E-learning Research Articles

Upskilling the cell therapy manufacturing workforce: design, implementation, and evaluation of a massive open online course.

Cell therapies have gained prominence as a promising therapeutic modality for treating a range of diseases. Despite the recent clinical successes of cell therapy products, very few formal training programs exist for cell therapy manufacturing. To meet the demand for a well-trained workforce, we assembled a team of university researchers and industry professionals to develop an online course on the principles and practice of cell therapy manufacturing. The course covers the basic cell and systems physiology underlying cell therapy products, in addition to explaining end-to-end manufacturing from cell acquisition through to patient treatment, industrialization, and regulatory processes. As of September 2023, >10,000 learners have enrolled in the course, and >90% of respondents to the course exit survey indicated that they were "very likely" or "likely" to recommend the course to a peer. In this article, we discuss our experience in the collaborative design and implementation of the online course as well as lessons learned from quantitative and qualitative student feedback. We believe that this course can serve as a model for how academia and industry can collaborate to create innovative, scalable training programs to meet the demands of the modern biotechnology workforce.NEW & NOTEWORTHY We assembled a team of university researchers and industry professionals to develop an online course on the principles and practice of cell therapy manufacturing. We believe that this course can serve as a model for how academia and industry can collaborate to create innovative, scalable training programs to meet the demands of the modern biotechnology workforce.

Play your way to an "A": helping students engage during the social isolation of remote learning.

With the increased attention focused on active learning, educators strive to find better and more innovative ways to engage students in the classroom. One of the hurtles that educators are presented with is that the classroom is no longer limited to a physical location but rather students and professor can meet via the internet, Before COVID-19, distance or remote learning was something that students, by and large, had the option of choosing in which whether to engage. Students had the option to take "online courses," whether those be synchronous remote learning or asynchronous online courses. Indeed, numerous studies have focused on investigating the efficacy of many different approaches to distance and online learning. Unfortunately, COVID 19 mandated a rapid transition to remote learning, and with this forced change has come what some students describe as "Zoom fatigue" (Wolf CR. Psychology Today, May 2020). Many students reported feeling exhausted, overwhelmed, and disengaged by the dramatic increase in mandated distance education required by the COVID pandemic. Video conferencing has become the "go-to" panacea for education during this time, and students are spending unprecedented amounts of time in front of a screen when normally they would be in a classroom. This heretofore singular and unique approach to education coupled with decreased peer-to-peer interaction has caused a problem with student engagement (Goodman BE, Barker MK, Cooke JE. Adv Physiol Educ 42: 417-423, 2018). Students' engagement and performance have decreased during COVID-19 because of forced online learning and lack of peer interaction. We hypothesize that creating a nongraded, fun, and relaxing physiology-focused "Trivia Night" will increase student engagement and performance on summative assessments. Using a master's level class progressing through the respiratory physiology module utilizing remote, synchronous lectures to deliver content, we introduced a voluntary Trivia Night review session with teams randomly assigned to increase interaction among peers and review respiratory physiology material.NEW & NOTEWORTHY This article presents the effectiveness of the use of the "pub Trivia Night" to facilitate learning, deconstruct misconceptions, and increase engagement during remote teaching due to the COVID-19 pandemic.

Virtual simulation-based educational training enhances nursing students’ preparedness to conduct perinatal intimate partner violence screenings

Intimate partner violence (IPV) is an unfortunately prevalent experience during the perinatal period and significantly impacts both maternal and fetal health. Ensuring nursing students are adequately prepared to conduct IPV screening and respond to patients’ disclosure of IPV upon graduation is essential to promoting enhanced maternal-child health outcomes. Seventy-eight undergraduate nursing students in their third semester of nursing school participated in the simulated scenario and provided complete data. Students engaged in a virtual simulation-based educational training (SBET) and completed a preposttest assessing their knowledge, skills, and attitudes related to IPV screening and response. Students’ knowledge and skills significantly improved as a result of the virtual SBET (t = −5.99, p < .001, d = −0.67 and t = −4.33, p < .001, d = −0.48, respectively). A relatively low-cost, low-burden virtual SBET can significantly improve nursing students’ perceived knowledge and skills in identifying and caring for pregnant patients experiencing IPV.

Application Research of 3D Virtual Interactive Technology in Interactive Teaching of Arts and Crafts

In the virtual reality teaching of arts and crafts, the uploaded image data often contains a lot of noise, which significantly reduces the quality of uploaded images and seriously affects the teaching effect of interactive teaching of arts and crafts based on virtual reality. For this reason, the research proposes an image denoising model based on convolutional neural network and wavelet transform, which adopts residual neural network structure and batch normalization algorithm, aiming at gradient explosion and gradient disappearance that may be caused by convolutional neural network, And the problem of low training efficiency has been optimized. The results show that when the noise standard deviation is 50, model 1 can achieve the target accuracy with only 82 iterations, while model 2 requires as many as 56 iterations. For images of different types and noise intensities, the average PSNR values of model 1 are 29.3dB, 30.5dB and 28.9dB, respectively. In addition, by applying Model 1 to VR teaching of arts and crafts, the average score of class 1 is 9.3 points higher than that of class 2. The proposed model effectively optimizes the virtual reality teaching process of arts and crafts, and provides a useful contribution to the application of virtual reality in the field of education.

DVRT: Design and Evaluation of a Virtual Reality Drone Programming Teaching System

Virtual Reality (VR) is an immersive virtual environment generated through computer technology. VR teaching, by utilizing an immersive learning model, offers innovative learning methods for Science, Technology, Engineering and Mathematics (STEM) education as well as programming education. This study developed a Drone Virtual Reality Teaching (DVRT) system aimed at beginners in drone operation and programming, with the goal of addressing the challenges in traditional drone and programming education, such as difficulty in engaging students and lack of practicality. Through the system's curriculum, students learn basic drone operation skills and advanced programming techniques. We conducted a course experiment primarily targeting undergraduate students who are beginners in drone operation. The test results showed that most students achieved scores above 4 out of 5, indicating that DVRT can effectively promote the development of users' comprehensive STEM literacy and computational thinking, thereby demonstrating the great potential of VR technology in STEM education. Through this innovative teaching method, students not only gain knowledge but also enjoy the fun of immersive learning.

Implementing an online course in the MOOC environment with a focus on science and mathematics education

Implementing an online course in the MOOC environment with a focus on science and mathematics education

Conversational Recommendation With Online Learning and Clustering on Misspecified Users

Conversational Recommendation With Online Learning and Clustering on Misspecified Users

Validity and reliability of the discrepancy evaluation instrument for measuring inequality in the online learning

Validity and reliability of the discrepancy evaluation instrument for measuring inequality in the online learning

Curious Explorer: a Provable Exploration Strategy in Policy Learning.

A coverage assumption is critical with policy gradient methods, because while the objective function is insensitive to updates in unlikely states, the agent may need improvements in those states to reach a nearly optimal payoff. However, this assumption can be unfeasible in certain environments, for instance in online learning, or when restarts are possible only from a fixed initial state. In these cases, classical policy gradient algorithms like REINFORCE can have poor convergence properties and sample efficiency. Curious Explorer is an iterative state space pure exploration strategy improving coverage of any restart distribution ρ. Using ρ and intrinsic rewards, Curious Explorer produces a sequence of policies, each one more exploratory than the previous one, and outputs a restart distribution with coverage based on the state visitation distribution of the exploratory policies. This paper main results are a theoretical upper bound on how often an optimal policy visits poorly visited states, and a bound on the error of the return obtained by REINFORCE without any coverage assumption. Finally, we conduct ablation studies with REINFORCE and TRPO in two hard-exploration tasks, to support the claim that Curious Explorer can improve the performance of very different policy gradient algorithms.

Comparing two online learning activities addressing interprofessional approach on health science students’ attitudes towards teamwork

BackgroundPhysical distancing restrictions during the COVID-19 pandemic led to the transition from in-person to online learning for many health sciences educators. This study was conducted to compare the effects of two online learning activities addressing interprofessional teamwork on health science students’ attitudes towards teamwork. MethodsThis study was designed as a randomized quasi-experimental study. The nursing (n = 30), physiotherapy and rehabilitation (n = 30), and nutrition-dietetics (n = 30) students were assigned to either online standardized patient simulation (n = 45) or online case analysis (n = 45) groups. Students’ attitudes towards interprofessional teamwork were collected pre- and post-test evaluation using the TeamSTEPPS Teamwork Attitudes Questionnaire (T-TAQ). ResultsNo significant differences were found between T-TAQ mean scores prior the intervention (p > .05). However, following the intervention, whilst the T-TAQ mean scores were not found to be statistically significant between the two groups (p > .05), the students’ pretest and posttest T-TAQ mean scores did show a statistically significant difference (p < .05). ConclusionWe found online learning activities to be a successful alternative to physical interprofesional education activities and a worthwhile opportunity to develop health science students to interprofesional teamwork.

Online Learning and Detecting Corrupted Users for Conversational Recommendation Systems

Online Learning and Detecting Corrupted Users for Conversational Recommendation Systems

Social work students’ perception towards online learning during the COVID-19 pandemic

Social work students’ perception towards online learning during the COVID-19 pandemic

Examining the trends of research on educators’ readiness in online teaching &amp; learning (2013-2022): a bibliometric analysis

Examining the trends of research on educators’ readiness in online teaching &amp; learning (2013-2022): a bibliometric analysis

An unsupervised online Tai Chi program for people with knee osteoarthritis (“My Joint Tai Chi”): study protocol for the RETREAT randomised controlled trial

BackgroundKnee osteoarthritis (OA) is a leading contributor to global disability, with exercise proven to be an effective treatment. Tai Chi is a recommended type of exercise, but it is primarily done in person which imposes an accessibility issue. This study aims to evaluate the effects of an online unsupervised program, when provided with online educational information and exercise adherence support, on changes in knee pain and physical function, when compared to online education control for people with knee OA. MethodsA two-arm, superiority parallel-design, pragmatic randomised controlled trial will be conducted involving 178 people with a clinical diagnosis of knee OA. After completing baseline assessment, participants will be randomly assigned to either: i) “My Joint Education”, an education control website containing OA information only; or ii) “My Joint Tai Chi”, an intervention website containing the same information as the control, a 12-week unsupervised online Tai Chi program to be undertaken at home 3 times a week, and information about an exercise adherence support app. All participants will be reassessed at 12 weeks after randomisation. Primary outcomes are overall knee pain during walking and physical function using the Western Ontario and McMaster Universities Osteoarthritis Index subscale. DiscussionThis randomised controlled trial will provide evidence about the effectiveness of the “My Joint Tai Chi” website compared to “My Joint Education” website on self-reported knee pain and physical function for people with knee OA. Trial RegistrationProspectively registered with the Australia New Zealand Clinical Trials Registry (ID: ACTRN12623000780651) on 18th July 2023.

Impact of students’ feedback on their engagement towards online classes during COVID-19 pandemic

Impact of students’ feedback on their engagement towards online classes during COVID-19 pandemic

Digital learning using ChatGPT in elementary school mathematics learning: a systematic literature review

Digital learning with ChatGPT in elementary school mathematics learning is urgently implemented. Several studies have explored digital learning with ChatGPT in elementary school mathematics learning, but studies using SLR are minimal. This article presents the 2022-2024 SLR study on digital learning with ChatGPT in elementary school mathematics learning. This SLR and PRISMA method is supported by Publish or Perish 8, VOSviewer version 1.6.20, Mendeley version 1.19.8, and ATLAS.ti version 7.5.16. The search results obtained 1,259 Scopus articles, which were filtered to 40 and analyzed using ATLAS.ti, then the results were described according to the research question. Digital learning with ChatGPT is a learning approach using the synchronous-asynchronous mode, virtual classrooms, distance, use of interactive digital tools, digital methods and media, innovation, digital modeling, use of robotics and AI ChatGPT for children with the principle of collaboration digital, and problem-solving with the support of digital resources. ChatGPT features multilingual, natural language, advanced AI, 24/7 availability, answering math questions, recurring training, and helping students with various math tasks. Implementation of digital learning with ChatGPT in elementary school mathematics learning for problem-solving, geometry, function limits in algebra, the material on flat shapes, geometric shapes, integrated PjBL, online, mixed and flipped classes.

Multi-Label Feature Aware XGBoost Model For Student Performance Assessment Using Behavior Data in Online Learning Environment

In light of recent outbreaks like COVID19, the use of online-based learning streams (i.e., e-Learning systems) has increased significantly. Institutional efforts to boost student achievement have made precise predictions of academic success a priority. To analyze student sessions-streams and anticipate academic success, e-Learning platforms are starting to combine Datamining (DM) with Machine-Learning (ML) techniques. Recent research highlights the difficulties that ML-based methods have while dealing with unbalanced data. In tackling ensemble-learning, we combine several ML algorithms to select the most appropriate approach for the given data. Current ensemble-based approaches for predicting student achievement, nevertheless, don't do exceptionally well, particularly when it comes to multi-label classification, because they don't factor the relevance of features into their approaches. This study presents multi-label feature aware XGBoost (MLFA-XGB) method that improves upon the previously used ensemble-learning technique. The MLFA-XGB makes use of a robust cross-validation approach for gaining a deeper understanding of feature relationships. The experimental results demonstrate that in comparison with the state-of-the-art ensemble-based student achievement predictive approach, this suggested MLFA-XGB based approach provides much higher accuracy for prediction.

An effective surrogate-assisted rank method for evolutionary neural architecture search

Evolutionary neural architecture search (ENAS) is able to automatically design high-performed architectures of deep neural networks (DNNs) for specific tasks. In recent years, surrogate models have gained significant traction because they can estimate the performance of neural architectures, avoiding excessive computational costs for training. However, most existing surrogate models primarily predict the performance of architectures directly or predict pairwise comparison relationships, which makes it challenging to obtain the rank of a group of architectures when training samples are limited. To address this problem, we propose TCMR-ENAS, an effective triple-competition model-assisted rank method for ENAS. TCMR-ENAS employs a novel triple-competition surrogate model combined with a score-based fitness evaluation method to predict group performance rank. Moreover, a progressive online learning method is proposed to enhance the predictive performance of the triple-competition surrogate model in the framework of modified genetic search. To validate the effectiveness of TCMR-ENAS, we conducted a series of experiments on NAS-Bench-101, NAS-Bench-201, NATS-Bench and NAS-Bench-301, respectively. Experimental results show that TCMR-ENAS can achieve better performance with lower computational resources. The accuracies of searched architectures achieve the best results compared with those of the state-of-the-art methods with limited training samples. In addition, the factors that may influence the effectiveness of TCMR-ENAS are explored in the ablation studies.

Online Learning of Temporal Association Rule on Dynamic Multivariate Time Series Data

Online Learning of Temporal Association Rule on Dynamic Multivariate Time Series Data

Hierarchical Partitioning Forecaster

In this work we consider a new family of algorithms for sequential prediction, Hierarchical Partitioning Forecasters (HPFs). Our goal is to provide appealing theoretical - regret guarantees on a powerful model class - and practical - empirical performance comparable to deep networks - properties at the same time. We built upon three principles: hierarchically partitioning the feature space into sub-spaces, blending forecasters specialized to each sub-space and learning HPFs via local online learning applied to these individual forecasters. Following these principles allows us to obtain regret guarantees, where Constant Partitioning Forecasters (CPFs) serve as competitor. A CPF partitions the feature space into sub-spaces and predicts with a fixed forecaster per sub-space. Fixing a hierarchical partition H and considering any CPF with a partition that can be constructed using elements of H we provide two guarantees: first, a generic one that unveils how local online learning determines regret of learning the entire HPF online; second, a concrete instance that considers HPF with linear forecasters (LHPF) and exp-concave losses where we obtain O(klogT) regret for sequences of length T where k is a measure of complexity for the competing CPF. Finally, we provide experiments that compare LHPF to various baselines, including state of the art deep learning models, in precipitation nowcasting. Our results indicate that LHPF is competitive in various settings.