing physically and emotionally, growing from childhood towards adulthood, and seeking to be grown up. It is a period of transition between childhood and adulthood. During this transition period dramatic physical, cognitive, psychosocial and psychosexual changes take place that are exciting and at the same time frightening Adolescence is a period where a vital physical and psychosexual changes takes place. In this regard, not only the pre-schooler, but also the older child that is the pre- adolescents between the age of 10 and 12 year should also receive adequate attention as a preparation for pubertal changes and menarche. Materials and Methods: A quantitative pre-experimental one group pre-test post-test design was used to assess the effectiveness of Structured Teaching Programme on knowledge regarding pubertal changes and its coping strategies among adolescent girls. The study conducted on 60 samples. Data was collected using structured knowledge questionnaire. Results: In the pre-test, the majority (72%) adolescent girls had inadequate knowledge regarding pubertal changes and its coping strategies. In the post test, the same group has gained adequate knowledge (97%). Thus the study findings indicate that the Structured Teaching Programme was effective in enhancing knowledge of adolescent girls regarding pubertal changes and its coping strategies. The variables with χ2 values for the variables age (χ2 =12.49), birth order (χ2 = 12.49), class of study (χ2=9.49), source of information (χ2 =12.49),type of family(χ2 =12.49),diet (χ2 =5.99), sleeping patterns (χ2=(9.49)are found to be significant at 0.05 level of significance with their mean pre- test knowledge scores. Conclusion: After the detailed analysis of the study findings showed that pre-test finding showed that, most of the adolescent girl had inadequate knowledge regarding pubertal changes and its coping strategies. but after the administration of structured teaching programme it enhancing knowledge of adolescent girls regarding pubertal changes and its coping strategies. Regarding the association of demographic variables, they show association with age, birth order, class of study, source of information, type of family, diet, sleeping pattern, are found to be significant at 0.05 level of significance with their pre-test knowledge score.

University lecturers’ conceptions of online teaching in distance education courses in Vietnamese higher education

This study aimed to characterise academics’ conceptions of teaching in fully online undergraduate distance education courses with no on-campus component. The study aimed to fill a gap in the literature, as previous research had examined conceptions of teaching in face-to-face courses, with a few studies of blended teaching via the Internet in on-campus courses. Fourteen academics from five faculties in a Vietnamese regional university were interviewed, with the study taking place shortly after the outbreak of Covid-19. Grounded theory was used for data analysis. The results revealed four categories of conceptions of online teaching, namely online teaching (1) as transmitting structured knowledge and skills, (2) as guiding students to acquire knowledge and skills, (3) as facilitating students’ understanding via interaction and (4) as developing students’ understanding and capabilities. The four categories of conceptions were defined and distinguished by a set of six dimensions, which included e-Learning/LMS. The set of categories had some similarities to those found for face-to-face teaching, but also some distinctions which could be explained by the nature of online teaching and learning. The study, therefore, makes a major contribution by establishing a category scheme for conceptions of teaching in online distance education, with detailed characterisation of the four categories of conceptions. The descriptors of the conceptions showed cultural influences, particularly from a Confucian heritage, which is of significance as research into face-to-face conceptions had not found cultural variations.

Ontological knowledge inferring approach: Introducing Directed Collocations (DC) and Joined Directed Collocations (JDC)

The growing need of utilizing unstructured knowledge embedded in open-domain natural language text into machine-processable forms requires the induction of hardly extracted structured knowledge into knowledge bases which makes the Semantic Web vision a reality. In this context, ontologies, and ontological knowledge (triples) plays a vital role. This research introduces two novel concepts named Directed Collocation (DC) and Joined Directed Collocation (JDC) along with a methodical application of them to infer new ontological knowledge. Introduced Quality-Threshold-Value (QTV) parameter improves the quality of the inferred ontological knowledge. Having set a moderate value (3) for QTV, this approach inferred 95,491 new ontological knowledge from 43,100 triples of open domain Sri Lankan English news corpus. Indeed, the outcome was approximately doubled in size as the source corpus. Some inferred ontological knowledge was identical with the original corpus content, which evidences the accuracy of this approach. The remaining were validated using inter-rater agreement method (high reliability) and out of which around 56% were estimated as effective. The inferred outcome which is in the triple format may use in any knowledge base. The proposed approach is domain independent. Thus, helps to construct/extend ontologies for any domain with the help of less or no human specialists.

Informal structures in knowledge and the effectiveness of the transfer process among construction workers in Dar es Salaam, Tanzania

The combination of both Structured Knowledge Transfer (SKT) and Unstructured Knowledge Transfer (UKT) processes is important for Knowledge Transfer Effectiveness (KTE) in construction workplace practices. However, the extant literature suggests that there is a high level of KTE in informal construction practices where UKTs are dominant while SKTs are rare as prevailing Knowledge Transfer Structures (KTSs). This study, therefore, analyzes the impact of knowledge transfer structures on the KTE in informal workplace practices. A questionnaire survey was used to collect data from 417 craftsmen under informal construction practices in Dar es Salaam, Tanzania. Using correlation and binary logistics regression analyses, the strength of association between KTE under different KTSs was evaluated. The study suggests that both SKT and UKT positively influence KTE under different conditions, although the magnitude of effect is relatively higher in SKT than UKT, even when the overall process is considered informal. In addition, the increase in KTS increases KTE, providing some justification for planned and organized forms of knowledge transfer and sharing, even in informal workplace practices. In contrast to SKT processes, UKT processes are significantly influenced by codification, motivation, trade specialization and transferor-transferee agreement. The findings from this study contribute to the understanding of KTSs in informal workplace practices and that the transfer, though dominated by UKT processes, enables practitioners to internalise a high degree of structures well.

A Co-relational Study to assess the Knowledge and Attitude of nurses regarding Biomedical Waste Management in selected hospitals of Gulbarga

Background of the Study: Biomedical waste has become a serious health hazard in many countries, including India. In many countries, hazardous and medical wastes are still handled and disposed together with domestic wastes, thus creating a great health risk to municipal workers, the public and the environment. The present study was conducted to assess the knowledge and attitude of nurses regarding biomedical waste management in selected hospitals of Gulbarga. Objectives of The Study: 1 To assess the Knowledge of nurses regarding Biomedical waste management. 2 To assess the Attitude of nurses regarding Biomedical waste management. 3 To correlate the knowledge and attitude of nurses regarding Biomedical waste management. 4 To find out the association between the Knowledge and Attitude of nurses regarding Biomedical waste management and selected socio-demographic variables. Method: A descriptive approach with descriptive survey design was used for this study. The study was carried out in selected hospitals of Gulbarga. The sample comprised of 100 nurses who met the inclusion criteria from the selected hospitals were chosen by purposive sampling technique. Data was collected by administering a structured knowledge questionnaire and Likert’s Attitude scale. The data was analyzed using descriptive and inferential statistics Results: Mean percentage of knowledge score was 57.3%. Majority 66% had moderate knowledge regarding biomedical waste management. Mean percentage of attitude score was 73.49%. Majority 60% had moderately favourable attitude regarding biomedical waste management. The correlation between knowledge and attitude was found to be significant (r- 0.617). No significant association was found between the knowledge and selected demographic variables such as gender, designation, previous information and working area (χ2 =1.846, 3.375, 11.237 and 11.647). But significant association was found between the knowledge of nurses and age, professional qualification, clinical experience and type of hospital (χ2 =20.225, 10.474, 19.717 and 6.221). No significant association was found between the attitude of nurses and selected demographic variables such as age, professional qualification, designation, clinical experience, previous information, type of hospital and working area (χ2 =3.219, 3.571, 2.168, 1.341, 2.800, 0.815 and 5.828). But a significant association was found between the attitude of nurses and gender (χ2 =4.920). Interpretation and Conclusion: The study had shown that majority of the nurses had moderate knowledge and moderately favourable attitude regarding biomedical waste management. Hence, it was concluded that nurses need to be educated on biomedical waste management in order to achieve better care.

A Study on Knowledge of Mothers regarding Behavioural Problems in children in selected hospital at Mangaluru

The study was conducted to assess the knowledge of mother’s regarding behavioural problems in children. A descriptive approach was adopted for this study. The study was conducted in Father Muller Medical college Hospital Mangaluru. The sample consists of 153 subjects selected by purposive sampling technique. A structured knowledge questionnaire was used to assess the knowledge of mothers regarding behavioural problems in children. The collected data was analyzed by using descriptive statistics. The result showed that about 11.1% had good knowledge, 62.7% had average knowledge and 26.1% had poor knowledge among mothers regarding behavioural problems in children. There was a significant association between the knowledge of mothers regarding behavioural problem in children and selected baseline variable like occupation. Findings of the present study indicate that majority of the mothers had average knowledge score. Hence there is a need of health awareness program to improve mother’s knowledge on behavioural problems.

A Study to Assess the Knowledge and Attitude towards Global Warming among B.Ed. students in a selected College of Education, Hubballi with a view to develop an Information Guide Sheet

Global warming is one of the important environmental problems since the Mid-20th century. The phenomena not only affecting human health but also plant, animals, as well as economy and unfortunately some adverse consequences like flood, heavy rain are becoming visible these days. According to best estimates the earth could warm up by a minimum of 3oC by 2100. There is a need to bring out awareness among the future teachers on what is global warming? How it affects our life, our economic prosperity and health and other welfare, hence the study aims in awareness status and attitude towards global warming. A descriptive study was conducted among 100final year B.Ed. students of KLE Society’s College of Education, Hubballi. Descriptive research design was used for the study. The sample was selected using Non-Probability; Purposive Sampling Technique. Data was collected by structured knowledge questionnaire and attitude scale. Data analysis was done using descriptive and inferential statistics. Overall result of the study revealed that out of 100 subjects, majority of the subjects 57 (57%) had average knowledge, 25(25%) had good knowledge and 18(18%) had poor knowledge regarding global warming. Regarding attitude, majority of subjects 63(63%) had neutral attitude, 21(21%) had positive attitude and 16(16%) had negative attitude towards global warming. There was positive correlation (rxy=0.577) between knowledge scores and attitude scores. The study concludes that, knowledge and attitude of Final year B.Ed. students regarding global warming was just satisfactory.

Multimodal learning on graphs for disease relation extraction

Disease knowledge graphs have emerged as a powerful tool for artificial intelligence to connect, organize, and access diverse information about diseases. Relations between disease concepts are often distributed across multiple datasets, including unstructured plain text datasets and incomplete disease knowledge graphs. Extracting disease relations from multimodal data sources is thus crucial for constructing accurate and comprehensive disease knowledge graphs. We introduce REMAP, a multimodal approach for disease relation extraction. The REMAP machine learning approach jointly embeds a partial, incomplete knowledge graph and a medical language dataset into a compact latent vector space, aligning the multimodal embeddings for optimal disease relation extraction. Additionally, REMAP utilizes a decoupled model structure to enable inference in single-modal data, which can be applied under missing modality scenarios. We apply the REMAP approach to a disease knowledge graph with 96,913 relations and a text dataset of 1.24 million sentences. On a dataset annotated by human experts, REMAP improves language-based disease relation extraction by 10.0% (accuracy) and 17.2% (F1-score) by fusing disease knowledge graphs with language information. Furthermore, REMAP leverages text information to recommend new relationships in the knowledge graph, outperforming graph-based methods by 8.4% (accuracy) and 10.4% (F1-score). REMAP is a flexible multimodal approach for extracting disease relations by fusing structured knowledge and language information. This approach provides a powerful model to easily find, access, and evaluate relations between disease concepts.

Application of Automatic Completion Algorithm of Power Professional Knowledge Graphs in View of Convolutional Neural Network

With the continuous development of electric power informatization, a large amount of electric power data information has been produced. The reasonable application of electric power database is of great significance. Building the automatic completion optimization algorithm of knowledge graphs (KGs) in power professional field provides a method to extract structured knowledge from a large number of power information and images, which has broad application value. The automatic completion algorithm of power professional KGs in view of convolutional neural network (CNN) is conducive to completing the analysis and management of power data, enabling the flexible use of data information generated by the power grid, and bringing ideas for the in-depth exploration and innovation of power grid data information application.

Diagnosis Kesulitan Belajar Siswa pada Materi Aritmatika Sosial Kelas VII SMPN 7 Sinjai

The purpose of this study was to determine students' learning difficulties in social arithmetic material in terms of structured knowledge. The subjects of this study were two seventh grade students with the lowest scores at SMP Negeri 7 Sinjai. This research method is a qualitative research with a case study approach. Data collection techniques used are observation, diagnostic tests, interviews, and documentation. The results of this study indicate that there are four types of learning difficulties, namely (1) students do not write down what is known and asked in the problem (2) students have difficulty identifying arithmetic operations according to what is asked; (3) reflected in the student's error in determining the formula; (4) students' difficulties in the calculation process. This shows that students cannot apply the formula correctly. Students also have no conclusions that can be drawn from the answers to the problems.

Toward Question-Answering with Multi-Hop Reasoning and Calculation over Knowledge Using a Neural Network Model with External Memories

The differentiable neural computer (DNC) is a neural network model with an addressable external memory that can solve algorithmic and question-answering tasks. Improved versions of the DNC have been proposed, including the robust and scalable DNC (rsDNC) and DNC-deallocation-masking-sharpness (DNC-DMS). However, integrating structured knowledge and calculations into these DNC models remains a challenging research question. In this study, we incorporate an architecture for knowledge and calculations into the DNC, rsDNC, and DNC-DMS to improve their abilities to generate correct answers for questions with multi-hop reasoning and provide calculations over structured knowledge. Our improved rsDNC model achieves the best performance for the mean top-1 accuracy, and our improved DNC-DMS model scores the highest for the top-10 accuracy in the GEO dataset. In addition, our improved rsDNC model outperforms other models in regards to the mean top-1 accuracy and mean top-10 accuracy in the augmented GEO dataset.

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs.We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences.Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance.Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. While primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

MKBQA: Question Answering over Knowledge Graph Based on Semantic Analysis and Priority Marking Method

In the field of question answering-based knowledge graphs, due to the complexity of the construction of knowledge graphs, a domain-specific knowledge graph often cannot contain some common-sense knowledge, which makes it impossible to answer questions that involve common-sense and domain knowledge at the same time. Therefore, this study proposes a knowledge graph-based question answering method in the computer science domain, which facilitates obtaining complete answers in this domain. In order to solve the problem of natural language problems being difficult to match with structured knowledge, a series of logic rules are first designed to convert natural language into triples of the question. Then, a semantic query expansion strategy based on WordNet is proposed and a priority marking algorithm is proposed to mark the order of triples of the question. Finally, when a question triple corresponds to multiple triples in the knowledge graph, it can be solved by the proposed SimCSE-based similarity method. The designed logic rules can deal with each type of question in a targeted manner according to the different question words and can effectively transform the question text into question triples. In addition, the proposed priority marking algorithm can effectively mark the order in the triple of the question. MKBQA can answer not only computer science-related questions but also extended open domain questions. In practical applications, answering a domain question often cannot rely solely on one knowledge graph. It is necessary to combine domain knowledge and common-sense knowledge. The MKBQA method provides a new idea and can be easily migrated from the field of computer science to other fields. Experiment results on real-world data sets show that, as compared to baselines, our method achieves significant improvements to question answering and can combine common-sense and domain-specific knowledge graphs to give a more complete answer.

Enhancing Answer Selection via Ad-Hoc Knowledge Extraction from Unstructured Web Texts

Answer selection aims to identify the most relevant answers to a given question from a set of candidates. It is the fundamental component of intelligent question answering system. To improve performance, it gradually becomes an effective strategy to integrate external structured knowledge bases (KBs) into the answer selection model. Due to expensive cost of construction and maintenance of such KBs, these models are suffering from domain barriers and information incompleteness. In this paper, we propose a two-stage extraction–comprehension answer selection model, which can extract ad-hoc knowledge from unstructured web texts to enhance the performance of answer selection. For the extraction, two types of snippets are extracted from unstructured web pages and utilized as the source of ad-hoc knowledge. For the comprehension, a selective attention mechanism is employed to extract and integrate ad-hoc knowledge from multiple text snippets obtained in the first stage, which can enrich the representation of question–answer pairs and more accurately identify the correct answers. By incorporating ad-hoc knowledge extracted from both types of snippets, the proposed model achieves state-of-the-art results on two public available benchmark datasets. In particular, on WikiQA, in terms of the two evaluation metrics (mean average precision and mean reciprocal rank), it achieves 9.9[Formula: see text] and 8.4[Formula: see text] higher than the previous non-pretraining-based models, and 3.4[Formula: see text] and 3.2[Formula: see text] higher than the pretraining-based models.

Terminology and ontology development for semantic annotation: A use case on sepsis and adverse events

Annotations enrich text corpora and provide necessary labels for natural language processing studies. To reason and infer underlying implicit knowledge captured by labels, an ontology is needed to provide a semantically annotated corpus with structured domain knowledge. Utilizing a corpus of adverse event documents annotated for sepsis-related signs and symptoms as a use case, this paper details how a terminology and corresponding ontology were developed. The Annotated Adverse Event NOte TErminology (AAENOTE) represents annotated documents and assists annotators in annotating text. In contrast, the complementary Catheter Infection Indications Ontology (CIIO) is intended for clinician use and captures domain knowledge needed to reason and infer implicit information from data. The approach taken makes ontology development understandable and accessible to domain experts without formal ontology training.

Knowledge regarding Hypothyroidism among Community People of Kavre

Background: Hypothyroidism is a condition in which the thyroid gland is unable to produce enough thyroid hormone. Thyroid dysfunction is one of the concern public health problem among the Nepalese population. It has been estimated that 0.2% of the deaths in Nepal result from endocrine disorders, among which Iodine deficiency has been a major cause. Therefore, this study helps to assess knowledge level regarding hypothyroidism among community people of Banepa-8, Kavre. Objectives: To assess the knowledge regarding hypothyroidism and to identify the association of knowledge with the selected demographic variables. Methodology: A descriptive cross-sectional study was conducted among 140 respondents of Banepa-8, Kavre district on April 2021. All the adult people of age 25-55 years were included in the study. Structured knowledge questionnaire was used after validity and reliability. Data was collected by interview Result: Among the 140 participants, statistical analysis showed that more than half of the respondents 46(37.9%) had low knowledge, majority 87(62.1%) of respondents had average knowledge and 7 (5%) had high knowledge regarding Hypothyroidism. Age, Gender, education, ethnicity, occupation, family history and marital status had no significant effect on the knowledge level of the respondents at (p > 0.05). Conclusion: Knowledge regarding hypothyroidism was found to be average among majority of people in Banepa-8, Kavre. There was no significant association with the knowledge and selected demographic variables.

EFFECTIVENESS OF STRUCTURED TEACHING PROGRAM ON KNOWLEDGE REGARDING PREVENTION OF FIVE F'S DISEASE TRANSMISSION AMONG MOTHERS OF UNDER FIVE CHILDREN AT SELECTED RURALAREAS OF JAMUHAR., SASARAM, BIHAR

Adequate sanitation, together with good hygiene and safe water, are fundamental to good health and to social and economic development. Improvements in one or more of these three components of good health can substantially reduce the rates of morbidity and the severity of various diseases and improve the quality of life of huge numbers of people, particularly children, in developing countries. A quasi-experimental research design was used for this study to select 60 mothers of under ve children by purposive sampling technique. Structured knowledge questionnaire was used to collect the data from the samples. The ndings of the study concluded that the STP was found to be effective in improving the knowledge of mothers regarding prevention of Five F's (food, nger, uids, ies and faeces) disease transmission

Institutional awareness campaign: COVID-19 vaccination drive among nursing students

ObjectivesPossessing accurate knowledge and utilization of reliable information sources may play a vital role in adoption of positive health behaviours, including acceptance of vaccine. The present study was conducted with an objective to assess the awareness and attitude of undergraduate nursing students regarding COVID-19 vaccine. MethodsA cross-sectional survey was conducted in Mid May 2021 using Google Forms through Google platform on-line. A total of 354 nursing students participated in the survey. A validated and pre-tested structured knowledge and Attitude, questionnaire was used to collect data from undergraduate nursing students regarding COVID-19 vaccine. Chi-square test, followed by binary logistic, was applied to determine factors associated with knowledge scores. ResultsThe mean knowledge score was 11.31 (SD ± 2.31, range: 2–15), with a correct response rate of 75.4%. However, the mean attitude score was 40.56 (SD ± 5.10, range: 28–55) with an unfavourable response towards COVID-19 vaccination (54.8%). The knowledge level was found to be significantly associated with student's professional qualifications and Vaccination status (P < 0.05). In binary logistic regression analyses, the knowledge score found to be significantly associated with participant's professional qualification i.e., B.Sc. (Hons.) Nursing 2 nd Year (AOR: 2.45, CI: 1.43–4.19, P < 0.001) and B.Sc. (Hons.) Nursing 3rd Year (AOR: 2.69, CI: 1.50–4.83, P < 0.001) and students who has received COVID-19 vaccination (AOR:3.08 CI: 1.81–5.25, P < 0.000). ConclusionThe current study findings represent adequate knowledge among undergraduate nursing students which is a very good sign. However, efforts must be taken to develop positive attitude towards COVID-19 Vaccination.

EFFECTIVENESSOFSELF-INSTRUCTIONAL MODULEONKNOWLEDGEREGARDING DIETARY APPROACHES TO STOP HYPERTENSION (DASH)

Background: A study was conducted to assess the effectiveness of Self-Instructional Module on knowledge regarding dietary approaches to stop hypertension among hypertensive patients in selected hospital, Sikar, Rajasthan. A total sample of 60, hypertensive patients were selected using purposive sampling technique. The objectives of the study were to assess the level of knowledge among hypertensive patientsregarding dietary approaches to stop hypertension (DASH) before and after intervention (Self Instructional module) and to determine the effectiveness of self-instructional module on level of knowledge regarding DASH among hypertensive patients. The final objective was to find out the association between pre-test level of knowledge and selected demographic variables. Materials and Methods: A one-group pre-test post-test design was used to conduct the study. 60 hypertensive patients from selected hospital were enrolled using non-probabilitypurposive sampling technique. The conceptual framework of the study was based on Ludwig von Bertalanffys General System Model. Tools used for data collection were demographic Performa and structured knowledge questionnaire. Results: Data analysis was done using descriptive and inferential statistics. Findings of the study revealed that the mean post-test knowledge score 23.00±2.7 was significantly higher than the mean pre-test knowledge score 10.00±2.3 with the Paired t value computed [31.60**, df=59] was statistically significant (P&lt;0.01 level). Significant association was observed between level of knowledge and gender at 0.05 level. Conclusion: The findings of the study confirmed tha the Self-instructional module was significantly effective in improving the knowledge regarding DASH among hypertensive patients.

ANALOGY PROBLEM POSING FOR ELEMENTARY SCHOOL STUDENTS ON WORD PROBLEMS

This study aims to determine the analogy characteristics of elementary school students in proposing word problems. Analogical reasoning is a complex process that involves retrieving structured knowledge from long-term memory, representing and manipulating role-filling ties in working memory, identifying elements that play appropriate roles, generating new conclusions, and learning abstract schemes. The characteristics of analogy are categorized into four: (1) reformulation, rearrangement of information by changing the order of numbers but still paying attention to the order of mathematical operations. (2) reconstruction, Students rearrange information in the initial problem so that the problem appears different but is identical to the initial problem. (3) reproduction, the resulting product creates a new problem that is different by changing the structure of the initial problem. (4) imitation, changing the structure of the given problem by expanding the goal statement so that the initial problem becomes a step of the solution process of the new problem. Students' failure in making problem posing is included in non-reformulation, where students cannot make problem posing on word problems according to the problem structure