Feedback Framework Research Articles

Learner perceptions of the feedback process in the online component of a blended course

Despite extensive research on feedback models, there is still sparse empirical evidence of their validity and application in higher education learning settings, whether online, hybrid, or face-to-face. Understanding how a feedback framework, integrated in the instructional cycle, is perceived by the learners can provide empirical support about its intended purpose and effectiveness. Recent reviews of research on student perceptions of feedback in different learning environments have revealed the need to research feedback as a process with the use of a more solid methodological approach. The aim of the present descriptive survey study is to investigate learners’ perceptions of an established feedback model, a Matrix of Feedback for Learning (Brooks et al., 2019), in the asynchronous online component of an undergraduate blended course. More specifically, the impact of its application on learners’ encounters with three types of feedback (feed-up, feed-back and feed-forward) and three levels of feedback (task, process and self-regulatory) is explored. A 36-item survey, previously piloted and preliminarily validated, was used to explore learners’ perceptions (N=135) of the three feedback types and levels. Approximately 68% of responses showed that students recognized feedback and the possibilities to use and act on feedback as helpful to their learning. This present survey supports a line of research on feedback model validation in online learning environments while offering meaningful insights from the learners that can inform both course design and interventions to support engagement with feedback.

Exploring a method to reduce inconsistency in the group Analytic Hierarchy Process using the Delphi method and Nudge theory

This paper reports a new method based on a Delphi process to nudge responses of participants toward consistency in an Analytic Hierarchy Process (AHP) study. We hypothesized that data collected during an early round of an AHP study could establish ranges of answers for later rounds that would improve consistency of responses. In our design, the Delphi method provided an effective framework for feedback of the bounds calculated between rounds. Using Delphi within the AHP to nudge answers of experts toward answers that promote consistency was the focus of our method. We propose an application using four mini scenarios depicting alternate futures for the management of genetic modification technologies. We found that in most instances our nudges improved the consistency of responses in sequential Delphi rounds; however, in some instances, the Delphi suggestions of bounds were not followed by a sufficient number of participants and a small number of inconsistencies remained. The value of this work may be in its warning to other researchers who attempt to achieve consistency by nudging responses in a paired comparison AHP study, to be quite explicit in instructions about remaining within the given bounds and perhaps creating incentives for respondents to do so.

Elevation-dependent warming: observations, models, and energetic mechanisms

Abstract. Observational data and numerical models suggest that, under climate change, elevated land surfaces warm faster than non-elevated ones. Proposed drivers of this “elevation-dependent warming” (EDW) include surface albedo and water vapour feedbacks, the temperature dependence of longwave emission, and aerosols. Yet the relative importance of each proposed mechanism both regionally and at large scales is unclear, highlighting an incomplete physical understanding of EDW. Here we expand on previous regional studies and use gridded observations, atmospheric reanalysis, and a range of climate model simulations to investigate EDW over the historical period across the tropics and subtropics (40° S to 40° N). Observations, reanalysis, and fully coupled models exhibit annual mean warming trends (1959–2014), binned by surface elevation, which are larger over elevated surfaces and broadly consistent across datasets. EDW varies by season, with stronger observed signals in local winter and autumn. Analysis of large ensembles of single-forcing simulations (1959–2005) suggests historical EDW is likely a forced response of the climate system rather than an artefact of internal variability and is primarily driven by increasing greenhouse gas concentrations. To gain quantitative insight into the mechanisms contributing to large-scale EDW, a forcing–feedback framework based on top-of-atmosphere energy balance is applied to the fully coupled models. This framework identifies the Planck and surface albedo feedbacks as being robust drivers of EDW (i.e. enhancing warming over elevated surfaces), with energy transport by the atmospheric circulation also playing an important role. In contrast, water vapour and cloud feedbacks along with weaker radiative forcing in elevated regions oppose EDW. Implications of the results for understanding future EDW are discussed.

Academia or industry? The effect of long-term performance feedback on collaborative innovation partner selection

ABSTRACT In this study, we enrich the framework of long-term performance feedback by incorporating different feedback states and aspirational referents, as well as integrating it into the research model of collaborative innovation partner selection. By categorising partner into academia and industry, we argue that firms with long-term historical underperformance (overperformance) are more likely to choose industry (academia) partners than academia (industry) ones. However, firm’s partner selection preference demonstrates an opposite tendency when peers are taken as the aspirational referent. Our hypotheses received general support by testing the sample of 114 public biopharmaceutical companies between 2008 and 2020 in China.

Understanding visual feedback mechanism from three-dimensional of information, time and display: A meta-analysis of feedback research for household electricity conservation

Visual feedback, which refers to the provision of energy information through visual interfaces to households, has emerged as a crucial approach for promoting energy conservation and addressing climate change. However, existing experimental studies lack a unified standard framework design for visual feedback, and have significant divergences in its effectiveness. Hence, we first propose a design framework for visual feedback on household electricity consumption, including information, time and display. On this basis, this study provides comprehensivemeta-analytic evidence on the effectiveness of visual feedback from 156 articles between 1976 and 2024, and finds that visual feedback has a moderate energy-saving effect. Further considering the interaction of 3D influencing factors, we explore the optimal combination of visual feedback using multilevel meta-regression. The results show that information, time and display are all important factors for visual feedback. When designing informational interventions, it is important to choose effective feedback frequency and feedback media. The optimal strategy of 3D visual feedback combination is “self-comparison + monetary incentives + low frequency + digital media”. These findings can help policy makers and utilities to develop effective feedback measures to promote household energy conservation.

A Scalable Deep Learning Framework for Dynamic CSI Feedback With Variable Antenna Port Numbers

A Scalable Deep Learning Framework for Dynamic CSI Feedback With Variable Antenna Port Numbers

A Novel Attitude Control Strategy for a Quadrotor Drone with Actuator Dynamics Based on a High-Order Sliding Mode Disturbance Observer

In the attitude control of quadrotor drones, it is necessary to cope with matched and unmatched disturbances caused by nonlinear couplings, model uncertainties, and external disturbances, as well as to consider the effects caused by actuator dynamics. Aiming to accurately track desired trajectories under the above factors, a novel control strategy is proposed by combining a state feedback control with a high-order sliding mode disturbance observer (HOSMDO). The HOSMDO is motivated by the higher-order sliding mode (HOSM) differentiator and extended state observer (ESO) technique, allowing for the exact robust estimation of disturbances and their arbitrary order derivatives in finite time. Unlike the control schemes based on back-stepping methods, the proposed controller is designed with a holistic mindset. Specifically, a baseline feedback framework is constructed firstly, and the disturbances and relevant derivatives required for the baseline framework are then generated by the HOSMDOs to obtain the overall control scheme. The stability conditions of the controllers designed with and without considering the actuator dynamics are analyzed separately. In the latter case, the actuator dynamics imposed additional constraints on the control parameters. Numerical simulations validate the effectiveness of the proposed control strategy.

Learning analytics of peer feedback on communicative skills in an EFL course across different learning modalities

This research investigates the improvement of communicative skills in university students via peer feedback activities in an English-as-a-foreign-language (EFL) course. Utilizing a comprehensive view of contemporary feedback frameworks, the study engaged 320 students across three cohorts. The last two cohorts transitioned to online Emergency Remote Teaching (ERT) due to the COVID-19 pandemic. The final cohort further employed a prototype learning analytics system to enhance peer feedback skills. Answering calls for more research on authentic task-based learning in tech-mediated contexts, this study offers empirical evidence supporting modern feedback frameworks. Results showed that ERT cohorts, especially those using the prototype system, outperformed the face-to-face cohort in communicative speaking tests, highlighting the effectiveness of peer feedback activities in boosting communicative skills.

Vision-based real-time process monitoring and problem feedback for productivity-oriented analysis in off-site construction

The widespread adoption of surveillance cameras in work environments has enabled the direct and non-intrusive detection of productivity-related issues in the field of construction. In this research, a process monitoring and problem feedback framework is developed based on closed-circuit television footage and computer vision analysis to achieve real-time visual control of the work process and facilitate data-driven decision-making in off-site construction. To enhance the automation of productivity-related problem recognition, a novel video analysis algorithm is developed to process the inputted video footage and provide feedback with respect to nine productivity issues. The z-score and Exponential Moving Average methods are employed to eliminate detection errors, and the spatial density analysis method is adopted to visually analyze spatial information. The observed performance of the proposed framework demonstrates that it can accurately acquire data from footage and provide process monitoring and problem feedback in real time.

Reweighted Extreme Learning Machine-Based Clutter Suppression and Range Compensation Algorithm for Non-Side-Looking Airborne Radar

Non-side-looking airborne radar provides important applications on account of its all-round multi-angle airspace coverage. However, it suffers clutter range dependence that makes the samples fail to satisfy the condition of being independent and identically distributed (IID), and it severely degrades traditional approaches to clutter suppression and target detection. In this paper, a novel reweighted extreme learning machine (ELM)-based clutter suppression and range compensation algorithm is proposed for non-side-looking airborne radar. The proposed method involves first designing the pre-processing stage, the special reweighted complex-valued activation function containing an unknown range compensation matrix, and two new objective outputs for constructing an initial reweighted ELM-based network with its training. Then, two other objective outputs, a new loss function, and a reverse feedback framework driven by the specifically designed objectives are proposed for the unknown range compensation matrix. Finally, aiming to estimate and reconstruct the unknown compensation matrix, special processes of the complex-valued structures and the theoretical derivations are designed and analyzed in detail. Consequently, with the updated and compensated samples, further processing including space–time adaptive processing (STAP) can be performed for clutter suppression and target detection. Compared with the classic relevant methods, the proposed algorithm achieves significantly superior performance with reasonable computation time. It provides an obviously higher detection probability and better improvement factor (IF). The simulation results verify that the proposed algorithm is effective and has many advantages.

Inline automatic quality control of 2D phase-contrast flow MRI for subject-specific scan time adaptation.

To develop an inline automatic quality control to achieve consistent diagnostic image quality with subject-specific scan time, and to demonstrate this method for 2D phase-contrast flow MRI to reach a predetermined SNR. We designed a closed-loop feedback framework between image reconstruction and data acquisition to intermittently check SNR (every 20 s) and automatically stop the acquisition when a target SNR is achieved. A free-breathing 2D pseudo-golden-angle spiral phase-contrast sequence was modified to listen for image-quality messages from the reconstructions. Ten healthy volunteers and 1 patient were imaged at 0.55 T. Target SNR was selected based on retrospective analysis of cardiac output error, and performance of the automatic SNR-driven "stop" was assessed inline. SNR calculation and automated segmentation was feasible within 20 s with inline deployment. The SNR-driven acquisition time was 2 min 39 s ± 67 s (aorta) and 3 min ± 80 s (main pulmonary artery) with a min/max acquisition time of 1 min 43 s/4 min 52 s (aorta) and 1 min 43 s/5 min 50 s (main pulmonary artery) across 6 healthy volunteers, while ensuring a diagnostic measurement with relative absolute error in quantitative flow measurement lower than 2.1% (aorta) and 6.3% (main pulmonary artery). The inline quality control enables subject-specific optimized scan times while ensuring consistent diagnostic image quality. The distribution of automated stopping times across the population revealed the value of a subject-specific scan time.

Influence of Peer Feedback on Motor Skill Learning and Self-Efficacy among Adolescents in Kenya

Purpose: The aim of the study was to influence of peer feedback on motor skill learning and self-efficacy among adolescents in Kenya
 Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
 Findings: The study on the influence of peer feedback on motor skill learning and self-efficacy among adolescents in Kenya found that peer feedback had a significant positive impact on motor skill learning. Adolescents who received feedback from their peers demonstrated improved motor skill performance compared to those who did not. Additionally, the study revealed that peer feedback also had a positive influence on self-efficacy, with adolescents reporting increased confidence in their motor skill abilities after receiving feedback from their peers
 Unique Contribution to Theory, Practice and Policy: Social cognitive theory, self-determination theory & feedback intervention theory may be used to anchor future studies on influence of peer feedback on motor skill learning and self-efficacy among adolescents in Kenya. Peer mentorship programs can create a structured framework for peer feedback and skill development, facilitating a sense of camaraderie and cooperation among adolescents. Educational policymakers should consider incorporating guidelines for peer feedback into curriculum standards for physical education.

Real-Time Classification of Anxiety in Virtual Reality Therapy Using Biosensors and a Convolutional Neural Network.

Virtual Reality Exposure Therapy is a method of cognitive behavioural therapy that aids in the treatment of anxiety disorders by making therapy practical and cost-efficient. It also allows for the seamless tailoring of the therapy by using objective, continuous feedback. This feedback can be obtained using biosensors to collect physiological information such as heart rate, electrodermal activity and frontal brain activity. As part of developing our objective feedback framework, we developed a Virtual Reality adaptation of the well-established emotional Stroop Colour-Word Task. We used this adaptation to differentiate three distinct levels of anxiety: no anxiety, mild anxiety and severe anxiety. We tested our environment on twenty-nine participants between the ages of eighteen and sixty-five. After analysing and validating this environment, we used it to create a dataset for further machine-learning classification of the assigned anxiety levels. To apply this information in real-time, all of our information was processed within Virtual Reality. Our Convolutional Neural Network was able to differentiate the anxiety levels with a 75% accuracy using leave-one-out cross-validation. This shows that our system can accurately differentiate between different anxiety levels.

Low-Complexity Deep Learning Framework for CSI Feedback in Massive MIMO System

Low-Complexity Deep Learning Framework for CSI Feedback in Massive MIMO System

A Hybrid Method Combining Data-Driven and Model-Based Algorithms for External Force-Sensing and Haptics Control of Cable-Pulley-Driven Surgical Robotic Manipulator

Abstract The implementation of the high-precision tracking control and external force-sensing ability of a manipulator is important for achieving refined surgical robot operation. In this paper, a hybrid method based on data-driven and model-based algorithms is proposed for the manipulator of a cable-pulley-driven surgical robot. This method integrates an artificial neural network and a dynamic model rotation angle estimation, and a full closed-loop control architecture is further constructed. The algorithm compensates for the hysteresis of the joint angle and effectively improves the tracking control precision. Based on the architecture, the external force estimator (EFE) using a joint torque disturbance observer and the force interaction teleoperation control strategy using a direct force feedback framework (DFF) are implemented. In the force loading experiment, it was shown that the EFE performs well for static and dynamic force estimation, and the teleoperated haptic control experiment showed that the DFF-EFE-based system has a high position-tracking accuracy with real-time external force-sensing ability.

Optimizing Near-Field XL-MIMO Communications: Advanced Feedback Framework for CSI

Optimizing Near-Field XL-MIMO Communications: Advanced Feedback Framework for CSI

Prescribed-time adaptive stabilization of high-order stochastic nonlinear systems with unmodeled dynamics and time-varying powers

<p>In this paper, the control problem of prescribed-time adaptive neural stabilization for a class of non-strict feedback stochastic high-order nonlinear systems with dynamic uncertainty and unknown time-varying powers is discussed. The parameter separation technique, dynamic surface control technique, and dynamic signals were used to eradicate the influences of unknown time-varying powers together with state and input unmodeled dynamics, and to mitigate the computational intricacy of the backstepping. In a non-strict feedback framework, the radial basis function neural networks (RBFNNs) and Young's inequality were deployed to reconstruct the continuous unknown nonlinear functions. Finally, by establishing a new criterion of stochastic prescribed-time stability and introducing a proper bounded control gain function, an adaptive neural prescribed-time state-feedback controller was designed, ensuring that all signals of the closed-loop system were semi-global practical prescribed-time stable in probability. A numerical example and a practical example successfully validated the productivity and superiority of the control scheme.</p>

AI for CSI Feedback Enhancement in 5G-Advanced

The 3rd Generation Partnership Project started the study of Release 18 in 2021. Artificial intelligence (AI)-native air interface is one of the key features of Release 18, where AI for channel state information (CSI) feedback enhancement is selected as the representative use case. This article provides an overview of AI for CSI feedback enhancement in 5G-Advanced. Several representative non-AI and AI-enabled CSI feedback frameworks are first introduced and compared. Then, the standardization of AI for CSI feedback enhancement in 5G-advanced is presented in detail. First, the scope of the AI for CSI feedback enhancement in 5G-Advanced is presented and discussed. Then, the main challenges and open problems in the standardization of AI for CSI feedback enhancement, especially focusing on performance evaluation and the design of new protocols for AI-enabled CSI feedback, are identified and discussed. This article provides a guideline for the standardization study of AI-based CSI feedback enhancement.

A Manifold Learning-based CSI Feedback Framework for FDD Massive MIMO

A Manifold Learning-based CSI Feedback Framework for FDD Massive MIMO

Noise and distortion suppression for industrial confocal microscopy

Industrial confocal microscopy is an optical imaging measurement technology used in the fields of material research, defect detection and industrial inspection. Noise will affect the quality of the image and thus affect its performance. We propose a denoising algorithm based on CNN minimisation in this paper. First, we give the object function of the algorithm and the adaptive calculation formula of the capped value. Then, by using the difference convex (DC) optimisation algorithm framework, the sub-gradient optimisation condition, and the feedback framework, we develop a simple and efficient adaptive-stop iterative optimisation algorithm to solve this model and provide the closed solution of the model at each iteration. The experimental results show that for industrial confocal images, our algorithm can not only effectively suppress noise, but also effectively avoid the distortion of denoised images. The signal-to-noise ratio of industrial confocal denoised images is increased by 1.93 times. In addition, experiments show that the algorithm has good noise reduction ability in other optical imaging instruments, and can suppress not only Gaussian noise, but also Gaussian-Poisson noise.