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48 results for “mobile learning”
User stories and xAPI statements for "A mobile campus application as a sensor node for Personal Learning Environments"
<p>This dataset provides the full user stories and xAPI statements as used in the prototype described in the article "A mobile campus application as a sensor node for Personal Learning Environments". It consists of two PDF documents described below. The files were created as part of the master thesis of Hendrik Geßner.</p> <p>"User Stories.pdf" contains a complete list of user stories with required context information, existing portlets and a category. The process that led to this collection is described very briefly in the article mentioned above, a graphical explanation is available in the attached image "Use case process complete.jpg"</p> <p>"xAPI Statements.pdf" contains all xAPI statements used in the prototype described in the article mentioned above. Dynamic elements such as names or IDs are highlighted on color. The statements appear in the following order: attended, used, loggedin, wasat, opened, closed, joined, left.</p>
Data for: Machine learning for predicting environmental mobility based on retention behaviour
<p>This repository contains the data and supplementary information for the paper: "Machine learning for predicting environmental mobility based on retention behaviour".</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 9. Proportion of indicators used in the case studies
<p>Figure 9 presents the indicators used for evaluating the effectiveness of mobile and ubiquitous learning practices. Learning achievements (64%) and perceived usefulness (56%) were the two most frequently used, followed by motivation (26%), ease of use (26%) and satisfaction (24%).<br> cognitive load (12%), system usage (8%), self-efficacy (6%) and social engagement (2%). Those indicators were usually adopted in the studies using qualitative methods for data collection. The results suggest a possible relationship between the data collection methods and the indicators. The choices of indicators represent, in principle, how the effectiveness of mobile and ubiquitous learning practices can be most appropriately evaluated and presented using particular study methods.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 8. Functions of the mobile devices used in the practices (Note: Each case could involve the use of more than one function.)
<p>Figure 8 captures the functions of mobile devices used in the practices. The results show that tailor-made applications for specific practices were most common (74%), followed by the use of a speaker (32%) and a camera (26%), where learners had to listen to audio materials using speakers or access online information by scanning QR-codes through cameras. In the various practices, other functions were also used, such as messaging (16%) for interacting with diverse parties and GPS (14%) for outdoor learning activities. For the practices using older models of mobile devices without cameras, tools such as an RFID reader (8%) were used for accessing information via communication tags.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 5. Level of intervention of the studies
<p>Figure 5 illustrates the levels of intervention of the studies. Most studies concentrated on the course level (76.9%) and some on either the programme level (13.5%) or the institutional level (9.6%). Two studies were conducted at more than one level. These results supplement the above number of participants where most of the studies were conducted on a small scale.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 6. Purposes of using mobile devices for learning
<p>Figure 6 shows the purposes of mobile and ubiquitous learning in the studies. A majority of the cases were multi-purpose (54%). Those with a single specific purpose were focused on “practice or revision” (34%) and “knowledge acquisition” (10%). There was a case where the application was to help new students to become familiar with the campus and teach them about the use of the facilities.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 1. Geographical distribution of the case studies
<p>The 50 cases of mobile and ubiquitous learning practices covered 14 countries/regions, including China, Japan, Taiwan, Korea, Sri Lanka, Turkey, Spain, Greece, the Netherlands, Britain, Australia, New Zealand, South Africa and the USA. Figure 1 shows the geographical distribution of the cases. Among the 50 cases, 70% were conducted in Asia, 16% in Europe, 4% in Oceania, 4% in Africa, and 2% in North America. Therefore, the results of this study represent more of the situation in Asia.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 7. Level of interactivity of the studies
<p>Figure 7 presents the level of interactivity in using mobile devices. Sixty percent of the cases involved only one-way access for information either online or offline, while 8% involved social interaction with information exchange among learners. Also 32% of the cases included both levels of interactivity in learning.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 4. Number of participants in the studies
<p>Figure 4 shows the number of participants in the studies, with the majority being on a small scale, having less than 100 participants (72%). Twenty percent of the studies involved over 100 but less than 1,000 participants, and only two cases included more than 1,000 participants (4%).</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 2. Data collection methods of the studies (Note: A study may involve more than one method.)
<p>Figure 2 shows the data collection methods applied in the studies. Surveys were used in most of the selected cases (94%), followed by interviews (38%) and experiments (34%). Apart from these approaches, observation (12%), field materials (8%), video recordings (6%) and discussion threads (2%) were also used in some of the studies. Thirty of the cases involved the use of more than one method, mostly combining a survey and another one or more method. These suggest that most of the studies involved quantitative data at least in part.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 3. Education level of the studies
<p>Figure 3 presents the education levels of the mobile and ubiquitous learning practices. Most studies took place at the tertiary level (66.7%). Also, 25.5% of the studies were conducted at the primary school level and 7.8% at the secondary school level.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 3. Screenshots of the new VC app
<p>The user will be able to stay logged in inside the app and to receive different types of notifications. The application will have the ability to be set up for working offline, while users have the possibility to change the application language, which can be useful for foreign or Erasmus students. Additionally, users will be able to customize the app based on some preferences available in a settings screen, check their grades, send direct messages to other students or professors and also save important documents under “My files” section. During the beta phase of development, a focus group with teachers will be organized in order to present the new apps and to understand what new features are needed to support the courses facilitation on the go.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 2. Screenshots of the Virtual Campus mobile application (d, e)
<p>The VC app was downloaded and used by 350 students, representing a percentage of 5% of the total number of the students enrolled on the platform courses. The application has features corresponding to the desktop version of the platform. In developing it, there were applied principles related to mobile learning usability and design, trying to provide enhanced possibilities for learning on-the-go and also specific notifications (Machun et al., 2012; Harrison et al., 2013; Mocofan, 2017). Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figire 1. Screenshots of the Virtual Campus mobile application (a, b, c)
<p>Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.</p>
Assessing Student Sustainable Learning Engagement in Mobile Learning through Social Cognitive Theory and Social Learning Theory
<p><span>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381)</em>.</span><span> the data was collected as part of ODDEA WP2.</span></p>
Assessing a Multidisciplinary Group of Undergraduate Students Applying the Challenge Based Learning Methodology to Learn Mobile Development
<p>That video presents the paper entitled "Assessing a Multidisciplinary Group of Undergraduate Students Applying the Challenge Based Learning Methodology to Learn Mobile Development" accepted in SBES 2020 conference (Education track).</p>
DAEMON: Network intelligence for aDAptive and sElf-Learning MObile Networks
<p>The DAEMON H2020 european project develops and implements innovative and pragmatic approaches to Network Intelligence (NI) design that enable high performance, sustainable and extremely reliable zero-touch network system. DAEMON designs an end-to-end NI-native architecture for Beyond 5G (B5G) that fully coordinates NI-assisted functionalities.</p> <p>Main website: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqblFJekN4dzdUcHYzQXlMd19rS0xjSDNleWVlZ3xBQ3Jtc0tsSmpkdWVUaXBSVDRHb2w5WDhGZnN3S05DM0FSRk9VS3RDc1hTT0l6MVhtd1pvWmZ2bEtRRUFxb0xkRmRQa1lVWVF0a1B2M1RwdXlmS2lIWEF0LWt4Q1ZIQndSNHNJcVJaSVNQODgtRThEaHh4TDBuaw&q=https%3A%2F%2Fh2020daemon.eu%2F&v=VNMVe8S-Ees">https://h2020daemon.eu/</a></p>
The Effect of Biomechanical Scapular Mobilization With Movement and Motor Learning
ClinicalTrials.gov study NCT04701814. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Clustered Embedding using Deep Learning to Analyze Urban Mobility based on Complex Transportation Data
<p>The subset of the anonymized dataset for personalized POI embedding.</p>
An Assessment of University Students' Perception Towards the Adoption and Use of Mobile Learning Technologies for Learning
<p>Mobile learning technologies serve has as a transformative tool in the educational sector which gives room for accessibility, flexibility and scalability. It also improves student learning outcomes. This study aims to review research on students' attitude and perception towards the adoption and implementation of mobile learning technologies using the Unified Theory and Acceptance and Use of Technology (UTAUT) model. From the studies, results showed that students' perception has a major impact on the adoption and use of mobile technologies. However, based on the various literature reviewed, students' perception is influenced by performance expectancy, effort expectancy (ease of use of the technology), social influence, perceived enjoyment and satisfaction. All these must be put into consideration before design and implementation for the effectiveness. Nevertheless, if all or some of these constructs were not incorporated in the development of mobile learning technology, obstructs the adoption and implementation of mobile learning.</p>
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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.