scholarly journals Educators' perceptions of automated feedback systems

2008 ◽  
Vol 24 (4) ◽  
Author(s):  
Justin C. W. Debuse ◽  
Meredith Lawley ◽  
Rania Shibl
Keyword(s):  
Academic Staff ◽  
Research Question ◽  
Feedback System ◽  
Feedback Systems ◽  
Constructive Feedback ◽  
Use Of Technology ◽  
Quality Feedback ◽  
Core Function ◽  
Assessment Of Student Learning ◽  
Automated Feedback

<span>Assessment of student learning is a core function of educators. Ideally students should be provided with timely, constructive feedback to facilitate learning. However, provision of high quality feedback becomes more complex as class sizes increase, modes of study expand and academic workloads increase. ICT solutions are being developed to facilitate quality feedback, whilst not impacting adversely upon staff workloads. Hence the research question of this study is 'How do academic staff perceive the usefulness of an automated feedback system in terms of impact on workloads and quality of feedback?' This study used an automated feedback generator (AFG) across multiple tutors and assessment items within an MBA course delivered in a variety of modes. All academics marking in the course completed a survey based on an adaptation of the </span><em>unified theory of acceptance and use of technology</em><span> (UTAUT) model. Results indicated that while the workload impact was generally positive with savings in both cost and time, improvements and modifications to the system could further reduce workloads. Furthermore, results indicated that AFG improves quality in terms of timeliness, greater consistency between markers and an increase in the amount of feedback provided.</span>

System interaction influences on cognitive-affective states to enhance performance, workload, and knowledge acquisition in teams conducting close air support simulations.

2016 ◽  
Vol 60 (1) ◽  
pp. 1260-1263
Author(s):  
Baron C. Summers ◽  
Herbert Hauser
Keyword(s):  
Knowledge Acquisition ◽  
Affective State ◽  
Feedback System ◽  
Affective States ◽  
Feedback Systems ◽  
Training Environment ◽  
Team Members ◽  
Human Computer Interfaces ◽  
Automated Feedback ◽  
Aerial Systems

The purpose of this research is to shed light on the effects of an automated feedback system to optimize cognitive-affective states and increase effectiveness of using remotely piloted aerial system team members training to conduct Close Air Support missions in a simulation training environment. Feedback manipulations in this study utilize attributes of engagement as an optimal cognitive-affective state in order to assess state and effectiveness differences. Understanding these effects could enable predictions of aspects that might be adapted to optimize future approaches in training teams in complex situations. If states of learners can be impacted via feedback experiences to an engagement like state and thereby benefit from increased learning and effectiveness, then training approaches utilizing feedback may advance in capability. Thus, designs of automated feedback systems in human-computer interfaces may help advance training of complex military tasks such as close air support with remotely piloted aerial systems through decreasing workload, increasing knowledge acquisition, and enabling better performance.

scholarly journals The Impact of Using Automated Writing Feedback in ESL/EFL Classroom Contexts

2021 ◽  
Vol 14 (12) ◽  
pp. 189
Author(s):  
Ameni Benali
Keyword(s):  
Language Learning ◽  
Feedback Systems ◽  
Technology In The Classroom ◽  
Writing Evaluation ◽  
Constructive Feedback ◽  
Automated Writing Evaluation ◽  
Writing Feedback ◽  
Automated Feedback ◽  
Essay Quality ◽  
The Impact

It is undeniable that attempts to develop automated feedback systems that support and enhance language learning and assessment have increased in the last few years. The growing demand for using technology in the classroom and the promotions provided by automated- written-feedback program developers and designers, drive many educational institutions to acquire and use these tools for educational purposes (Chen &amp; Cheng, 2008). It remains debatable, however, whether students&rsquo; use of these tools leads to improvement in their essay quality or writing outcomes. In this paper I investigate the affordances and shortcomings of automated writing evaluation (AWE) on students&rsquo; writing in ESL/EFL contexts. My discussion shows that AWE can improve the quality of writing and learning outcomes if it is integrated with and supported by human feedback. I provide recommendations for further research into improving AWE tools to give more effective and constructive feedback.

scholarly journals An evaluation of semi-automated, collaborative marking and feedback systems: Academic staff perspectives

2011 ◽  
Vol 27 (7) ◽  
Author(s):  
Steven Burrows ◽  
Mark Shortis
Keyword(s):  
Large Class ◽  
State Of The Art ◽  
Academic Staff ◽  
Feedback System ◽  
The State ◽  
Functional Requirements ◽  
Feedback Systems ◽  
Comprehensive Collection ◽  
Home Institution ◽  
Key Features

<span>Online marking and feedback systems are critical for providing timely and accurate feedback to students and maintaining the integrity of results in large class teaching. Previous investigations have involved much in-house development and more consideration is needed for deploying or customising off the shelf solutions. Furthermore, keeping up to date with the state of the art from both academia and industry is essential. This paper is motivated by a project aiming to identify a marking and feedback system for deployment at the authors' university. A detailed investigation is described which is open minded towards adopting or modifying an existing product, or the implementation of a new solution, with key features and shortcomings described in detail. </span><em>Moodle Workshops, Turnitin GradeMark, Waypoint</em><span> and </span><em>WebMark</em><span> were shortlisted and carried forward for user analysis testing. The outcomes have not only provided key conclusions concerning the suitability of existing solutions, but resulted in a comprehensive collection of functional requirements that leaders of new projects should consider. This paper should be of interest for anyone considering the adoption or upgrade of any marking and feedback system at their home institution.</span>

Optimal Model Reference Control Scheme Design for Nonlinear Strict-Feedback Systems

2020 ◽  
Vol 38 (9A) ◽  
pp. 1342-1351
Author(s):  
Musadaq A. Hadi ◽  
Hazem I. Ali
Keyword(s):  
Reference Model ◽  
Feedback System ◽  
Optimization Method ◽  
Feedback Systems ◽  
Model Reference ◽  
Scheme Design ◽  
Model Reference Control ◽  
Control Scheme ◽  
Lyapunov Stability Analysis ◽  
Strict Feedback

In this paper, a new design of the model reference control scheme is proposed in a class of nonlinear strict-feedback system. First, the system is analyzed using Lyapunov stability analysis. Next, a model reference is used to improve system performance. Then, the Integral Square Error (ISE) is considered as a cost function to drive the error between the reference model and the system to zero. After that, a powerful metaheuristic optimization method is used to optimize the parameters of the proposed controller. Finally, the results show that the proposed controller can effectively compensate for the strictly-feedback nonlinear system with more desirable performance.

scholarly journals 1113. A Novel Means of Acquiring High Quality Feedback for Training Programs –The Program Director Ombudsperson

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S587-S587
Author(s):  
David J Riedel ◽  
Rana Malek
Keyword(s):  
Training Programs ◽  
Evaluation Process ◽  
Program Director ◽  
Additional Time ◽  
High Quality ◽  
Constructive Feedback ◽  
Quality Feedback ◽  
Program Leadership ◽  
Constructive Criticism ◽  
Academic Year

Abstract Background Trainees may not always be comfortable providing frank feedback or constructive criticism to their Program Director (PD) due to fears of negative repercussions. PDs likewise may lack the means to obtain high quality feedback from trainees, particularly among smaller training programs, e.g. fellowships. An ombudsperson is defined as an independent appointee whose task is to investigate and attempt to resolve complaints and problems. Methods The PDs from Infectious Disease (ID) and Endocrinology fellowships partnered to meet with fellows from the opposite program. Meetings were held with fellows in December and June of the academic year. Each PD started their meeting with 2 questions on a 1-10 scale: How do you rate the fellowship program? How do you rate the job the PD and program leadership are doing? Additional questions covered the fellow evaluation process, faculty feedback, education vs. service balance, and gaps in training. Additional time was allotted for fellow-raised concerns. Meeting notes were summarized anonymously and returned to the PD of the other program. Fellows were asked to complete a brief questionnaire about their experience. Results A total of 15 fellows completed the survey (6 Endocrinology, 9 ID; Table). All 15 fellows agreed that the goal of the ombudsperson review meeting (i.e. to address fellow concerns in a more confidential setting) was achieved and were comfortable sharing concerns and feedback to the ombudsperson. The majority of fellows (53%) was more comfortable sharing concerns regarding the fellowship to the ombudsperson than directly to the PD. Thirteen (87%) agreed that concerns raised during the first ombudsperson meeting were addressed by the Program in subsequent months. All fellows (100%) found it helpful that the ombudsperson was another PD and recommended that the ombudsperson review meetings should continue in the fellowship. Table. Characteristics of participants and outcomes of questions about fellowship ombudsperson program Conclusion The fellowship PD ombudsperson meeting is a novel means for soliciting constructive feedback from trainees at small training programs. Fellows were comfortable with the ombudsperson arrangement, felt the meetings achieved their goal, and recommended that the meetings continue in the future. Disclosures All Authors: No reported disclosures

scholarly journals Automated Feedback Is Nice and Human Presence Makes It Better: Teachers’ Perceptions of Feedback by Means of an E-Portfolio Enhanced with Learning Analytics

2021 ◽  
Vol 11 (6) ◽  
pp. 278
Author(s):  
Pihel Hunt ◽  
Äli Leijen ◽  
Marieke van der Schaaf
Keyword(s):  
Learning Analytics ◽  
Electronic Portfolio ◽  
Human Interaction ◽  
Web Based ◽  
Teachers Perceptions ◽  
Use Of Technology ◽  
Group Interviews ◽  
Automated Feedback ◽  
Development Course

While there is now extensive research on feedback in the context of higher education, including pre-service teacher education, little has been reported regarding the use of feedback from teachers to other teachers. Moreover, literature on the potential advantages that the use of technology, for example electronic portfolios and learning analytics, has in improving feedback in the in-service workplace practices, is also sparse. Therefore, the aim of this exploratory case study was to explore how in-service teachers perceived the peer feedback they received and provided through a web-based electronic portfolio during a professional development course carried out in their workplace. Questionnaire and interview data were collected from 38 teachers who received feedback through a learning analytics enhanced electronic portfolio and from 23 teachers who received feedback only by the electronic portfolio. Additionally, one individual and four focus group interviews were conducted with 15 teachers who were the feedback providers. Several common topics were identified in the interviews with the feedback receivers and providers, involving the benefits and challenges of human interaction and the flexibility of the feedback process that the electronic portfolio offered. The results also revealed better feedback experience within the group of teachers who received extra feedback by means of learning analytics. It is concluded that although an electronic portfolio provides a useful tool in terms of flexibility in the provision and receipt of feedback, the need for human interaction was acknowledged.

Criteria for Recovery of Zero Initial Condition Responses of Unstable Plants Under Input Saturation

2000 ◽  
Author(s):  
Wei Wu ◽  
Suhada Jayasuriya
Keyword(s):  
Initial Conditions ◽  
Input Saturation ◽  
Input Sequence ◽  
Feedback System ◽  
Open Loop ◽  
Feedback Systems ◽  
Bounded Control ◽  
Analysis And Design ◽  
Study Results ◽  
Unstable Plant

Abstract In this paper, we consider the sufficient and/or necessary conditions under which responses of unstable plants with zero initial conditions would be bounded under step inputs. Several possible unstable pole patterns are examined, and corresponding criteria are derived. It is shown that an unstable plant can be stabilized to have bounded responses using an alternate step input sequence. Step inputs simulate the saturated inputs in a feedback system with bounded control, where the closed-loop stability of an unstable plant is really difficult to study. Results from this open-loop study may lend some insight into the analysis and design of such feedback systems under input saturation nonlinearities.

scholarly journals Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues

2021 ◽  
Vol 3 ◽  
Author(s):  
Anirudh Som ◽  
Sujeong Kim ◽  
Bladimir Lopez-Prado ◽  
Svati Dhamija ◽  
Nonye Alozie ◽  
...  
Keyword(s):  
Recommendation System ◽  
Feedback System ◽  
Individual Student ◽  
Teaching Staff ◽  
Student Group ◽  
Student Groups ◽  
Group Collaboration ◽  
Individual Level ◽  
Constructive Feedback ◽  
Spatio Temporal

Early development of specific skills can help students succeed in fields like Science, Technology, Engineering and Mathematics. Different education standards consider “Collaboration” as a required and necessary skill that can help students excel in these fields. Instruction-based methods is the most common approach, adopted by teachers to instill collaborative skills. However, it is difficult for a single teacher to observe multiple student groups and provide constructive feedback to each student. With growing student population and limited teaching staff, this problem seems unlikely to go away. Development of machine-learning-based automated systems for student group collaboration assessment and feedback can help address this problem. Building upon our previous work, in this paper, we propose simple CNN deep-learning models that take in spatio-temporal representations of individual student roles and behavior annotations as input for group collaboration assessment. The trained classification models are further used to develop an automated recommendation system to provide individual-level or group-level feedback. The recommendation system suggests different roles each student in the group could have assumed that would facilitate better overall group collaboration. To the best of our knowledge, we are the first to develop such a feedback system. We also list the different challenges faced when working with the annotation data and describe the approaches we used to address those challenges.

Modeling head tracking of visual targets

2002 ◽  
Vol 12 (1) ◽  
pp. 25-33
Author(s):  
K.J. Chen ◽  
E.A. Keshner ◽  
B.W. Peterson ◽  
T.C. Hain
Keyword(s):  
Control System ◽  
Visual Feedback ◽  
Human Head ◽  
Feedback System ◽  
Optimization Methods ◽  
Head Tracking ◽  
Feedback Systems ◽  
Control Gain ◽  
Head Control ◽  
Visual Targets

Control of the head involves somatosensory, vestibular, and visual feedback. The dynamics of these three feedback systems must be identified in order to gain a greater understanding of the head control system. We have completed one step in the development of a head control model by identifying the dynamics of the visual feedback system. A mathematical model of human head tracking of visual targets in the horizontal plane was fit to experimental data from seven subjects performing a visual head tracking task. The model incorporates components based on the underlying physiology of the head control system. Using optimization methods, we were able to identify neural processing delay, visual control gain, and neck viscosity parameters in each experimental subject.

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