Stages Of Model Development Research Articles

USING ARTIFICIAL NEURAL NETWORKS AND SPACE SYNTAX TECHNIQUES TO UNDERSTAND MASS HOUSING DESIGN PARAMETERS

The design of mass housing is a complex process that involves the use of a large number of components and parameters. The field of design has unavoidably been changed by the impact of digitalization, which has resulted in the proliferation of computational design models, data structures, artificial intelligence, and an algorithmic way of thinking. Artificial neural networks, space syntax methodologies, predefined rules will help shape the steps of the schematic design process and establish certain limitations. Within the confines of this research, predefined guidelines were used to bring about geometric variances in the design of mass houses. Both traditional and digital instruments were utilized in the process. Methodologies based on artificial neural network models and space syntax techniques were utilized to investigate case studies and develop prototypes. The artificial neural network model is designed to understand the factors affecting mass housing design parameters. The importance percentages of the parameters were determined according to the outputs of this model. Besides, methodologies based on space syntax have had a significant impact, both on decision-making processes and on feedback-based design. In this study, several digital tools were used to analyze such as visibility graph analyzes, node-based techniques, and isovist analysis. In the section devoted to the conclusion, the comparison of the various prototypes that were obtained, the findings of the space syntax analysis, and the various stages of model development are discussed.

Organizational Framework Development: Formulating Strategy to Increase Digital Maturity

<em>This paper aims to develop an organizational framework for formulating a strategy to increase digital maturity. The research stages consist of: (1) Problem observation, (2) Literature review, (3) Model development, (4) Discussion, and (5) Conclusion. Specifically, the model development stage becomes this research's findings and originality. The results show that the organizational framework proposed in this research can help companies formulate strategies from operational business processes through a structured and systematic organizational framework. This framework consists of four systematic steps: (1) digital maturity assessment, (2) business process mapping, (3) business process characteristic identification, and (4) strategy formulation. Also, the strategy that arises by implementing the proposed organizational framework is obvious and structured because the resulting strategy considers business processes' characteristics. Specifically, this research suggests that the company may develop a web-based knowledge-sharing community for externalizing tacit knowledge into explicit knowledge to solve tacit knowledge needs to be managed. The research does not discuss the suggestion to develop a web-based knowledge-sharing community as a strategy's practical implication in detail. Also, considering the web-based knowledge-sharing community is a strategy designed in this case based on the characteristics and knowledge needs of prioritized business processes, the strategy formulation needs adjustment if the business process has turned into different characteristics. The organizational framework for formulating a strategy to increase digital maturity from the operational business process point of view has not been investigated previously.</em>

The Classification of Research Models and the Development of Tested Models

본 연구는 연구에서 널리 활용되고 있는 모형을 분류하고, 다양한 모형을 이해하는 데 있다. 이를 위하여 모형의 개념을 재정의하고, 모형의 분류 준거를 분석하고, 새로운 모형 분류 기준을 제시하였다. 본 연구에서 모형이란, “연구자의 이론, 철학, 신념 등을 반영한 것으로 논리적으로 제안된 그 무엇이다”라고 정의하였다. 여러 학문 분야의 모형 분류 준거가 다양하고, 대부분 이분화된 준거를 사용하였다. 이에 따라 연구자도 검정가능성을 모형의 분류 준거로 하여 검정모형과 비검정모형으로 분류하였다. 이 준거에 의하면 검정모형 제안자들은 데이터를 사용하여 자신이 개발한 모형을 검정하여 제시하고 있다. 그러나 비검정모형은 엄격한 검정이 이루어지 않고 제안되었다고 할 수 있다. 그리고 비검정모형들은 개발과정의 일반적인 절차를 따르지 않고 있다. 이러한 이유에서 검정모형 개발 5단계를 제안하였다. 물론 연구자가 제안한 검정 모형 개발 5단계는 하나의 ‘’비검정 모형‘이라고 할 수 있다. 검정모형과 비검정 모형의 개발은 누구나 할 수 있지만, 비검정모형이 아닌, 검정모형은 논리적인 절차에 따라 개발되고 검정될 필요가 있다.The purpose for this study is to classify models widely used in research and to understand various models. To this end, the concept of the model was redefined, the classification criteria of models were analyzed, and new model classification criteria were presented. In this study, a model is defined as “something that is logically proposed as a reflection of the researcher's theory, philosophy, beliefs, etc.”. There are various criteria for classification of models in many academic fields, but most of them use the dichotomous method. Accordingly, this researcher classified all models into a tested model and the non-tested model based on the test feasibility. The tested model refers to a model tested by the proposers using data. On the other side, nnn-tested model is a model proposed without rigorous testing. Tested models require rigorous development procedures, but non-tested models do not require such procedures. For this reason, five stages of test model development were proposed. Of course, the 5 stages of test model development proposed by the researcher can be said to be a ‘non-testing model’. Anyone can develop a model whether it is a test model or a non-test model, but a test model, not a non-test model, needs to be developed and tested strictly.

DOP23 Decrements in quality of life associated with symptoms of Inflammatory Bowel Disease: results from the UK IBD-BOOST survey

Abstract Background Inflammatory Bowel Disease (IBD) affects about 7 million people worldwide. People with active disease often experience symptoms of abdominal pain, fatigue and incontinence which may persist even in remission. We assess the impact of these symptoms, and the further contribution of anxiety and depression, on health-related quality of life (QoL) of people with IBD. Methods In the IBD-BOOST survey, over 12,500 people with IBD in the UK were approached across outpatient clinics, the IBD-BioResource participants and the Crohn’s and Colitis UK charity membership; 8486 self-completed surveys were returned. Participants’ QoL was measured using the EQ-5D-5L questionnaire, a generic health-related QoL instrument. Patients’ QoL, ranging from 1 (perfect health) to -0.594 (worst health) was calculated using UK value set. Our approach involved three pre-specified stages of linear regression model development to assess the effects of symptoms on QoL. At stage 1 the model included IBD type, IBD activity score, IBD control score, socio-demographic characteristics and co-morbidities. At stage 2, the three key IBD symptoms: pain, fatigue and bowel incontinence were added. At stage 3, anxiety and depression were also included. Results Table 1 presents selected characteristics of the 8486 IBD-BOOST survey participants (mean age 50 years, 58% women, 49% Crohn’s disease). The EQ-5D-5L questionnaire was completed by 8114 (96%) participants reporting problems across QoL domains (Figure 1: 30% problems with mobility, 13% with self-care, 43% with usual activities, 60% pain/discomfort and 50% anxiety/depression). The mean QoL was 0.76 (SD 0.22). Health-related QoL was substantially reduced in respondents reporting higher IBD activity or worse IBD control (Table 2; stage 1). These associations weakened substantially following adjustment for the key IBD symptoms of pain, fatigue and bowel incontinence for which strong and graded associations with higher symptom severity associated with lower QoL were observed (Table 2; stage 2). The further inclusion of adjustments for anxiety and depression only moderately weakened previous associations and strong and graded associations between more severe anxiety or depression and lower QoL were also observed (Table 3; stage 3). Being male, obese, unemployed/retired, or having physical or mental health comorbidity were associated with lower QoL, and being in a relationship with higher QoL (results not shown). Conclusion We report graded associations between symptoms of pain, fatigue, bowel incontinence, anxiety and depression and QoL in IBD. These estimates could inform assessments of interventions to improve IBD management.

Quality Assurance in Education: The Development of An Employment Contract-Based Madrassa Principal Supervision Model At Madrassa Aliyah In West Aceh Regency

The development of an employment contract-based madrassa principal supervision model is designed with a contract of employment approach for the principal and teachers (the parties) so that the parties share the responsibility and commitment in implementing the academic supervision program at the madrassa. This study aims to develop a model for the supervision of the principal based on a contract of employment. The research uses the Research and Development method by following the steps, including the preliminary study stage, the model development stage, the model validation stage, and the model improvement stage. In the preliminary study stage, the 1st focus group discussion (FGD) and field studies were carried out, in order to obtain factual data on the implementation of academic supervision so far, and continued with literature studies in order to obtain the results of previous research deemed relevant. At the model design development stage, the and FGD activity was carried out to socialize the conceptual model design and ask for suggestions and input from the madrassa principal and supervisor to improve the developed model. At the model validation stage, the and FGD activity was carried out with experts to test the feasibility of the model on a limited basis through the Delphi technique, to find out how far the effectiveness of the model can be carried out by the madrassa principal. At the last stage, undertaking improvements or revisions to the model according to suggestions and input from the head of the madrassa, supervisors and experts to finalize the model. Then, testing the validity of the data through triangulation of sources. The results of this study indicate that: 1) the current implementation of academic supervision has not received serious attention from the madrassa principal, both in terms of planning academic supervision programs and in implementing academic supervision programs as well as evaluating and following up on the results of observation of class visits supervision, 2) development The employment contract-based madrassa principal supervision model can be an alternative academic supervision model with a contract agreement approach for the principal and teachers (the parties) so that they both have the responsibility and commitment in implementing the academic supervision program at the madrassa. Researchers suggest other researchers to conduct trials with a wider scale in order to find out more accurately and validly about the level of effectiveness of this model.

A decision-support framework for evaluating supplier performance in trial production stage of new car model development: A hybrid multi-criteria decision making approach.

A decision-support framework for evaluating supplier performance in trial production stage of new car model development: A hybrid multi-criteria decision making approach.

Identifikasi Serangan Low-Rate DDOS Berbasis Deep Learning

LowRate DDoS (LDDoS) is a variation of DDoS attack that sends fewer packets than conventional DDoS attacks. However, by sending a smaller number of packets and using a unique attack period, low-rate DDoS is very effective in reducing the quality of an internet network-based service due to full access. On the other hand, the low-rate DDoS with its nature also makes it difficult to detect because it looks more mixed with normal user access. The Deep Learning model that will be used in this research is the RNN LSTM (Long Short Term Memory) model. LSTM is a neural network architecture which is good enough to process sequential data. This model is better than the simple RNN model. The research method is adapted to the SKKNI No. 299 of 2020. However, this research will be carried out until the model development stage, namely the evaluation model. From the results of the research that has been done, it can be concluded that the RNN LSTM model can be used to classify low-rate DDOS attacks using feature selection. The accuracy of the training data on the validation data is around 98% and after visualizing the data for accuracy and loss, it can be concluded that the model is quite good, aka there is no underfitting or overfitting. While the accuracy obtained for testing data is 0.97%.

HOTS-Based PjBL Model Development to Increase Children's Creativity in Elementary School

The PjBL (Project Based Learning) model is important to develop in learning at elementary schools, because the PjBL model provides opportunities for students to develop creativity and improve learning outcomes. The PjBL model involves higher order thinking skills, or HOTS. One of the current learning demands in the 4.0 era requires students to be able to develop their creativity. Creativity is needed by students now and in the future to overcome various challenges in life. The observation results show that SD Kertak Hanyar has not implemented learning using the HOTS-based PjBL model yet. The purpose of this research is to find learning model steps that are able to develop creativity, as well as to analyze students' learning outcomes. The combination of PjBL model steps and higher order thinking skills (HOTS) is a learning strategy that is expected to be implemented effectively. The method used in this research is Research and Development, to produce a product. The research stages can be broken down as follows: (1) the preparation stage, (2) the HOTS-based PjBL Model Development stage, (3) the model effectiveness analysis stage. The results of the HOTS-based PjBL Model development research obtained an increase in students' creativity of 42.50, and learning outcomes with an average score of 86.14 (2) the HOTS-based PjBL Model Development stage, (3) the model effectiveness analysis stage. The results of the HOTS-based PjBL Model development research obtained an increase in students' creativity of 42.50, and learning outcomes with an average score of 86.14 (2) the HOTS-based PjBL Model Development stage, (3) the model effectiveness analysis stage. The results of the HOTS-based PjBL Model development research obtained an increase in students' creativity of 42.50, and learning outcomes with an average score of 86.14 student creativity score of 0.75. The achievement at SDN Kertak Hanyar is in the high category. The conclusion from the results of the study is that the HOTS-based PjBL model is effectively implemented in learning at SDN Kertak Hanyar, in other words it is able to increase students' creativity and learning outcomes. As a follow-up to the results of this research, a model step socialization will be carried out to teachers at SD Kertak Hanyar.

A Method to Classify Steel Plate Faults Based on Ensemble Learning

With the industrial revolution 4.0, machine learning methods are widely used in all aspects of manufacturing to perform quality prediction, fault diagnosis, or maintenance. In the steel industry, it is important to precisely detect faults/defects in order to produce high-quality steel plates. However, determining the exact first-principal model between process parameters and mechanical properties is a challenging process. In addition, steel plate defects are detected through manual, costly, and less productive offline inspection in the traditional manufacturing process of steel. Therefore, it is a great necessity to enable the automatic detection of steel plate faults. To this end, this study explores the capabilities of the following three machine learning models Adaboost, Bagging, and Random Forest in detecting steel plate faults. The well-known steel plate failure dataset provided by Communication Sciences Research Centre Semeion was used in this study. The aim of many studies using this dataset is to correctly classify defects in steel plates using traditional machine learning models, ignoring the applicability of the developed models to real-world problems. Manufacturing is a dynamic process with constant adjustments and improvements. For this reason, it is necessary to establish a learning process that determines the best model based on the arrival of new information. Contrary to previous studies on the steel plate failure dataset, this article presents a systematic modelling approach that includes the normalization step in the data preparation stage to reduce the effects of outliers, the feature selection step in the dimension reduction stage to develop a machine learning model with fewer inputs, and hyperparameter optimization step in the model development stage to increase the accuracy of the machine learning model. The performances of the developed machine learning models were compared according to statistical metrics in terms of precision, recall, sensitivity, and accuracy. The results revealed that AdaBoost performed well on this dataset, achieving accuracy scores of 93.15% and 91.90% for the training and test datasets, respectively.

Developing a Psychometric Self- Assessment Tool “PG Scan” for Examining PhD Candidates’ Readiness

The Malaysian government and universities provide numerous scholarships for PhD candidates; unfortunately, an observable number of the recipients were unable to complete their studies due to various factors. To date, no specific instrument has been introduced for Doctoral candidates towards minimising the risks of abortive academic plans. Henceforth, the goals of this study are; i) to examine the factors in selecting suitable PhD candidates, ii) to develop an instrument to gauge the applicants' PhD readiness, and iii) to develop a proposed model of PhD readiness. Library research was conducted to examine the factors relating to instrument development. The present study is the preliminary stage of item and model development. The importance and originality of this study are manifested in the outputs of the research project with the establishment of a new comprehensive self- assessment tool to gauge PhD readiness and the PhD readiness model. The implications of the self- assessment tool and model fulfil the Twelfth Malaysian Plan and Sustainable Development Goals (SDG).

A new practical approach to design a Phase Change Material-Water longitudinally finned tube heat exchanger

Phase change materials have demonstrated interesting advantages in solar energy storage systems to produce domestic hot water although more efficient heat exchangers are required. Among the different exchanger typologies, longitudinally finned exchangers are widespread. However, according to scientific literature, there is a lack of analytical models able to estimate the thermal power transferred by this kind of exchangers easily. This paper shows a practical and easy to apply analytical model that has been built for that type of exchangers. Thus, based on a change of the solution given by Heisler and Gröber for an infinitely long cylinder, this new analytical approach characterises the thermal energy discharged in terms of dimensionless numbers. The adjustment of the analytical model was achieved from the results provided by a CFD numerical model which was previously validated from experimental data. 544 numerical simulations of the system were carried out varying the Biot number and a surface factor number from 1 to 100 and 3 to 15, respectively. An analytical equation that estimates the energy discharged is provided as a function of Fourier, Biot, and Stefan numbers and the surface factor number, related to the exchanger geometry. An average value of the root mean square error smaller than 4% indicates that the proposed model is sufficiently accurate and might be used in the design stages of this kind of devices. A further validation of the analytical model against experimental observations will be carried out in a future stage of model development.

Increasingly Sophisticated Climate Models Need the Out-Of-Sample Tests Paleoclimates Provide.

Climate models are becoming increasingly sophisticated as climate scientists continually work to improve the realism with which the processes influencing Earth's climate are represented. One example is the treatment of cloud microphysics: as complexity is added to cloud microphysical schemes, Earth's energy budget can respond to changes in climate forcings, such as carbon dioxide or aerosols, in new ways. This increase in degrees of freedom has illuminated larger spread in climate sensitivity across the latest generation of climate models participating Coupled Model Intercomparison Project, Phase 6, with more high climate sensitivity models (Zelinka etal., 2020, https://doi.org/10.1029/2019gl085782). Whilst the historical record gives us just over a century of data to apply toward climate sensitivity constraints (e.g., Nijsse etal., 2020, https://doi.org/10.5194/esd-11-737-2020), the ocean is still taking up much of the heat trapped by anthropogenic greenhouse gas emissions and the climate system is far from equilibrium which limits our understanding how climate sensitivity might change in response to long-term forced climate change. Here we discuss the valuable tests that paleoclimate reconstructions can provide the latest generation of climate models, as demonstrated by the recent study of Zhu etal., 2022, https://doi.org/10.1029/2021ms002776. Their study provides an example of the benefits for climate model development when climate models are confronted with simulating climates very different from today. Ideally the climate model development stage under future iterations of CMIP will involve such tests as an effort to constrain global climate sensitivity and the regional patterns of climate, such as polar amplification and subtropical aridification.

The role of computational models in mechanobiology of growing bone.

Endochondral ossification, the process by which long bones grow in length, is regulated by mechanical forces. Computational models, specifically finite element models, have been used for decades to understand the role of mechanical loading on endochondral ossification. This perspective outlines the stages of model development in which models are used to: 1) explore phenomena, 2) explain pathologies, 3) predict clinical outcomes, and 4) design therapies. As the models progress through the stages, they increase in specificity and biofidelity. We give specific examples of models of endochondral ossification and expect models of other mechanobiological systems to follow similar development stages.

Machine Learning for Microcontroller-Class Hardware: A Review.

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.

The Development and Implementation of a Model to Facilitate Self-Awareness of Professionalism for Enrolled Nurses.

Within the South African context, the nursing profession comprises different nursing cadres. The enrolled nurse is considered a sub-category of nursing and therefore does not carry the title of 'professional' as in a professional nurse. The purpose of the study was to develop, describe, implement, and evaluate a model for the facilitation of self-awareness for the professionalism of enrolled nurses at a specific nursing agency in Gauteng. A theory generating, qualitative, exploratory, descriptive and contextual design was used and was conducted following Chinn and Kramer's four stages of model development. The model can benefit nursing education because it relates to an essential aspect of growth and maturity in one's career. Ultimately, the facilitation of self-awareness for professionalism can advance one's career, or the lack of self-awareness may impede one's career. Developing, describing, implementing and evaluating this model to facilitate self-awareness for the professionalism of enrolled nurses at a specific nursing agency in Gauteng provides an original contribution to the theory in nursing professionalism and ethos. This model can be utilised as a tool to facilitate self-awareness for the professionalism of enrolled nurses at a nursing agency.

Inside the “black box”: Embedding clinical knowledge in data-driven machine learning for heart disease diagnosis

Ischemic heart disease (IHD) caused by the narrowing of coronary arteries is a major cause of morbidity and mortality worldwide. Clinical diagnosis involves complex, costly, and potentially invasive procedures. To address this problem, we introduce a novel clinical knowledge-enhanced machine learning (ML) pipeline to assist in timely and cost-effective IHD prediction. Unlike conventional data-driven "black box" ML approaches, we propose an effective mechanism to engage clinical expertise and gain insight into the "black box" at each stage of model development, including data analysis, preprocessing, selecting the most clinically discriminative features, and model evaluation. One-hot feature encoding is introduced to expose hidden bias and highlight the important elements and features. Experimental results on the benchmark Cleveland IHD dataset showed that the proposed clinical knowledge-enhanced ML pipeline overperformed state-of-the-art data-driven ML models, using even fewer features. Our model based on one-hot feature encoding and support vector machine achieved the best accuracy of 94.4% and sensitivity 95% by using only 7 discriminative attributes. We share insights and discuss the effectiveness of incorporating clinical input in machine learning to improve model performance, as well as addressing some practical issues such as data bias and interpretability. We hope this preliminary study on engaging clinical expertise to explore the "black box" would improve the trustworthiness of AI and its potential wider uptake in the medical field.

Development of Athletic Learning Model Based on Traditional Games in Class IV Students Primary School (SD)

Athletics is a sport that grows and develops along with natural human activities, running, jumping and throwing are an inseparable part of the long history of Indonesian life. The purpose of this study was to determine the results of the development of a traditional game-based athletic learning model for fourth grade elementary school students. The sample of the small group in this study were students of SD Negeri 87 Palembang, totaling 27 people, while the large group sample was SD Negeri 88 Palembang and SD Negeri 89 Palembang, amounting to 65 people. The stages of the development model carried out are the stages of model analysis, design, development, implementation and evaluation (ADDIE). The instrument used in this research is a questionnaire. The results of the expert validity test using the Content Validity Index (CVI) and Content Validity Ratio (CVR) in the Engklek Question game with an average CVR value of -0.225. Based on these results, it can be said that the development of a traditional game-based athletic learning model for fourth grade elementary school students has good quality as a learning resource.

Enhancing Data Quality and Integrity in Machine Learning Pipelines: Approaches for Detecting and Mitigating Bias

Machine learning (ML) has become a cornerstone of innovation in numerous industries, including healthcare, finance, marketing, and criminal justice. However, the growing reliance on ML models has revealed the critical importance of data quality and integrity in ensuring fair and reliable predictions. As AI technologies are deployed in sensitive decision-making areas, the presence of hidden biases within data has become a major concern. These biases can perpetuate systemic inequalities and result in unethical outcomes, undermining trust in AI systems. The accuracy and fairness of ML models are directly influenced by the data used to train them, and poor-quality data—whether due to missing values, noise, or inherent biases—can degrade performance, skew results, and exacerbate societal inequalities. This paper explores the complex relationship between data quality, data integrity, and bias in machine learning pipelines. Specifically, it examines the different types of bias that can emerge at various stages of data collection, preprocessing, and model development, and the negative impacts these biases have on model performance and fairness. Furthermore, the paper outlines a range of bias detection and bias mitigation techniques, which are essential for developing trustworthy and ethical AI systems. From data preprocessing methods like imputation and normalization to advanced fairness-aware algorithms and post-processing adjustments, several approaches are available to improve data quality and eliminate bias from machine learning pipelines. Additionally, the paper emphasizes the importance of ongoing monitoring and validation of ML models to detect emerging biases and ensure that they continue to operate fairly as they are exposed to new data. The integration of regular audits, fairness metrics, and data drift detection mechanisms are discussed as crucial steps in maintaining model integrity over time. By focusing on the processes and strategies required to enhance both data quality and integrity, this paper aims to contribute to the development of more equitable, transparent, and reliable AI systems. The goal is to ensure that machine learning technologies can be used responsibly and in ways that promote fairness, equality, and trust, ultimately benefiting all sectors of society.

Prediction of field winter wheat yield using fewer parameters at middle growth stage by linear regression and the BP neural network method

Simple and reliable methods for wheat yield forecasting at the field scale are significant for farmers to formulate effective and timely field management and estimate economic revenue. In this study, the linear regression method (least absolute shrinkage and selection operator regression, LASSO) and BP neural network method (back propagation neural network, BPNN) were chosen for field yield prediction. The four variable types considered are climate, soil moisture, crop growth, and flag leaf photosynthesis. Crop-related data were collected in 12 winter wheat varieties that are widely grown in the North China Plain from 2018 to 2021. The same data set was used for both LASSO and BPNN models’ development and validation. The results show that models based on LASSO method had the determination coefficient (R2) from 0.23 to 0.78, and the root mean square error (RMSE) from 548 to 1023 kg ha−1, while they were 0.66–0.94 and 302–684 kg ha−1 in models based on the BPNN method. This showed that the BPNN method performed better in field wheat yield prediction than the LASSO method. For BPNN method, using only a single variable at the early grain-filling stage had the similar prediction accuracy to using single or comprehensive variables in multiple growth periods, indicating that the field wheat yield can be predicted earlier by BPNN method with fewer indicators. Last, an optimal field wheat prediction model was developed by the BPNN method using crop growth variables (dry matter and leaf area index) at the early grain-filling stage, with the R2 and RMSE of 0.93 and 288 kg ha−1 in the model development stage, respectively, and correspondingly 0.86 and 481 kg ha−1 in the model validation stage. The significant point of this model is that the field wheat yield can be predicted with only two growth indicators approximately 35 days before harvest for 12 mostly cultivated wheat varieties.

학사경고자를 위한 학업적 정서조절 개입모형 개발

Objectives The purpose of this study is to develop an academic emotion regulation intervention model for academic probation students. Methods The study was conducted according to the ADDIE model. First, in the draft model development stage, the process, details, and approach of academic emotion regulation intervention for academic probation students were derived through literature analysis, and each key element of the model was designed so that it could be systematically sequenced to develop a draft model. Second, in the implementation process, the developed preliminary model was applied to academic probation students of actual university sites to confirm the effectiveness of the model. The results of ANCOVA of 22 academic probation students(11 experiments, 11 controls) showed a significant effect on improving academic emotion regulation. In order to analyze the effect, participants' evaluations were analyzed and reflected in the intervention. Third, the evaluation process verified the usefulness and value of the preliminary model. An expert formation evaluation was conducted in a focus group interview composed of nine learning experts. The output from the implementation was reviewed and the value of the developed model was evaluated. Finally, the final model was developed by modifying and supplementing the model. Results The research results of this study are as follows. First, first, this model organically linked the academic warning overcoming process, academic emotion, and academic emotion regulation process. Second, this model suggests a systematic procedure for learning experts to effectively solve the academic emotional regulation problem of academic warnings. Third, this model presented a 4x3 matrix type of intervention method according to the approach for academic emotion regulation intervention. Fourth, in this model, the academic emotion regulation was cyclically performed in consideration of the academic emotions of the academic warning. Conclusion This study is meaningful in developing a model that can be used by actual learning experts to intervene in academic emotional regulation for academic warnings by fully reflecting the emotional characteristics of academic warnings and the experiences and needs of learning experts. Conclusions This study is significant in developing a model that can be used by actual learning experts to intervene in academic emotional regulation for academic probations by fully reflecting the emotional characteristics of academic probations and the experiences and needs of learning experts.