Online Learning Approach Research Articles

The BIORES-21 Survey: Insights Into Remote and Online Education in Biomechanics and Mechanobiology.

The COVID-19 pandemic necessitated mainstream adoption of online and remote learning approaches, which were highly advantageous yet challenging in many ways. The online modality, while teaching biomedical engineering-related topics in the areas of biomechanics, mechanobiology, and biomedical sciences, further added to the complexity faced by the faculty and students. Both the benefits and the challenges have not been explored systematically by juxtaposing experiences and reflections of both the faculty and students. Motivated by this need, we designed and conducted a systematic survey named BIORES-21, targeted toward the broader bio-engineering community. Survey responses and our inferences from survey findings cumulatively offer insight into the role of employed teaching/learning technology and challenges associated with student engagement. Survey data also provided insights on what worked and what did not, potential avenues to address some underlying challenges, and key beneficial aspects such as integration of technology and their role in improving remote teaching/learning experiences. Overall, the data presented summarize the key benefits and challenges of online learning that emerged from the experiences during the pandemic, which is valuable for the continuation of online learning techniques as in-person education operations resumed broadly across institutions, and some form of online learning seems likely to sustain and grow in the near future.

Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking.

Tracking visual objects from a single initial exemplar in the testing phase has been broadly cast as a one-/few-shot problem, i.e., one-shot learning for initial adaptation and few-shot learning for online adaptation. The recent few-shot online adaptation methods incorporate the prior knowledge from large amounts of annotated training data via complex meta-learning optimization in the offline phase. This helps the online deep trackers to achieve fast adaptation and reduce overfitting risk in tracking. In this paper, we propose a simple yet effective recursive least-squares estimator-aided online learning approach for few-shot online adaptation without requiring offline training. It allows an in-built memory retention mechanism for the model to remember the knowledge about the object seen before, and thus the seen data can be safely removed from training. This also bears certain similarities to the emerging continual learning field in preventing catastrophic forgetting. This mechanism enables us to unveil the power of modern online deep trackers without incurring too much extra computational cost. We evaluate our approach based on two networks in the online learning families for tracking, i.e., multi-layer perceptrons in RT-MDNet and convolutional neural networks in DiMP. The consistent improvements on several challenging tracking benchmarks demonstrate its effectiveness and efficiency.

Finding the optimal exploration-exploitation trade-off online through Bayesian risk estimation and minimization

We propose endogenous Bayesian risk minimization (EBRM) over policy sets as an approach to online learning across a wide range of settings. Many real-world online learning problems have complexities such as action- and belief-dependent rewards, time-discounting of reward, and heterogeneous costs for actions and feedback; we find that existing online learning heuristics cannot leverage most problem-specific information, to the detriment of their performance. We introduce a belief-space Markov decision process (BMDP) model that can capture these complexities, and further apply the concepts of aleatoric, epistemic, and process risks to online learning. These risk functions describe the risk inherent to the learning problem, the risk due to the agent's lack of knowledge, and the relative quality of its policy, respectively. We demonstrate how computing and minimizing these risk functions guides the online learning agent towards the optimal exploration-exploitation trade-off in any stochastic online learning problem, constituting the basis of the EBRM approach. We also show how Bayes' risk, the minimization objective in stochastic online learning problems, can be decomposed into the aforementioned aleatoric, epistemic, and process risks.In simulation experiments, EBRM algorithms achieve state-of-the-art performance across various classical online learning problems, including Gaussian and Bernoulli multi-armed bandits, best-arm identification, mixed objectives with action- and belief-dependent rewards, and dynamic pricing, a finite partial monitoring problem. To our knowledge, it is also the first computationally efficient online learning approach that can provide online bounds on an algorithm's Bayes' risk. Finally, because the EBRM approach is parameterized by a set of policy algorithms, it can be extended to incorporate new developments in online learning algorithms, and is thus well-suited as the foundation for developing real-world learning agents.

Exploring the potential impact of applying web-based training program on nurses' knowledge, skills, and attitudes regarding evidence-based practice: A quasi-experimental study.

Evidence-based practice (EBP) has gained significant importance in clinical practice worldwide, including in nursing. This study aimed to explore the potential impact of applying a web-based training program on nurses' knowledge, skills, and attitudes regarding EBP. A quasi-experimental pretest-posttest research design was utilized with a purposive sample of 64 professional nurses who agreed to participate. The study took place in different hospitals and primary healthcare centers in the Bisha Governorate, Aseer region, Saudi Arabia. A four-week standardized web-based training program was implemented using an online learning approach. Nurses were provided with an online self-rated data collection tool through the Google Forms platform. The findings indicated a highly significant difference in the total knowledge and EBP skills mean scores of the post-intervention (53.08±15.9) and (66.03±8.95), respectively compared to pre-intervention (P<0.05). Additionally, there was marked improvement in the mean scores of the positive attitude of the training sessions post-intervention compared to pre-intervention. The program was also well-received by the nurses in terms of quality and usability. The program has the potential to enhance nurses' knowledge, skills, and attitudes toward EBP. Therefore, healthcare organizations may consider adopting web-based training as a means of continuing professional education to promote EBP competencies among nurses.

Reinforcement Learning Building Control: An Online Approach With Guided Exploration Using Surrogate Models

Abstract The incorporation of emerging technologies, including solar photovoltaics, electric vehicles, battery energy storage, smart devices, Internet-of-Things devices, and sensors in buildings, desirable control objectives are becoming increasingly complex, calling for advanced controls approaches. Reinforcement learning (RL) is a powerful method for this. RL can adapt and learn from environmental interaction, but it can take a long time to learn and can be unstable initially due to limited environmental knowledge. In our research, we propose an online RL approach for buildings that uses data-driven surrogate models to guide the RL agent during its early training. This helps the controller learn faster and more stably than the traditional direct plug-and-learn online learning approach. In this research, we propose an online approach in buildings with RL where, with the help of data-driven surrogate models, the RL agent is guided during its early exploratory training stage, aiding the controller to learn a near-optimal policy faster and exhibiting more stable training progress than a traditional direct plug-and-learn online learning RL approach. The agents are assisted in their learning and action with information gained from the surrogate models generating multiple artificial trajectories starting from the current state. The research presented an exploration of various surrogate model-assisted training methods and revealed that models focusing on artificial trajectories around rule-based controls yielded the most stable performance. In contrast, models employing random exploration with a one-step look-ahead approach demonstrated superior overall performance.

Online entrepreneurship teaching and learning approaches: A South African conceptual perspective

In this conceptual paper, we review literature on entrepreneurship education with the aim of determining viable approaches for teaching entrepreneurship using online platforms. We discuss existing studies on constructivism as a theoretical framework and examine how entrepreneurship education can be offered through online teaching, as well as the effectiveness and the design of online teaching for entrepreneurship education. We used a conceptual analysis methodology and also conducted a library search to examine previous pertinent literature on online entrepreneurship education. Our findings indicate that online entrepreneurship education should be grounded in constructivism as a learning theory. Additionally, during online entrepreneurship learning, lecturers should introduce and explain new concepts by using students' prior knowledge and should assign practical and experiential tasks. Our findings also show that online activities should allow students to interpret and assimilate new knowledge, and that online entrepreneurship modules should incorporate learning by doing and experiential learning. Lecturers should also provide feedback as part of online entrepreneurship learning and, at the end of such learning, lecturers should assign online questions that provide an opportunity for students to reflect on their learning and knowledge.

The Digital Generation: An Analysis of the Seminal Impact of Internet Usage on Children

Abstract — The increased use of smart devices is causing a movement away from traditional learning methods and towards online learning approaches. Concerns have been expressed concerning the harmful impact of increased internet usage among children on a variety of factors, including health, social activities, outdoor participation, academic performance, and behavior. The purpose of this study is to look into how children's internet use affects their academic achievement, outdoor activities, social interactions, behavior, and health. This study presents a model to show the potential correlations between children's internet usage, academic performance, outdoor activities, social interactions, behavior, and health. The study intends to create links and comprehend the total influence of internet usage on children by analyzing these characteristics. A questionnaire with closed-ended questions was used as a survey strategy to collect data for the study. In and around Navi Mumbai, India, 502 responses were collected from parents of school-aged children aged 6 to 12 years. A random sample method was used in the survey research design, and information was gathered using a pre-structured questionnaire.The information gathered was coded and entered into SPSS 20 for analysis. To analyses the data, descriptive statistics and the Chi Square test were used, and conclusions were drawn based on the results. A significant correlation was discovered between children's internet usage, academic achievement, outdoor activities, social interactions, behavior, and health, according to the research. The study's model successfully establishes probable correlations between these factors, and statistical tests confirm these linkages further. Finally, the growing popularity of online learning methods, fueled by the rising use of smart devices, has sparked concerns regarding the influence of internet consumption on children. This study looks into the consequences of internet use on several elements of children's lives. The study proposed model aids in understanding the connections between internet usage, academic performance, outdoor activities, social interactions, behavior, and health. The statistical study supports the probable correlations between these variables, providing important insights into the implications of children's internet usage in the digital age. Keywords— Internet, Internet Usage, Academic Performance, Outdoor Activities, Social Relationship, Behavior, Health.

Transcoding-Enabled Cloud–Edge–Terminal Collaborative Video Caching in Heterogeneous IoT Networks: An Online Learning Approach With Time-Varying Information

Transcoding-Enabled Cloud–Edge–Terminal Collaborative Video Caching in Heterogeneous IoT Networks: An Online Learning Approach With Time-Varying Information

Online Mindset Training for Prelicensure Nursing Students: A Randomized Longitudinal Study.

Research suggests students with a growth mindset are more successful. Utilizing Dweck's growth mindset model, the study explored the benefits of mindset training within nursing education. To determine the impact of an online mindset training intervention on first-semester prelicensure nursing students' mindset beliefs and learning and studying strategies. This study employed a randomized longitudinal design, conducted entirely online. Sixty-eight participants completed all phases of the study. Data were collected using the Williams Inventory of Learning Strategies tool before and after participants viewed an online training. Findings indicate that online mindset training positively influenced student learning, reducing fear of failure and increasing willingness to remediate. These results highlight the promising impact of an online learning approach in fostering a growth mindset among nursing students; suggesting the potential for integrating mindset training into the nursing curriculum to enhance student success, with recommendations for further research in larger-scale studies.

The Essentials or Fundamentals for Harnessing Technologies to Improve Teaching and Learning through Online Learning as Part of Digital Transformation in Higher Education

The Covid-19 pandemic accelerated digital transformation and usage of the fourth industrial revolution tools in most developing countries where teaching and learning most occurred through face-to-face interaction. To reap successes in the digitally transforming teaching and learning in universities, it is crucial to reconcile the key essential (factors) that need consideration for digital transformation and the challenges hindering the phenomenon. This study explored the fundamental factors for harnessing technologies for teaching and learning in State universities in Zimbabwe. The study employed a desk research approach. The study revealed that tertiary institutions were struggling to effectively implement online learning due to factors such as lack of strategy and vision on the implementation process, lack of financial resources, poor technological infrastructure, poor digital competences among lecturers and students as well as lack of adequate technical support. Instructors in these institutions mostly resorted to asynchronous online learning approaches because they lacked up to date gadgets for synchronous teaching and learning. Similarly, students preferred asynchronous learning due to data challenges as it offered self-pacing and flexibility. In limited cases lecturers make use of WhatsApp, Google Classroom, Google Meet and Zoom. The findings were that in order to institutionalise online learning as key component of digital transformation in state universities the fundamentals to be addressed include of procurement current and compatible IT infrastructure, comprehensive training and education to improve digital competences, incorporate digital transformation and the fourth industrial revolution into university curriculum to equip students, provision of internet data and improved internet connectivity.

Investigation of Self-Regulated Online Learning According to Demographic Variables and Their Relationship to Learning Approaches

Self-regulation in online learning environments is of great importance in terms of ensuring effective learning since it makes the learner autonomous. In this context, the research aims to compare the self-regulated online learning of education faculty students according to certain variables and to reveal the relationship between their self-regulated online learning and learning approaches. The research is conducted using a correlational research design. Firstly, the general view of students' self-regulated online learning and learning approaches was determined. After that, self-regulated online learning averages were compared according to their gender and academic averages, and finally, the relationship between self-regulated online learning and learning approaches was presented. The study group of the research, which continues their education through distance education due to the pandemic in the 2021-2022 academic year; consisted of a total of 376 students who studied in different departments of the education faculty of Necmettin Erbakan University in Konya. The research findings demonstrate the fact that students' self-regulated online learning corresponds to an above-average value and their self-regulated online learning differs according to their gender and academic achievement. Moreover, there is a moderately positive correlation between the deep learning approach and self-regulated online learning skills, and a low negative correlation between the superficial learning approach and self-regulated online learning skills.

Development of Audio-Visual Media on the Physical Motor Development of Students in Big Kindergarten

This study aims to determine the effect of the development of audio-visual media on the motor-physical development of students in BIG Kindergarten. This study involved early childhood students aged 5-6 years as participants. An audio-visual-based online learning approach was used in this study to introduce students to interesting physical activities and support the development of their motor skills. This research method use appropriate methods. The proposed solution is the use of audio-visual media which contains games as access to learning materials to develop children's gross motor skills. LCD/Projector is used to display learning videos. The results showed that the audio-visual media developed, including jumping over shapes, hot ball cold ball, cloth rolling ball, light ball, stepping on a tiger's tail, kangaroo ball game, and motion exercises and songs, had a positive influence on students' physical-motor development. Assessments from physical education experts, sports experts, and teachers show that this media gets a high score in the very good category. Based on the results of this study, it can be concluded that the development of audio-visual media in learning gross motor skills has a positive impact on the physical-motor development of early-age students. This media also received a positive response from students and educators, indicating a higher level of motivation and involvement in sports learning. This research contributes to the development of an innovative and effective learning approach in improving gross motor skills of early childhood students.

Student Profiling for Online Learning During Covid-19: A Systematic Review

The Covid-19 epidemic has spurred significant changes in learning paradigms in several countries, including Malaysia. Particularly, the Malaysian government's Movement Control Order (MCO) forced both students and teachers to turn to online learning. It is true that its adoption is rife with fresh difficulties, including network coverage, device appropriateness, and socio-economic status of students, and digital competency, which makes teaching and learning at home incredibly difficult. Since students have a variety of learning needs, abilities, skills, and goals, it is crucial to develop learner profiles that can aid in the development of appropriate online learning approaches in order to address such issues. A systematic review of the most recent literature was conducted using three major databases: Scopus, ERIC, and Mendeley, as was envisioned in this context. The review helped identify 23 relevant publications that were then subjected to a thematic analysis, which helped reveal three (3) key themes for online learning: online platform, learner profile, and learning experience. It might be argued that understanding these thematic elements can assist teaching professionals in creating and putting into practice efficient online learning strategies for students of all educational levels. The results of this study emphasize the value of adopting student or learner profiles as a guideline to assist teaching practitioners in developing and implementing effective online teaching practices, which can ultimately make online learning sustainable long after the Covid-19 outbreak has passed.

Online Learning With Incremental Feature Space and Bandit Feedback

Online learning is a fundamental paradigm for learning from continuous data stream. Tradition online learning approaches usually assume that the feature space of data stream is fixed and the incoming instance can always get the true label after making its prediction. However, in many real-world applications, such as the personalized recommender systems, the feature space may keep expanding due to the accumulation of user behaviors. Besides, we may only get bandit feedback, i.e., we only know whether the prediction is correct or not. To solve this important but rarely studied problem, we propose a novel algorithm LIFBF, together with its two variants LIFBF-I and LIFBF-II, to learn from data stream with incremental feature space and bandit feedback. Specifically, when an instance arrives with augmented features, we first utilize the exploration-exploitation strategy to guess its best label, then, a new loss function considering both bandit feedback and guessed label is proposed. Finally, we design a highly dynamic multi-class classifier, which updates the shared and augmented features by adopting the passive-aggressive rule and structural risk minimization principle, respectively. We theoretically analyze the cumulative loss bound of LIFBF. Besides, empirical studies on various datasets further validate the effectiveness of our proposed algorithms.

Manaaki Tauira Course Enhancements Program

This paper reports on a pilot study of a course enhancements program at Lincoln University, New Zealand, designed to enhance learner success for all students, but particularly those from minority groups. The pilot study includes two phases, each of six-months duration – course redevelopment and course delivery. During the first phase, 10 academics were selected to engage in self-reflection, complete a four-week online asynchronous course, develop an action plan for course redevelopment, attend a two-day in-person workshop to implement the action plan, and meet several times with an academic development facilitator. This blended approach resulted in academics experiencing a variety of in-person and online learning activities and approaches that could be used in their own courses, and the opportunity to collaborate and share their course redevelopment journey with other academics. This paper highlights the approach taken in the pilot study and the strategies used to bring academics on the journey.

Impact of Online Learning on the Performance of Business Students

The utilization of information and communication technologies (ICTs) has significantly transformed the education sector by introducing modifications to the execution of diverse educational endeavors. An expanding number of business programs are integrating online and blended learning approaches into their undergraduate, graduate, and executive education programs as a means to address the changing needs of students. The objectives of the study were to find out the effects of online learning on the performance of business students. The qualitative descriptive method was incorporated into the design of this study. Students who were enrolled in the business education program at one of the public institutions in Lahore constituted the participants of this research. The dependent variable was the performance of business pupils, while the independent variable was online education. A structured interview was employed as the instrument for gathering data. The researcher gathered data via in-person visits. Prior to data collection, participants were informed of the purpose of the study through prior appointments, and the confidentiality of their responses was guaranteed. Thematic analysis was employed to examine the data. Results of the study shows that online education has both positive and negative impacts on business education. Moreover, study found some strategies to mitigate the problems during online learning of business students. Ina addition study has some implications for future researches.

An extended weak aggregating algorithm for a two dimensional data-driven multi-stage newsvendor problem

We investigate a multi-stage newsvendor problem with advance purchase discount (APD) in this paper. At the beginning of a stage, the decision maker (DM) makes the advance ordering decision for all the periods in this stage; at the start of every period within the stage, the DM makes the regular ordering decision. In this problem, the only available information we can observe is the past demands. To solve this problem, we extend the weak aggregating algorithm (WAA) with one decision variable, an online learning approach based on the theory of prediction and learning with expert advice, to a two-dimensional problem that involves advance ordering decisions in stages and regular ordering decisions nested in each stage. The difficulty of the problem lies in transferring learned knowledge of demand information from stage to stage. We design a cross-stage knowledge transfer scheme and obtain online ordering solutions for both advance-order and regular-order. We show that our solutions converge to the optimal solutions asymptotically. In addition, we derive theoretical guarantees for total gains in one stage and cumulative gains for all stages in the planning horizon. Through numerical studies, we find that our solutions are competitive to those offered by the best experts in hindsight. Finally, we do the sensitivity analysis to illustrate the effectiveness of our algorithm under different parameter values.

The implementation of character education during the COVID-19 pandemic in state islamic senior high schools in Indonesia

Character education is one of the focuses of national education programs, including during the COVID-19 pandemic. This research aimed to investigate the implementation of character education during COVID-19 in Madrasah Aliyah Negeri (State Islamic Senior High School) in Indonesia. The study used a qualitative method with 65 people as the subject, consisting of 5 madrasah (Islamic school) principals and 60 teachers of 5 madrasahs in four districts of North Sumatra and Aceh Province, Indonesia. The subjects of the study were chosen purposively. The data collection techniques were interviews, observations, and documentation. The results indicated that the strategy of implementing character education during the pandemic at madrasahs in North Sumatra Province and Aceh Province involved integrating the online learning and modelling approach and internalizing the values of the nation's character in the learning and self-development activities in madrasahs. This study also found some obstacles to the implementation of character education during the pandemic, including teachers' lack of ability to use the online learning system and poor internet connections. The solution is to develop teacher skills for online learning systems and improve internet connections and technological facilities in schools.

An Online Active Broad Learning Approach for Real-Time Safety Assessment of Dynamic Systems in Nonstationary Environments.

Real-time safety assessment of the complex dynamic systems in nonstationary environments is of great significance for avoiding the potential hazards. In this case, the update procedure with high assessment accuracy and training speed is crucial and meaningful in the dynamic streaming setting. Generally, the performance of most online learning approaches will be negatively affected by limited annotated samples in such a setting. Moreover, the time cost of advanced conventional methods with retaining procedures is relatively high, constraining their practicality. In this article, a novel online active broad learning approach, termed OABL, is proposed. In detail, the effectiveness of the broad learning system in the framework of online active learning is first revealed and verified. A reasonable dynamic asymmetric query strategy is then designed with a limited annotation budget to actively annotate the relatively valuable samples, which is beneficial to mitigating the negative effects of class imbalance. In this context, the advantage of the human-in-the-loop characteristic is also effectively used to control the evolution direction of the learner during the incremental update, which makes it better able to adapt to complex and nonstationary environments. Several related experiments are conducted with the realistic data of JiaoLong deep-sea manned submersible. Results show the effectiveness and practicality of the proposal compared with the existing advanced approaches.

Neural network observer based on fuzzy auxiliary sliding-mode-control for nonlinear systems

This research suggests the use of neural network observers based on fuzzy auxiliary sliding mode control for the fault estimation and isolation of quadrotor unmanned aerial vehicle sensors. The fuzzy auxiliary sliding-mode-control-based adaptive approach for neural network observer is used for the approximation, reconstruction, and isolation of unknown faults by utilizing the multi-layer neural network. The neural network weight parameters are updated adaptively by using the fuzzy auxiliary sliding-mode-control approach. In conventional neural networks, the gradient descent approach-based back-propagation procedures are adapted for the training of the neural network. In this research, a new concept of the nonlinear controller such as the fuzzy auxiliary sliding-mode-control method is adapted for neural network online training, in which the fuzzy auxiliary sliding-mode-control is used as the learning approach; the neural network is used as a control process that calculates the stable and dynamic learning rates. By the consideration of unknown faults diagnosis and isolation, the online learning approach used in this research has shown the faults estimation, reconstruction, and isolation abruptly and with high accuracy compared to conventional approaches as well the approach used in literature. Strategies adopted in the literature are not capable of fault detection, estimation, and reconstruction with high accuracy and abruptness compared to the method adopted in this research. The presented approach is validated by using the nonlinear dynamics of quadrotor unmanned aerial vehicles, results show the accuracy, abruptness, and efficiency compared to the algorithms adapted in the literature. It is suggested that the proposed strategy can be integrated into nonlinear systems fault diagnosis, fault isolation, and for increasing the system performance.