Feedback Framework Research Articles

Preservice teachers’ perceptions of feedback: The importance of timing, purpose, and delivery

If the purpose of feedback is to reduce the discrepancy between the established goal and what is recognized, then how can this discrepancy be minimized through support and guidance? Feedback is instrumental to a preservice teacher development during their teacher preparation program. This qualitative study examines 31 first year teachers’ previous experiences with feedback during their undergraduate practicums. The two research questions addressed: What can be learned from PSTs’ perceptions of feedback practices utilized in teacher preparation programs? and What modifications or adaptations can be made to current feedback practices and structures in teacher preparation programs to enhance teacher efficacy and classroom readiness? Semi structured interviews provided a comparison of qualitative data and an opportunity for open ended questioning. Using descriptive analysis, researchers discovered that current feedback loops and structures can inhibit pre-service teachers’ ability to make meaning from the information and move their learning and instruction forward. As teacher preparation programs work to establish more dialogic approaches to feedback that provide pre-service teachers with multiple opportunities to reflect individually and collaboratively with university faculty, timing, purpose, and delivery are important components to consider. Although this article is written based on preservice teacher perceptions, the implications pertain to multiple fields and authors share a universal framework for feedback.

A Validity Framework for Effective Analysis and Interpretation of Milestones Data.

A Validity Framework for Effective Analysis and Interpretation of Milestones Data.

The unsung role of assessment and feedback in self-directed learning (SDL)

ABSTRACT Assessment and feedback have been intensively studied to promote effective learning. However, less attention is given on its role in self-directed learning (SDL) context. Despite being recognised as a powerful educational tool, critical examination of SDL and assessment and feedback literature reveals that these bodies of literature rarely intersect with one another. This study, which aims to investigate teacher educators’ practices of assessment and feedback in the context of SDL, begins to address the gap. Twenty Malaysian teacher educators were interviewed, and the interview data were analysed using a hybrid thematic analysis approach. Findings suggest the importance of (i) reducing anxiety in assessment and feedback, (ii) practicing sustainable formative assessment, (iii) ensuring timely feedback, and (iv) responding to cultural norms. These findings address key features of assessment and feedback in promoting SDL, however greater emphasis is needed in developing a framework of assessment and feedback that supports SDL.

Customer online reviews and hospitality employees’ helping behavior: moderating roles of self-efficacy and moral identity

PurposeOnline reviews are perceived as credible and trustworthy across various business sectors; thus, they influence customers’ purchase decisions. However, the potential role of customer online reviews as feedback for employee performance and employee reactions to customer reviews remain largely unclear. To address this knowledge gap, this study proposes that employee characteristics, namely, self-efficacy (Study 1) and moral identity (Study 2), moderate the effect of the valence of customer reviews on hospitality employees’ helping behavior.Design/methodology/approachThe authors used a scenario-based, quasi-experimental design in two studies. They recruited a total of 215 frontline employees at independent casual dining restaurants in Istanbul, Turkey (Study 1) and 226 US residents who have worked in the restaurant industry for more than six months (Study 2). Multiple linear regressions via PROCESS and moderation analysis via Johnson–Neyman technique were used.FindingsStudy 1 demonstrates that when employees’ self-efficacy is low, positive (vs negative) customer reviews enhance employees’ helping behavior. By contrast, when employees’ self-efficacy is high, their helping behavior is invariantly high regardless of the valence of customer reviews. Study 2 reveals that when employees’ moral identity is low, their helping behavior decreases in the presence of negative (vs positive) customer reviews. Conversely, when employees’ moral identity is high, their helping behavior is similarly high regardless of the valence of customer reviews.Practical implicationsHospitality managers may need to develop training programs to enhance their employees’ self-efficacy and moral identity. They may also provide necessary organizational support to induce their employees’ self-efficacy and moral identity, given that such psychological resources help buffer the dampening effect of negative reviews on helping behavior. Last, hospitality managers may consider incorporating customer reviews as part of employee performance feedback.Originality/valueThis study advances the understanding of employees’ responses to customer reviews, with the performance appraisal feedback framework as fresh theoretical lens. This study is among the first to demonstrate the relationship between the valence of customer reviews and subsequent helping behavior of employees toward customers. It also contributes to the emerging literature that identifies boundary conditions for employees’ responses to customer reviews.

Adaptive fuzzy finite-time control of stochastic nonlinear systems with actuator faults

This paper considers the finite-time control problem for a class of nonlinear stochastic systems with actuator faults/failure. A fast convergence feedback control algorithm based on backstepping finite-time command filtering is proposed. Under the framework of adaptive feedback, the fuzzy logic system is used to deal with the uncertainties of the system. Taking into account the actuator faults of both loss of effectiveness and lock-in-place, an adaptive fuzzy controller is developed. Since there is no need to calculate the derivative of the virtual control signal, the presented scheme overcomes the “explosion of complexity” problem inherent in conventional methods. A compensation mechanism is also introduced to compensate for errors caused by the filter. The proposed method ensures not only that all signals of the closed-loop system are finite-time bounded, but also that the tracking error converges to a small neighborhood around the origin. The effectiveness of the proposed method is demonstrated in the simulation results.

Investigation of Dynamic Coupling Coordination between Urbanization and the Eco-Environment—A Case Study in the Pearl River Delta Area

The interaction between urbanization and the eco-environment is usually viewed as an effect–feedback framework. Its coupling system is composed of urbanization and eco-environment subsystems. In this paper, the coupling degree (CD) and the coupling coordinated degree (CCD) are used to reflect the coupling interaction and coupling coordination between the urbanization subsystem and the eco-environment subsystem. Based on the dynamic relative quantities of urbanization and eco-environment data in the Pearl River Delta, CD and CCD values were calculated, and the spatiotemporal evolution trend of coordination was analyzed. The results show that (1) from 2000 to 2015, the nine cities in the Pearl River Delta had high CD values and CCD values. Though they had different performances in different periods, they were all in a coordinated class, including good coordination (GC), moderate coordination (MC), and bare coordination (BC). (2) In terms of temporal evolution, the coupling coordination between urbanization and the eco-environment in the entire Pearl River Delta greatly improved. (3) From the perspective of spatial distribution, the coupling coordination of the central region was higher than that of the peripheral regions, and that of the west bank of the Pearl River was higher than that of the east bank of the Pearl River. These results can help local policy makers enact appropriate measures for sustainable development.

Climate More Responsive to Marine Cloud Brightening Than Ocean Albedo Modification: A Model Study

AbstractSolar radiation modification has been suggested as a backup option to reduce anthropogenic warming. Marine cloud brightening (MCB) and ocean albedo modification (OAM) are two proposed approaches to intentionally reflect sunlight back to space over oceanic regions. Using the NCAR Community Earth System Model, we compare climate response to MCB and OAM under the framework of fast adjustment and slow feedback. We implement MCB and OAM uniformly over the global ocean to offset CO2‐induced warming. We find that to offset 3.3 K global mean warming from a doubling of CO2, diagnosed effective radiative forcing is −4.8 and −3.6 W m−2 for OAM and MCB, respectively. Correspondingly, radiative forcing efficacy of OAM is about 70% of MCB. Fast climate adjustment differs in response to MCB and OAM forcing. MCB cools the lower atmosphere by reflecting sunlight from cloud, causing a reduction in sunlight absorption in the atmosphere. In contrast, OAM, by reflecting more sunlight from surface, increases shortwave heating of the lower atmosphere, leading to a decrease in low marine clouds and hence a positive cloudy‐sky shortwave forcing that partly compensates the negative clear‐sky shortwave forcing. The slow climate response and pattern of equilibrium climate change are similar between MCB and OAM. As for hydrological cycle, relative to the climate under a doubling of CO2, both MCB and OAM produce an increase in precipitation and runoff over tropical land.

Online estimation of lithium‐ion batteries state of health during discharge

In order to solve the problem that the unsatisfactory accuracy of SOH estimation method, which seeks the relationship between battery life and external characteristics through experiments, is restricted by battery consistency in a large number of battery applications, this paper proposes an SOH estimation framework which can automatically correct the errors caused by the battery consistency problem online. The SOH framework realizes the automatic online fast correction of SOH estimation error through the designed closed-loop feedback framework. Another advantage of this framework is that it can achieve accurate estimation for the batteries state of health (SOH) during the irregular charging and discharging process of electric vehicles. And in this framework, a new equivalent circuit based on the autoregressive (AR) model is proposed to reduce the complexity of the battery method while ensuring the accuracy of the estimation, which has better robustness in practical applications. Finally, it is proved that the online estimation of lithium-ion batteries SOH during discharge proposed in this paper has better practicability and higher estimation accuracy by comparing with the traditional SOH method of external feature relationship.

WITHDRAWN: Enhanced adaptive creation of visualisation network by detection of leaves

Leaf imaging yield estimation is an important research problem for computer vision. In many fields, an effective earnings forecasting system can be beneficial, including agriculture, cattle farming and other related industries. Leaf pictures are obtained from random field observers. These random website photos are analysed to identify any diseases on the ground. A health map that divides the field into safe and unhealthy regions is obtained with the position tags of these images. The present and potential disease distribution can be detected using different health maps. This helps to propose precautionary steps to curb transmission and increase yields. This paper deals with a new method of predicting yield based on enhanced learning and uses a neural network of translation learning convoluted to identify diseases. A framework for real-time feedback is also developed that proposes disease remedies and then revalue the field after a time to verify whether or not the remedies function. If the proposed solution fails, a better remedy will be proposed and its effect on the field will be analysed. Testing the system in real-world environments showed that the integrated system proposed was one form and overwhelmed by more than 20 per cent in terms of the yield prediction, 35 per cent for quality controls and feedback remedies systems, such as convolutionary neural networks (CNN), deep convolutionary networks (DNN) and other specially implemented neural systems (NN). The assessment of various crop types was performed and the findings were consistent.

Real-Time Multi-Modal Sensing and Feedback for Catheterization in Porcine Tissue.

Objective: In this study, we introduce a multi-modal sensing and feedback framework aimed at assisting clinicians during endovascular surgeries and catheterization procedures. This framework utilizes state-of-the-art imaging and sensing sub-systems to produce a 3D visualization of an endovascular catheter and surrounding vasculature without the need for intra-operative X-rays. Methods: The catheterization experiments within this study are conducted inside a porcine limb undergoing motions. A hybrid position-force controller of a robotically-actuated ultrasound (US) transducer for uneven porcine tissue surfaces is introduced. The tissue, vasculature, and catheter are visualized by integrated real-time US images, 3D surface imaging, and Fiber Bragg Grating (FBG) sensors. Results: During externally-induced limb motions, the vasculature and catheter can be reliably reconstructed at mean accuracies of mm and mm, respectively. Conclusions: The conventional use of intra-operative X-ray imaging to visualize instruments and vasculature in the human body can be reduced by employing improved diagnostic technologies that do not operate via ionizing radiation or nephrotoxic contrast agents. Significance: The presented multi-modal framework enables the radiation-free and accurate reconstruction of significant tissues and instruments involved in catheterization procedures.

Fully connected feedforward neural networks based CSI feedback algorithm

In modern wireless communication systems, the accurate acquisition of channel state information (CSI) is critical to the performance of beamforming, non-orthogonal multiple access (NOMA), etc. However, with the application of massive MIMO in 5G, the number of antennas increases by hundreds or even thousands times, which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme. In this paper, by using deep learning technology, we develop a system framework for CSI feedback based on fully connected feedforward neural networks (FCFNN), named CF-FCFNN. Through learning the training set composed of CSI, CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.

On the Throughput of Wireless Communication With Combined CSI and H-ARQ Feedback

In this paper, we characterize the combined channel state information (CSI) and re-transmission feedback on wireless communication. Feedback, often assumed free, has traditionally been used to either learn the channel state, or to request the re-transmission of a failed reception. We propose here a generalized framework for feedback that captures the channel time-variation. We consider two-way communication model where all the resources needed for training, feedback (CSI and automatic repeat request), and data. In order to maximize the throughput and optimize resource allocations, we provide the expressions of outage probabilities for different rate/power adaptation and re-transmission protocols. The simulation results show that, besides signal-to-noise ratio (SNR) and Doppler, the optimal pilot overhead depends on whether we are using rate/power adaptation and the number of re-transmissions. We also prove that in slow-fading channels with high SNR regimes, varying the rate provides the best throughput. When dealing with fast time-varying channels and low SNR regimes, using constant power and rate gives the best throughput performance.

Progressive and Corrective Feedback for Latent Fingerprint Enhancement Using Boosted Spectral Filtering and Spectral Autoencoder

The objective of this research is to design an efficient algorithm that can successfully enhance a targeted latent fingerprint from various complex backgrounds under an uncontrolled environment. Most algorithms in literature exploited dictionary learning schemes and deep learning architectures to capture latent fingerprints from complicated backgrounds and noise. However, an algorithm learned from other high-quality fingerprint images may not solve all possible cases within a given unseen image. We propose a new feedback framework to distinguish latent fingerprints from complex backgrounds and gradually improve friction-ridge quality using the information provided inside the given unseen image. We combine two efficient mechanisms. The first mechanism enhances high-quality areas in priority and feeds the enhanced areas back to improve the quality of latent fingerprints in the nearby area. The second mechanism is to verify that the first mechanism works correctly by detecting anomalously enhanced fingerprint patterns. The second mechanism employs a spectral autoencoder that learns from good fingerprint spectra in the frequency domain. The anomalous fingerprint area is sent back to the first mechanism for further improving the enhanced result. We benchmark the proposed algorithm against available state-of-the-art algorithms using two fingerprint matching systems (one commercial off-the-shelf and one open-source) on two public latent fingerprint databases. The experimental results show that the proposed algorithm outperforms most state-of-the-art algorithms in the literature.

An optimization–simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty

This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for driving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.

Analytical Review of Cybersecurity for Embedded Systems

To identify the key factors and create the landscape of cybersecurity for embedded systems (CSES), an analytical review of the existing research on CSES has been conducted. The common properties of embedded systems, such as mobility, small size, low cost, independence, and limited power consumption when compared to traditional computer systems, have caused many challenges in CSES. The conflict between cybersecurity requirements and the computing capabilities of embedded systems makes it critical to implement sophisticated security countermeasures against cyber-attacks in an embedded system with limited resources, without draining those resources. In this study, twelve factors influencing CSES have been identified: (1) the components; (2) the characteristics; (3) the implementation; (4) the technical domain; (5) the security requirements; (6) the security problems; (7) the connectivity protocols; (8) the attack surfaces; (9) the impact of the cyber-attacks; (10) the security challenges of the ESs; (11) the security solutions; and (12) the players (manufacturers, legislators, operators, and users). A Multiple Layers Feedback Framework of Embedded System Cybersecurity (MuLFESC) with nine layers of protection is proposed, with new metrics of risk assessment. This will enable cybersecurity practitioners to conduct an assessment of their systems with regard to twelve identified cybersecurity aspects. In MuLFESC, the feedback from the system-components layer to the system-operations layer could help implement “Security by Design” in the design stage at the bottom layer. The study provides a clear landscape of CSES and, therefore, could help to find better comprehensive solutions for CSES.

Stochastic Model Predictive Control Using Simplified Affine Disturbance Feedback for Chance-Constrained Systems

This letter covers the model predictive control of linear discrete-time systems subject to stochastic additive disturbances and chance constraints on their state and control input. We propose a simplified control parameterization under the framework of affine disturbance feedback, and we show that our method is equivalent to parameterization over the family of state feedback policies. Using our method, associated finite-horizon optimization can be computed efficiently, with a slight increase in conservativeness compared with conventional affine disturbance feedback parameterization.

Deep Learning-Based CSI Feedback for Beamforming in Single- and Multi-Cell Massive MIMO Systems

The potentials of massive multiple-input multiple-output (MIMO) are all based on the available instantaneous channel state information (CSI) at the base station (BS). Therefore, the user in frequency-division duplexing (FDD) systems has to keep on feeding back the CSI to the BS, thereby occupying large uplink transmission resources. Recently, deep learning (DL) has achieved great success in the CSI feedback. However, the existing works just focus on improving the feedback accuracy and ignore the effects on the following modules, e.g., beamforming (BF). In this paper, we propose a DL-based CSI feedback framework for BF design, called CsiFBnet. The key idea of the CsiFBnet is to maximize the BF performance gain rather than the feedback accuracy. We apply it to two representative scenarios: single- and multi-cell systems. The CsiFBnet-s in the single-cell system is based on the autoencoder architecture, where the encoder at the user compresses the CSI and the decoder at the BS generates the BF vector. The CsiFBnet-m in the multi-cell system has to feed back two kinds of CSI: the desired and the interfering CSI. The entire neural networks are trained by an unsupervised learning strategy. Simulation results show the great performance improvement and complexity reduction of the CsiFBnet compared with the conventional DL-based CSI feedback methods.

Fixed time output feedback containment for uncertain nonlinear multiagent systems with switching communication topologies

This paper presents a fixed time containment protocol within output feedback framework for uncertain nonlinear multiagent systems (MASs) exposed to unknown leader dynamics under switching communication topologies. To characterize dynamic communication links among agents in uncertain environments, a Markov jumping process with partially known transition probability is introduced to circumvent the difficulties of requiring the exact elements of transition rates as a prior. Then, in order to eliminate the necessity of broadcasting local velocity states between neighboring agents over the wireless and reject the comprehensive uncertainties including unknown model nonlinearities, external disturbances as well as switching topology jumps, a fixed time extended state observer (FTESO) only using the relative position information as input data is established for follower agents to achieve a uniform fast and precise estimation capability. A fixed time output feedback containment protocol by means of observer is presented to ensure that individual follower is guided into the geometric area constituted by a fleet of unknown leaders with a consistent convergence time irrelevant of initial conditions. The outstanding features of the developed algorithm are threefold: First, a prescribed fixed time containment consensus with reduced communication burden and enhanced robustness can be implemented. Second, a Markov jumping process with a partially known transition probability is considered to stimulate switching communication topologies. Third, a FTESO is constructed for each agent to realize a fixed-time estimation without requiring accurate modeling information. Finally, the fixed time stabilization of cascaded system is illuminated by applying the Lyapunov-based methods and bi-limit homogeneity. Simulation examples validate the feasibility and strengths of developed control protocol.

Simulating the interstellar medium of galaxies with radiative transfer, non-equilibrium thermochemistry, and dust

ABSTRACT We present a novel framework to self-consistently model the effects of radiation fields, dust physics, and molecular chemistry (H2) in the interstellar medium (ISM) of galaxies. The model combines a state-of-the-art radiation hydrodynamics module with a H and He non-equilibrium thermochemistry module that accounts for H2 coupled to an empirical dust formation and destruction model, all integrated into the new stellar feedback framework SMUGGLE. We test this model on high-resolution isolated Milky-Way (MW) simulations. We show that the effect of radiation feedback on galactic star formation rates is quite modest in low gas surface density galaxies like the MW. The multiphase structure of the ISM, however, is highly dependent on the strength of the interstellar radiation field. We are also able to predict the distribution of H2, that allow us to match the molecular Kennicutt–Schmidt (KS) relation, without calibrating for it. We show that the dust distribution is a complex function of density, temperature, and ionization state of the gas. Our model is also able to match the observed dust temperature distribution in the ISM. Our state-of-the-art model is well-suited for performing next-generation cosmological galaxy formation simulations, which will be able to predict a wide range of resolved (∼10 pc) properties of galaxies.

Evaluating the nature of summative feedback on electronically submitted work at a UK University: implications for post-compulsory education

ABSTRACT Summative feedback provides individualised support for students in formal learning situations; however, numerous studies show that academic practice in summative feedback is highly uneven, that the purposes are poorly understood and that the provision of developmental feedback is often subsidiary to the justification of the assessment decisions. This study considers the nature of marker feedback on 105 pieces of summative work that were electronically submitted across 16 first degrees in sports at a UK University. The study explores the patterns of feedback practice and relates these to ideas of dialogic feedback, to the concept of Feedback Mark 2, and to feedback frameworks in the literature. The analysis shows that feedback practice is often monologistic, predominantly addressing the structure of student work and is characterised by the identification of deficiencies rather than the support of reflective learning. However, the study finds that the feedback on non-anonymised texts was more extensive and dialogic than that on anonymised texts. Overall, the study confirms that feedback practice is more aligned to transmissive approaches than to the empowerment of students through and identifies the implications for practice in post-compulsory education.