Artificial intelligence in constructing personalized and accurate feedback systems for students

2021 ◽  
Author(s):  
Wenzhong Xu ◽  
Jun Meng ◽  
S. Kanaga Suba Raja ◽  
M. Padma Priya ◽  
M. Kiruthiga Devi
Keyword(s):  
Artificial Intelligence ◽  
Teaching Methods ◽  
Feedback System ◽  
Academic Experience ◽  
Digital Learning ◽  
Feedback Process ◽  
Personalized Feedback ◽  
Feedback Systems ◽  
Collaborative Process ◽  
Performance Rate

Artificial Intelligence (AI) systems have evolved with digital learning developments to provide thriving soft groups with digital opportunities in response to feedback. When it comes to learning environments, educators’ training feedback is often used as a response recourse. Through the use of final evaluations, students receive feedback that improves their education abilities. To improve academic achievement and explore knowledge in the learning process, this section provides an AI-assisted personalized feedback system (AI-PFS). An individualized feedback system is implemented to learn more about the student’s lack of academic experience interactivity and different collaboration behaviors. According to their benchmark, PFS aims to establish a personalized and reliable feedback process for each class based on their collaborative process and learn analytics modules. It has been proposed to use multi-objective implementations to evaluate students regarding the learning results and teaching methods. With different series of questions sessions for students, AI-PFS has been designed, and the findings showed that it greatly enhances the performance rate of 95.32% with personalized and reasonable predictive.

scholarly journals SOCIOLOGY OF KNOWLEDGE SECURITY IN THE DIGITAL EDUCATIONAL SPACE

2020 ◽  
pp. 154-159
Author(s):  
M. Yu. Zakharov ◽  
I. E. Starovoytova ◽  
A. V. Shishkova
Keyword(s):  
Artificial Intelligence ◽  
Teaching Methods ◽  
Sociology Of Knowledge ◽  
Digital Technologies ◽  
Big Data Analysis ◽  
Learning Technologies ◽  
Educational Process ◽  
Digital Learning ◽  
Digital Space ◽  
The Social

The article deals with the spread of digital technologies in education and digital learning as a special stage of digital socialization, changing the social roles of the teacher and student. Artificial intelligence and machine learning technologies, big data analysis, robotics and virtualization have taken their place in the educational process, and traditional offline teaching methods require revision. The problem of knowledge alienation in the digital educational space has been analysed and ways to solve this problem have been proposed. Particular attention has been paid to the problem of the safety of knowledge in the educational space. It has been concluded about the need for such a reorganization of the digital space in which cognitive conditions for the safe functioning of knowledge will be provided.

scholarly journals Giving away some of their powers! Towards learner agency in digital assessment and feedback

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Diogo Casanova ◽  
Graham Alsop ◽  
Isabel Huet
Keyword(s):  
Feedback System ◽  
Formative Feedback ◽  
Assessment Literacy ◽  
Feedback Process ◽  
Feedback Systems ◽  
Assessment Practice ◽  
Research And Practice ◽  
The Past ◽  
Digital Assessment ◽  
Learner Agency

AbstractDigital assessment and feedback have been a growing area of research and practice in the past decade in higher education. Within this theme, research has been published highlighting the importance of learner agency in the assessment and feedback process as a way to develop assessment literacy in contrast with the existing lecturer-led approach. In this research, we aimed to find out whether lecturers are willing to let go of some of the power they currently have in the digital assessment and feedback process and how they see opportunities for agency being developed in the digital assessment and feedback systems. We collected data from 10 sandpits with 58 lecturers in which, using a storytelling technique and one mock-up of a digital assessment and feedback system, we discussed and critiqued an assessment scenario intending to collect perceptions about digital assessment and feedback and the constraints felt by lecturers in their assessment practice. Based on these perceptions, we identify recommendations that may improve digital assessment and feedback systems and practices. We discuss the data and the recommendations based on three clusters of themes: (i) preparation for the assessment, (ii) formative feedback and (iii) feedback post-submission.

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 Indirect Feedback Measurement of Flow in a Water Pumping Network Employing Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 75
Author(s):  
Thommas Kevin Sales Flores ◽  
Juan Moises Mauricio Villanueva ◽  
Heber P. Gomes ◽  
Sebastian Y. C. Catunda
Keyword(s):  
Artificial Intelligence ◽  
Nonlinear Models ◽  
Control Strategies ◽  
Experimental Tests ◽  
Feedback System ◽  
Indirect Measurement ◽  
High Price ◽  
Maximum Relative Error ◽  
Pumping System ◽  
Water Pumping

Indirect measurement can be used as an alternative to obtain a desired quantity, whose physical positioning or use of a direct sensor in the plant is expensive or not possible. This procedure can been improved by means of feedback control strategies of a secondary variable, which can be measured and controlled. Its main advantage is a new form of dynamic response, with improvements in the response time of the measurement of the quantity of interest. In water pumping networks, this methodology can be employed for measuring the flow indirectly, which can be advantageous due to the high price of flow sensors and the operational complexity to install them in pipelines. In this work, we present the use of artificial intelligence techniques in the implementation of the feedback system for indirect flow measurement. Among the contributions of this new technique is the design of the pressure controller using the Fuzzy logic theory, which rules out the need for knowing the plant model, as well as the use of an artificial neural network for the construction of nonlinear models with the purpose of indirectly estimating the flow. The validation of the proposed approach was carried out through experimental tests in a water pumping system, fully automated and installed at the Laboratory of Hydraulic and Energy Efficiency in Sanitation at the Federal University of Paraiba (LENHS/UFPB). The results were compared with an electromagnetic flow sensor present in the system, obtaining a maximum relative error of 10%.

Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG

2021 ◽  
pp. 2150031
Author(s):  
Qin Tao ◽  
Yajing Si ◽  
Fali Li ◽  
Peiyang Li ◽  
Yuqin Li ◽  
...  
Keyword(s):  
Frontal Lobe ◽  
Visual Information ◽  
Hidden Markov ◽  
Decision Feedback ◽  
Markov Modeling ◽  
Feedback Process ◽  
Feedback Systems ◽  
Left Frontal Lobe ◽  
Hidden Markov Modeling ◽  
Independent Decision

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

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.

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.

Digital Literacy and Motivation

2020 ◽  
pp. 268-288
Author(s):  
Jane Vinther ◽  
Jørgen T. Lauridsen
Keyword(s):  
Intrinsic Motivation ◽  
Language Learning ◽  
Teaching Methods ◽  
Digital Literacy ◽  
Student Attitudes ◽  
Willingness To Communicate ◽  
Digital Learning ◽  
Affective Factors ◽  
Learning Anxiety ◽  
Digital Classroom

The contribution of this chapter to the knowledge of motivation in relation to the learning of a foreign language lies in the extensive examination of student attitudes to a detailed field of involved factors. This chapter gives an account of affective factors in the digital classroom on the basis of the level of the digital literacy of the participating students. The digital learning of an L2 is correlated to the level of language learning anxiety, willingness to communicate in the L2, attitude to feedback, and preference for teaching methods. The results show that the level of digital literacy is so high that it should not in itself negatively impact attitude to other factors. The central finding of the investigation is that intrinsic motivation is the strongest motivational drive when compared to other motivational factors.

scholarly journals Adaptive Dynamic Surface Control for a Class of Nonlinear Pure-Feedback Systems with Parameter Drift

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Cheng He ◽  
Jian Wu ◽  
Jin Ying ◽  
Jiyang Dai ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Tracking Error ◽  
Feedback System ◽  
Dynamic Surface Control ◽  
Projection Algorithm ◽  
Feedback Systems ◽  
Simulation Experiments ◽  
Dynamic Surface ◽  
Parameter Drift ◽  
System A ◽  
Surface Control

In order to solve the problem of unknown parameter drift in the nonlinear pure-feedback system, a novel nonlinear pure-feedback system is proposed in which an unconventional coordinate transformation is introduced and a novel unconventional dynamic surface algorithm is designed to eliminate the problem of “calculation expansion” caused by the use of backstepping in the pure-feedback system. Meanwhile, a sufficiently smooth projection algorithm is introduced to suppress the parameter drift in the nonlinear pure-feedback system. Simulation experiments demonstrate that the designed controller ensures the global and ultimate boundedness of all signals in the closed-loop system and the appropriate designed parameters can make the tracking error arbitrarily small.

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