Collaborative Learning Framework Research Articles

Educator capacity for online global collaborative learning: developing a framework

ABSTRACT Despite recent moves to online learning, online global collaborative learning does not seem to have been taken up as a daily practice by most educators in the K–12 context. Yet it is evident that early adopters are seeing the benefits of such practice, not only for students but for their own professional learning. The research described here uses single case-study methodology to investigate a small group of educators, located in a range of countries and with varied teaching experiences, who have identified the potential of global interactions. Data analysis, using manual and software-supported coding, identified three key influences on the educators’ personal pedagogies: their disposition to online learning, their approach to professional learning, and conceptual change. The findings were used to construct an online global collaborative learning framework, which has the potential to inform considerations about building educator capacity in digital pedagogical knowledge.

A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes.

Recent advances in spatial transcriptomics technologies have provided multi-modality data integrating gene expression, spatial context, and histological images. Accurately identifying spatial domains and spatially variable genes is crucial for understanding tissue structures and biological functions. However, effectively combining multi-modality data to identify spatial domains and determining SVGs closely related to these spatial domains remains a challenge. In this study, we propose spatial transcriptomics Multi-modality and Multi-granularity Collaborative Learning (spaMMCL). For detecting spatial domains, spaMMCL mitigates the adverse effects of modality bias by masking portions of gene expression data, integrates gene and image features using a shared graph convolutional network, and employs graph self-supervised learning to deal with noise from feature fusion. Simultaneously, based on the identified spatial domains, spaMMCL integrates various strategies to detect potential SVGs at different granularities, enhancing their reliability and biological significance. Experimental results demonstrate that spaMMCL substantially improves the identification of spatial domains and SVGs. The code and data of spaMMCL are available on Github: Https://github.com/liangxiao-cs/spaMMCL. Supplementary data are available at Bioinformatics online.

Decentralized collaborative machine learning for protecting electricity data

In recent years, there has been a noticeable surge in electric power load due to economic development and improved living standards. The growing need for smart power solutions, such as leveraging user electricity data to forecast power peaks and utilizing power data statistics to enhance end-user services, has been on the rise. However, the misuse and unauthorized access of data have prompted stringent regulations to safeguard data integrity. This paper presents a novel decentralized collaborative machine learning framework aimed at predicting peak power loads while protecting the privacy of users’ power data. In this scheme, multiple users engage in collaborative machine learning training within a peer-to-peer network free from a centralized server, with the objective of predicting peak power loads without compromising users’ local data privacy. The proposed approach leverages blockchain technology and advanced cryptographic techniques, including multi-key homomorphic encryption and consistent hashing. Key contributions of this framework include the development of a secure dual-aggregate node aggregation algorithm and the establishment of a verifiable process within a decentralized architecture. Experimental validation has been conducted to assess the feasibility and effectiveness of the proposed scheme, demonstrating its potential to address the challenges associated with predicting peak power loads securely and preserving user data privacy.

Meshwork choreography: a pragmatic approach to collaborative embodied learning in South African higher education

ABSTRACT The diversity of the South African higher educational student population requires innovative pedagogical strategies that facilitate transformative and collaborative embodied learning. This article demonstrates how the choreographic process emerges from the context of multiculturalism through a pragmatic learning approach called meshwork choreography. Meshwork suggests an interwoven relation that emphasises the ever-evolving nature of relationships within an environment. Choreography as meshwork is where the choreographer does not impose preconceived ideas on the dancers but rather facilitates a collaborative and exploratory process. The research employed a conceptual framework of phenomenography, embodied and collaborative learning to frame the discussion towards a model of how choreography functions as meshwork. Composition and design processes in and of themselves embrace transformative learning through collaborative, embodied, and relational practices and are bolstered when they converge within the creation of choreographic work. This article reflects on students’ experiences in the Performing Arts Dance Stream at Tshwane University of Technology in conceptualising choreography as meshwork. Phenomenography was adopted as the research methodology, where data was analysed within phenomenographic categories. The article offers a South African perspective, informed by Ubuntu, of a meshwork model for the benefits of applying transformative learning through choreography within a higher education dance context.

A Federated Transfer Fault Diagnosis Method for Cross-Domain and Incomplete Data

In fact, multiple clients often use similar devices and collect fault data separately, so joint multi-client collaborative fault diagnosis modeling can solve the problem of data scarcity, but this poses great challenges to data privacy protection. In this paper, we propose a federated transfer fault diagnosis method based on federated learning for cross-domain incomplete data. The proposed method only exchanges the parameters of the local training model, which achieves the privacy protection of the client’s local data. We construct a multi-client collaborative learning framework to address the problem of weak generalization ability caused by the lack of terms in single client training samples. We also propose a targeted semi-supervised fine-tuning strategy based on relative distance to reduce the probability of negative fine-tuning of out-of-distribution samples and improve the accuracy of diagnostic models. The results of cross-condition and cross-equipment experiments demonstrate that the proposed method has obvious advantages over the existing fault diagnosis methods.

Device-Performance-Driven Heterogeneous Multiparty Learning for Arbitrary Images.

Multiparty learning (MPL) is an emerging framework for privacy-preserving collaborative learning. It enables individual devices to build a knowledge-shared model and remaining sensitive data locally. However, with the continuous increase of users, the heterogeneity gap between data and equipment becomes wider, which leads to the problem of model heterogeneous. In this article, we concentrate on two practical issues: data heterogeneous problem and model heterogeneous problem, and propose a novel personal MPL method named device-performance-driven heterogeneous MPL (HMPL). First, facing the data heterogeneous problem, we focus on the problem of various devices holding arbitrary data sizes. We introduce a heterogeneous feature-map integration method to adaptively unify the various feature maps. Meanwhile, to handle the model heterogeneous problem, as it is essential to customize models for adapting to the various computing performances, we propose a layer-wise model generation and aggregation strategy. The method can generate customized models based on the device's performance. In the aggregation process, the shared model parameters are updated through the rules that the network layers with the same semantics are aggregated with each other. Extensive experiments are conducted on four popular datasets, and the result demonstrates that our proposed framework outperforms the state of the art (SOTA).

Effective knowledge representation and utilization for sustainable collaborative learning across heterogeneous systems

AbstractThe increasingly decentralized and private nature of data in our digital society has motivated the development of collaborative intelligent systems that enable knowledge aggregation among data owners. However, collaborative learning has only been investigated in simple settings. For example, clients are often assumed to train solution models de novo, disregarding all prior expertise. The learned model is typically represented in task‐specific forms that are not generalizable to unseen, emerging scenarios. Finally, a universal model representation is enforced among collaborators, ignoring their local compute constraints or input representations. These limitations hampers the practicality of prior collaborative systems in learning scenarios with limited task data that demand constant knowledge adaptation and transfer across information silos, tasks, and learning models, as well as the utilization of prior solution expertise. Furthermore, prior collaborative learning frameworks are not sustainable on a macro scale where participants desire fairness allocation of benefits (e.g., access to the combined model) based on their costs of participation (e.g., overhead of model sharing and training synchronization, risk of information breaches, etc.). This necessitates a new perspective of collaborative learning where the server not only aggregates but also conducts valuation of the participant's contribution, and distribute aggregated information to individuals in commensurate to their contribution. To substantiate the above vision, we propose a new research agenda on developing effective and sustainable collaborative learning frameworks across heterogeneous systems, featuring three novel computational capabilities on knowledge organization: model expression, comprehension, and valuation.

“Never Met Them in Person, But We Help Each Other”: Black Women’s Experiences in Online Undergraduate Engineering Programs

In this empirical analysis, we provide a qualitative investigation of online learning among Black women engineering majors at one institution. Two research questions guide this analysis: (1) How do Black women describe their online program learning experiences? and (2) how do participants describe their interactions with other students and faculty in their online programs? We used an exploratory, qualitative case study research design to collect data for this project and we were guided by Harasim’s online collaborative learning framework. After conducting multiple rounds of qualitative coding, authors arrived at four major themes in response to these research questions. These themes were that participants: (1) experienced isolation, (2) witnessed varying impacts of COVID-19, (3) observed benefits and limitations of flexibility of online programs, and (4) engaged in virtual connection-building. We conclude our piece by discussing implications for faculty development and course designers. Key words: Black women, COVID-19, engineering, flexibility, online learning

Collaborative framework to assess achievement of American Association of Colleges of Nursing and National Organization of Nurse Practitioner Faculties patient-centered competencies using evolving case studies

BackgroundThe American Association of Colleges of Nursing (AACN) and National Organization of Nurse Practitioner Faculties (NONPF) emphasize the role of clinical reasoning in nurse practitioner (NP) competencies. Evidence-based clinical reasoning is vital to patient safety. Collaborative technology tools can aid in assessing progress towards achieving clinical reasoning competency. Purpose/aimsThe purpose of this article is to describe an electronic, collaborative learning framework to teach and assess second year NP students in systematically selecting and eliminating diagnoses and forming treatment plans. Design/methodsPost gap analysis, the collaborative learning framework was created. This visual, collaborative resource was scaffolded across two sequential advanced NP second year clinical synthesis courses and embedded with evolving case studies. Students identified pertinent positives and negatives from the history, physical, and diagnostic findings. Each student developed a unique differential diagnosis and plan of care and critiqued their peers. Result/findingsThe tool exceeded expectations. Faculty were able to visualize data, provide clarification on interpretation of data and pharmacology, and grade in small groups. ConclusionThe collaborative learning framework provided real-time visualization of students' work in clinical reasoning. It was easy to use and integrate into second year NP courses to meet learning objectives and assess clinical reasoning competency.

BearingFM: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning

Monitoring bearing failures in production equipment can effectively prevent finished product quality issues and unplanned factory downtime, thereby reducing supply chain uncertainties and risk. Therefore, monitoring bearing failures in production equipment is important for improving supply chain sustainability. Due to the generalization limitations of neural network models, specific models must be trained for specific tasks. However, in real industrial scenarios, there is a severe lack of labeled samples, making it difficult to deploy fault diagnosis models across massive amounts of equipment in workshops. In order to solve the above issue, this paper proposes a cloud-edge-end collaborative semi-supervised learning framework, which provides multi-level computing power and data support for building a foundation model. A data augmentation method based on the bearing fault mechanism is proposed, which effectively preserves the inherent essential characteristics in vibration signals by normalizing frequency and adding noise in specific frequency bands. A novel contrastive learning model is designed, which narrows the distances between positive samples and widens the distances between negative samples in the high-dimensional space through cross comparisons in the time dimension and knowledge dimension, thereby extracting the most essential characteristics from the unlabeled signals. Multiple sets of experiments conducted on four datasets demonstrate that the proposed approach achieves an approximately 98% fault classification accuracy with only 1.2% labeled samples.

A New Framework of Collaborative Learning for Adaptive Metric Distillation.

This article presents a new adaptive metric distillation approach that can significantly improve the student networks' backbone features, along with better classification results. Previous knowledge distillation (KD) methods usually focus on transferring the knowledge across the classifier logits or feature structure, ignoring the excessive sample relations in the feature space. We demonstrated that such a design greatly limits performance, especially for the retrieval task. The proposed collaborative adaptive metric distillation (CAMD) has three main advantages: 1) the optimization focuses on optimizing the relationship between key pairs by introducing the hard mining strategy into the distillation framework; 2) it provides an adaptive metric distillation that can explicitly optimize the student feature embeddings by applying the relation in the teacher embeddings as supervision; and 3) it employs a collaborative scheme for effective knowledge aggregation. Extensive experiments demonstrated that our approach sets a new state-of-the-art in both the classification and retrieval tasks, outperforming other cutting-edge distillers under various settings.

DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.

Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. Datasets and source codes are available at https://github.com/Biohang/DiSMVC.

Data distribution inference attack in federated learning via reinforcement learning support

Federated Learning(FL) is currently a widely used collaborative learning framework, and the distinguished feature of FL is that the clients involved in training do not need to share raw data, but only transfer the model parameters to share knowledge, and finally get a global model with improved performance. However, recent studies have found that sharing model parameters may still lead to privacy leakage. From the shared model parameters, local training data can be reconstructed and thus lead to a threat to individual privacy and security. We observed that most of the current attacks are aimed at client-specific data reconstruction, while limited attention is paid to the information leakage of the global model. In our work, we propose a novel FL attack based on shared model parameters that can deduce the data distribution of the global model. Different from other FL attacks that aim to infer individual clients’ raw data, the data distribution inference attack proposed in this work shows that the attackers can have the capability to deduce the data distribution information behind the global model. We argue that such information is valuable since the training data behind a well-trained global model indicates the common knowledge of a specific task, such as social networks and e-commerce applications. To implement such an attack, our key idea is to adopt a deep reinforcement learning approach to guide the attack process, where the RL agent adjusts the pseudo-data distribution automatically until it is similar to the ground truth data distribution. By a carefully designed MDP process, our implementation ensures our attack can have stable performance and experimental results verify the effectiveness of our proposed inference attack.

CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions.

Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the exploration of data-driven deep learning technologies as a viable, efficient, and accurate alternative. Nonetheless, most current deep learning-based methods regarded a pair of proteins to be predicted for possible interaction as two separate entities when extracting PPI features, thus neglecting the knowledge sharing among the collaborative protein and the target protein. Aiming at the above issue, a collaborative learning framework CollaPPI was proposed in this study, where two kinds of collaboration, i.e., protein-level collaboration and task-level collaboration, were incorporated to achieve not only the knowledge-sharing between a pair of proteins, but also the complementation of such shared knowledge between biological domains closely related to PPI (i.e., protein function, and subcellular location). Evaluation results demonstrated that CollaPPI obtained superior performance compared to state-of-the-art methods on two PPI benchmarks. Besides, evaluation results of CollaPPI on the additional PPI type prediction task further proved its excellent generalization ability.

SDCL: A Framework for Secure, Distributed, and Collaborative Learning in Smart Grids

SDCL: A Framework for Secure, Distributed, and Collaborative Learning in Smart Grids

CoLLaRS : A cloud–edge–terminal collaborative lifelong learning framework for AIoT

AIoT applications often encounter challenges such as terminal resource constraints, data drift, and data heterogeneity in real world, leading to problems such as catastrophic forgetting, low generalization ability, and low accuracy during model training. To address these challenges, we proposed CoLLaRS, a cloud–edge–terminal collaborative lifelong learning framework for AIoT applications. In the CoLLaRS framework, we alleviate the problem of terminal resource constraints by uploading terminal tasks at the edge. CoLLaRS uses continuous training at the edge to achieve lifelong learning training of the model and solve the problem of catastrophic forgetting. CoLLaRS employs federated optimization in the cloud to perform personalized aggregation of different edge models and solve the problem of weak model generalization ability. Finally, the model is fine-tuned at the terminal to further optimize its accuracy in local tasks. Our experiments on real-world datasets showed that CoLLaRS has an 8% improvement in accuracy and a 5% improvement in backward transfer(BWT) and forward transfer(FWT) compared to other baseline algorithms. The results of the ablation experiments further confirmed the effectiveness of CoLLaRS.

Advancing cross-subject olfactory EEG recognition: A novel framework for collaborative multimodal learning between human-machine

Odor sensory evaluation is broadly applied in food, clothing, cosmetics, and other fields. Traditional artificial sensory evaluation has poor repeatability, and the machine olfaction represented by the electronic nose (E-nose) is difficult to reflect human feelings. Olfactory electroencephalogram (EEG) contains features associated with human olfactory preference, which has unique advantages in odor sensory evaluation. However, the difficulty of cross-subject olfactory EEG recognition dramatically limits its application. In this paper, a human–machine collaborative multimodal learning method is proposed for cross-subject olfactory preference recognition. Firstly, the olfactory EEG and E-nose multimodal data acquisition and preprocessing paradigms are established. Secondly, a complementary multimodal data mining strategy is proposed to effectively mine the individual and common features representing subjects' olfactory preferences from multimodal data. Finally, the cross-subject olfactory preference recognition is achieved in 24 subjects by fusing the extracted common and individual features, and the recognition effect is superior to the state-of-the-art recognition methods. Furthermore, the advantages of the proposed method in cross-subject olfactory preference recognition indicate its potential for practical odor evaluation applications.

Revolutionizing online business education: Leveraging the Collaborative Engagement Experience-Based Learning (CEEBL) Framework in a Post-Pandemic World

In this study, the CEEBL teaching framework is further explored across three academic levels of online business courses. These classes emphasize collaborative teamwork using hands-on activities with projects aimed at tackling real world challenges. Students form affinity groups to co-create solutions for the projects. The study covers the building of the courses utilizing the framework along with an assessment of student feedback post course, obtained via surveys. Results provide valuable insights for expanding the framework. These findings offer a pedagogical method for educators seeking to address the key-challenge of harmonizing academic rigor with practical application in business education.

Increasing Reciprocal Social Interactions Between Children Who Use Augmentative and Alternative Communication and Peers Using a Collaborative Learning Framework.

Children with complex communication needs who rely on augmentative and alternative communication (AAC) strategies to express themselves face significant difficulties interaction with peers. This study sought to design, implement, and evaluate a collaborative photography intervention designed to increase reciprocal social interaction between children who use AAC and their same-age peers. A single-subject, withdrawal design (ABAB) was used to explore the functional relationship between engagement in a collaborative photography intervention and the frequency of reciprocal social interactions between children who use AAC and their same-age peers. Partial-interval time sampling was used to code the number of reciprocal social interactions across four dyads. Increased frequencies of reciprocal social interactions were observed in intervention phases across all four dyads. Very large levels of effect and 100% nonoverlapping data were noted for Dyads 1 and 4. Moderate levels of effect and 70% of nonoverlapping data were noted for Dyads 2 and 3. Collaborative learning frameworks may be used to increase reciprocal social interactions between children who use AAC and their peers. Speech-language pathologists should consider utilizing collaborative learning elements in activities with children who use AAC. Future research is needed to further explore collaborative learning frameworks for interventions for children who use AAC. https://doi.org/10.23641/asha.25464064.

Collaborative Learning across Heterogeneous Systems with Pre-Trained Models

The increasingly decentralized and private nature of data in our digital society has motivated the development of personalized, collaborative intelligent systems that enable knowledge aggregation across multiple data owners while accommodating for their data privacy and system constraints. However, collaborative learning has only been investigated in simple and limited settings: isolated task scenarios where learning begins from scratch and does not build on prior expertise; learned model is represented in task-specific forms which are not generalizable to unseen, emerging scenarios; and more often, a universal model representation is assumed across collaborators, ignoring their local compute constraints or input representations. This restricts its practicality in continual learning scenarios with limited task data, which demand continuous adaptation and knowledge transfer across different information silos, tasks, and learning models, as well as the utilization of prior solution expertises. To overcome these limitations, my research has been focused on developing effective and scalable resource-aware collaborative learning frameworks across heterogeneous systems.