Framework For Online Learning Research Articles

Educational Technology for Active Connections in Blended Learning Environments

This conference focuses on employing educational technology for connections. It assumes that these connections will be active, and not latent – otherwise these will be meaningless and ineffective. The emphasis is thus on creating effective learning environments. Such learning environments are not just online or digital, but can also be physical learning spaces in which educational technology can play a key role.A key strategy to ensure that educational technology connections are indeed active is to employ educational technology within an active learning framework for both online and on-campus learning i.e. blended learning. Educational technology on its own does not lead to active learning – only when it is used within well-founded learning designs – of which constructive alignment is highlighted below. I am thus agreeing with Veletsianos and Moe (2017) that educational technology by itself is not “education’s silver bullet” but should be located within “the essentials of teaching and learning: theory, pedagogy and emergent trends in the research.” Such active learning will lead to learner engagement, leading to effective learning and learner success. The vision for instance for educational technologies at Charles Sturt University is to “support educational practices focussed on student success by providing cutting edge, stable learning environments”.

Learning and Management for Internet of Things: Accounting for Adaptivity and Scalability

Internet of Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to a massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.

DESAIN MODEL PEMBELAJARAN ONLINE SEBAGAI UPAYA MEMFASILITASI BELAJAR DI TEMPAT KERJA


 Vocational high school in Indonesia is familiar with the concept of dual system education program. There are two places of learning such as school-based learning and work-based learning. A few vocational education institutions have claimed that during carry out work-based learning, they ask students to master some competencies through self-regulated learning without a learning environment that planned. Therefore, this article aims to give an integrative learning design framework for online learning as an effort to guide educators and instructional designers in designing and developing online learning environment that meet with the students need. Shifting roles of educators and instructional designers in online learning, each component within integrative learning design framework for online learning, as well as the features of online learning model are discussed here. So that, educators and instructional designers who want to design online learning model could not only avoid mistakes but also reduce the failure.

The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling

Recent years have seen a growing focus on automated personalized services, with music recommendations a particularly prominent domain for such contributions. However, while most prior work on music recommender systems has focused on preferences for songs and artists, a fundamental aspect of human music perception is that music is experienced in a temporal context and in sequence. Hence, listeners’ preferences also may be affected by the sequence in which songs are being played and the corresponding song transitions. Moreover, a listener’s sequential preferences may vary across circumstances, such as in response to different emotional or functional needs, so that different song sequences may be more satisfying at different times. It is therefore useful to develop methods that can learn and adapt to individuals’ sequential preferences in real time, so as to adapt to a listener’s contextual preferences during a listening session. Prior work on personalized playlists either considered batch learning from large historical data sets, attempted to learn preferences for songs or artists irrespective of the sequence in which they are played, or assumed that adaptation occurs over extended periods of time. Hence, this prior work did not aim to adapt to a listener’s current song and sequential preferences in real time, during a listening session. This paper develops and evaluates a novel framework for online learning of and adaptation to a listener’s current song and sequence preferences exclusively by interacting with the listener, during a listening session. We evaluate the framework using both real playlist datasets and an experiment with human listeners. The results establish that the framework effectively learns and adapts to a listeners’ transition preferences during a listening session, and that it yields a significantly better listener experience. Our research also establishes that future advances of online adaptation to listener’s temporal preferences is a valuable avenue for research, and suggests that similar benefits may be possible from exploring online learning of temporal preferences for other personalized services.

Learning peer recommendation using attention-driven CNN with interaction tripartite graph

Learning peer recommendation using attention-driven CNN with interaction tripartite graph

Adaptive regularization for Lasso models in the context of nonstationary data streams

Large‐scale, streaming data sets are ubiquitous in modern machine learning. Streaming algorithms must be scalable, amenable to incremental training, and robust to the presence of nonstationarity. In this work we consider the problem of learningℓ1regularized linear models in the context of streaming data. In particular, the focus of this work revolves around how to select the regularization parameter when data arrives sequentially and the underlying distribution is nonstationary (implying the choice of optimal regularization parameter is itself time‐varying). We propose a framework through which to infer an adaptive regularization parameter. Our approach employs anℓ1penalty constraint where the corresponding sparsity parameter is iteratively updated via stochastic gradient descent. This serves to reformulate the choice of regularization parameter in a principled framework for online learning. The proposed method is derived for linear regression and subsequently extended to generalized linear models. We validate our approach using simulated and real data sets, concluding with an application to a neuroimaging data set.

Toward Energy-Awareness Smart Building: Discover the Fingerprint of Your Electrical Appliances

Energy efficiency raises significant concerns as it is one of the most promising ways to mitigate climate change. Disaggregation and identification of individual electrical appliances activities are one of the essentials for energy preservation especially for smart buildings. This paper proposes a lightweight electrical appliance activity detection approach for smart building, which leverages a single smart metering device to establish a learning and detection processing for multiple appliances. In this system, data interpolation and transition detection algorithm are proposed to effectively reduce the cost of model training and optimize the detection accuracy. The concept of appliance fingerprint is proposed and a variety of fingerprints, including appliance-based and context-based, are defined to depict fine-grained appliance characteristics. Based on these fingerprints, the paper proposes a multisource fingerprint-weighting KNN (FWKNN) classification algorithm and presents a boosting framework for continuous online learning and detection. A prototype system is implemented and demonstrated in IBM Bluemix PaaS cloud platform. Experimental result and analysis prove that FWKNN outperforms other benchmark methods in detection accuracy.

A new approach for clustering in desulfurization system based on modified framework for gypsum slurry quality monitoring

A new approach for clustering in desulfurization system based on modified framework for gypsum slurry quality monitoring

A Framework of Online Learning with Imbalanced Streaming Data

A challenge for mining large-scale streaming data overlooked by most existing studies on online learning is the skewdistribution of examples over different classes. Many previous works have considered cost-sensitive approaches in an online setting for streaming data, where fixed costs are assigned to different classes, or ad-hoc costs are adapted based on the distribution of data received so far. However, it is not necessary for them to achieve optimal performance in terms of the measures suited for imbalanced data, such as Fmeasure, area under ROC curve (AUROC), area under precision and recall curve (AUPRC). This work proposes a general framework for online learning with imbalanced streaming data, where examples are coming sequentially and models are updated accordingly on-the-fly. By simultaneously learning multiple classifiers with different cost vectors, the proposed method can be adopted for different target measures for imbalanced data, including F-measure, AUROC and AUPRC. Moreover, we present a rigorous theoretical justification of the proposed framework for the F-measure maximization. Our empirical studies demonstrate the competitive if not better performance of the proposed method compared to previous cost-sensitive and resampling based online learning algorithms and those that are designed for optimizing certain measures.

Online learning and joint optimization of combined spatial-temporal models for robust visual tracking

Online learning and joint optimization of combined spatial-temporal models for robust visual tracking

A statistical framework for online learning using adjustable model selection criteria

A statistical framework for online learning using adjustable model selection criteria

Kernel online learning with adaptive kernel width

Kernel online learning with adaptive kernel width

Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine

A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, including batch learning and online learning. Second, an adaptive selective ensemble framework for online learning is designed to balance the accuracy and speed of the algorithm. Experiments for both regression and classification problems with UCI data sets are carried out. Comparisons between OS-ELM, simple ensemble OS-ELM (EOS-ELM), genetic algorithm based selective ensemble (GASEN) of OS-ELM, and the proposed particle swarm optimization based selective ensemble of OS-ELM empirically show that the proposed algorithm achieves good generalization performance and fast learning speed.

A Learner-Centered Design Framework for E-Learning

This paper presents a design framework for online learning. The framework is based upon the taxonomy devised by Dettmer (2006). In a learner-centered focus, it draws together the cognitive, affective, social, and sensorimotor domains of learning, and is situated with the concept of online personal learning spaces and environments. It is at this intersection of learning domains that the graduate attributes and general capabilities of students are able to be intentionally supported and demonstrated. The proposed framework draws on recognized theory, principles, and practical considerations of contemporary online learning to clarify considerations of the learning outcomes, learning processes and learner performance when designing online learning that is mediated by education technology. This integrative approach will support mentored, reflective learning design and design-based research aimed at improving both the experience and outcomes of online students. The paper contends that this framework presents a useful design that reflects the nature of contemporary online learning environments.

Reconceptualizing the community of inquiry framework: An exploratory analysis

Reconceptualizing the community of inquiry framework: An exploratory analysis

Feature-aware regularization for sparse online learning

Learning a compact predictive model in an online setting has recently gained a great deal of at-tention. The combination of online learning with sparsity-inducing regularization enables faster learning with a smaller memory space than the previous learning frameworks. Many optimization methods and learning algo-rithms have been developed on the basis of online learning with L 1-regularization. L 1-regularization tends to truncate some types of parameters, such as those that rarely occur or have a small range of values, unless they are emphasized in advance. However, the inclusion of a pre-processing step would make it very difficult to pre-serve the advantages of online learning. We propose a new regularization framework for sparse online learning. We focus on regularization terms, and we enhance the state-of-the-art regularization approach by integrating information on all previous subgradients of the loss function into a regularization term. The resulting algorithms enable online learning to adjust the intensity of each features truncations without pre-processing and eventually eliminate the bias of L 1-regularization. We show theoretical properties of our framework, the computational complexity and upper bound of regret. Experiments demonstrated that our algorithms outperformed previous methods in many classification tasks.

A framework for learning and planning against switching strategies in repeated games

Intelligent agents, human or artificial, often change their behaviour as they interact with other agents. For an agent to optimise its performance when interacting with such agents, it must be capable of detecting and adapting according to such changes. This work presents an approach on how to effectively deal with non-stationary switching opponents in a repeated game context. Our main contribution is a framework for online learning and planning against opponents that switch strategies. We present how two opponent modelling techniques work within the framework and prove the usefulness of the approach experimentally in the iterated prisoner's dilemma, when the opponent is modelled as an agent that switches between different strategies (e.g. TFT, Pavlov and Bully). The results of both models were compared against each other and against a state-of-the-art non-stationary reinforcement learning technique. Results reflect that our approach obtains competitive results without needing an offline training phase, as opposed to the state-of-the-art techniques.

Efficient online subspace learning with an indefinite kernel for visual tracking and recognition.

We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an incremental KPCA in Krein space that does not require the calculation of preimages and therefore is both efficient and exact. Our approach has been motivated by the application of visual tracking for which we wish to employ a robust gradient-based kernel. We use the proposed nonlinear appearance model learned online via KPCA in Krein space for visual tracking in many popular and difficult tracking scenarios. We also show applications of our kernel framework for the problem of face recognition.

A Theoretical Framework for Effective Online Learning

ABSTRACT A key, overarching goal for any committed educator is to ensure that the learner has a meaningful and memorable learning experience while achieving the desired learning outcomes. In this paper it is argued that in order to achieve such a goal, a strategy needs to be put in place that is capable of providing students with a fully integrated, all-encompassing learning environment. The reasoning, simply, is that learning will not necessarily emanate from one specific source and when it happens, it will occur through different means, for different people. One of the great strengths of the online learning space is that, harnessing the power of the various information and communication technologies (ICTs), there is greater scope for catering for individual learning needs. With this in mind, the paper puts forward a framework that comprises a number of overlapping ‘sub-environments’ which, together, provide the scaffolding considered essential for the construction of a truly holistic learning environment. To illustrate how this framework for effective online learning can be operationalised, the authors refer to the case of Universitas 21 Global, an institution which offers completely online programs to post-graduate students in more than 40 countries around the world.

Towards incremental classifier fusion

To process the large amounts of data industrial systems are producing nowadays, machine learning techniques have shown their usefulness in many applications. As the amounts of data being generated are getting huge, the need for machine learning methods which can deal with them in an appropriate way - i.e. methods which can be adapted incrementally - becomes very important. Ensembles of classifiers have been shown to be able to improve the predictive accuracy as well as the robustness of single classification methods. In this work novel incremental variants of several well-known classifier fusion methods (Fuzzy Integral, Decision Templates, Dempster-Shafer Combination and Discounted Dempster-Shafer Combination) are presented. Furthermore, a novel incremental classifier fusion method called Incremental Direct Cluster-based ensemble will be introduced, which exploits an evolving clustering approach. A flexible and interactive framework for on-line learning will be introduced, in which the ensemble methods are adapted incrementally in a sample-wise manner together with their base classifiers. The performance of this framework and the proposed incremental classifiers fusion methods therein are evaluated on five real-world visual quality inspection tasks, captured on-line from an industrial CD imprint production process, together with five data sets from the UCI repository.