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Artykuły w czasopismach na temat "Online continual learning"
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Li, Guozheng, Peng Wang, Qiqing Luo, Yanhe Liu i Wenjun Ke. "Online Noisy Continual Relation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 11 (26.06.2023): 13059–66. http://dx.doi.org/10.1609/aaai.v37i11.26534.
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Recent work for continual relation learning has achieved remarkable progress. However, most existing methods only focus on tackling catastrophic forgetting to improve performance in the existing setup, while continually learning relations in the real-world must overcome many other challenges. One is that the data possibly comes in an online streaming fashion with data distributions gradually changing and without distinct task boundaries. Another is that noisy labels are inevitable in real-world, as relation samples may be contaminated by label inconsistencies or labeled with distant supervision. In this work, therefore, we propose a novel continual relation learning framework that simultaneously addresses both online and noisy relation learning challenges. Our framework contains three key modules: (i) a sample separated online purifying module that divides the online data stream into clean and noisy samples, (ii) a self-supervised online learning module that circumvents inferior training signals caused by noisy data, and (iii) a semi-supervised offline finetuning module that ensures the participation of both clean and noisy samples. Experimental results on FewRel, TACRED and NYT-H with real-world noise demonstrate that our framework greatly outperforms the combinations of the state-of-the-art online continual learning and noisy label learning methods.
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Alfarra, Motasem, Zhipeng Cai, Adel Bibi, Bernard Ghanem i Matthias Müller. "SimCS: Simulation for Domain Incremental Online Continual Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 10 (24.03.2024): 10795–803. http://dx.doi.org/10.1609/aaai.v38i10.28952.
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Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual learning approaches mostly focus on image classification in the class-incremental setup with clear task boundaries and unlimited computational budget. This work explores the problem of Online Domain-Incremental Continual Segmentation (ODICS), where the model is continually trained over batches of densely labeled images from different domains, with limited computation and no information about the task boundaries. ODICS arises in many practical applications. In autonomous driving, this may correspond to the realistic scenario of training a segmentation model over time on a sequence of cities. We analyze several existing continual learning methods and show that they perform poorly in this setting despite working well in class-incremental segmentation. We propose SimCS, a parameter-free method complementary to existing ones that uses simulated data to regularize continual learning. Experiments show that SimCS provides consistent improvements when combined with different CL methods.
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Shim, Dongsub, Zheda Mai, Jihwan Jeong, Scott Sanner, Hyunwoo Kim i Jongseong Jang. "Online Class-Incremental Continual Learning with Adversarial Shapley Value". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 11 (18.05.2021): 9630–38. http://dx.doi.org/10.1609/aaai.v35i11.17159.
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As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption. While memory replay techniques have shown exceptional promise for this task of continual learning, the best method for selecting which buffered images to replay is still an open question. In this paper, we specifically focus on the online class-incremental setting where a model needs to learn new classes continually from an online data stream. To this end, we contribute a novel Adversarial Shapley value scoring method that scores memory data samples according to their ability to preserve latent decision boundaries for previously observed classes (to maintain learning stability and avoid forgetting) while interfering with latent decision boundaries of current classes being learned (to encourage plasticity and optimal learning of new class boundaries). Overall, we observe that our proposed ASER method provides competitive or improved performance compared to state-of-the-art replay-based continual learning methods on a variety of datasets.
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Cheng, Kan, Yongxin Ma, Guanglu Wang, Linlin Zong i Xinyue Liu. "NLOCL: Noise-Labeled Online Continual Learning". Electronics 13, nr 13 (29.06.2024): 2560. http://dx.doi.org/10.3390/electronics13132560.
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Continual learning (CL) from infinite data streams has become a challenge for neural network models in real-world scenarios. Catastrophic forgetting of previous knowledge occurs in this learning setting, and existing supervised CL methods rely excessively on accurately labeled samples. However, the real-world data labels are usually misled by noise, which influences the CL agents and aggravates forgetting. To address this problem, we propose a method named noise-labeled online continual learning (NLOCL), which implements the online CL model with noise-labeled data streams. NLOCL uses an empirical replay strategy to retain crucial examples, separates data streams by small-loss criteria, and includes semi-supervised fine-tuning for labeled and unlabeled samples. Besides, NLOCL combines small loss with class diversity measures and eliminates online memory partitioning. Furthermore, we optimized the experience replay stage to enhance the model performance by retaining significant clean-labeled examples and carefully selecting suitable samples. In the experiment, we designed noise-labeled data streams by injecting noisy labels into multiple datasets and partitioning tasks to simulate infinite data streams realistically. The experimental results demonstrate the superior performance and robust learning capabilities of our proposed method.
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Li, Jiyong, Dilshod Azizov, Yang LI i Shangsong Liang. "Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 12 (24.03.2024): 13554–62. http://dx.doi.org/10.1609/aaai.v38i12.29259.
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Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://github.com/lijy373/CCLIS.
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Kim, Doyoung, Dongmin Park, Yooju Shin, Jihwan Bang, Hwanjun Song i Jae-Gil Lee. "Adaptive Shortcut Debiasing for Online Continual Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 12 (24.03.2024): 13122–31. http://dx.doi.org/10.1609/aaai.v38i12.29211.
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We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred by continuously changing environment. By the observed high-attention property of the shortcut bias, highly-activated features are considered candidates for debiasing. More importantly, resolving the limitation of the online environment where prior knowledge and auxiliary data are not ready, two novel techniques---feature map fusion and adaptive intensity shifting---enable us to automatically determine the appropriate level and proportion of the candidate shortcut features to be dropped. Extensive experiments on five benchmark datasets demonstrate that, when combined with various OCL algorithms, DropTop increases the average accuracy by up to 10.4% and decreases the forgetting by up to 63.2%.
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Liu, Bing. "Learning on the Job: Online Lifelong and Continual Learning". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 09 (3.04.2020): 13544–49. http://dx.doi.org/10.1609/aaai.v34i09.7079.
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One of the hallmarks of the human intelligence is the ability to learn continuously, accumulate the knowledge learned in the past and use the knowledge to help learn more and learn better. It is hard to imagine a truly intelligent system without this capability. This type of learning differs significantly than the classic machine learning (ML) paradigm of isolated single-task learning. Although there is already research on learning a sequence of tasks incrementally under the names of lifelong learning or continual learning, they still follow the traditional two-phase separate training and testing paradigm in learning each task. The tasks are also given by the user. This paper adds on-the-job learning to the mix to emphasize the need to learn during application (thus online) after the model has been deployed, which traditional ML cannot do. It aims to leverage the learned knowledge to discover new tasks, interact with humans and the environment, make inferences, and incrementally learn the new tasks on the fly during applications in a self-supervised and interactive manner. This is analogous to human on-the-job learning after formal training. We use chatbots and self-driving cars as examples to discuss the need, some initial work, and key challenges and opportunities in building this capability.
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Ha, Donghee, Mooseop Kim i Chi Yoon Jeong. "Online Continual Learning in Acoustic Scene Classification: An Empirical Study". Sensors 23, nr 15 (3.08.2023): 6893. http://dx.doi.org/10.3390/s23156893.
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Numerous deep learning methods for acoustic scene classification (ASC) have been proposed to improve the classification accuracy of sound events. However, only a few studies have focused on continual learning (CL) wherein a model continually learns to solve issues with task changes. Therefore, in this study, we systematically analyzed the performance of ten recent CL methods to provide guidelines regarding their performances. The CL methods included two regularization-based methods and eight replay-based methods. First, we defined realistic and difficult scenarios such as online class-incremental (OCI) and online domain-incremental (ODI) cases for three public sound datasets. Then, we systematically analyzed the performance of each CL method in terms of average accuracy, average forgetting, and training time. In OCI scenarios, iCaRL and SCR showed the best performance for small buffer sizes, and GDumb showed the best performance for large buffer sizes. In ODI scenarios, SCR adopting supervised contrastive learning consistently outperformed the other methods, regardless of the memory buffer size. Most replay-based methods have an almost constant training time, regardless of the memory buffer size, and their performance increases with an increase in the memory buffer size. Based on these results, we must first consider GDumb/SCR for the continual learning methods for ASC.
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Hihn, Heinke, i Daniel A. Braun. "Online continual learning through unsupervised mutual information maximization". Neurocomputing 578 (kwiecień 2024): 127422. http://dx.doi.org/10.1016/j.neucom.2024.127422.
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Ye, Fei, Adrian G. Bors i Kun Zhang. "Continual Unsupervised Generative Modelling via Online Optimal Transport". Proceedings of the AAAI Conference on Artificial Intelligence 39, nr 21 (11.04.2025): 22092–100. https://doi.org/10.1609/aaai.v39i21.34362.
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Lately, deep generative models have achieved excellent results after learning pre-defined and static data distribution. Meanwhile, their performance on continual learning suffers from degeneration, caused by catastrophic forgetting. In this paper, we study the unsupervised generative modelling in a more realistic continual learning scenario, where class and task information are absent during both training and inference learning phases. To implement this goal, the proposed memory approach consists of a temporary memory system, which stores data examples while a dynamic expansion memory system would gradually preserve those samples that are crucial for long-term memorization. A novel memory expansion mechanism is then proposed, by employing optimal transport distances between the statistics of memorized samples and each newly seen datum. This paper proposes the Sinkhorn-based Dual Dynamic Memory (SDDM) method, by considering Sinkhorn distance as an optimal transport measure, for evaluating the significance of the data to be stored in the memory buffer. The Sinkhorn transport algorithm leads to preserving a diversity of samples within a compact memory capacity. The memory buffering approach does not interact with the model's training process and can be optimized independently in both supervised and unsupervised learning without any modifications. Moreover, we also propose a novel dynamic model expansion mechanism to automatically increase the model's capacity whenever necessary, which can deal with infinite data streams and further improve the model's performance. Experimental results show that the proposed approach achieves state-of-the-art performance in both supervised and unsupervised learning.
Rozprawy doktorskie na temat "Online continual learning"
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Yang, Rui. "Online continual learning for 3D detection of road participants in autonomous driving". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2023. http://www.theses.fr/2023UBFCA021.
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La conduite autonome a connu des progrès remarquables au cours des dernières décennies, et la perception machine se présente comme un enjeu fondamental crucial, englobant la détection et le suivi des participants à la circulation tels que les véhicules, les piétons et les cyclistes. Bien que la détection d'objets basée sur la vision ait réalisé des progrès significatifs grâce aux techniques d'apprentissage profond, des défis subsistent dans la détection en 3D.Tout d'abord, les capteurs non visuels, tels que le LiDAR 3D, démontrent des avantages inégalés en matière de détection précise et d'adaptabilité aux conditions d'éclairage variables. Cependant, la complexité liée à la manipulation des données de nuages de points, qui peuvent être difficiles à interpréter, associée au coût élevé de l'annotation manuelle, posent des défis majeurs dans l'utilisation du LiDAR 3D.Deuxièmement, des préoccupations découlent du manque d'interprétabilité des modèles d'apprentissage profond, associée à leur forte dépendance à des données d'entraînement étendues, ce qui nécessite souvent une rétroaction coûteuse pour obtenir une performance acceptable lors de l'adaptation à de nouveaux environnements.Cette thèse aborde ces défis selon trois principaux axes : Génération d'échantillons, Préservation des connaissances et Évitement de l'oubli catastrophique. Nous introduisons le concept d'Apprentissage Continu en Ligne et proposons un cadre général. Ce cadre permet aux modèles de se mettre à jour en temps réel, préservant les connaissances plutôt que les données brutes, et atténuant efficacement la dégradation des performances causée par l'oubli.Les principaux travaux de cette thèse comprennent les trois contributions suivance : 1) Génération d'échantillons : Pour résoudre le problème des nuages de points clairsemés générés par un LiDAR 3D et de l'annotation manuelle laborieuse, nous tirons parti des avantages des données multi-capteurs et utilisons un cadre efficace d'apprentissage en ligne par transfert. Ce cadre transfère efficacement les capacités matures de détection basée sur des images aux détecteurs basés sur le LiDAR 3D. Un aspect innovant est le processus d'apprentissage par utilisation, réalisé grâce à la détection en boucle fermée. Une nouvelle stratégie de fusion d'informations est proposée pour combiner les corrélations spatio-temporelles, améliorant l'efficacité du transfert de connaissances. 2) Préservation des connaissances : L'Apprentissage en Ligne est introduit pour assurer la préservation des connaissances sans conserver les données d'entraînement. Un modèle amélioré de Forêt Aléatoire (Random Forest) en Ligne est intégré, permettant un entrainement rapide du modèle avec des ressources informatiques limitées et un déploiement immédiat. Les paramètres du modèle Forêt Aléatoire en Ligne sont partagés dynamiquement tout au long du processus d’entrainement pour répondre à la distribution inconnue des données. L'exploration des structures d'arbres Forêt Aléatoire en Ligne garantit l'indépendance du processus d’entrainement, renforçant la capacité du modèle à capturer des schémas et des variations complexes. 3) Évitement de l'oubli catastrophique : Pour faire face au problème inévitable de l'oubli dans le cadre d'apprentissage en ligne lors d'un déploiement à long terme, nous proposons le cadre de l'Apprentissage en Ligne à Long-court Terme (ALLCT). L'ALLCT combine de multiples apprenants à court terme basés sur l'apprentissage en ensemble avec un contrôleur à long terme doté d'un mécanisme de décision probabiliste. Ce cadre assure une maintenance efficace des connaissances et s'adapte aux changements lors d'un déploiement à long terme, sans faire d'hypothèses sur les types de modèles et la continuité des données. Les évaluations croisées de jeux de données sur des tâches telles que la détection 3D de participants à la circulation routière démontrent l'efficacité de l'ALLCT dans l'évitement de l'oubliAutonomous driving has witnessed remarkable progress over the past decades, and machine perception stands as a critical foundational issue, encompassing the detection and tracking of road participants such as vehicles, pedestrians, and cyclists. While vision-based object detection has achieved significant progress thanks to deep learning techniques, challenges still exist in 3D detection.Firstly, non-visual sensors, such as 3D LiDAR, demonstrate unparalleled advantages in achieving precise detection and adaptability to varying lighting conditions. However, the complexity of handling points cloud data, which can be challenging to interpret, coupled with the high cost of manual annotation, pose primary challenges in the use of 3D LiDAR.Secondly, concerns arise from the lack of interpretability in deep learning models, coupled with their heavy reliance on extensive training data, which often necessitates costly retraining for acceptable generalization performance when adapting to new scenes or environments.This dissertation addresses these challenges from three main perspectives: Generation of Samples, Preservation of Knowledge, and Avoidance of Catastrophic Forgetting. We introduce the concept of Online Continual Learning (OCL) and propose a general framework that encompasses detection, tracking, learning, and control. This framework enables models to update in real-time, preserving knowledge rather than raw data, and effectively mitigating the performance degradation caused by catastrophic forgetting.The main work of this dissertation includes: 1) Generation of Samples: To address sparse point clouds generated by 3D LiDAR and the labor-intensive manual annotation, we leverage the advantages of multi-sensor data and employ an efficient online transfer learning framework. This framework effectively transfers mature image-based detection capabilities to 3D LiDAR-based detectors. An innovative aspect is the "learn-by-use" process, achieved through closed-loop detection, facilitating continuous self-supervised learning. A novel information fusion strategy is proposed to combine spatio-temporal correlations, enhancing the effectiveness of knowledge transfer. 2) Preservation of Knowledge: Online Learning (OL) is introduced to address knowledge preservation without retaining training data. An improved Online Random Forest (ORF) model is incorporated, enabling rapid model training with limited computational resources and immediate deployment. The ORF model's parameters are dynamically shared throughout the training process to address the unknown data distribution. The exploration of ORF tree structures ensures independence in training processes, enhancing the model's ability to capture complex patterns and variations. Implementing octrees improves storage efficiency and model access. 3) Avoidance of Catastrophic Forgetting: To tackle the inevitable forgetting problem in online learning frameworks during long-term deployment, we propose the Long Short-Term Online Learning (LSTOL) framework. LSTOL combines multiple short-term learners based on ensemble learning with a long-term controller featuring a probabilistic decision mechanism. This framework ensures effective knowledge maintenance and adapts to changes during long-term deployment, without making assumptions about model types and data continuity. Cross-dataset evaluations on tasks such as 3D detection of road participants demonstrate the effectiveness of LSTOL in avoiding forgetting
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Pham, Cuong X. "Advanced techniques for data stream analysis and applications". Thesis, Griffith University, 2023. http://hdl.handle.net/10072/421691.
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Deep learning (DL) is one of the most advanced AI techniques that has gained much attention in the last decade and has also been applied in many successful applications such as market stock prediction, object detection, and face recognition. The rapid advances in computational techniques like Graphic Processing Units (GPU) and Tensor Processing Units (TPU) have made it possible to train large deep learning models to obtain high accuracy surpassing human ability in some tasks, e.g., LipNet [9] achieves 93% of accuracy compared with 52% of human to recognize the word from speaker lips movement. Most of the current deep learning research work has focused on designing a deep architecture working in a static environment where the whole training set is known in advance. However, in many real-world applications like predicting financial markets, autonomous cars,
and sensor networks, the data often comes in the form of streams with massive volume, and high velocity has affected the scalability of deep learning models. Learning from such data is called continual, incremental, or online learning.
When learning a deep model in dynamic environments where the data come from streams, the modern deep learning models usually suffer the so-called catastrophic forgetting problem, one of the most challenging issues that have not been solved yet. Catastrophic forgetting occurs when a model learns new knowledge, i.e., new objects or classes, but its performance in the previously learned classes reduces significantly. The cause of catastrophic forgetting in the deep learning model has been identified and is related to the weight-sharing property. In detail, the model updating the corresponding weights to capture
knowledge of the new tasks may push the learned weights of the past tasks away and cause the model performance to degrade. According to the stability-plasticity dilemma [17], if the model weights are too stable, it will not be able to acquire new knowledge,
while a model with high plasticity can have large weight changes leading to significant forgetting of the previously learned patterns. Many approaches have been proposed to tackle this issue, like imposing constraints on weights (regularizations) or rehearsal from experience, but significant research gap still exists.
First, current regularization methods often do not simultaneously consider class imbalance and catastrophic forgetting. Moreover, these methods usually require more memory to store previous versions of the model, which sometimes is not able to hold a copy of a substantial deep model due to memory constraints. Second, existing rehearsal approaches pay little attention to selecting and storing critical instances that help the model to retain as much knowledge of the learned tasks. This study focuses on dealing with these challenges by proposing several novel methods.
We first proposed a new loss function that combines two loss terms to deal with class imbalance data and catastrophic forgetting simultaneously. The former is a modification of a widely used loss function for class imbalance learning, called Focal loss, to handle the exploding gradient (loss goes to NaN) and the ability to learn from highly confident data points. At the same time, the latter is a novel loss term that addresses the catastrophic forgetting within the current mini-batch. In addition, we also propose an online convolution neural network (OCNN) architecture for tabular data that act as a base classifier in an ensemble system (OECNN). Next, we introduce a rehearsal-based method to prevent catastrophic forgetting. In which we select a triplet of instances within each mini-batch to store in the memory buffer. We find that these instances are identified as crucial instances
that can help either remind the model of easy tasks or revise for the hard ones. We also propose a class-wise forgetting detector that monitors the performance of each class encountered so far in a stream. If a class’s performance drops below a predefined
threshold, that class is identified as a forgetting class. Finally, based on the nature of data which often comprises many modalities, we study online multi-modal multi-task (M3T) learning problems. Unlike the traditional methods in stable environments, online M3T learning need to be considered in many scenarios like missing modalities and incremental tasks. We establish the setting for six frequently happened scenarios for M3T. Most of the existing works in M3T fail to run on all of these scenarios. Therefore, we propose a novel M3T deep learning model called UniCNet that can work on all of these scenarios and achieves superior performance compared with state-of-the-art M3T methods.
To conclude, this dissertation contributes to novel computational techniques that deal with catastrophic forgetting problem in continual deep learning.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
Full Text
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Strutynskiy, Maksym. "A concept of an intent-based contextual chat-bot with capabilities for continual learning". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99102.
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Chat-bots are computer programs designed to conduct textual or audible conversations with a single user. The job of a chat-bot is to be able to find the best response for any request the user issues. The best response is considered to answer the question and contain relevant information while following grammatical and lexical rules. Modern chat-bots often have trouble accomplishing all these tasks. State-of-the-art approaches, such as deep learning, and large datasets help chat-bots tackle this problem better. While there is a number of different approaches that can be applied for different kind of bots, datasets of suitable size are not always available. In this work, we introduce and evaluate a method of expanding the size of datasets. This will allow chat-bots, in combination with a good learning algorithm, to achieve higher precision while handling their tasks. The expansion method uses the continual learning approach that allows the bot to expand its own dataset while holding conversations with its users. In this work we test continual learning with IBM Watson Assistant chat-bot as well as a custom case study chat-bot implementation. We conduct the testing using a smaller and a larger datasets to find out if continual learning stays effective as the dataset size increases. The results show that the more conversations the chat-bot holds, the better it gets at guessing the intent of the user. They also show that continual learning works well for larger and smaller datasets, but the effect depends on the specifics of the chat-bot implementation. While continual learning makes good results better, it also turns bad results into worse ones, thus the chat-bot should be manually calibrated should the precision of the original results, measured before the expansion, decrease.
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Wagner, Baptiste. "Apprentissage continu en ligne pour la classification d'images et la détection d'objets". Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALT111.
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Dans cette thèse, nous nous intéressons au problème de l'apprentissage continu en ligne des réseaux de neurones artificiels, qui consiste à apprendre en permanence sur un flux de données. La difficulté majeure réside dans le fait qu'intégrer les nouvelles informations présentes dans le flux tend à effacer les connaissances précédemment acquises, un phénomène appelé oubli catastrophique.Dans ce domaine de l'apprentissage continu en ligne, notre recherche porte sur deux applications importantes en vision par ordinateur : la classification d'images et la détection d'objets. Le flux de données est alors constitué d'une suite d'images.En classification d'images le réseau de neurones doit apprendre progressivement à classifier les images des nouvelles classes sans oublier les anciennes. La méthode la plus répandue pour résoudre ce problème est le rejeu de données, qui consiste à ré-entraîner le modèle avec des images d'anciennes classes stockées dans une mémoire externe. Cependant, cette méthode est peu adaptée lorsque la capacité de stockage et la capacité de calcul pour l'entraînement du modèle sont limitées. Nous proposons une nouvelle méthode qui repose sur un schéma d'entraînement basé sur un classificateur un-contre-tous pour répondre à cette limitation. Notre méthode appelée ILOVA (Incremental Learning of One-Vs-All classifiers) offre un meilleur compromis entre précision, oubli, temps de calcul et empreinte mémoire, que les méthodes de l'état de l'art et se montre particulièrement efficace avec des mémoires très réduites, jusqu'à une seule image par classe.En détection d'objets, de nombreux scénarios de test sont construits à partir de séquences vidéo réelles dans lesquelles des objets peuvent réapparaître à plusieurs reprises à différents moments dans le flux de données. Cependant, ce phénomène de revisite que nous appelons rejeu naturel, est peu documenté et son impact sur les performances et l'oubli reste mal compris. Nous proposons alors une métrique, appelée NRS (Natural Replay Score), permettant de quantifier le degré de rejeu naturel d'un scénario, et montrons qu'il est impossible d'évaluer correctement l'oubli d'un modèle en sa présence. La suite de notre étude porte sur l'analyse de l'oubli dans l'architecture Faster R-CNN lorsqu'elle est utilisée en détection d'objets en ligne. D'une part, nos résultats montrent que des rappels réguliers réduisent l'oubli. D'autre part, nous proposons un nouveau protocole, appelé Module Probing, qui permet de mesurer localement l'oubli dans l'architecture. Nous montrons que l’oubli est concentré dans la couche de classification du Faster R-CNN. Enfin, ces analyses nous mènent à proposer la méthode Configurable Recall, basée sur le rejeu de données. Notre méthode permet d'optimiser la fréquence et la durée des rappels et utilise une fonction de perte modifiée qui limite l'oubli dans la couche de classification. En combinant ces deux éléments, nous parvenons à réduire significativement l'oubli pour l’architecture Faster R-CNNIn this thesis, we focus on the problem of online continual learning in artificial neural networks, which involves learning continuously from a data stream. The main challenge is that integrating new information from the stream tends to overwrite previously acquired knowledge, a phenomenon known as catastrophic forgetting.In the field of online continual learning, our research focus on two important applications in computer vision: image classification and object detection. In these cases, the data stream consists of a sequence of images.In image classification, the neural network must progressively learn to classify images from new classes without forgetting the previous ones. The most common method to address this problem is experience replay, which involves retraining the model with images from previously seen classes stored in external memory. However, this method is less suitable when both storage capacity and computational resources are limited. We propose a new method based on a one-vs-all classifier training scheme to overcome this limitation. Our method, called ILOVA (Incremental Learning of One-Vs-All classifiers), offers a better trade-off between accuracy, forgetting, computational time, and memory footprint compared to state-of-the-art methods and proves particularly effective with very limited memory, down to a single image per class.In object detection, many test scenarios are constructed from real video sequences in which objects can reappear multiple times at different moments in the data stream. However, this phenomenon of reappearance, which we call natural replay, is poorly documented, and its impact on performance and forgetting remains poorly understood. We propose a new metric, called NRS (Natural Replay Score), which quantifies the degree of natural replay in a scenario, and show that it is impossible to properly evaluate model forgetting in its presence. The next part of our study focuses on analyzing forgetting in the Faster R-CNN architecture when used for online object detection. On the one hand, our results show that periodic recalls reduce forgetting. On the other hand, we propose a new protocol, called Module Probing, which allows us to measure forgetting locally within the architecture. We show that forgetting is concentrated in the classification layer of Faster R-CNN. Finally, these analyses lead us to propose a method called Configurable Recall, based on experience replay. Our method optimizes the frequency and duration of the recalls and uses a modified loss function to limit forgetting in the classification layer. By combining these two elements, we significantly reduce forgetting in the Faster R-CNN architecture
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Peters, Mitchell Joseph. "The contribution of lifelong learning ecologies in online higher education: graduate student learning across contexts". Doctoral thesis, Universitat Oberta de Catalunya, 2020. http://hdl.handle.net/10803/670302.
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Les institucions d'ensenyament superior s'han hagut d'adaptar cada vegada més a la digitalització de l'educació, així com a una àmplia demografia d'estudiants que es tornen a incorporar a la formació acadèmica en diferents etapes de la seva carrera. Aquests estudiants, que aprenen al llarg de la vida, sovint han de combinar els estudis amb responsabilitats professionals paral·leles. El propòsit d'aquest estudi interpretatiu és comprendre les experiències dels estudiants en diferents contextos -des d'una perspectiva d'ecologies d'aprenentatge (learning ecologies, LE)- en el context de l'ensenyament superior en línia. L'estudi de cas múltiple de mètodes mixtos principalment qualitatius es va desenvolupar en tres programes de postgrau totalment en línia. Els resultats obtinguts mitjançant l'ús d'un marc analític de LE han demostrat l'efectivitat del constructe a l'hora d'analitzar la complexitat de l'aprenentatge en diferents contextos. Els resultats destaquen la centralitat de l'activitat de l'alumne com un component clau que impulsa les LE d'un individu en combinació amb el suport dels companys i els recursos d'aprenentatge digital en sistemes oberts, dinàmics i fluids que abasten múltiples contextos.Las instituciones de educación superior han tenido que adaptarse cada vez más a la digitalización de la educación, así como a una amplia demografía de estudiantes que vuelven a incorporarse a la educación formal en diferentes etapas de su carrera. Estos estudiantes, que aprenden a lo largo de su vida, a menudo deben combinar los estudios con responsabilidades profesionales paralelas. El propósito de este estudio interpretativo es comprender las experiencias de los estudiantes en distintos contextos -desde una perspectiva de ecologías de aprendizaje (learning ecologies, LE)- en el contexto de la educación superior en línea. El estudio de caso múltiple de métodos mixtos principalmente cualitativos se desarrolló en tres de programas de posgrado totalmente en línea. Los resultados obtenidos mediante el uso de un marco analítico de LE han demostrado la efectividad del constructo a la hora de analizar la complejidad del aprendizaje en distintos contextos. Los resultados destacan la centralidad de la actividad del alumno como un componente clave que impulsa las LE de un individuo en combinación con el apoyo de los compañeros y los recursos de aprendizaje digital dentro de sistemas abiertos, dinámicos y fluidos que abarcan múltiples contextos.
Higher Education institutions have increasingly had to adapt to both the digitalization of education and a broad global demographic of students re-entering formal education at different stages of their careers as lifelong learners, often in combination with parallel professional responsibilities. The purpose of this interpretive study is to understand student experiences and conceptions of learning across contexts - from a learning ecologies perspective - in the context of online HE. The primarily qualitative mixed methods multiple case study was developed across three sites of fully online graduate level programs (master's or 1st year doctoral students) at the UOC and at UIUC. The results obtained using a Learning Ecologies (LE) analytical framework have demonstrated the effectiveness of the construct for analyzing the complexity of learning across multiple contexts. The findings highlight the centrality of learner activity as a key component which drives an individual's LE in combination with peer support and digital learning resources within open, dynamic and fluid systems spanning multiple contexts.
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Zulatto, Rúbia Barcelos Amaral. "A natureza da aprendizagem matemática em um ambiente online de formação continuada de professores /". Rio Claro : [s.n.], 2007. http://hdl.handle.net/11449/102133.
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Orientador: Miriam Godoy PenteadoBanca: João Pedro Mendes da Ponte
Banca: Marcelo de Carvalho Borba
Banca: Maria Elizabeth Bianconcini Trindade Morato Pinto de Almeida
Banca: Vani Moreira Kenski
Resumo: A presente pesquisa analisa a natureza da aprendizagem matemática em um curso online de formação continuada de professores, denominado Geometria com Geometricks. Nele, alunos-professores de uma mesma rede de escolas, situadas em diferentes localidades do país, desenvolveram atividades de Geometria utilizando-se do software Geometricks, e se encontravam para discuti-las. Esses encontros aconteceram a distância, em tempo real, por chat ou videoconferência. Nessa proposta pedagógica, a telepresença condicionou a comunicação e oportunizou o estar-junto-virtual-com-mídias. De modo singular, os recursos da videoconferência permitiram que construções geométricas fossem compartilhadas visualmente e realizadas por todos os envolvidos, fomentando a interação e a participação ativa, constituindo, por meio do diálogo, uma comunidade virtual de aprendizagem. Os resultados levam a inferir que, nesse contexto, a aprendizagem matemática teve natureza colaborativa, na virtualidade das discussões, tecidas a partir das contribuições de todos os participantes; coletiva, na medida em que a produção matemática era condicionada pelo coletivo pensante de seres-humanos-com-mídias; e argumentativa, uma vez que conjecturas e justificativas matemáticas se desenvolveram intensamente do decorrer do processo, contando para isso com as tecnologias presentes na interação ocorrida de forma constante e colaborativa.
Abstract: This study was conducted to analyze the nature of mathematical learning in an online continuing education course for teachers entitled Geometry with Geometricks. Teachers employed in a nation-wide network of privately-supported schools developed geometry activities using the software Geometricks and discussed them in virtual meetings, in real time, via chat or video-conference. In this pedagogical proposal, tele-presence conditioned the communication and provided the opportunity for virtual-togetherness-with-media. In a unique way, the resources of the videoconference made it possible for everyone to participate in and visually share geometrical constructions, encouraging interaction and active participation and constituting a virtual learning community through dialogue. The results indicate that, in this context, mathematical learning nature was characterized by: collaboration, in the virtual discussions that were woven from the contributions of all the participants; collectivity, to the degree to which mathematical production was conditioned by the humans-with-media thinking collective; and argumentation, as the development of mathematical conjectures and justifications was intense throughout the process, aided by the technologies that were present in the constant, collaborative interaction.
Doutor
7
Zulatto, Rúbia Barcelos Amaral [UNESP]. "A natureza da aprendizagem matemática em um ambiente online de formação continuada de professores". Universidade Estadual Paulista (UNESP), 2007. http://hdl.handle.net/11449/102133.
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A presente pesquisa analisa a natureza da aprendizagem matemática em um curso online de formação continuada de professores, denominado Geometria com Geometricks. Nele, alunos-professores de uma mesma rede de escolas, situadas em diferentes localidades do país, desenvolveram atividades de Geometria utilizando-se do software Geometricks, e se encontravam para discuti-las. Esses encontros aconteceram a distância, em tempo real, por chat ou videoconferência. Nessa proposta pedagógica, a telepresença condicionou a comunicação e oportunizou o estar-junto-virtual-com-mídias. De modo singular, os recursos da videoconferência permitiram que construções geométricas fossem compartilhadas visualmente e realizadas por todos os envolvidos, fomentando a interação e a participação ativa, constituindo, por meio do diálogo, uma comunidade virtual de aprendizagem. Os resultados levam a inferir que, nesse contexto, a aprendizagem matemática teve natureza colaborativa, na virtualidade das discussões, tecidas a partir das contribuições de todos os participantes; coletiva, na medida em que a produção matemática era condicionada pelo coletivo pensante de seres-humanos-com-mídias; e argumentativa, uma vez que conjecturas e justificativas matemáticas se desenvolveram intensamente do decorrer do processo, contando para isso com as tecnologias presentes na interação ocorrida de forma constante e colaborativa.
This study was conducted to analyze the nature of mathematical learning in an online continuing education course for teachers entitled Geometry with Geometricks. Teachers employed in a nation-wide network of privately-supported schools developed geometry activities using the software Geometricks and discussed them in virtual meetings, in real time, via chat or video-conference. In this pedagogical proposal, tele-presence conditioned the communication and provided the opportunity for virtual-togetherness-with-media. In a unique way, the resources of the videoconference made it possible for everyone to participate in and visually share geometrical constructions, encouraging interaction and active participation and constituting a virtual learning community through dialogue. The results indicate that, in this context, mathematical learning nature was characterized by: collaboration, in the virtual discussions that were woven from the contributions of all the participants; collectivity, to the degree to which mathematical production was conditioned by the humans-with-media thinking collective; and argumentation, as the development of mathematical conjectures and justifications was intense throughout the process, aided by the technologies that were present in the constant, collaborative interaction.
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Leite, Maria Teresa Meirelles [UNIFESP]. "Cursos em educação médica continuada on-line na América Latina: um olhar pedagógico". Universidade Federal de São Paulo (UNIFESP), 2008. http://repositorio.unifesp.br/handle/11600/9286.
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Previous issue date: 2008-11-28Leite, Maria Teresa Meirelles. Cursos em educação médica continuada on-line na América Latina: um olhar pedagógico. [Online Continuing Medical Education Courses in Latina America: a pedagogical approach]. Orientador: Daniel Sigulem. São Paulo: s.n., 2008. [146]. Dissertação(Mestrado em Ciências)-Universidade Federal de São Paulo. Escola Paulista de Medicina. Resumo: Introdução: A educação médica continuada on-line oferecida por universidades parece priorizar os conteúdos transmitidos, a qualidade estética e a sofisticação tecnológica, dedicando pouca atenção aos aspectos pedagógicos. Os objetivos desta pesquisa são: sistematizar aspectos teóricos de educação, de educação a distância e de educação médica continuada (EMC) e identificar as abordagens pedagógicas prevalentes nos cursos em EMC on-line no Brasil e América Latina, por meio de artigos publicados. Metodologia: Após a elaboração do quadro teórico e do estabelecimento do escopo da pesquisa foram conduzidas buscas bibliográficas em bases de dados eletrônicas internacionais em saúde, medicina e humanidades. A seguir, foram realizadas pesquisas complementares, em websites de universidades da América do Sul. Resultados: Apenas quatro relatos obedeciam plenamente aos critérios estabelecidos para a investigação, sendo que três (3) parecem obedecer à abordagem cognitivista e um (1) à abordagem tradicional. Após a busca complementar, constatou-se que de um total de duzentas e vinte e seis (226) escolas médicas presentes em treze (13) países da América do Sul, estão disponíveis para consulta na Internet treze (13) experiências de EMC on-line. Entre elas, houve referência a concepções de educação em apenas oito (8). Discussão: Foram discriminados os limites impostos a esta pesquisa, observaram-se as características de uma busca bibliográfica envolvendo a área de humanas, estabeleceu-se que os resultados não constituem um referencial para avaliar os cursos quanto à concepção de educação, considerou-se a legitimidade dos resultados como amostragem significativa para ilustrar o volume de artigos disponíveis sobre o tema. Foram analisados os artigos incluídos, assim como diversos achados relevantes. Foram discutidos outros aspectos relacionados ao quadro teórico inicial, como perfil do docente e pós-modernidade. Conclusões: 1. Praticamente inexiste, na comunidade acadêmica latino-americana que desenvolve estudos pedagógicos nesta área, um envolvimento com a publicação científica nas bases de dados pesquisadas. 2. A falta de informações sobre aspectos pedagógicos pode revelar a ausência de um especialista em educação no planejamento dos cursos e pode apontar para a necessidade dos cursos de Pedagogia brasileiros investirem na formação do profissional capaz de atender essa demanda. 3. Há grande tendência das instituições privadas oferecerem educação continuada a distância, o que ficou evidente a partir da pesquisa complementar nos sites das universidades. 4. A partir dos resultados, foi possível constatar também que vários autores concordam que, com as recentes mudanças sociais, muitas transformações são esperadas da Educação. 5. O quadro teórico desenvolvido pode auxiliar a estabelecer fundamentos para reconhecer onde o conhecimento pedagógico se insere no contexto da EaD. 6. Além da pesquisa sobre o número de cursos oferecidos na América Latina e suas características, pode ser relevante utilizar o mesmo referencial teórico para estabelecer critérios de análise qualitativa dos cursos oferecidos por uma instituição específica, indicando possibilidade de novas pesquisas..
Introduction: Continuing medical education online offered by universities in Brazil seems to prioritize aesthetic quality and technological sophistication, paying little attention to pedagogical aspects. The objectives of this research are: to systematize theoretical aspects to the concepts of education, distance learning and continuing medical education (CME) and to identify the pedagogical approaches adopted in CME online courses in Brazil and Latin America by means of published articles. Method: After the construction of the theoretical basis and the establishment of the research limits, bibliographical queries in international electronic databases in health, medicine and humanities were carried through. Following, complementary research was performed in South American universities websites. Results: Only four articles obeyed the established criteria, from which three (3) seem to hold a cognitivist pedagogical approach and one (1) held a traditional approach of education. After the complementary search, it was found that, from a total of two hundred and twenty and six (226) medical schools in thirteen (13) South American countries, there were thirteen (13) online CME courses available for consultation in the Internet. Among them, only eight (8) referred to the education conceptions. Discussion: The limits for the query were discriminated, the characteristics of a bibliographical query involving the humanities was observed, it was established that the present results cannot constitute a referential tool to evaluate the courses, the legitimacy of the results as a significant sampling to illustrate the volume of available articles on the subject was discussed, the four articles were deeply analyzed, as well as diverse other findings that were not included. Besides, other aspects were discussed, as teacher profile and post modernity. Conclusions: 1. There is practically no involvement with scientific publication in the searched databases by the Latin American researchers in educational approaches for online continuing medical education. 2. The lack of information on pedagogical aspects may reveal the absence of an education specialist in the courses design and may suggest for the necessity for Brazilian Pedagogy courses to form a professional who is capable of answering this demand. 3. The trend of private institutions to offer online CME was evidenced from the complementary research. 4. From the results, it was possible to see that some authors agree that with the recent social changes, many transformations are expected from Education. 5. The developed theoretical basis may be able to contribute to establish connections to understand how the pedagogical knowledge inserts in the context of distance learning. 6. Beyond the research on the number of courses offered in Latin America and its characteristics, it is desirable to use the same theoretical referential to establish criteria for qualitative analysis for the courses offered by a specific institution, indicating a possibility of continuing this research.
TEDE
BV UNIFESP: Teses e dissertações
9
Henderson, Filho José Ruy. "Formação continuada de professores de música em ambiente de ensino e aprendizagem online". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/11229.
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Este estudo teve como objetivo investigar a viabilidade da educação online como estratégia de formação continuada de professores de música em exercício na educação básica. O método de pesquisa utilizado foi a pesquisa-ação, que consistiu de um curso online sobre tecnologia na educação musical destinado a professores de música de escolas públicas situadas no Estado do Pará. Oito professores aceitaram participar da pesquisa. Foram utilizadas três técnicas de pesquisa: questionário auto-administrado, observação participante e entrevista semiestruturada. Os resultados da presente pesquisa indicam que as bases para criação de programas de formação continuada de professores de música em exercício na educação básica, por meio da educação online, estão na concepção metodológica que a orienta, na gestão eficiente do ambiente, na adequação do material didático disponibilizado ao conteúdo do curso e no apoio institucional.This study aimed to investigate the viability of online education as a strategy for inservice music teacher education. The research method used was the actionresearch, which consisted of an online course on music technology for in-service music teachers of public schools situated in the State of Pará, Brazil. Eight teachers agreed to participate in this research. Three research techniques have been used: self-administered questionnaires, observation and semi-structured interviews. The results point out that the basis for the creation of in-service music teacher education programs, using online education, resides on the conception that orient the program, on efficient administration of the environment, on adequation of the teaching material available to the content of the course and on the institutional support.
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Grau, Valldosera Josep. "A dropout definition for continuance intention and effective re-enrolment models in online distance learning". Doctoral thesis, Universitat Oberta de Catalunya, 2020. http://hdl.handle.net/10803/668911.
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Aquesta recerca intenta trobar una definició d'abandonament (dropout) empírica i adaptada als estudiants de la UOC i, per extensió, potencialment adaptable a altres institucions d'aprenentatge a distància en línia. Hem fet una anàlisi de la quantitat de semestres consecutius de descans (N) que fan que es consideri que un estudiant d'un programa determinat ha abandonat els estudis (amb una probabilitat inferior al 5% de retorn o error màxim). Aquesta N és diferent per a cada programa, com també ho és el percentatge global d'abandonament. Per a tots els programes hi ha una alta probabilitat d'abandonament després d'una pausa, especialment quan aquesta interrupció és durant el segon o tercer semestre (early dropout). La recerca continua posant el focus d'anàlisi en l'early dropout, amb la definició de dos models per al conjunt de nous estudiants de grau que descansen durant el segon semestre: el model d'intenció de continuar i el de rematrícula efectiva (o prolongació de descans), ambdós durant el tercer semestre.Esta investigación trata de encontrar una definición de abandono (dropout) empírica y adaptada a los estudiantes de la UOC y, por extensión, potencialmente adaptable a otras instituciones de aprendizaje a distancia en línea. Hemos realizado un análisis de la cantidad de semestres consecutivos de descanso (N) que hacen que se considere que un estudiante de un programa determinado ha abandonado los estudios (con una probabilidad inferior al 5 % de retorno o error máximo). Esta N es diferente para cada programa, así como el porcentaje global de abandono. Para todos los programas, hay una alta probabilidad de abandono tras una pausa, especialmente cuando esta interrupción es en el segundo o tercer semestre (early dropout). La investigación continúa poniendo el foco de análisis en el early dropout, con la definición de dos modelos para el conjunto de nuevos estudiantes de grado que descansan en el segundo semestre: el modelo de intención de continuar y el de rematrícula efectiva (o prolongación de descanso), ambos en el tercer semestre.
The aim of this research is to establish an empirical definition of dropout that is tailored to UOC students and can be adapted later on to other online distance learning institutions. To do so, we analyse the number of consecutive rest periods (N) needed for a student on a particular programme to be considered to have dropped out (with a probability of less than 5% return or maximum error). This N proves to be different for each programme, as does the overall dropout rate. All programmes show a high probability of students' leaving after a break, especially when this interruption in their studies takes place in the second or third semester (early dropout). The research then focuses on early dropout, defining two models for the total number of new bachelor's degree students taking a break in the second semester: the continuance intention model and the effective re-enrolment model (or extended rest period), both for the third semester.
Książki na temat "Online continual learning"
1
London, Manuel, red. The Oxford Handbook of Lifelong Learning, Second Edition. Oxford University Press, 2020. http://dx.doi.org/10.1093/oxfordhb/9780197506707.001.0001.
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This handbook is currently in development, with individual articles publishing online in advance of print publication. At this time, we cannot add information about unpublished articles in this handbook, however the table of contents will continue to grow as additional articles pass through the review process and are added to the site. Please note that the online publication date for this handbook is the date that the first article in the title was published online. For more information, please read the site FAQs.
2
Archibald, Robert B. Technological Threat. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190251918.003.0008.
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This chapter explores the rapid growth of online education. It evaluates the likelihood that distance learning will break the rise in college cost and disrupt the traditional model of campus-based programming. There are many ways that the rapid entrepreneurial development of digital learning can change how higher education is provided, and there are many ways that online education may work in the labor market of the future. Although digital techniques will continue to improve and develop, this chapter does not forecast an upheaval in the way higher education is delivered to students in the traditional age range of eighteen to twenty-four. Online education will continue to expand the opportunities for older and returning students, while traditional campus-based programming will continue to offer the best alternative for younger first-time students.
3
Carew, Thomas J., red. The Oxford Handbook of the Neurobiology of Learning and Memory. Oxford University Press, 2020. http://dx.doi.org/10.1093/oxfordhb/9780190069162.001.0001.
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This handbook is currently in development, with individual articles publishing online in advance of print publication. At this time, we cannot add information about unpublished articles in this handbook, however the table of contents will continue to grow as additional articles pass through the review process and are added to the site. Please note that the online publication date for this handbook is the date that the first article in the title was published online. For more information, please read the site FAQs.
4
Chakraborty, Mou, Samantha Harlow i Heather Moorefield-Lang, red. Sustainable Online Library Services and Resources. ABC-CLIO, LLC, 2022. http://dx.doi.org/10.5040/9798216184935.
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COVID-19 shifted library services and resources; this book includes narratives of lessons learned and sustainable practices to prepare educators and librarians for any challenges that might arise in the future. All different types of libraries (academic, public, special, and school) were impacted by the pandemic, and librarians learned valuable lessons about how to shift and transition in a challenging time. In this informative book, librarians, instructional designers, educators, and faculty from all over the world write about how they pivoted services and resources online to continue to serve patrons during a pandemic and beyond, as well as which services and programs will be sustainable and scalable. Online delivery of programs and services allows librarians to respond to many different situations, emergencies, and challenges, and this book is a record of the lessons that librarians learned and the practices they'll implement in the future based on their experiences with COVID-19. Sustainable Online Library Services and Resources showcases a diverse range of perspectives on how online learning has changed and grown with a focus on what library services and resources are here to stay.
5
Spisak, Jen. Multimedia Learning Stations. ABC-CLIO, LLC, 2015. http://dx.doi.org/10.5040/9798400688317.
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Learn how to use rotating multimedia learning stations, employing databases, websites, education apps, videos, audio podcasts, online games, books, and more to build a strong, collaborative library program that helps you strengthen student understanding of the research process. Libraries across America are losing funding and suffering from cuts in positions and programs. The process presented here will help you increase library use and prove that school libraries—and librarians—are a necessity. Written for middle and secondary school librarians, the book provides a guide to using standards-based and content-focused learning stations in the library to facilitate instruction and strengthen students' research skills. You'll learn what multimedia stations are, why they should be used, and how to use them to enhance and extend direct instruction. Plus, the book breaks down the steps for building sets of stations and shows you how to organize and implement them for maximum impact. In addition to describing the "hows," the book provides sound arguments for why multimedia learning stations work. The method ensures that students gain continued practice with resources and build the skills and dispositions you want them to have. It also increases the amount of collaboration you'll have with teachers and enhances your interactions with and influence on your students. Teacher and student testimonials are interspersed throughout the book, and appendices offer you specific examples from which to draw.
6
Barnhurst, Kevin G. Longer News Turned Elite. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040184.003.0003.
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This chapter considers the elitism in digital media. During the early years of the Internet in the 1990s, there were high expectations for new media and harsh criticism for legacy news. A decade later a majority of U.S. newspapers had an online presence, and reporters and editors claimed that technology was changing what they do. However, U.S. news followed, and even continued in digital venues, the century-spanning trend of growing longer. The chapter argues that long stories are a sign of status in line with the elitism of American modernism. Elite writers appear to write the longest and elite readers to read the longest daily news. Efforts are made to serve the elites because they are the ones most likely to contribute to political parties and run for political office. In contrast, short, realist news articles match the predilections and limited time and resources of the non-elite: the wage laborer, the working parent, the immigrant learning the language, the less educated, the young, the poor.
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Roberts, Ann. Designing Adult Services. ABC-CLIO, LLC, 2017. http://dx.doi.org/10.5040/9798400639388.
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Focusing on adult patrons ages 19 through senior citizens, this book explains how libraries can best serve this portion of their community's population at different life stages and foster experiences that are "worth the trip"whether actual or virtual. Adult library patrons are busier than ever beforeworking, taking classes and studying for advanced degrees, caring for children, helping their aging parents, taking care of their homes or rental properties, planning and nurturing careers, managing investments and retirement funds, and inevitably retiring. Each of these endeavors can require highly specific learning and education. Throughout their lives, adults continue to have different information needs that the library and its services can fill. Designing Adult Services: Strategies for Better Serving Your Community discusses the many ways libraries can serve adults of various ages and at different life stages, covering online services, collection development, programming, and lifelong learning. This guide's unique approach simplifies the processes of designing and carrying out a successful adult services program for adult library users in all the various stages of life. The book is organized by age groups, with the respective information needs and life challenges. Each chapter suggests programs, services, and collection development strategies for the life stages. Public library administrators and managers as well as adult services librarians in public libraries will find this guide a must-read.
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Hangan, Horia, i Ahsan Kareem, red. The Oxford Handbook of Non-Synoptic Wind Storms. Oxford University Press, 2020. http://dx.doi.org/10.1093/oxfordhb/9780190670252.001.0001.
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This handbook is currently in development, with individual articles publishing online in advance of print publication. At this time, we cannot add information about unpublished articles in this handbook, however the table of contents will continue to grow as additional articles pass through the review process and are added to the site. Please note that the online publication date for this handbook is the date that the first article in the title was published online. For more information, please read the site FAQs. Wind storms impact human lives, their built as well as natural habitat. During the last century, society’s vulnerability to wind storms has been reduced by enhanced knowledge of their impact and by controlling exposure through better design. However, only two of the wind systems have so far been considered in the design of buildings and structures, i.e., synoptic winds resulting from macroscale weather systems spanning thousands of kilometers, e.g., extratropical storms, and mesoscale tropical storms spanning hundreds of kilometers and traveling fast, e.g., hurricanes/typhoons/cyclones. During the last two decades, enough evidence has surfaced to support that a third type of very localized wind storms, the non-synoptic winds, are the most damaging in some regions of the world. Thus far there are no design provisions established for the codification of these wind storms. Their characterization in terms of climatology, wind field and intensity, frequency and occurrence, as well as their impact on the built environment, is slowly developing. This handbook presents the state-of-the-art of knowledge related to all these features including their risk, insurance issues, and economics. The research in this area is on the one hand more arduous given the reduced scale, the three-dimensionality, and nonstationary aspects of these non-synoptic winds while, at the same time, its understanding and modeling are being aided by the emergence of novel modeling and simulation techniques which are addressed in this handbook. This will serve as a guiding resource for those interested in learning about and contributing to the advancement of the field.
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Pope, Elizabeth M., Catarina Brandão i Cedric C. Sanders. Scientific Congresses: What is Our Future? Ludomedia, 2022. http://dx.doi.org/10.36367/ntqr.11.2022.editorial.
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As we write these words, the COVID-19 pandemic has become part of our lives in a much more controlled way. For instance, some of our habits have changed and we are able to resume our activities in the way of a “new normal,” returning to social contact with family, friends, and colleagues. In returning to a life without the constraint of the virus at such a high level, the academy tries to resume its rituals, including scholarly events. Email boxes and physical boards at universities are once again filling up with calls for submission of abstracts for congresses, seminars, and workshops. As these events are happening again, academia seems to be reflecting on the pros and cons of onsite scientific events. While acknowledging the importance of such scientific events and their potential for strengthening scholarly communities and collaborations, many academics have begun questioning the real impact of being physically present. This questioning seems to be based on several factors. On the one hand, it is clear that universities have been increasingly devaluing academics’ presence in congresses (unless by invitation). They allocate less funding for these activities, especially for those academics who wish to attend an event without presentation. With no presentation, institutions devalue attendance in performance appraisal processes. Increasingly, academic institutions value publications (indexed, despite some positive movement seeking to counter the tyranny of the “publish or perish” motto), and an academics ability to raise funding. Yet, not all congresses are associated with publication processes in indexed journals or proceedings. Books of abstracts (once edited by any congress) are almost extinct, namely because of their devaluation by institutes of higher learning (and funding entities). On the other hand, the massive and necessary use of online scientific events in 2020 and 2021 allowed us to realize that it is possible, efficient, and effective to hold these events in a format different from the traditional one. The internet offers versatility and more and more congresses are now offered online or in hybrid formats. These formats allow academics to overcome financial and physical complications caused by in-person scholarly events. Academics can request less funding and, at the same time, mitigate concerns of acceptance without presentations, covering classes while away, or having to supplement university sponsorship with personal funds. At some universities, funding comes after attendance regardless of availability of those funds and academics are asked to pay registration fee, plane tickets, and lodging with the expectation of being reimbursed upon return. This is particularly challenging given the present economic situation around the globe. At the same time, while physically at the event and away from families, work continues to accumulate for academics. They then must wade through this excess upon returning home, adding to an already excessive workload. This makes maintaining a work-life balance challenging. We at New Trends in Qualitative Research (NTQR) believe it is particularly relevant to discuss this topic within the context of the release of NTQR Volume 11. NTQR is an indexed journal associated with international scientific events in the field of qualitative research - Congreso Ibero-Americano en Investigación Cualitativa (CIAIQ) and the World Conference on Qualitative Research (WCQR). Specifically, the volume that we edit here aggregates works that, having been originally presented at WCQR2022 (held in an online format), went through a double-blind review process. This volume, annually edited (as WCQR is an annual event), allows us, as editors, to condense a diverse set of qualitative research work, focusing on different topics, and with different methodological designs. And, our concern as editors has always been to assure the quality of the published works, namely through a careful review and editing process. We do not know if we are ready to give up our physical presence at scientific events. But, with opportunities such as online presentations and online publishing venues, we may now be much more judicious in this presence. We may now take time to ponder the relevance of investing in attending a scientific event, and selecting (hand-drawn) two or three events per year, at most. WCQR has a strong emphasis in the building of a scientific community (in this case, bonded by the interest in qualitative research), reconciling physical and online presence, and is associated with quality journals. These aspects help academics to select it as one of the events where it is important to be present. Sincerely, The Editors
Części książek na temat "Online continual learning"
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Dam, Tanmoy, Mahardhika Pratama, MD Meftahul Ferdaus, Sreenatha Anavatti i Hussein Abbas. "Scalable Adversarial Online Continual Learning". W Machine Learning and Knowledge Discovery in Databases, 373–89. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_23.
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Parisi, German I., i Vincenzo Lomonaco. "Online Continual Learning on Sequences". W Recent Trends in Learning From Data, 197–221. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43883-8_8.
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Gunasekara, Nuwan, Heitor Gomes, Albert Bifet i Bernhard Pfahringer. "Adaptive Online Domain Incremental Continual Learning". W Lecture Notes in Computer Science, 491–502. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15919-0_41.
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Fini, Enrico, Stéphane Lathuilière, Enver Sangineto, Moin Nabi i Elisa Ricci. "Online Continual Learning Under Extreme Memory Constraints". W Computer Vision – ECCV 2020, 720–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58604-1_43.
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Wang, Zhen, Liu Liu, Yajing Kong, Jiaxian Guo i Dacheng Tao. "Online Continual Learning with Contrastive Vision Transformer". W Lecture Notes in Computer Science, 631–50. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20044-1_36.
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Davalas, Charalampos, Dimitrios Michail, Christos Diou, Iraklis Varlamis i Konstantinos Tserpes. "Computationally Efficient Rehearsal for Online Continual Learning". W Image Analysis and Processing – ICIAP 2022, 39–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06433-3_4.
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Ye, Fei, i Adrian G. Bors. "Continual Variational Autoencoder Learning via Online Cooperative Memorization". W Lecture Notes in Computer Science, 531–49. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20050-2_31.
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Liu, Zhanyang, i Jinfeng Liu. "Online Continual Learning via Maximal Coding Rate Reduction". W Lecture Notes in Computer Science, 176–87. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5594-3_15.
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Achbany, Youssef, Francois Fouss, Luh Yen, Alain Pirotte i Marco Saerens. "Optimal Tuning of Continual Online Exploration in Reinforcement Learning". W Artificial Neural Networks – ICANN 2006, 790–800. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_82.
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Gunasekara, Nuwan, Heitor Gomes, Albert Bifet i Bernhard Pfahringer. "Adaptive Neural Networks for Online Domain Incremental Continual Learning". W Discovery Science, 89–103. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18840-4_7.
Pełny tekst źródłaStreszczenia konferencji na temat "Online continual learning"
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Raghavan, Siddeshwar, Jiangpeng He i Fengqing Zhu. "DELTA: Decoupling Long-Tailed Online Continual Learning". W 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 4054–64. IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00409.
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Wang, Maorong, Nicolas Michel, Ling Xiao i Toshihiko Yamasaki. "Improving Plasticity in Online Continual Learning via Collaborative Learning". W 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 23460–69. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02214.
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Wang, Guanglu, Xinyue Liu, Wentao Yang, Wenxin Liang, Linlin Zong i Xianchao Zhang. "Online Contrastive Continual Learning with Hard Negative Samples". W ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888382.
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Luong, Kevin, i Michael Thielscher. "Hierarchically Gated Experts for Efficient Online Continual Learning". W 17th International Conference on Agents and Artificial Intelligence, 507–18. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013190000003890.
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Wei, Xiwen, Guihong Li i Radu Marculescu. "Online-LoRA: Task-Free Online Continual Learning via Low Rank Adaptation". W 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 6634–45. IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00646.
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Shahbazinia, Amirhossein, Flavio Ponzina, Jose A. Miranda, Jonathan Dan, Giovanni Ansaloni i David Atienza. "Resource-Efficient Continual Learning for Personalized Online Seizure Detection". W 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1–7. IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781699.
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Pan, Youqi, Wugen Zhou, Yingdian Cao i Hongbin Zha. "Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning". W 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 18019–28. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01706.
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Wang, Guanglu, Xinyue Liu, Wenxin Liang, Linlin Zong i Xianchao Zhang. "Online Class-incremental Continual Learning with Maximum Entropy Memory Update". W 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650909.
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Ye, Fei, i Adrian G. Bors. "Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster Memory". W 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 26202–12. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02476.
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Ganie, Irfan, i S. Jagannathan. "Online Continual Safe Reinforcement Learning-based Optimal Control of Mobile Robot Formations". W 2024 IEEE Conference on Control Technology and Applications (CCTA), 519–24. IEEE, 2024. http://dx.doi.org/10.1109/ccta60707.2024.10666606.
Pełny tekst źródłaRaporty organizacyjne na temat "Online continual learning"
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Noakes, Sierra, Allison Modica i Kristyn Palazzolo. Edcamp Design for Learning Series: A New Bridge Between Research and Professional Learning. Digital Promise, luty 2022. http://dx.doi.org/10.51388/20.500.12265/149.
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In the face of a worldwide pandemic, educators rose to the challenge of engaging students and families remotely to not only continue learning but also to provide as much normalcy as possible at a time of chaos and uncertainty. Many educators were looking for quick, meaningful ways to learn new skills to effectively teach remotely and connect with learners from afar. The Edcamp Community by Digital Promise saw this as an opportunity to further demonstrate how the Edcamp model of professional learning paired with learning sciences research could quickly meet the needs of educators and create spaces for shared learning and networking. The Edcamp team created the Edcamp: Design for Learning (DfL) series. Supported by the Chan Zuckerberg Initiative and in partnership with Transcend, a national nonprofit organization focused on innovation in school design, we refined our model for online Edcamps to emphasize bringing together learning sciences expertise within Digital Promise and educator context and pedagogy expertise from the Edcamp Community to co-design a series of four online Edcamps. This report shares the key lessons learned from that series.
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Akhmedjanova, Diana, i Komiljon Karimov. Covid-19’s Effects on Higher Education in Uzbekistan: The Case of Westminster International University in Tashkent. TOSHKENT SHAHRIDAGI XALQARO VESTMINSTER UNIVERSITETI, listopad 2020. https://doi.org/10.70735/azco9450.
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Higher education (HE) in Uzbekistan moved to distance learning since the beginning of the pandemic with uneven results. Universities responded with different approaches to ensure safety of their communities and continue teaching and learning, often online. Westminster International University in Tashkent (WIUT) faced considerable challenges with distance learning including access to technology and the Internet, lack of skills for teaching online and understanding how students learn best in the new mode. Only 14% of faculty used this approach before. Based on WIUT’s initial experience, we recommend: Universities and educational leaders analyze the technology, software, and skills of academic staff and students—can they access and feel comfortable with online learning? Use a learning management system (LMS) that will meet the requirements and needs of academic staff and students. Teachers should supplement with other tools or methods as useful. Design and deliver professional development workshops and trainings for academic staff and students for effective online learning and use of technologies.
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Ahmed AlGarf, Yasmine. From Self-Awareness to Purposeful Employment: Guiding Egyptian youth using arts-based learning. Oxfam IBIS, sierpień 2021. http://dx.doi.org/10.21201/2021.7932.
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Alwan wa Awtar (A&A), a partner of the Youth Participation and Employment (YPE) programme, implements a youth programme supporting young people to shape their prospects through professional and soft skill development, safe learning space and non-formal education. A&A has learned important lessons throughout its journey. A safe learning environment, flexible learning techniques, visual and performing arts in education and participatory management are key approaches for successful youth programmes. During the COVID-19 period, many of the professional development programmes have been delivered online, which was a good example of adaptation to changing circumstances that ensured the sustainability and continued effectiveness of the programme.
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Vaughter, Philip, Ying-Syuan (Elaine) Huang i Jonghwi Park. Climate Change Displacement and the Right to Education in Small Island Developing States. United Nations University Institute for the Advanced Study of Sustainability, lipiec 2023. http://dx.doi.org/10.53326/lnzk2579.
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This policy brief addresses issues of education rights in the context of climate change and potential climate change displacement for SIDS residents. Challenges to adapting learning systems in the context of climate change are common to many SIDS and other countries, but SIDS also face unique challenges due to their geography, culture, and economic activities. It provides the following recommendations to build the resilience of education systems in SIDS to meet the needs of people displaced by climate change: (i) build and maintain multiple facilities that can serve as evacuation centres so schools can continue as learning facilities in the aftermath of extreme weather events; (ii) continue to develop teaching materials and pedagogies within SIDS’ education systems to be employed during times of disruption, while ensuring learners’ socialisation needs are met in the case of online or distance learning; (iii) create parallel curricular competencies between SIDS and destination countries, and advocate for access to education and training in any bilateral or multilateral arrangements for mobility and migration.
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Ahouansou, Wildfrid, Fadhel Medard Salifou-Bio i Arnaud Dangvenon. Academic success of students and educational trajectories: Characteristics and needs for a digital learning environment at the University of Abomey-Calavi. Mary Lou Fulton Teachers College, luty 2024. http://dx.doi.org/10.14507/mcf-eli.i15.
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The upsurge of COVID-19 proved the need to improve higher education capabilities to continue training even during class discontinuity. In UAC’s context, adopting e-learning is also a way to mitigate the significant number of students enrolled yearly (approximately 60.000) and provide quality education to all. Therefore, this research on students’ success and needs aims to identify the optimal learning conditions conducive to academic and professional success. Targeting two schools of UAC, we investigated the online learning environment, considering material and virtual environments, as well as pedagogical support provided to students at UAC. We analyzed the choice made at UAC to promote blended learning. We conducted in-person interviews with students, faculty, and staff and held a focus group with selected students. We collected information about what is being done regarding e-learning and users' perceptions and requested their needs for an optimal learning environment. We found that UAC does not have optimal conditions to provide quality e-learning to students. Many do not have access to devices (laptops, smartphones, or tablets) or a quality Internet connection. Faculty members still seek training, even if some have received Instructional Design and Pedagogy training from Arizona State University. Students and faculty members also express a need to receive coaching, tutoring, and pedagogical support from the technical staff, which are in a low number (only 3 in charge of supporting e-learning at the whole university). Based on this, we recommended that UAC: 1) develop a better material learning environment for students, faculty members, and staff through the acquisition of devices and improvement of the Internet connection, 2) provide training to students on how to learn online, and 3) develop staff capabilities to coach, tutor and support students and faculty members on the integration of e-Learning in academic practices.
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Desk, Front. Social Return on Investment Analysis of Support from the Commonwealth of Learning for the Open and Innovative Schooling Model. Commonwealth of Learning (COL), październik 2021. http://dx.doi.org/10.56059/11599/3934.
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As the Commonwealth of Learning (COL) was in the last year of its 2015–2021 strategic plan, COL sought to evaluate the impact of its interventions in open and innovative schooling (OIS) in the period 2018–2021 through an external review. An online quantitative study was carried out in Belize, Malawi, Mozambique, Trinidad and Tobago, and Zambia, followed by a contextual cost–benefit analysis, and a social return on investment (SROI) analysis. Overall, COL’s contribution to the OIS programme in terms of Phase 1 and Phase 2 activities is perceived to have significantly contributed to the success noted so far in the five countries under study. The SROI ratios for all countries were positive, indicating that the social returns on this intervention are desirable, justifying a case for continued support of such OIS initiatives.
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Roschelle, Jeremy, Britte Haugan Cheng, Nicola Hodkowski, Julie Neisler i Lina Haldar. Evaluation of an Online Tutoring Program in Elementary Mathematics. Digital Promise, kwiecień 2020. http://dx.doi.org/10.51388/20.500.12265/94.
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Many students struggle with mathematics in late elementary school, particularly on the topic of fractions. In a best evidence syntheses of research on increasing achievement in elementary school mathematics, Pelligrini et al. (2018) highlighted tutoring as a way to help students. Online tutoring is attractive because costs may be lower and logistics easier than with face-to-face tutoring. Cignition developed an approach that combines online 1:1 tutoring with a fractions game, called FogStone Isle. The game provides students with additional learning opportunities and provides tutors with information that they can use to plan tutoring sessions. A randomized controlled trial investigated the research question: Do students who participate in online tutoring and a related mathematical game learn more about fractions than students who only have access to the game? Participants were 144 students from four schools, all serving low-income students with low prior mathematics achievement. In the Treatment condition, students received 20-25 minute tutoring sessions twice per week for an average of 18 sessions and also played the FogStone Isle game. In the Control condition, students had access to the game, but did not play it often. Control students did not receive tutoring. Students were randomly assigned to condition after being matched on pre-test scores. The same diagnostic assessment was used as a pre-test and as a post-test. The planned analysis looked for differences in gain scores ( post-test minus pre-test scores) between conditions. We conducted a t-test on the aggregate gain scores, comparing conditions; the results were statistically significant (t = 4.0545, df = 132.66, p-value < .001). To determine an effect size, we treated each site as a study in a meta-analysis. Using gain scores, the effect size was g=+.66. A more sophisticated treatment of the pooled standard deviation resulted in a corrected effect size of g=.46 with a 95% confidence interval of [+.23,+.70]. Students who received online tutoring and played the related Fog Stone Isle game learned more; our research found the approach to be efficacious. The Pelligrini et al. (2018) meta-analysis of elementary math tutoring programs found g = .26 and was based largely on face-to-face tutoring studies. Thus, this study compares favorably to prior research on face-to-face mathematics tutoring with elementary students. Limitations are discussed; in particular, this is an initial study of an intervention under development. Effects could increase or decrease as development continues and the program scales. Although this study was planned long before the current pandemic, results are particularly timely now that many students are at home under shelter-in-place orders due to COVID-19. The approach taken here is feasible for students at home, with tutors supporting them from a distance. It is also feasible in many other situations where equity could be addressed directly by supporting students via online tutors.
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Abera, Mikyas, Jean Claude Byungura, Raymond Ndikumana, Solomon Mekonnen Abebe, Pierre Celestin Bimenyimana, Rediet Gizaw i Mustofa Worku Jemal. Implementing e-Learning in low-resourced university settings: A policy and institutional perspectives at the University of Gondar and University of Rwanda. Mary Lou Fulton Teachers College, grudzień 2023. http://dx.doi.org/10.14507/mcf-eli.j11.
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The COVID-19 pandemic outbreak affected most universities, and it severely disrupted their face-to-face teaching and learning processes. The University of Gondar (UoG) and the University of Rwanda (UR) were no exceptions. Before the pandemic, E-learning was not an education norm in both institutions. Education was mainly face-to-face, inside a four-wall classroom experience. As COVID-19 restricted such experience, the two universities adopted a range of online platforms to support teaching, learning, and access to learning resources. Across the globe, E-learning solutions promise institutional resilience and innovative teaching and learning activities in tertiary education – but only if their development is embedded within enabling institutional culture, structure, policy, and processes. Against this backdrop, we designed a study to explore leadership and policy perspectives, institutional contexts, potentials/prospects, challenges, and best practices of educational digital solutions. In this exploratory study, we used accessibility and inclusivity as key motifs to frame discussions of results. We used a cross-sectional design and employed qualitative methods to collect data, i.e., document reviews, key informant interviews, and focus group discussions. We adopted a descriptive thematic analysis procedure to organize, analyze, and interpret the data. Overall, the results indicate that education leaders, faculty, and students were not equipped to smoothly transition from face-to-face learning to e-learning in the aftermath of the COVID-19 pandemic. Not only meeting technological requirements, the accelerated deployment of E-learning tools implied a change in pedagogy. We found that institutional policies were not designed to fully accommodate the change (except for some guidelines proposed during COVID-19). The lack of an e-learning strategy and resource limitations have hindered and continue to impact e-learning uptake in both institutions. We also found that poor internet connectivity, lack of tech devices and software, inadequate leadership commitment, power interruptions or outages, inadequate pedagogical training, low community perception, and poor administrative and technical skills are the challenges of the two institutions to effectively manage full-fledged e-learning programs. These challenges were usually amplified by the nature of national, local, and institutional contexts (e.g., a multi-campus, multi-college setting of UR and a war outbreak in northern Ethiopia). Noting that face-to-face education is still seen as premium, there is a need for a blended approach to e-learning and policies that would improve accessibility to and affordability of E-resources to diverse groups of staff and students. With varying degrees, we found that the two institutions are engaging in activities to promote e-learning. For instance, groups of e-learning Champions are advocating in both institutions for engagements in accelerated change efforts (be it on an e-learning platform, capacity building, access devices, and strategy). Both institutions have units that coordinate e-learning uptake and ensure its inclusivity. Both institutions could also benefit from increased governmental and development partners’ attention to the possibility and support of digital education. However, the issue of social equity and e-learning ecosystem management remains paramount in launching e-learning programs. In sum, we observed that e-learning is still in its nascent stages at both institutions although not at the same level. Their respective e-learning initiatives must integrate global best practices and specific local contexts and priorities. This requires that state and institutional leaders embrace and encourage co-creation, knowledge, and expertise sharing among institutions in low-resource and similar settings.
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Raymond, Kara, Laura Palacios, Cheryl McIntyre i Evan Gwilliam. Status of climate and water resources at Saguaro National Park: Water year 2019. Redaktor Alice Wondrak Biel. National Park Service, grudzień 2021. http://dx.doi.org/10.36967/nrr-2288717.
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Climate and hydrology are major drivers of ecosystems. They dramatically shape ecosystem structure and function, particularly in arid and semi-arid ecosystems. Understanding changes in climate, groundwater, and water quality and quantity is central to assessing the condition of park biota and key cultural resources. The Sonoran Desert Network collects data on climate, groundwater, and surface water at 11 National Park Service units in south-ern Arizona and New Mexico. This report provides an integrated look at climate, groundwater, and springs conditions at Saguaro National Park (NP) during water year 2019 (October 2018–September 2019). Annual rainfall in the Rincon Mountain District was 27.36" (69.49 cm) at the Mica Mountain RAWS station and 12.89" (32.74 cm) at the Desert Research Learning Center Davis station. February was the wettest month, accounting for nearly one-quarter of the annual rainfall at both stations. Each station recorded extreme precipitation events (>1") on three days. Mean monthly maximum and minimum air temperatures were 25.6°F (-3.6°C) and 78.1°F (25.6°C), respectively, at the Mica Mountain station, and 37.7°F (3.2°C) and 102.3°F (39.1°C), respectively, at the Desert Research Learning Center station. Overall temperatures in WY2019 were cooler than the mean for the entire record. The reconnaissance drought index for the Mica Mountain station indicated wetter conditions than average in WY2019. Both of the park’s NOAA COOP stations (one in each district) had large data gaps, partially due to the 35-day federal government shutdown in December and January. For this reason, climate conditions for the Tucson Mountain District are not reported. The mean groundwater level at well WSW-1 in WY2019 was higher than the mean for WY2018. The water level has generally been increasing since 2005, reflecting the continued aquifer recovery since the Central Avra Valley Storage and Recovery Project came online, recharging Central Arizona Project water. Water levels at the Red Hills well generally de-clined starting in fall WY2019, continuing through spring. Monsoon storms led to rapid water level increases. Peak water level occurred on September 18. The Madrona Pack Base well water level in WY2019 remained above 10 feet (3.05 m) below measuring point (bmp) in the fall and winter, followed by a steep decline starting in May and continuing until the end of September, when the water level rebounded following a three-day rain event. The high-est water level was recorded on February 15. Median water levels in the wells in the middle reach of Rincon Creek in WY2019 were higher than the medians for WY2018 (+0.18–0.68 ft/0.05–0.21 m), but still generally lower than 6.6 feet (2 m) bgs, the mean depth-to-water required to sustain juvenile cottonwood and willow trees. RC-7 was dry in June–September, and RC-4 was dry in only September. RC-5, RC-6 and Well 633106 did not go dry, and varied approximately 3–4 feet (1 m). Eleven springs were monitored in the Rincon Mountain District in WY2019. Most springs had relatively few indications of anthropogenic or natural disturbance. Anthropogenic disturbance included spring boxes or other modifications to flow. Examples of natural disturbance included game trails and scat. In addition, several sites exhibited slight disturbance from fires (e.g., burned woody debris and adjacent fire-scarred trees) and evidence of high-flow events. Crews observed 1–7 taxa of facultative/obligate wetland plants and 0–3 invasive non-native species at each spring. Across the springs, crews observed four non-native plant species: rose natal grass (Melinis repens), Kentucky bluegrass (Poa pratensis), crimson fountaingrass (Cenchrus setaceus), and red brome (Bromus rubens). Baseline data on water quality and chemistry were collected at all springs. It is likely that that all springs had surface water for at least some part of WY2019. However, temperature sensors to estimate surface water persistence failed...
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McKenna, Patrick, i Mark Evans. Emergency Relief and complex service delivery: Towards better outcomes. Queensland University of Technology, czerwiec 2021. http://dx.doi.org/10.5204/rep.eprints.211133.
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Emergency Relief (ER) is a Department of Social Services (DSS) funded program, delivered by 197 community organisations (ER Providers) across Australia, to assist people facing a financial crisis with financial/material aid and referrals to other support programs. ER has been playing this important role in Australian communities since 1979. Without ER, more people living in Australia who experience a financial crisis might face further harm such as crippling debt or homelessness. The Emergency Relief National Coordination Group (NCG) was established in April 2020 at the start of the COVID-19 pandemic to advise the Minister for Families and Social Services on the implementation of ER. To inform its advice to the Minister, the NCG partnered with the Institute for Governance at the University of Canberra to conduct research to understand the issues and challenges faced by ER Providers and Service Users in local contexts across Australia. The research involved a desktop review of the existing literature on ER service provision, a large survey which all Commonwealth ER Providers were invited to participate in (and 122 responses were received), interviews with a purposive sample of 18 ER Providers, and the development of a program logic and theory of change for the Commonwealth ER program to assess progress. The surveys and interviews focussed on ER Provider perceptions of the strengths, weaknesses, future challenges, and areas of improvement for current ER provision. The trend of increasing case complexity, the effectiveness of ER service delivery models in achieving outcomes for Service Users, and the significance of volunteering in the sector were investigated. Separately, an evaluation of the performance of the NCG was conducted and a summary of the evaluation is provided as an appendix to this report. Several themes emerged from the review of the existing literature such as service delivery shortcomings in dealing with case complexity, the effectiveness of case management, and repeat requests for service. Interviews with ER workers and Service Users found that an uplift in workforce capability was required to deal with increasing case complexity, leading to recommendations for more training and service standards. Several service evaluations found that ER delivered with case management led to high Service User satisfaction, played an integral role in transforming the lives of people with complex needs, and lowered repeat requests for service. A large longitudinal quantitative study revealed that more time spent with participants substantially decreased the number of repeat requests for service; and, given that repeat requests for service can be an indicator of entrenched poverty, not accessing further services is likely to suggest improvement. The interviews identified the main strengths of ER to be the rapid response and flexible use of funds to stabilise crisis situations and connect people to other supports through strong local networks. Service Users trusted the system because of these strengths, and ER was often an access point to holistic support. There were three main weaknesses identified. First, funding contracts were too short and did not cover the full costs of the program—in particular, case management for complex cases. Second, many Service Users were dependent on ER which was inconsistent with the definition and intent of the program. Third, there was inconsistency in the level of service received by Service Users in different geographic locations. These weaknesses can be improved upon with a joined-up approach featuring co-design and collaborative governance, leading to the successful commissioning of social services. The survey confirmed that volunteers were significant for ER, making up 92% of all workers and 51% of all hours worked in respondent ER programs. Of the 122 respondents, volunteers amounted to 554 full-time equivalents, a contribution valued at $39.4 million. In total there were 8,316 volunteers working in the 122 respondent ER programs. The sector can support and upskill these volunteers (and employees in addition) by developing scalable training solutions such as online training modules, updating ER service standards, and engaging in collaborative learning arrangements where large and small ER Providers share resources. More engagement with peak bodies such as Volunteering Australia might also assist the sector to improve the focus on volunteer engagement. Integrated services achieve better outcomes for complex ER cases—97% of survey respondents either agreed or strongly agreed this was the case. The research identified the dimensions of service integration most relevant to ER Providers to be case management, referrals, the breadth of services offered internally, co-location with interrelated service providers, an established network of support, workforce capability, and Service User engagement. Providers can individually focus on increasing the level of service integration for their ER program to improve their ability to deal with complex cases, which are clearly on the rise. At the system level, a more joined-up approach can also improve service integration across Australia. The key dimensions of this finding are discussed next in more detail. Case management is key for achieving Service User outcomes for complex cases—89% of survey respondents either agreed or strongly agreed this was the case. Interviewees most frequently said they would provide more case management if they could change their service model. Case management allows for more time spent with the Service User, follow up with referral partners, and a higher level of expertise in service delivery to support complex cases. Of course, it is a costly model and not currently funded for all Service Users through ER. Where case management is not available as part of ER, it might be available through a related service that is part of a network of support. Where possible, ER Providers should facilitate access to case management for Service Users who would benefit. At a system level, ER models with a greater component of case management could be implemented as test cases. Referral systems are also key for achieving Service User outcomes, which is reflected in the ER Program Logic presented on page 31. The survey and interview data show that referrals within an integrated service (internal) or in a service hub (co-located) are most effective. Where this is not possible, warm referrals within a trusted network of support are more effective than cold referrals leading to higher take-up and beneficial Service User outcomes. However, cold referrals are most common, pointing to a weakness in ER referral systems. This is because ER Providers do not operate or co-locate with interrelated services in many cases, nor do they have the case management capacity to provide warm referrals in many other cases. For mental illness support, which interviewees identified as one of the most difficult issues to deal with, ER Providers offer an integrated service only 23% of the time, warm referrals 34% of the time, and cold referrals 43% of the time. A focus on referral systems at the individual ER Provider level, and system level through a joined-up approach, might lead to better outcomes for Service Users. The program logic and theory of change for ER have been documented with input from the research findings and included in Section 4.3 on page 31. These show that ER helps people facing a financial crisis to meet their immediate needs, avoid further harm, and access a path to recovery. The research demonstrates that ER is fundamental to supporting vulnerable people in Australia and should therefore continue to be funded by government.