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76 results for “online learning”
Students' perceived obstacles with Forced Online Distance Learning during the CoVID-19 outbreak and their preferences to continue with the introduced teaching methods after the reopening of the University of Maribor [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics,</li> <li>and SPSS dataset.</li> </ul>
BIM online learning content in Brazil
<p>We present the result of a survey that collected three types of content on the Internet on Building Information Modeling: (i) dissemination material, (ii) training courses, and (iii) tutorials, available online for open access in Brazil or abroad.</p> <p>The identified BIM content was categorized by the following fields:</p> <ul> <li> <p>NAME: title of content;</p> </li> <li> <p>LINK: url for online location;</p> </li> <li> <p>COMPONENT OF COMPETENCE: indicates whether the component of competence is conceptual (theoretical knowledge) or applied (skill) according to Succar, Scher and Williams (2013);</p> </li> <li> <p>COMPETENCE CLASSIFICATION: level of competence (domain or executive) according to the BIMe Initiative competence table;</p> </li> <li> <p>FORMAT: website, youtube channel, or podcast;</p> </li> <li> <p>CONTENT: dissemination, tutorial, online training course;</p> </li> <li> <p>TOOL: if the content has a specific focus on a software, its name is listed;</p> </li> <li> <p>HOURS: class hours (when applicable);</p> </li> <li> <p>CERTIFICATE: yes or no (when applicable) and</p> </li> <li> <p>NOTE: explanatory text.</p> </li> </ul>
Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs - Dataset
<p>Dataset providing a set of measurement of performance and power consumpetion of a virtualized Base Station (srseNB).</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 1. The AGLO online assessment approach
<p>In Figure 1, we present the lifetime of AGLOs in the context of online student assessment following a set of steps. In the backend, the tutor develops an AGLO model respecting a predefined meta-model. The model is intuitive, it has a few sections where symbols are defined using formulas and random numbers and then used in a section of a presentation for the student. When such models are created they are stored in a storage facility like a database to be selected by the student through the web application frontend. In the frontend, the students access the web application using a web browser from a workstation, tablet or smartphone. In the assessment process, the student will access several AGLOs. At this step, the accessed AGLOs are instantiated with random numbers, formulas are evaluated to fulfill the designed learning or testing scenario and to create the presentation content for the student. Nevertheless, the instantiated symbols will be used for the automatic assessment of the answers correctness</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 4. Online test assessment example
<p>Thus, applying these restrictions the computed solution is C, E, G, J, L, H, I and is unique. Node C is the starting node since it is the first from the lexicographical point of view. The first step CE is the only choice coping with the restrictions from the [CE, CG, and CJ] edges. Next, EG is the first edge in the list of [EG, EJ]. The next step is GJ which is the only choice. Edge JL is another unique choice. Edge LH is the next step from the list [LH, LI]. Finally, the last edge is obtained by backtracking to node L and then taking edge LI. These restrictions allow us to drive the student to build only one solution from the possible set of solutions. This will determine an easier way of comparing the student’s answer with the answer of the computer. Another more general solution is to use validation functions which require implementation in domain libraries written in JavaScript. </p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
Saving carbon emissions through online learning for overseas students [Data]
<p>We estimated the savings in CO<sub>2</sub> emissions by a cohort of master’s students who studied fully online from their home countries, rather than traveling to the UK and living there while attending university.</p> <p>Data come from International Civil Aviation Organization (ICAO) carbon emissions calculator <a href="https://www.icao.int/environmental-protection/CarbonOffset/Pages/default.aspx">https://www.icao.int/environmental-protection/CarbonOffset/Pages/default.aspx</a>; and CO₂ and Greenhouse Gas Emissions <a href="https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions">https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions</a></p>
Supplementary materials of the active learning methodology for online engineering education
<p>These are the supplementary materials for the active learning methodology for engineering education integrating online and mobile learning. The methodology is focused on teaching electronics, physical computing, basic robotics, and programming. The purpose of the methodology was to provide active learning, experimentation, and reflection in online classes to the students increasing their motivation and self-efficacy.</p> <p>The file contains the following elements:</p> <ol> <li>Rubric employed to evaluate the student-created videos and blogs.</li> <li>The survey's questions of the different courses that employ online and mobile learning modalities.</li> <li>A set of URLs with examples of the blogs and videos constructed by the students.</li> </ol>
Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques - Dataset
<p>This dataset includes the detailed values and scripts used to study behavioral aspects of users searching online for Art and Culture by analyzing quantitative data collected by the Art Boulevard search engine using machine learning techniques. This dataset is part of the core methodology, results and discussion sections of the research paper entitled "<strong>Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques</strong>"</p>
Dataset of paper Privacy Orientation during Online Teaching-Learning Activities: Practices Adopted and Lessons Learned
<p>Dataset of the paper accepted for publication in the Journal on Interactive Systems (JIS).</p> <p>DA SILVA, M.; VITERBO, J.; SALGADO, L. C. C.; MOURÃO, E. Privacy Orientation during Online Teaching-Learning Activities:<br> Practices Adopted and Lessons Learned. Journal on Interactive Systems, Porto Alegre, RS, 2023.</p>
Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning
<p>Dataset of optimisation runs performed for a study comparing reinforcement learning and Bayesian optimisation for online continuous tuning at the example of a linear particle accelerator tuning task.</p> <p> </p> <p><strong>Abstract of the Paper on the Study</strong></p> <p>Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study at the example of a routine task on a real particle accelerator, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study’s results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering<br> advancements.</p>
Questionnaire Draft and Respons for Study of Online Learning for Accounting Subjects in Vocational School During the Pandemic Period
<p>The questionnaire was designed to be completed by accounting-major vocational school students via an online survey platform. The data collection phase of the survey was conducted in August 2020, to analyze the learning process from March to July 2020. During that time, all schools in Indonesia implemented Study From Home (SFH). The questionnaire has 36 items, divided into 7 sections. Respondents were asked to choose one-point Likert scale from one to five depending on their online learning conditions. </p>
Self-Directed Online Machine Learning for Topology Optimization
<p>Code and results of the paper "Self-Directed Online Machine Learning for Topology Optimization".</p> <p>See latest updates at https://github.com/deng-cy/deep_learning_topology_opt</p>
Demonstrating a Bayesian Online Learning forEnergy-Aware Resource Orchestration in vRANs
<p>Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, converge an order of magnitude faster than other machine learning methods-and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the advantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implemented our approach into O-RAN's non-real-time RAN Intelligent Controller (RIC).</p>
Building Interprofessional Learning in the Midst of the COVID-19 Pandemic: Implementation of Online Simulation for Students in Health Profession
<p><strong>Abstract</strong></p> <p>COVID-19 has significantly affected the learning process of health institutions. It is inevitable that face-to-face learning activities will shift to online learning, including Interprofessional Education (IPE). The purpose of this article is to describe our experiences in developing a simulation of a two-day interprofessional learning and teaching session for students in nursing, midwifery, pharmacy, environmental sanitation, medical laboratory technology, dental health, and nutrition. A total of 956 students, 112 lecturers, and 15 standardized patients (SP) took part in this research. The study conducted in 6 schools of health profession in Indonesia. Students were assigned to two simulation models. The data were collected using an online questionnaire with the variables readiness to cooperate, interprofessional group discussions, and facilitator role with a 5-point Likert scale. In the implementation of the two simulation models, significant results have been demonstrated and their uniqueness is revealed. The simulation models that make direct anamnesis of the SP results in a higher level of readiness for IPE learning, and is significantly different. Meanwhile, the simulation model that is preceded by a scenario discussion process before making anamnesis to the SP shows a significantly positive attitude to the interprofessional groups discussion and the role of the facilitator.</p> <p><strong>Keywords:</strong> interprofessional education, COVID-19 pandemic, online simulation, health professionals, standardized patient</p>
Arctic PASSION Online Seminar on "Learning from the Sharing Circle"
<p>In Autumn 2023, an APECS & Arctic PASSION <a href="https://arcticpassion.eu/sharingcircle/">Sharing Circle</a> gathered a group of early career professionals and Arctic youth in the homelands of the Sámi, in Northern Finland. What have we learned from this event? Did we achieve our goal to foster dialogue and open new perspectives on Arctic intercultural collaborations and co-management? How can this experience be used for future educational efforts?</p> <p> </p> <p><strong>Panelists and participants of the Sharing Circle:</strong></p> <p>Elise Brown-Dussault (Canada)</p> <p>Jessica Hall (Norway)</p> <p>Pavel Tkach (Finland)</p> <p>Harmony Wayner (USA)</p> <p>Moderation: Lisa Grosfeld and Josefine Lenz (APECS, AWI)</p> <p> </p> <p><strong>Watch the webinar here: </strong>https://www.youtube.com/watch?v=mlyK-5Od9uc</p> <p> </p> <p><strong>Related links: </strong>https://arcticpassion.eu/sharingcircle/</p> <p> </p>
Planning as Optimization: Online Learning of Situations and Optimal Configurations - SASO 2019 - Accompanying material
<p>These files are accompanying material for our submission "" to SASO 19:</p> <p>Many approaches apply optimization techniques in SASs, mostly within the planning procedure, to generate new system configurations or adaptation plans. We performed an analysis of these techniques based on approaches published during the last ten years in conferences and journals related to self-adaptive systems (SASs), namely ACM Transactions on Autonomous and Adaptive System (TAAS), the International Conference on Autonomic Computing and Communications (ICAC), the International Conferences on Self-Adaptive and Self-Organizing Systems (SASO), the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), and the Symposium on the Foundations of Software Engineering (FSE). We identified the use of 29 different techniques in 51 publications. This list shows that a large set of techniques from different classes such as probabilistic, combinatorial, evolutionary, stochastic, mathematical, and meta-heuristic optimization are applied in SASs.</p> <p> </p> <p>We provide two files:</p> <p>- List of References (SASO - References - Planning_as_Optimization.pdf)</p> <p>- Dataset (SASO - Dataset - Planning_as_Optimization.xlsx)</p>
Dataset on an online collaborative learning situation in acomputer networks course
<p>Here you can find a dataset of a collaborative learning situation. Students were enrolled in two undergradute courses on computer networks where they were required to carry out a set of learning activities supported by Moodle and an online collaborative environment called CoTrackV2. The data collected includes logs of the writing process of shared documents, logs of the chat messages between the group members, and logs from Moodle with coarser-grained information about course-level interactions. This dataset has been generated with the aim of allowing researchers to study self-and socially-shared regulation in online environments.</p> <p>There will be 6 files:</p> <ul> <li>document_logs.csv</li> <li>chat_logs.csv</li> <li>moodle_logs.csv</li> <li>individual_submissions.csv</li> <li>learning_design.csv</li> <li>final_questionnaire.csv</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.