This research was conducted because the process of implementing the 2013 curriculum at SD Negeri Kp. Bulak III Pamulang has not been optimal, so learning is not optimal due to the lack of several ...technological tools that support the learning process in the 2013 curriculum. The purpose of this study is to determine the implementation process, and assessment. This research is a qualitative descriptive study. The subjects of this study were grade 1 - 6 teachers at SDN Kp. Bulak III Pamulang. While the object of his research is the implementation of the 2013 curriculum learning process in these elementary schools. Data collection techniques were obtained through observation and interviews. The data analysis technique uses the steps of reduction, presentation and conclusion drawing. The results showed that the implementation of the 2013 curriculum learning process in SDN Bp. Bulak III Pamulang, Pamulang Subdistrict, South Tangerang City was as follows: (1). Planning has gone according to the rules of the learning material. (2). The learning process in class is in accordance with the lesson plan, but there are still obstacles in time management and learning media. (3). Assessment has not gone well because the implementation has used thematic, but in its assessment the teacher must conduct an assessment per subject.
Fiber reinforced polymer (FRP) composites are susceptible to material degradation when exposed to environmental effects. To predict the residual tensile strength and modulus of pultruded FRP ...composites, an XGBoost decision tree model was developed in this work. XGBoost decision tree, as a machine learning technique, is able to provide accurate predictions for tabular dataset with a good prediction interpretability. In this work, the methodology of XGBoost decision tree was presented in detail. Datasets for training and testing included a total of 746 data points which were collected from an existing database. XGBoost decision tree model predictions were cross-validated with 149 test data, and an excellent agreement was observed, showing R2 values of 0.93 and 0.85 for tensile strength and modulus, respectively. In addition, attribute importance analysis was conducted to quantitatively evaluate the attributes pertaining to FRP degradations, including exposure time, exposure temperature, pH value of environment, fiber volume fraction, plate thickness, fiber type and matrix type. Exposure time and temperature were observed to have the greatest impacts on residual tensile properties. The proposed XGBoost decision tree model provides a new approach for predicting the long-term degradations of FRP composites subjected to environmental effects.
The study aimed to establish the influence of formative assessment on the attainment of scientific and technological competencies in a school in Lima, Peru. We formulated and subsequently tested ...hypothesis of the positive impact of formative evaluation on science and technology competencies. The authors used the questionnaire associated with formative evaluative practices and to measure the development of competencies gradings of 116 second-grade high school students. It was found that formative assessment significatively influences the development of scientific and technological competencies, predicting a 0.708 increase in educational outcomes (pseudo R-squared Nagelkerke). This influence on competency attainment was examined through regression analysis. It was concluded that formative assessment, along with all its dimensions, influences the achievement of competencies related to science and technology. We recommend that its implementation in classrooms should receive greater dissemination.
The relationship between walking and the built environment is gaining increased attention for promoting sustainable transport and healthy communities. However, while pedestrians engage with the ...street environment, walkability assessments often overlook human-scale characteristics, focusing mainly on the neighborhood-level. Furthermore, traditional studies on walkability rely on limited and time-bound methods. To address these research gaps and obtain insights into the connection between walking and the built environment, this study utilizes machine learning techniques to scrutinize mobile-app data on pedestrian traffic alongside street characteristics. Tree-based algorithms are deployed to identify the association between walking volume and built environment features at the street-level, spanning distinct time periods. The pedestrian traffic data was gathered in Tel Aviv, Israel, while accounting for seasonal variations, weekdays, and time of day. Examining 20 street-level characteristics across 8000 segments furnishes new insights into the relative significance of various characteristics for walking, as well as street profiles linked to greater vs. lesser pedestrian activity. Notably, time variables emerge as crucial, with street features varying in importance across different time definitions. The study offers implications for decision-makers and urban planners by informing them of pedestrians' behaviors and preferences at the street-level, facilitating more efficient infrastructure investments and supporting planning decisions.
•Pedestrian data is analyzed in relation to 20 street-level features using ML.•Street features were found with different importance in varying time definitions.•Street profiles linked to greater vs. lesser pedestrian activity are presented.•Among the most important features found are closeness, building height and lighting.•Topological features showed greater importance on weekends, compared with weekdays.
Cognitive model is playing very important role in predicting students’ performance and recommending learning resources. Thus, it has received a great deal of attention from researchers. However, most ...of the existing work design models from the aspect of students, ignoring the internal relation between problems and skills. To address this problem, we propose a deep cognitive diagnosis framework to obtain students’ mastery of skills and problems by enhancing traditional cognitive diagnosis methods with deep learning. First, we model the skill proficiency of students according to their responses to objective and subjective problems. Second, students’ mastery on problems is modeled based on attention mechanism and neural network, considering both the importance and the interactions of skills. Finally, considering the facts that students may carelessly select or simply guess the answer, we predict students’ performance via the proposed model. Extensive experiments are carried out on two real-world data sets, and the results have proved the effectiveness and interpretability of this work.
•We model the skill proficiency with IRT.•The importance of the skills is acquired based on attention mechanism.•The skill interaction is obtained and quantified by a neural network.
The findings of observations collected at school showed that students in class XI PPLG B still had relatively low levels of learning interest and that those students' interests were heavily ...influenced by those of their peers, which served as the basis for this study. This study used the Crowd Behavior Theory to analyze students' learning interests about the interests of their peers. Descriptive-analytical study using a qualitative approach was the methodology employed. Thirty-five students from class XI PPLG B and one teacher in the PPLG area of competence participated in the study. Purposive sampling was the method used to choose the participants. Data for the study were gathered through interviews and observation. While the interview instrument employed multiple question items that were submitted to the informant, the class teacher, the observation instrument used an observation sheet with several statement items. The data analysis results indicate that students' learning interests are significantly influenced by their peers' learning interests. According to the teacher's interview, groups with similar goals and perspectives might arise from the interactions of peers with different behavioral tendencies. The influence of peers has a direct effect on other students' learning spirits, as evidenced by the observational data. The idea that an individual's interest in learning may be influenced by their group is supported by Le Bon's (2002) Crowd Behavior Theory, which is consistent with this.
Programming typically involves humans formulating instructions for a computer to execute computations. If we adhere to this definition, a machine would seemingly lack the capability to autonomously ...design algorithms. However, recent generative Artificial Intelligence models, such as GPT, have demonstrated an impressive ability to perform complex human tasks with remarkable precision. In this paper, we initially showcase how an AI model can successfully complete an entire college-level programming course, akin to one of the top-performing students in the class. We then put forward strategies for crafting programming exercises that enable educators to effectively integrate these innovative technologies into their teaching methods. Lastly, we illustrate how these models can transition from being perceived as a potential threat to educators to becoming a valuable opportunity when employed judiciously.
Background
The digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes.
Objective
This study aims to use a machine ...learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy.
Methods
We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics.
Results
A higher engagement rate was associated with higher weight loss at 8 weeks (r=−0.59; P<.001) and 24 weeks (r=−0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R2 0.416, SD 0.006). The prediction of short-term weight change (mean R2 0.382, SD 0.015) was associated with 13 different digital phenotypes (ie, lower intake of high-calorie food and carbohydrate and higher intake of low-calorie food). Finally, 8 measures of digital phenotypes (ie, lower intake of carbohydrate and evening snacks and higher motivation) were associated with a long-term weight change (mean R2 0.590, SD 0.011).
Conclusions
Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics.
Trial Registration
ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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•GNA platforms in closely-packed and columnar nanorod structures are fabricated.•GNA platforms provides high SEIRA activity with a high signal-to-noise ratio.•PCA, HCA, SIMCA, and LDA ...of SEIRA attains bacterial clustering with 100 % accuracy.•The proposed system enables bacterial detection with high sensitivity and, accuracy.
Infrared (IR) spectroscopy is a unique and powerful method in the identification, characterization, and classification of chemical and biological molecules. However, the low absorbance of biological molecules has arisen as a major bottleneck and inhibits the application of IR in practical applications. To overcome this limitation, in the last four decades, surface-enhanced IR absorption (SEIRA) spectroscopy has been proposed and has become the focus of interest in various applications. In this study, for the first time, we proposed the employment of 3D anisotropic gold nanorod arrays (GNAs) as a highly active SEIRA platform in bacterial detection. For this, GNA platforms were fabricated through an oblique angle deposition (OAD) approach by using a physical vapor deposition (PVD) system. OAD of gold at proper deposition angle (10°) created closely-packed and columnar gold nanorod structures onto the glass slides in a well-controlled manner. GNA platform was tested as a SEIRA system in three different species of bacteria (Escherichia coli, Staphylococcus aureus, and Bacillus subtilis) by collecting IR spectra of each bacteria from different parts of GNA. The employment of GNA provided robust IR spectra with high reproducibility and signal-to-noise ratio. For the comparison, IR spectra of each bacteria were collected from aluminum foil and a smooth gold surface (SGS). No or very low IR spectra were observed in comparison to the GNA platform for these substrates. Unsupervised (PCA, HCA) and supervised (SIMCA, LDA, and SVM classification) machine learning analysis of bacteria spectra obtained from GNA substrate indicated that all bacteria samples can be detected and identified without using a label-containing biosensor, in a fast and simple manner.
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