Interactive Feedback Research Articles

The Impact of Climate Change and Urbanization on Groundwater Levels: A System Dynamics Model Analysis

Climate change and population growth have placed increasing stress on groundwater resources. Effective management of groundwater resources is crucial for promoting sustainable development, especially in areas heavily reliant on groundwater supply. However, sustainable groundwater management is more complex due to various factors affecting key drivers and their feedback interactions. In this regard, this study aims to examine the key driving forces and their interplay concerning fluctuations in groundwater levels within a study area in Illinois, United States, which heavily relies on groundwater for water supply. To achieve this objective, a system dynamics (SD) simulation model was developed, utilizing hydrology, climate, and urbanization data spanning from 2010 to 2020. Through calibration and validation of the SD model with historical data, the model demonstrated reliable simulation capabilities for groundwater levels. Additionally, sensitivity analysis and exploration of water resource scenarios were conducted to evaluate the critical factors influencing groundwater levels. Results revealed that population dynamics, agricultural land, and groundwater recharge were analyzed as key drivers influencing the change in groundwater level for the study area, among the climate and urbanization factors that had different contributions to the groundwater dynamics. A 10% increase in population, agricultural land, and groundwater recharge led to a decrease of approximately 5.5% and increases of 4.5% and 4.27% in groundwater levels, respectively. The results of the four-scenario analysis indicated that both climate change and urbanization exerted significant and adverse impacts on the groundwater level in the study area. These scenarios represented prominent and worst-case situations, highlighting the potential challenges and risks associated with the combination of these factors, which resulted in about a 77 mm decrease in groundwater levels by 2030. Overall, given limited data availability for SD model parameterization, the study emphasizes the need to consider the effects of both urbanization and climate change on groundwater supplies in decision-making for sustainable groundwater management, as well as the need for adequate management measures to maintain the long-term usage of groundwater resources.

A systems framework for investigating the roles of multiple transporters and their impact on drug resistance.

Efflux transporters are a fundamental component of both prokaryotic and eukaryotic cells, play a crucial role in maintaining cellular homeostasis, and represent a key bridge between single cell and population levels. From a biomedical perspective, they play a crucial role in drug resistance (and especially multi-drug resistance, MDR) in a range of systems spanning bacteria and human cancer cells. Typically, multiple efflux transporters are present in these cells, and the efflux transporters transport a range of substrates (with partially overlapping substrates between transporters). Furthermore, in the context of drug resistance, the levels of transporters may be elevated either due to extra or intracellular factors (feedforward regulation) or due to the drug itself (feedback regulation). As a consequence, there is a real need for a transparent systems-level understanding of the collective functioning of a set of transporters and their response to one or more drugs. We develop a systems framework for this purpose and examine the functioning of sets of transporters, their interplay with one or more drugs and their regulation (both feedforward and feedback). Using computational and analytical work, we obtain transparent insights into the systems level functioning of a set of transporters arising from the interplay between the multiplicity of drugs and transporters, different drug-transporter interaction parameters, sequestration and feedback and feedforward regulation. These insights transparently arising from the most basic consideration of a multiplicity of transporters have broad relevance in natural biology, biomedical engineering and synthetic biology. Insight, Innovation, Integration: Innovation: creating a structured systems framework for evaluating the impact of multiple transporters on drug efflux and drug resistance. Systematic analysis allows us to evaluate the effect of multiple transporters on one/more drugs, and dissect associated resistance mechanisms. Integration allows for elucidation of key cause-and-effect relationships and a transparent systems-level understanding of the collective functioning of transporters and their impact on resistance, revealing the interplay of key underlying factors. Systems-level insights include the essentially different behaviour of transporters as part of a group; unintuitive effects of influx; effects of elevated transporter-levels by feedforward and drug-induced mechanisms. Relevance: a systems understanding of efflux, their role in MDR, providing a framework/platform for use in designing treatment, and in synthetic biology design.

Pola Manejemen Kelas Guru Sekolah Dasar Di Kota Surabaya Terhadap Persoalan Motivasi Belajar Siswa Rendah

Learning in elementary schools involves various challenges and problems, especially those related to students' developmental characteristics. Teachers are expected to be able to design and implement learning that is appropriate to students' cognitive, social and emotional development. Students' learning motivation in elementary schools can be a serious obstacle, affecting the achievement of learning outcomes. Classroom management is considered the key to creating an effective learning atmosphere and motivating students. This research aims to determine the classroom management patterns implemented by elementary school teachers in the city of Surabaya regarding the problem of low student learning motivation. This research is qualitative research with a multi case study type, using the Explanatory Survey method. The informants consisted of 23 elementary school teachers in the city of Surabaya, selected using a cluster random sampling technique. Data collection was carried out through interviews, and data analysis was carried out descriptively qualitatively. The research results show that the main obstacle in learning is students' passivity and noise which is triggered by low learning motivation. Some teachers apply a Student-Centered Learning (SCL) approach to overcome this problem, placing students as active subjects. Classroom rules, learning contracts, and inspirational stories are considered effective classroom management strategies. Teachers also provide interactive feedback and emphasize an emotional approach to increase students' sensitivity.

Adaptively Enhancing Facial Expression Crucial Regions via a Local Non-local Joint Network

AbstractFacial expression recognition (FER) is still challenging due to the small interclass discrepancy in facial expression data. In view of the significance of facial crucial regions for FER, many existing studies utilize the prior information from some annotated crucial points to improve the performance of FER. However, it is complicated and time-consuming to manually annotate facial crucial points, especially for vast wild expression images. Based on this, a local non-local joint network is proposed to adaptively enhance the facial crucial regions in feature learning of FER in this paper. In the proposed method, two parts are constructed based on facial local and non-local information, where an ensemble of multiple local networks is proposed to extract local features corresponding to multiple facial local regions and a non-local attention network is addressed to explore the significance of each local region. In particular, the attention weights obtained by the non-local network are fed into the local part to achieve interactive feedback between the facial global and local information. Interestingly, the non-local weights corresponding to local regions are gradually updated and higher weights are given to more crucial regions. Moreover, U-Net is employed to extract the integrated features of deep semantic information and low hierarchical detail information of expression images. Finally, experimental results illustrate that the proposed method achieves more competitive performance than several state-of-the-art methods on five benchmark datasets.

AI Adaptivity in a Mixed-Reality System Improves Learning

AbstractAdaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a new genre of Intelligent Science Stations that bring intelligent tutoring into the physical world. Intelligent Science Stations are mixed-reality systems that bridge the physical and virtual worlds to improve children’s inquiry-based STEM learning. Automated reactive guidance is made possible by a specialized AI computer vision algorithm, providing personalized interactive feedback to children as they experiment and make discoveries in their physical environment. We report on a randomized controlled experiment where we compare learning outcomes of children interacting with the Intelligent Science Station that has task-loop adaptivity incorporated, compared to another version that provides tasks randomly without adaptivity. Our results show that adaptivity using Bayesian Knowledge Tracing in the context of a mixed-reality system leads to better learning of scientific principles, without sacrificing enjoyment. These results demonstrate benefits of adaptivity in a mixed-reality setting to improve children’s science learning.

Can human-machine feedback in a smart learning environment enhance learners' learning performance? A meta-analysis.

The human-machine feedback in a smart learning environment can influences learners' learning styles, ability enhancement, and affective interactions. However, whether it has stability in improving learning performance and learning processes, the findings of many empirical studies are controversial. This study aimed to analyze the effect of human-machine feedback on learning performance and the potential boundary conditions that produce the effect in a smart learning environment. Web of Science, EBSCO, PsycINFO, and Science Direct were searched for publications from 2010 to 2022. We included randomized controlled trials with learning performance as outcome. The random effects model was used in the meta-analysis. The main effect tests and the heterogeneity tests were used to evaluate the effect of human-machine feedback mechanism on learning performance, and the boundary conditions of the effect were tested by moderating effects. Moreover, the validity of the meta-analysis was proved by publication bias test. Out of 35 articles identified, 2,222 participants were included in this study. Human-machine interaction feedback had significant effects on learners' learning process (d = 0.594, k = 26) and learning outcomes (d = 0.407, k = 42). Also, the positive effects of human-machine interaction feedback were regulated by the direction of feedback, the form of feedback, and the type of feedback technique. To enhance learning performance through human-machine interactive feedback, we should focus on using two-way and multi-subject feedback. The technology that can provide emotional feedback and feedback loops should be used as a priority. Also, pay attention to the feedback process and mechanism, avoid increasing students' dependence on machines, and strengthen learners' subjectivity from feedback mechanism.

The art of staying in touch – exploring daily feedback interactions between a leader and a subordinate in remote work

PurposeThis study aims to widen the understanding of how remote work shapes the feedback environment by examining the perceptions of leaders and subordinates of daily, dyadic feedback interactions. The emphasis is on understanding how reciprocity within leader-member exchange (LMX) relationships manifests and how it influences the feedback dynamics.Design/methodology/approachTemplate analysis of a qualitative data set consisting of 81 semi-structured interviews with leaders (n = 29) and remote working subordinates (n = 52) was performed.FindingsDrawing on the theoretical frameworks of the feedback environment and the leader-member exchange, the findings demonstrate the imbalance between the efforts of leaders and subordinates in building and maintaining a favourable feedback environment in the remote work context. The results of this study highlight the importance of the dyadic nature of feedback interactions, calling for a more proactive role from subordinates.Practical implicationsGiven the estimation that the COVID-19 pandemic has permanently changed the way organizations work, leaders, subordinates and HR practitioners will benefit from advancing their understanding of the characteristics of dyadic, daily feedback interaction in remote work.Originality/valueQualitative research on feedback and leader-member exchange interactions in remote work that combines the perceptions of leaders and subordinates is sparse.

Abiotic legacies mediate plant–soil feedback during early vegetation succession on rare earth element mine tailings

Abstract An increasing number of studies have shown how feedback interactions between plants and soil can influence primary and secondary succession. However, very little is known about the patterns and mechanisms of such plant–soil feedbacks on stressed mine tailings ecosystem, which can be severely contaminated by a range of toxic elements. In a two‐phase plant–soil feedback experiment based on the rare earth element (REE) mine tailing soil, we investigated biotic (changes in bacterial and fungal communities) and abiotic (changes in chemical properties) legacies of three pioneer grass species, and examined feedback effects of three grasses, two legumes and two woody plants with different root traits. Positive plant–soil feedbacks were found in Miscanthus sinensis, Paspalum thunbergii and Tephrosia candida, and neutral feedbacks were observed in the other four plants. These effects corresponded with an increase in nutrients and total organic carbon, as well as a decrease in acidity and extractable aluminium and REEs. There were less signs of biotic changes in the conditioned tailings. The correlation analysis suggested a relationship between plants' responses to soil legacies and root traits, as well as root economics spectrum. On the mine tailings, acquisitive species with higher specific root length appeared to have greater potential for positive feedback. Synthesis and application. Our study shows that early succession on contaminated rare earth element mine tailings may lead to more positive plant–soil feedback than predicted based on results of non‐contaminated soils, mainly due to the alleviation of abiotic stress in tailings. Therefore, the improvement of specific abiotic soil stress and the trait‐based selection of acquisitive plants should be preferentially considered to promote the primary restoration of degraded land.

Research on Personalized Marketing Strategy of Financial Products Based on Big Data

With the rapid development of information technology, big data has become one of the most important tools for personalized marketing in the financial industry. Based on big data analysis, this study investigates the practice and impact of personalized marketing strategies for financial products in the current economic environment. Financial institutions can create customer portraits and tailored products and services by gathering and studying data on consumers' buying patterns, behavioral preferences, social circles, and interactive feedback. Using various methods, including case studies, in-depth interviews, and quantitative analysis, we took a closer look at how big data can help financial institutions improve marketing efficiency, optimize product design, increase customer satisfaction, and improve customer loyalty. Research shows that big data can greatly support the marketing of financial products, improving market segmentation, providing better opportunities for cross-selling and value-added services, and optimizing marketing resources through predictive analytics, instant marketing, and highly personalized customer experiences. Nonetheless, financial institutions must demonstrate effective skills in managing data, using analysis technology, protecting privacy, and implementing personalized marketing strategies to fully benefit from these advantages.

Effects of Social Media on Higher Education

Social media has had a significant impact on all aspects of life and society in general, transforming the world into what is now referred to as the information society with high-tech connectivity penetrating almost every aspect of human life. Social media platforms, emerging as technological advancements, have played a crucial role in driving rapid development in the economy, business, politics, and particularly in higher education. Their emergence has led to increased global communication, enabling people to access educational information they desire anytime, anywhere, on any digital device, as well as interactive feedback.

ASL champ!: a virtual reality game with deep-learning driven sign recognition

We developed an American Sign Language (ASL) learning platform in a Virtual Reality (VR) environment to facilitate immersive interaction and real-time feedback for ASL learners. We describe the first game to use an interactive teaching style in which users learn from a fluent signing avatar and the first implementation of ASL sign recognition using deep learning within the VR environment. Advanced motion-capture technology powers an expressive ASL teaching avatar within an immersive three-dimensional environment. The teacher demonstrates an ASL sign for an object, prompting the user to copy the sign. Upon the user’s signing, a third-party plugin executes the sign recognition process alongside a deep learning model. Depending on the accuracy of a user’s sign production, the avatar repeats the sign or introduces a new one. We gathered a 3D VR ASL dataset from fifteen diverse participants to power the sign recognition model. The proposed deep learning model’s training, validation, and test accuracy are 90.12%, 89.37%, and 86.66%, respectively. The functional prototype can teach sign language vocabulary and be successfully adapted as an interactive ASL learning platform in VR.

Development of a strategy for the expansion of online teaching at the University of Würzburg based on the experiences of lecturers and students in the pandemic years 2020/21.

Owing to the COVID-19 pandemic, the summer of 2020 saw face-to-face teaching replaced by online teaching. The question arose as to how digitalisation may be implemented meaningfully. The views of lecturers and students on past online programmes were gathered in order to identify potential and future prospects. An exploratory, guidelines-based interview study was conducted during the clinical phase of the medicine degree at the Faculty of Medicine in Würzburg. Five lecturers and five students were interviewed in the winter semester of 2020/21. This was followed by a content analysis evaluation according to Kuckartz, with the help of MAXQDA. Online teaching offers more flexibility and security for the future. Hybrid formats (e.g., blended learning) are in demand. While theoretical knowledge can be taught online, face-to-face teaching remains essential in practical training. Digital elements must be developed didactically and anchored in the curriculum. Interaction and direct feedback between students and lecturers are key aspects of this. Online teaching in medicine offers numerous potentials and didactic design options that can improve the degree programme in a competency-based manner. Combined teaching formats are particularly effective in this regard. Fittingly conceived, multimedia teaching formats enable students to approach their studies in a focused manner. The points raised during the interviews correspond with the fundamental principles of the ARCS model, which was developed to strengthen continuous motivation in students. Well-thought-out design and integration of online teaching can contribute to attractive, efficient, and future-oriented teaching/learning activities. Decisive factors are the collaboration of everyone involved and adequate provision of both time and financial resources.

A new methodological approach to characterize selective motor control in children with cerebral palsy.

Despite being a primary impairment in individuals with cerebral palsy (CP), selective motor control (SMC) is not routinely measured. Personalized treatment approaches in CP will be unattainable without the ability to precisely characterize the types and degrees of impairments in motor control. The objective of this study is to report the development and feasibility of a new methodological approach measuring muscle activation patterns during single-joint tasks to characterize obligatory muscle co-activation patterns that may underly impaired SMC. Muscle activation patterns were recorded during sub-maximal voluntary isometric contraction (sub-MVIC) tasks at the hip, knee, and ankle with an interactive feedback game to standardize effort across participants. We calculated indices of co-activation, synergistic movement, mirror movement, and overflow (indices range 0-2, greater scores equal to greater impairment in SMC) for each isolated joint task in 15 children - 8 with typical development (TD) (mean age 4.7 ± 1.0 SD years) and 7 with CP (mean age 5.8 ± 0.7 SD years). Indices were compared with Mann-Whitney tests. The relationships between the indices and gross motor function (GMFM-66) were examined with Pearson's r. Mean indices were higher in the CP vs. the TD group for each of the six tasks, with mean differences ranging from 0.05 (abduction and plantarflexion) to 0.44 (dorsiflexion). There was great inter-subject variability in the CP group such that significant group differences were detected for knee flexion mirroring (p = 0.029), dorsiflexion coactivation (p = 0.021), and dorsiflexion overflow (p = 0.014). Significant negative linear relations to gross motor function were found in all four indices for knee extension (r = -0.56 to -0.75), three of the indices for ankle dorsiflexion (r = -0.68 to -0.78) and in two of the indices for knee flexion (r = -0.66 to -0.67), and ankle plantarflexion (r = -0.53 to -0.60). Indices of coactivation, mirror movement, synergy, and overflow during single-joint lower limb tasks may quantify the type and degree of impairment in SMC. Preliminary concurrent validity between several of the indices of SMC and gross motor function was observed. Our findings established the feasibility of a new methodological approach that quantifies muscle activation patterns using electromyography paired with biofeedback during single-joint movement.

Exploring Pedagogical Interactions: A Critical Discourse Analysis of College English Teaching in Chinese Media Colleges

This study employs Critical Discourse Analysis (CDA) to investigate the complexities of interactive classroom discourse within the context of College-level English education in Chinese media colleges. Specifically, the study focuses on a reading session that took place in a freshman English class, with the aim of evaluating classroom interactions. The primary objective is to interpret the recurring patterns of interactive feedback exchanged between teachers and students during these sessions. The investigation draws insights from the tape-recorded and closely examined lessons of two English teachers, revealing the dynamics of language use within the educational context. By applying CDA, this study seeks to reveal the underlying structures and power relations embedded in classroom discourse, shedding light on how language functions as a tool for both teaching and learning. The significance of this research lies in its potential to enhance our understanding of effective pedagogical strategies and communication dynamics in language education settings. The findings aim to contribute valuable insights to educators, curriculum developers, and researchers, promoting a more informed, innovative and thorough approach to language instruction in the college context.

Factors Influencing Higher-order Thinking Skills in Blended Learning

The purpose of the study is to investigate how learning self-efficacy (LS), learning interaction (LI), and learning feedback (LF) are significant determinants of higher-order thinking skill (HOTS) growth. The methodology of the current study examines research on HOTS that was published in the Scopus database from 2012 to 2022 with an emphasis on LF and LS. This review was based on the blended learning (BL) model. While some study has been done on the connections between HOTS and feedback, HOTS and engagement, and HOTS and self-efficacy. However, there is limited research that considers feedback, interaction, and self-efficacy as significant components impacting the development of HOTS, and it is still unclear how HOTS, learning feedback, learning self-efficacy, and learning interaction are related. There is little research on self-efficacy or confidence in HOTS. The analytic findings are discussed, and ideas for future study directions are also put out.

Islamic Mindfulness-Based Play Activities: Enhancing Child Mental Health in the Post-Pandemic Era

This research focuses on developing Islamic mindfulness-based play activities to mitigate child mental health issues in the post-pandemic context. The study was conducted in Jakarta-Depok kindergartens using a modified Research and development (R&D) approach tailored for educational settings, as per Borg and Gall. The primary aim was to create and evaluate educational tools to foster Islamic mindfulness among young learners. The research involved identifying children's learning challenges post-pandemic and developing and refining educational games and teaching methods. Data collection encompassed observational studies, educator feedback, and direct interaction with children using the developed tools. The analysis evaluated these tools' usability, engagement level, and educational impact. The results highlighted the development of a physical model comprising a 14-chapter guidebook for tea. Still, it is a procedural model that condenses the original ten stages into seven. Essential components include yoga based on prayer movements, activities for enhancing food relationships, self-body awareness exercises, sensory training, gratitude journal writing, and positive affirmations based on Asmaul Husna. Evaluations by material and learning design experts and one-to-one teacher tests affirmed the model's quality. A field trial with 20 children showed enjoyment and preference for gratitude journal writing. Conclusively, the research effectively developed a model for Islamic mindfulness-based play activities, demonstrating the potential to enhance children's mental health post-pandemic. However, limitations such as geographic confinement and individual response variability suggest the need for broader application and further research to assess long-term impacts. This study contributes significantly to educational and psychological interventions, offering a culturally sensitive, holistic child development approach integrating mental health, spirituality, and education.

수학, 과학 교육용 저작 도구를 활용한 수업 설계 및 실행에서 교사들이 경험한 어려움 분석

Objectives In this study, we explored and discussed the difficulties experienced by teachers in designing and implementing instruction using authoring tools for mathematics and science education from the perspective of TPACK.
 Methods For this purpose, 23 elementary and secondary school teachers were recruited, and 15 teams were organized according to the level and subject of interest to participate in instruction design and implementation using authoring tools for mathematics and science education. Thematic analysis was conducted from the results of reflection journals and interviews to derive the difficulties experienced by teachers, and related TPACK components were coded.
 Results Teachers experienced difficulties in ‘using the block coding function of the authoring tool’, ‘efficient time management’, ‘meta-cognitive shift phenomena’, ‘use of curriculum materials and setting learning goals’, ‘anticipation of learners' thinking and responses’, and ‘designing interactive feedback’.
 Conclusions It is necessary to find a support plan for strengthening mathematics and science teachers’ TPACK expertise such as TK related to understanding the block coding environment and using it appropriately for instruction design, TPK related to efficiently managing time and overcoming extreme didactic phenomena such as metacognitive shift in instructional design and implementation using authoring tools, TCK related to using curriculum materials and clearly setting learning goals in instructional design and implementation, TPACK related to designing interactive feedback and anticipating learner’s thinking and reaction.

Identifying key factors of dynamic ADDIE model for instructional virtual reality design: an exploratory study

ABSTRACT Due to the features of immersive learning and without the restrictions of time and place, virtual reality (VR) is commonly used in learning areas. For investigating functional design in the VR learning environment, previous literature proposed a series of theoretical models and strategies without considering the key factors for their development. Therefore, this study explored the key factors based on the dynamic ADDIE model for the design of VR instructional systems. To augment the assessment integrity, this study adopted the fuzzy Delphi method (FDM). Open and closed questions were also integrated into the online FDM-based questionnaire to assess the opinions of expert groups, with 9 professors and 9 school teachers. The results showed that the final selection encompassed 5 criteria, 2 sub-criteria, and 23 key factors. In the overall priority order, the “interactive feedback design” was ranked as the most influential factor, while the “learner opinion survey” was the least. Most importantly, since the experts considered “formative evaluation” to be the highest significant sub-criterion, the reliability of the dynamic ADDIE model was confirmed. In addition, the divergent opinion from expert groups could provide references for future developers with different backgrounds.

ChatGPT as a Tool for Language Development: Investigating Its Impact on Proficiency and Self-Evaluation Accuracy in Indonesian Higher Education

English language learning plays a crucial role in today’s globalized world, and the integration of technology, particularly chatbots, has shown promise in enhancing language learning experiences. However, there is a noticeable gap in the literature regarding the application of chatbots in the specific context of Indonesia University. This research employs a mixed-methods approach, combining quantitative and qualitative data collection methods. A sample of 100 students from Indonesia University participated in pre-test and post-test assessments to measure language development. Additionally, the accuracy of students’ self-assessments was evaluated before and after using ChatGPT. The main findings of this study indicate that personalized feedback provided by ChatGPT significantly influenced students’ language development, as evidenced by the notable improvement in post-test scores. Furthermore, there was a considerable increase in self-assessment accuracy after students engaged with ChatGPT, demonstrating the effectiveness of personalized feedback in enhancing students’ ability to evaluate their language proficiency. Participants reported that ChatGPT facilitated language learning by providing interactive feedback and targeted guidance, allowing learners to practice and improve their language skills at their own pace. English language learners perceive benefits such as interactive feedback, increased motivation, and resource accessibility. While challenges exist, the advantages outweigh the limitations. These findings contribute to understanding the potential of chatbots, specifically ChatGPT, as effective language learning tools in self-directed learning environments. This finding offers valuable insights for educators and practitioners in implementing chatbots effectively within language pedagogy, ultimately contributing to more effective and engaging English language learning experiences in the Indonesian university context.

Simulation Study on Rain-Flood Regulation in Urban “Gray-Green-Blue” Spaces Based on System Dynamics: A Case Study of the Guitang River Basin in Changsha

Urban rainstorms and flood disasters are the most common and severe environmental problems worldwide. Many factors influence rain-flood control simulation, forming a complex network system of interconnected and mutually constraining elements. In terms of spatial scale selection, existing research on rain-flood disaster risk largely relies on a single-scale infrastructure index system and has not yet focused on urban “gray-green-blue” spatial scale simulations for rain-flood storage. Regarding research methodology, applying system dynamics methods to the simulation of rain-flood storage and disaster prevention planning in watershed cities is still in its initial stages. System dynamics models can simulate the feedback interactions among various sub-elements in the coupled mega-system, fully addressing complex issues within the system structure that involve multiple variables, non-linear relationships, and numerous feedback loops, thereby compensating for the inadequacies of traditional linear models in the collaborative management of rain-flood risks. Taking the Changsha Guitang River Basin as an example, this paper constructs a system dynamics model covering four dimensions: natural environment, socio-economics, internal structure, and policy development. It aims to derive the optimal planning scheme for gray-green-blue spatial coordination in rain-flood storage by weighing four different development scenarios. The simulation results show: (1) Simply changing the surface substrates without considering rainwater discharge and the plan that emphasizes the construction of municipal drainage facilities will see the capacity gap for rain-flood storage-space construction continue to widen by 2035. This indicates that the plans mentioned above will struggle to bear the socio-economic losses cities face during rain-flood disasters. (2) The plan of combining gray and green infrastructures sees the rain-flood storage construction capacity turn from negative to positive from 2024, rising to 52.259 billion yuan by 2035. This reflects that the plan can significantly reduce the rainwater volume in the later stages of low-impact development infrastructure construction, mitigate rain-flood disaster risks, and reduce government investment in rain-flood disaster risk management, making it a relatively excellent long-term rain-flood storage space planning option. (3) The rain-flood regulation space planning scheme, under the combined effect of the urban “gray-green-blue” network system, sees the capacity for rain-flood storage construction turn positive a year earlier than the previous plan, reaching 54.232 billion yuan by 2035. This indicates that the scheme can not only effectively respond to extreme flood and rainstorm disasters but also maintain ecological environment benefits and mitigate the socio-economic losses caused by disasters, making it the optimal choice for future government disaster management planning. The research results provide a theoretical framework and practical insights for territorial spatial planning, rain-flood control management, and resilient city construction in watershed areas.