Expert Observers Research Articles

The Use of Audio-Visual Media in Improving Students' Reading Comprehension and Sholat Movements in Online Learning

In the Covid-19 pandemic situation, learning that requires practice and habituation patterns such as prayer is experiencing obstacles. Teachers and parents also have problems finding the right learning media so that children's prayer understanding is better. Preliminary study obtained data on the ability of elementary school children in Class I is still far from the Minimum Completeness Criteria (KKM) when online learning is implemented since the child enters school. This research used classroom action research, which aims to describe 1)how to apply prayer audio visual media, and 2) how is the student's understanding of prayer which includes aspects of the ability to recite prayer readings and pray according to the sequence of prayer movements. The research subjects were 18 students of class I, with the researcher as a class teacher and the principal as an expert observer and assessor. Methods of data collection using observation and interview. The instrument consists of media assessment by experts, teacher observation instruments and student abilities. The data analysis used is narrative-qualitative, which describes the findings obtained in the implementation of the action in the form of diagrams and tables. The results of the action after using audio-visual media, students' understanding experienced an increase in prayer readings from 11% which was good to 95% good, and movement ability from 6% to 94%. The conclusion is that the use of audio-visual media in online learning can improve students' understanding of prayer on the ability to read and perform movements in accordance with the guidance of the Prophet SAW.

The Role of the Archeological Heritage Sites in the Process of Urban Regeneration of UNESCO’s Cities—Boka Bay Case Study

The role of the archeological and cultural heritage sites in the process of urban regeneration in UNESCO’s protected areas becomes more and more important for the natural and cultural heritage protection in the touristic cities with urban expansion. However, there is no clear methodological approach for the inclusion of these important natural and cultural heritage sites in the process of urban regeneration of the cities. The purpose of this study is to analyze how two contemporary urban planning tools, sustainable urban mobility plans and heritage impact assessments, contribute to the protection and sustainable use of archeological sites. The methodology that is used in this study is dual, theoretical, through literature review, and empirical, through the appliance of the case study method and expert observation and mapping of the most valuable archeological sites in Boka Bay. The results of this study show that practical usage of the two proposed tools in the process of urban regeneration could help in cultural and natural heritage protection and their inclusion as drivers of sustainable urban planning and cultural heritage management. The results of the study confirm the authors’ hypothesis that the role of the archeological sites in the process of urban regeneration is evident in the touristic cities in the coastal area of Montenegro, concluding that urban mobility principles and heritage impact assessment studies must be considered in the process of urban regeneration while at the same time cultural (archeological) heritage management is an integral part of this process.

Machine learning-based automatic sleep apnoea and severity level classification using ECG and SpO2 signals

Sleep apnoea is a potentially serious sleep disorder that is characterised by repetitive episodes of breathing interruptions. Traditionally, sleep apnoea is commonly diagnosed in an attended sleep laboratory setting using polysomnography (PSG). The manual diagnosis of sleep apnoea using PSG is, however complex, and time-consuming, as many physiological variables are usually measured overnight using numerous sensors attached to patients. In PSG sleep laboratories, an expert human observer is required to work overnight, and the diagnosis accuracy is dependent on the physician’s experience. A quantitative and objective method is required to improve the diagnosis efficacy, decrease the complexity and diagnosis time and to ensure a more accurate diagnosis. The purpose of this study was then to develop an automatic sleep apnoea and severity classification using a simultaneously recorded electrocardiograph (ECG) and saturation of oxygen (SpO2) signals based on a machine learning algorithm. Different ECG and SpO2 time domain and frequency domain features were extracted for training different machine learning algorithms. For sleep apnoea classification, an accuracy of 99.1%, specificity of 98.1% and sensitivity of 100% were achieved using a support vector machine (SVM) based on combined ECG and SpO2 features. Similarly, for severity classification, an 88.9% accuracy, 90.9% specificity and 85.7% sensitivity have been obtained. For both apnoea and severity classification, using the combined features was found to be more accurate, and this is typically important when either channel is poor quality, the system can make an analysis based on the other channel and achieve good accuracy.

Detection of underwater acoustic target using beamforming and neural network in shallow water

Due to the complexity of marine environment, underwater acoustic target detection based on experience of expert observers is not fully reliable. We proposed a two-stage approach for underwater acoustic target detection task. In the first stage, the features extracted by beamforming-based direction of arrival estimation can carry essential information of the targets, and are taken as the input of the second stage. In the second stage, a feedforward neural network (FNN) is used as the kernel network to discover the potential pattern and construct a reliable detection model. The performance of the proposed method is evaluated on ocean underwater acoustic data. Convolutional neural network (CNN) is adopted as the baseline method for comparison. In three different sea areas, the accuracy of detection with the proposed method can reach 93.81%, 97.94%, and 97.32%, respectively, while CNN presents an average accuracy of only 74.17%. The experimental results demonstrate that the proposed method is effective in complex and changing underwater environments with little precise environmental information.

Digital workflows for pathological assessment of rat estrous cycle stage using images of uterine horn and vaginal tissue

Assessment of the estrous cycle of mature female mammals is an important component of verifying the efficacy and safety of drug candidates. The common pathological approach of relying on expert observation has several drawbacks, including laborious work and inter-viewer variability. The recent advent of image recognition technologies using deep learning is expected to bring substantial benefits to such pathological assessments. We herein propose 2 distinct deep learning-based workflows to classify the estrous cycle stage from tissue images of the uterine horn and vagina, respectively. These constructed models were able to classify the estrous cycle stages with accuracy comparable with that of expert pathologists. Our digital workflows allow efficient pathological assessments of the estrous cycle stage in rats and are thus expected to accelerate drug research and development.

Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

Mental health signifies the emotional, social, and psychological well-being of a person. It also affects the way of thinking, feeling, and situation handling of a person. Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life. Mental health problems are rising globally and constituting a burden on healthcare systems. Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated. The machine learning models are highly prevalent for medical data analysis, disease diagnosis, and psychiatric nosology. This research addresses the challenge of detecting six major psychological disorders, namely, Anxiety, Bipolar Disorder, Conversion Disorder, Depression, Mental Retardation and Schizophrenia. These challenges are mined by applying decision level fusion of supervised machine learning algorithms. A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the models. Furthermore, to reduce the impact of a conflicting decision, a voting scheme Shrewd Probing Prediction Model (SPPM) is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine (RF + GBM). This research provides an intuitive solution for mental disorder analysis among different target class labels or groups. A framework is proposed for determining the mental health problem of patients using observations of medical experts. The framework consists of an ensemble model based on RF and GBM with a novel SPPM technique. This proposed decision level fusion approach by combining RF + GBM with SPPM-MIN significantly improves the performance in terms of Accuracy, Precision, Recall, and F1-score with 71\%, 73\%, 71\% and 71\% respectively. This framework seems suitable in the case of huge and more diverse multi-class datasets. Furthermore, three vector spaces based on TF-IDF (unigram, bi-gram, and tri-gram) are also tested on the machine learning models and the proposed model.

A New Adaptive Interpretable Fault Diagnosis Model for Complex System Based on Belief Rule Base

This paper develops a new adaptive interpretable fault detection and diagnosis model (AIFD ) based on belief rule base (BRB) for complex system. The developed AIFD model aims to solve three problems in engineering practice: few fault samples, complex system mechanism and loss of interpretability in modeling process. The first two problems can be addressed by fusing expert knowledge and observation data in the BRB expert system. Moreover, to address the problem of loss of interpretability of AIFD model in its modeling process, a new belief rule weight adjustment method is proposed based on its sensitivity coefficient. The new adjustment method can improve the confidence degree of the users to the FDD output. The developed adjustment method can guarantee the fault diagnosis accuracy and the model interpretability. Then, to further address the influence of uncertain expert knowledge, a new optimization model is developed for the BRB based fault diagnosis model. To illustrate the effectiveness of the developed model, a case study of electric servo mechanism is conducted in this paper.

Validity of the dental operating microscope and selective dentin removal with ultrasonic tips for locating the second mesiobuccal canal (MB2) in maxillary first molars: An in vivo study.

BackgroundSeveral investigations have determined whether the use of a dental operating microscope (DOM) in combination with selective dentine removal with ultrasonic tips increases the percentage of location of the Mesiobuccal 2 (MB2) root canal in maxillary first molars (MFM). However, these studies did not report the performance of in vivo measurements with the comparison with a gold standard. The aim of this study was to determine the validity of the DOM and selective dentin removal with ultrasonic tips to locate the MB2 root canal in MFM using Cone Beam Computed Tomography (CBCT) as the gold standard. Material and MethodsThe initial sample size was 91 patients, but 7 were excluded, so the sample size was 84 patients who signed the informed consent. Inclusion criteria: MFM indicated for root canal treatment. An expert blinded observer identified the MB2 in the CBCT. Two standardized examiners (Kappa=91%) performed the clinical assessment in three stages: Stage 1, canal location with an endodontic explorer and a mirror; Stage 2, use of DOM and Stage 3, use of DOM plus selective dentine removal with ultrasonic tips. The validity of each stage was calculated. ResultsThe prevalence of MB2 using CBCT was 79%, by clinical location was 68%. Sensitivity was 79%, 82%, 86% for stage 1, 2 and 3, respectively. Specificity and Positive Predictive Values were 100% for all methods. Negative Predictive Value was 56%, 60%, 67%, respectively. Positive Likelihood Ratio tends to infinity for all methods, Negative Likelihood Ratio= 0.21, 0.18 and 0.14 and Accuracy= 83%, 86% and 89%, respectively. ConclusionsThe use of DOM with selective dentine removal with ultrasonic tips is the most valid method for locating MB2 canal in MFM. There was an increase in the location of the MB2 root canal with the DOM and ultrasonic tips, which definitely help the clinician. Key words:Cone beam computed tomography, microscopy, second mesiobuccal canal, sensitivity and specificity, validity.

ОСНОВНЫЕ ЭЛЕМЕНТЫ СИСТЕМЫ УПРАВЛЕНИЯ ВНЕШНИМИ РИСКАМИ В ЦЕЛЯХ РОСТА ОРГАНИЗАЦИОННО-ТЕХНОЛОГИЧЕСКОЙ НАДЕЖНОСТИ СТРОИТЕЛЬСТВА

The article considers the relationship between construction risks and organizational and technological reliability of construction, their mutual influence on each other in order to increase the economic efficiency of construction production. The main purpose of the study is to identify the main elements of risk management in order to improve the organizational and technological reliability of construction. It is noted that the construction is characterized by a number of features that determine the types and degree of influence of risks. It is noted that external risks cannot be influenced by the enterprise, therefore they should be predicted and try to reduce their negative impact on construction. Examples of the main risks in the construction of facilities in the region, as well as the main methods of risk assessment are given. The scheme of functioning of the risk management system in a construction organization is proposed. A system analysis that determines the impact of risks on the performance indicators of construction production contributes to improving the organizational and technological reliability of a construction enterprise. When writing the article, the method of expert assessment, observation, analysis and synthesis was used.

Unusual expert observation of death of a man from falling into a working sawmill

An unusual expert observation of the death of a man from falling into a working disk sawmill is presented. As a result of the forensic medical examination of the corpse, a sawn wound of the right lower limb with subtotal traumatic amputation at the level of the upper third of the lower leg was found. The above observation complements the forensic understanding of the relatively rare sawn injuries caused by circular saws in production conditions. Despite the surgical intervention that took place in the described case, which led to a modification of the primary picture of sawn damage, it remains possible to diagnose the damaging factor, some of its characteristics, the mechanism of action and solve the situational problem.

Timescapes beyond the Metropolises

The past decades have witnessed a rise of culture-led urban regeneration. The successful cultural models have travelled throughout the world, and applied to cities and urban areas regardless of their size and location. Culture, ranging between high culture and contemporary creative economies, acquires potential to contribute to physical, social and economic aspects of urban regeneration. Successful examples of culture-led urban regeneration have tempted small cities to invest in traveling global cultural policies. Academic community has criticized these travelling policies for over-simplifying the abstract notion of culture, overrating the benefits of culture-led urban regeneration and ignoring local temporal specifics. This paper argues that a temporal analysis framework would enable a holistic approach to culture-led urban regeneration, and embrace the temporal uniqueness of urban contexts. This article discusses the temporal characteristics of culture-led regeneration in a provincial city context within an empirical case study analysis of Myllytulli in Oulu, Northern Finland. Myllytulli represents a district of regional cultural relevance where cultural amenities range from museums and educational facilities to creative bottom-up initiatives. This study reframes Myllytulli’s urban regeneration process using temporal conceptions of recent interdisciplinary academic discourse. The empirical data set consists of expert interviews, observation material and municipal planning documents. The results analyse the urban regeneration process within the linear temporal ideals of rational-comprehensive planning, reactive experiential urbanism and relational dimensions of time. The paper suggests time-sensitive approaches for future research and practice of urban regeneration.

Qualitative Modeling for Bridging Expert-Knowledge and Social-Ecological Dynamics of an East African Savanna

Sub-Saharan social-ecological systems are undergoing changes in environmental conditions, including modifications in rainfall pattern and biodiversity loss. Consequences of such changes depend on complex causal chains which call for integrated management strategies whose efficiency could benefit from ecosystem dynamic modeling. However, ecosystem models often require lots of quantitative information for estimating parameters, which is often unavailable. Alternatively, qualitative modeling frameworks have proved useful for explaining ecosystem responses to perturbations, while only requiring qualitative information about social-ecological interactions and events and providing more general predictions due to their validity for wide ranges of parameter values. In this paper, we propose the Ecological Discrete-Event Network (EDEN), an innovative qualitative dynamic modeling framework based on “if-then” rules generating non-deterministic dynamics. Based on expert knowledge, observations, and literature, we use EDEN to assess the effect of permanent changes in surface water and herbivores diversity on vegetation and socio-economic transitions in an East African savanna. Results show that water availability drives changes in vegetation and socio-economic transitions, while herbivore functional groups have highly contrasted effects depending on the group. This first use of EDEN in a savanna context is promising for bridging expert knowledge and ecosystem modeling.

Investigation of the Effect of Inclusive Play and Special Movement Education to Social Communication in Disadvantaged and Peer Children in Preschool (Erzurum Sample)

The aim of this research is to investigate the effect of inclusive play and special movement education applied to disadvantaged and peer preschool children in Erzurum city center on their basic skills.22 students (10 disadvantaged children + 12 peer group children) consisting of disadvantaged children and peer children who were educated in special education and rehabilitation centers in the city center of Erzurum and participated voluntarily. Before the study, two seminars were given to the parents of the students, which determined the purpose and objective of the research. Students participated in a specially prepared game and movement training program for 45-60 minutes, 2 days a week during the summer period. Before starting the research, an interview and observation form consisting of 10 questions was prepared for the parents of the children and the results were recorded. At the end of the 2-month (8-week) period, interviews and observations were made with the parents of the children, and the last situation was recorded and compared with the previous situation.In the first parent interviews, the results of the research; it was observed that the children hesitated to participate in the study, they do not want to get close to other children, refrain from sharing game materials and avoid playing together and do not help. While they stated that they did not comply with the game discipline, they stated that at the end of the study program, their initial negative behavior developed completely in a positive way. In expert observations; it was observed that at the beginning, the children did not want to participate in the studies, did not listen to the volunteers, acted randomly, fought over the play materials used, and avoided playing together. While they observed that they behaved randomly, it was stated that the observations developed completely positively at the end of the research process.As a result, it was concluded that inclusive play and special movement training applied to disadvantaged preschool children and peers contributed positively to the social development of children.

Skin Color Match in Head and Neck Reconstructive Surgery.

To quantify the degree of color match achieved during microvascular facial reconstruction, and to describe a novel technique for improving reconstructive skin color match. We hypothesize that split-thickness skin grafts (STSG) placed atop de-epithelialized free tissue produces better facial skin color match than free tissue with intact epithelium. Cross sectional photographic study of reconstructed facial skin color match. Sixty-eight adults, who underwent head and neck reconstructive surgery, were divided into six categories based on cutaneous reconstructive technique: cervicofacial flap, radial forearm free flap (RFFF), fibula free flap, anterolateral thigh free flap (ALT), STSG over adiopofascial flap (STAFF), and STSG over myogenous flap (STMF). Averaged color samplings of the reconstructed defect and adjacent normal skin were taken from digital photographs. The color difference was calculated using the delta-E calculation. Blinded expert observers also rated the degree of color match. Nonparametric cohort contrast and correlation statistical analyses were performed. The mean delta-E's and 10-point Likert ratings for the ALT, fibula, RFFF, STAFF, STMF, and cervicofacial flaps were 11.6, 10.0, 7.7, 6.3, 8.8, and 4.7, and 5.1, 6.4, 2.4, 3.2, 2.7, and 1.1, respectively. Likert scale inter-rater correlation was strong, with coefficient=0.80. On average, STSG over de-epithelialized myogenous and adipofascial free tissue transfers produced a better color match than the skin paddles of donor sites, with the exception of the radial forearm donor site. Delta-E values obtained from photos correlated well with expert ratings of color match. This reliable technique for quantifying color match may be used in future studies. 3 Laryngoscope, 132:1753-1759, 2022.

Use of Artificial Intelligence as a Predictor of the Response to Treatment in Alopecia Areata

Background Artificial intelligence (AI) has emerged in dermatology with some studies focusing on skin disorders such as skin cancer, atopic dermatitis, psoriasis, and onychomycosis. Alopecia areata (AA) is a dermatological disease whose prevalence is 0.7%-3% in the United States, and is characterized by oval areas of nonscarring hair loss of the scalp or body without evident clinical variables to predict its response to the treatment. Nonetheless, some studies suggest a predictive value of trichoscopic features in the evaluation of treatment responses. Assuming that black dots, broken hairs, exclamation marks, and tapered hairs are markers of negative predictive value of the treatment response, while yellow dots are markers of no response to treatment according to recent studies, the absence of these trichoscopic features could indicate favorable disease evolution without treatment or even predict its response. Nonetheless, no studies have reportedly evaluated the role of AI in AA on the basis of trichoscopic features. Objective This study aimed to develop an AI algorithm to predict, using trichoscopic images, those patients diagnosed with AA with a better disease evolution. Methods In total, 80 trichoscopic images were included and classified in those with or without features of negative prognosis. Using a data augmentation technique, they were multiplied to 179 images to train an AI algorithm, as previously carried out with dermoscopic images of skin tumors with a favorable response. Subsequently, 82 new images of AA were presented to the algorithm, and the algorithm classified these patients as responders and non-responders; this process was reviewed by an expert trichologist observer and presented a concordance higher than 90% with the algorithm identifying structures described previously. Evolution of the cases was followed up to truly determine their response to treatment and, therefore, to assess the predictive value of the algorithm. Results In total, 32 of 40 (80%) images of patients predicted as nonresponders scarcely showed response to the treatment, while 34 of 42 (81%) images of those predicted as responders showed a favorable response to the treatment. Conclusions The development of an AI algorithm or tool could be useful to predict AA evolution and its response to treatment. However, further research is needed, including larger sample images or trained algorithms, by using images previously classified in accordance with the disease evolution and not with trichoscopic features. Conflicts of Interest None declared.

An algorithm for analyzing cloze test results

We present an algorithm for the automatic processing of cloze test results that are traditionally analyzed and adjusted by hand. The algorithm accurately includes inflected and derived forms of the target word as correct responses, as well as minor spelling mistakes and typographical errors. We provide a test of the algorithm against the coding of one expert human observer and find that the algorithm performs with equal accuracy. We further examine the utility of the algorithm for the analysis of data involving word identification with typed responses. The routines are implemented in R-software and are freely available.

A digital cardiac disease biomarker from a generative progressive cardiac cine-MRI representation.

Cardiac cine-MRI is one of the most important diagnostic tools used to assess the morphology and physiology of the heart during the cardiac cycle. Nonetheless, the analysis on cardiac cine-MRI is poorly exploited and remains highly dependent on the observer's expertise. This work introduces an imaging cardiac disease representation, coded as an embedding vector, that fully exploits hidden mapping between the latent space and a generated cine-MRI data distribution. The resultant representation is progressively learned and conditioned by a set of cardiac conditions. A generative cardiac descriptor is achieved from a progressive generative-adversarial network trained to produce MRI synthetic images, conditioned to several heart conditions. The generator model is then used to recover a digital biomarker, coded as an embedding vector, following a backpropagation scheme. Then, an UMAP strategy is applied to build a topological low dimensional embedding space that discriminates among cardiac pathologies. Evaluation of the approach is carried out by using an embedded representation as a potential disease descriptor in 2296 pathological cine-MRI slices. The proposed strategy yields an average accuracy of 0.8 to discriminate among heart conditions. Furthermore, the low dimensional space shows a remarkable grouping of cardiac classes that may suggest its potential use as a tool to support diagnosis. The learned progressive and generative representation, from cine-MRI slices, allows retrieves and coded complex descriptors that results useful to discriminate among heart conditions. The cardiac disease representation expressed as a hidden embedding vector could potentially be used to support cardiac analysis on cine-MRI sequences.

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach.

Simple SummaryBreast cancer is one of the foremost causes of cancer-related mortality in women. It is curable and controllable only if detected early. Microcalcifications in breast tissue are essential predictors for radiologists to detect early-stage breast cancer. This study proposes a method for detecting and classifying microcalcifications in mammogram images to predict breast lesions, using machine learning coupled with an interpretable radiomics approach. The method was evaluated using a publicly accessible dataset, which may aid radiologists and clinicians in identifying breast cancer in their regular clinical practices. This study contributes to the field of predictive modeling in healthcare.Microcalcifications in breast tissue can be an early sign of breast cancer, and play a crucial role in breast cancer screening. This study proposes a radiomics approach based on advanced machine learning algorithms for diagnosing pathological microcalcifications in mammogram images and provides radiologists with a valuable decision support system (in regard to diagnosing patients). An adaptive enhancement method based on the contourlet transform is proposed to enhance microcalcifications and effectively suppress background and noise. Textural and statistical features are extracted from each wavelet layer’s high-frequency coefficients to detect microcalcification regions. The top-hat morphological operator and wavelet transform segment microcalcifications, implying their exact locations. Finally, the proposed radiomic fusion algorithm is employed to classify the selected features into benign and malignant. The proposed model’s diagnostic performance was evaluated on the MIAS dataset and compared with traditional machine learning models, such as the support vector machine, K-nearest neighbor, and random forest, using different evaluation parameters. Our proposed approach outperformed existing models in diagnosing microcalcification by achieving an 0.90 area under the curve, 0.98 sensitivity, and 0.98 accuracy. The experimental findings concur with expert observations, indicating that the proposed approach is most effective and practical for early diagnosing breast microcalcifications, substantially improving the work efficiency of physicians.

Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis.

The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations, advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults' rather than children's faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity=91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition. LAY SUMMARY: Core symptoms in children with ASD involve social communication and interaction. ASD assessment includes expert observations in neutral settings, which show limitations and biases related to lack of objectivity and do not capture performance in real settings. To overcome these limitations, this work aimed to distinguish between autistic and typically developing children in visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to, and extraction of, socially relevant information.

Pengembangan Media Buletin Berbasis Etnomatematika untuk Meningkatkan Kemampuan Pemahaman Matematis

This research focuses on the development of ethnomathematical bulletin media to improve students' mathematical understanding. Research Development with ADDIE design consists of five stages, they are analysis, design, development, implementation, and evaluation. The sample taken is class IV SDN 1 Buyut. Sampling was done by purposive method. The research instruments used were mathematical understanding tests, expert validation questionnaires, observation sheets, and interview guidelines. Based on the validation of material and media experts, the ethnomathematics bulletins were considered valid with a total percentage of 80 and 76%, respectively. After doing the feasibility test, the ethnomathematical bulletin media was considered suitable for use with a percentage of 78%. In a limited trial, it was also known that the bulletin media could increase students' understanding. These results are in line with the findings at the implementation stage, where it is known that the bulletin media has an effect on increasing students' mathematical understanding.