This study tested the construct validity, factorial validity, and measurement invariance of the learning gains scale based on survey responses of a large sample (n = 536) of undergraduate students in two colleges at a university in Ethiopia. The anal...
4 November 2021
This study aimed to determine whether learning engagement plays a mediating effect on the relationship between family capital and students’ higher education gains in mainland China. We used family capital, learning engagement, and higher education ga...
To respond to global issues positively, education systems in higher education institutions play a significant role in empowering learners as well as promoting sustainable development goals. By implementing curricula that cultivate cross-cutting and t...
To realize the high-performance load torque tracking of an electric dynamic load simulator system with random measurement noises and strong position disturbances, a PD-type iterative learning control (ILC) algorithm with adaptive learning gains is pr...
The rapid development of tourism has put forward new requirements for the training of tourism talents. This study conducted a cross-regional questionnaire survey on tourism management undergraduate students from 28 tourism colleges in seven regions o...
Within the dynamic area of global higher education, international student education plays an important role as an educational resource for students from different countries around the world and ensures inclusive and equitable quality education to cre...
Serious Games (SG) provide a comfortable learning environment and are productive for various disciplines ranging from Science, Technology, Engineering, and Mathematics (STEM) to computer programming. The Object Oriented (OO) paradigm includes objects...
Online courses are an important form of educational delivery worldwide, yet students differ in how well they learn from them. Following psychological and educational research, students’ goals can be considered relevant personal predictors of th...
In this paper, a parameter optimal gain-arguable iterative learning control algorithm is proposed for a class of linear discrete-time systems with quantized error. Based on the lifting model description for ILC systems, the iteration time-variable de...
In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM v...
In many applications, intelligent agents need to identify any structure or apparent randomness in an environment and respond appropriately. We use the relative entropy to separate and quantify the presence of both linear and nonlinear redundancy in a...
This paper considers management education and specifically how student learning has been impacted by the online replacement teaching offered by universities during the COVID-19 pandemic. The study utilizes a learning gain model which considers the st...
Intelligent driving is a key technology in the field of automotive manufacturing due to its advantages in environmental protection, energy efficiency, and economy. However, since the intelligent driving model is an uncertain multi-input multi-output...
This paper presents innovative reinforcement learning methods for automatically tuning the parameters of a proportional integral derivative controller. Conventionally, the high dimension of the Q-table is a primary drawback when implementing a reinfo...
In the traditional iterative learning control (ILC) method, the operational time interval is conventionally fixed to facilitate a seamless learning process along the iteration axis. However, this condition may frequently be contravened in real-time a...
Background/Objectives: While machine learning has made substantial strides in pronunciation detection in recent years, there remains a notable gap in the literature regarding research on improvements in the acquisition of speech sounds following a tr...
Heuristic optimization algorithms have been proved to be powerful in solving nonlinear and complex optimization problems; therefore, many effective optimization algorithms have been applied to solve optimization problems in real-world scenarios. This...
This paper presents a reinforcement learning (RL)-based methodology for the online fine-tuning of PD controller gains, with the goal of bridging the gap between simulation-trained controllers and real-world quadrotor applications. As a first step tow...
It may be helpful to integrate multiple aircraft communication and navigation functions into a single software-defined radio (SDR) platform. To transmit these multiple signals, the SDR would first sum the baseband version of the signals. This outgoin...
Despite increasing focus on the importance of self–regulated learning for undergraduate students in universities in recent years, very little is known about its specific features in universities in developing countries, in general, and Ethiopia...
As generative artificial intelligence (GenAI) chatbots gain traction in educational settings, a growing number of studies explore their potential for personalized, scalable learning. However, methodological fragmentation has limited the comparability...
The timely identification of suicidal ideation on social media is pivotal for global suicide prevention efforts. Addressing the challenges posed by the unstructured nature of social media data, we present a novel Chinese-based dual-channel model, DSI...
Research has shown the effectiveness of designing a Learning Analytics Dashboard (LAD) for learners and instructors, including everyone’s levels of progress and performance. An intertwined relationship exists between learning analytics (LA) and...
In this study, we evaluate how ChatGPT complements and enriches the traditional flipped learning strategy in higher education, particularly in engineering courses. Using an experimental design involving 356 students from basic programming courses in...
The post-pandemic stage has accelerated the search for innovative ways that impact the teaching–learning process. Flipped learning and gamification have been used as active learning strategies to increase motivation and student learning gains....
To suppress the speed ripple of a permanent magnet synchronous motor in a seeker servo system, we propose an accelerated iterative learning control with an adjustable learning interval. First, according to the error of current iterative learning for...
Mathematics is a fundamental subject for learning at all levels of education. Thus, developing effective methods for increasing children’s learning performance in mathematics is important. In this study, first graders who were identified to hav...
Modern audio production workflows often require significant manual effort during the initial session preparation phase, including track labeling, format standardization, and gain staging. This paper presents a rule-based and Machine Learning-assisted...
Obesity and its attendant conditions have become major health problems worldwide, and obesity is currently ranked as the fifth most common cause of death globally. Complex environmental and genetic factors are causes of the current obesity epidemic....
An adaptive fuzzy iterative learning control (ILC) algorithm is designed for the iterative variable reference trajectory problem of nonlinear discrete-time systems with input saturations and unknown control directions. Firstly, an adaptive fuzzy iter...
Integral Sliding Mode Control (ISMC) is widely employed in motor position control systems due to its robustness against uncertainties. However, its control performance is critically dependent on the selection of the switching gain. Although Disturban...
17 September 2024
This study investigated whether a social–emotional learning program, implemented over a one-year period, could lead to gains in social–emotional competencies and to a reduction in internalizing and externalizing problems in the context of...
In this paper, the potential of visuo-haptic simulators to help engineering students to understand the nature of electric forces between different electric charge distributions is addressed. Three visuo-haptic simulators were designed to perceive the...
The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. However, WOA still has some limitations, including limited global search eff...
To reduce the influence of gain-phase errors and improve the performance of direction-of-arrival (DOA) estimation, a robust sparse Bayesian two-dimensional (2D) DOA estimation method with gain-phase errors is proposed for L-shaped sensor arrays. The...
Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Usin...
This systematic review examines the potential of digital language learning in contributing to students’ cognitive gains. The study reviews existing research on the relationship between digital language learning and cognitive benefits, with a fo...
Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controll...
This paper proposes a novel network anomaly detection framework based on data balance and feature selection. Different from the previous binary classification of network intrusion, the network anomaly detection strategy proposed in this paper solves...
The paper considers the detection of multiple targets by a group of mobile robots that perform under uncertainty. The agents are equipped with sensors with positive and non-negligible probabilities of detecting the targets at different distances. The...
This paper proposes a finite-time adaptive observer with online disturbance learning for time-varying disturbed systems. By integrating parameter-dependent Lyapunov functions and slack matrix techniques, the method eliminates conservative static dist...
Recent advancements in software-defined networking (SDN) make it possible to overcome the management challenges of traditional networks by logically centralizing the control plane and decoupling it from the forwarding plane. Through a symmetric and c...
This article formalizes AI-assisted assessment as a discrete-time policy-level design for iterative feedback and evaluates it in a digitally transformed higher-education setting. We integrate an agentic retrieval-augmented generation (RAG) feedback e...
With the increasing complexity of modern industrial processes, fault occurrences may lead to catastrophic consequences, making incipient fault detection crucial for industrial safety. This critical task confronts a key challenge: insufficient cross-d...
Background/Objectives: COVID-19 and its variants continue to pose significant threats to public health, with considerable uncertainty surrounding their impact. As of September 2024, the total number of deaths reached 8.8 million worldwide. Vaccinatio...
Cardiovascular diseases (CVDs) rank among the leading global causes of mortality, underscoring the necessity for early detection and effective management. This research presents a novel prediction model for CVDs utilizing a bagging algorithm that inc...
Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as an effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets...
Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely...
As an irreplaceable structural and functional material in strategic equipment, uranium and uranium alloys are generally susceptible to corrosion reactions during service, and predicting corrosion behavior has important research significance. There ha...
Addressing the issues of prolonged training times and low recognition rates in large model applications, this paper proposes a weight training method based on entropy gain for weight initialization and dynamic adjustment of the learning rate using th...
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