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9 results for “Learning Management Systems”
Learning Elements in Learning Management Systems (LMSs)
<p>Results of a survey in the higher education area. Participants are professors, lecturers, and tutors.</p> <p> </p> <p>The final definitions for the elements are:</p> <ul> <li>Brief Overview (BO): Short summary or recap without details of the actual learning material</li> <li>Quiz (QU): Quiz questions related to the content taught</li> <li>Learning Goal (LG): Description of the competences, skills or abilities that the learners should acquire in relation to a specific learning content</li> <li>Manuscript (MS): Complete or brief elaboration of a speech, a lecture, a course, or similar</li> <li>Exercise (EX): Opportunity to apply and deepen the learned. Varied tasks are possible beside the classic exercise sheet</li> <li>Summary (SU): Elementalization (reduction to the essentials) of the actual content with details</li> <li>Auditory additional material (AAM): Material with the aim of applying and deepening the learned with audio files</li> <li>Textual additional material (TAM): Material with the aim of applying and deepening the learned with textual further information (also named additional literature)</li> <li>Visual additional material (VAM): Material with the aim of applying and deepening the learned with videos or similar</li> <li>Collaboration Tool (CT): Cooperative and interactive communication medium with the aim of knowledge sharing between learners and learners and/or lecturers, and is used for collaborative work</li> </ul> <p>The corresponding scientific paper can be found via ORCID as of December 2023.</p> <p> </p> <p>The presented work is supported by the ‘German Federal Ministry of Research, Technology and Space’ (BMFTR) through the granting of the funding project HASKI (FKZ: 16DHBKI035).</p>
University Learning Management System xAPI data for the ILEDA project
<p>Anonymized data in xAPI format collected from learning management systems (Moodle and LAMS) for the ILEDA (2021-1-BG01-KA220-HED-000031121) project https://ileda.eu/ileda-project. The dataset contains 306,741 records of 829 individuals participating in eight different blended learning courses following either a flipped classroom methodology or a project-based learning methodology from four universities: University of Eastern Finland (Finland), University of León (Spain), Belgrade Metropolitan University (Serbia) and Sofia University (Bulgaria).</p>
Unveiling Hurdles in Software Engineering Education: The Role of Learning Management Systems
<p>Learning management systems (LMSs) are established tools in higher education, especially in the field of software engineering (SE). The onset of the COVID-19 pandemic further amplified the utilization of these systems, which necessitated their integration into educational curricula for both lecturers and students. However, adopting LMSs within SE education has presented distinctive challenges impeding their seamless incorporation into the courses. This paper aims to scrutinize the challenges and requirements encountered by professors, lecturers, and students in the domain of SE education when using LMSs. We conducted an empirical study that included (i) a survey with 47 professors/lecturers and 133 students, (ii) an analysis of the ensuing data, and (iii) 18 additional interviews conducted with professors and lecturers to delve into nuanced variations in viewpoints. The findings derived from our study reveal that the challenges and requirements pertaining to LMSs are rather specific depending on the scope and size of the respective courses. Nevertheless, many participants have a consensus on numerous challenges and requirements for improving certain features of LMSs in order to improve their usage in SE education. The findings are valuable for advancing research and development in the field of LMSs and provide guidance for lecturers in SE education.</p>
Eye Tracking based Learning Style Identification for Learning Management Systems
<h2>Abstract: </h2> <p>In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.</p> <h3>Research: </h3> <p>As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary: </p> <ul> <li>Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, <em>In 2024 Symposium on Eye Tracking Research and Applications.</em> ETRA.</li> <li>Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence. Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany</li> <li>Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In <em>ICERI2023 Proceedings</em> (pp. 5153-5162). IATED.</li> <li>Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In <em>Proceedings of the 5th European Conference on Software Engineering Education</em> (pp. 138-147). ECSEE.</li> </ul> <p>The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly. </p> <h3>Experimental Setup:</h3> <p>In the following section, crucial notes on the circumstances and the experiment itself as well as the equipment are given. <br>In order to reduce the external influence on the experiment, variables such as:</p> <ul> <li>order, number, and presentation of the stimuli,</li> <li>instruction to the participant prior to the experiment,</li> <li>position of the participant in respect to the Eye Tracking equipment,</li> <li>environment such as illuminance and ambient noise for the participant,</li> <li>Eye Tracking equipment, software, settings such as sampling frequency and latency as well as calibration</li> </ul> <p>were attempted to keep constant and consistent throughout the experiment. </p> <h3>Equipment: </h3> <p>In this study, the <strong>Tobii Pro Fusion</strong> (<a href="https://go.tobii.com/tobii-pro-fusion-user-manual">https://go.tobii.com/tobii-pro-fusion-user-manual</a>) eye tracker is utilized without a chin rest along with the <strong>Tobii IVT filter</strong> for fixation detection and <strong>Tobii Pro Lab</strong> software for data collection. The Tobii Pro Fusion is categorised as a video-based combined pupil and corneal reflection technology. This tracker provides several advantages, such as the collection of comprehensive data, comprising gaze, pupil, and eye-opening metrics. The eye tracker captures up to 250 images per second (250Hz), enhancing its precision and eye movement analysis. In addition, Tobii Pro Fusion is capable of performing under different lighting conditions, thus making this portable device ideal for off-site studies.</p> <p>Ensuring consistent quality across all experiment participants is crucial. Prior to each individual experiment, eye trackers are calibrated, aiming for a maximum reproduction error of less or equal than 0.2 degree during calibration to minimize deviations. The calibration is excluded from the experiment recording. Each participant is given the same instructions for their single trial of the experiment. The stimuli is displayed on a 24-inch monitor in a 16:9 format, positioned approximately 65cm away from the participants' eyes. Any effect related to the characteristics of the participants, such as age, visual acuity, eye colour, pupil size, etc., are considered in the experiment design. </p> <h3>Procedure: </h3> <p>Initially, the participants are requested to confirm their ability to conduct the experiment based on their current condition. Subsequently, the participant must be positioned comfortably and accurately in relation to the eye tracker. The eye tracker calibration is carried out for each participant to ensure a suitable experimental configuration. Once a successful calibration is achieved, the Eye Tracking experiment begin with introductions prior to each task. The stimuli presentation is unrestricted by time constraints, and no prior knowledge of the stimuli contents is necessary. Employing a within-subject design, each stimulus is exposed to each subject. Following completion of the experiment, participants anonymously answer the ILS questionnaire. To prevent any impact on the experiment, it is important that the questionnaire only be seen and completed after the experiment. </p> <h3>Stimuli: </h3> <p>The specially designed stimuli shown to participants during the study are illustrated in the left-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. For this research, only specific regions of a stimulus, referred to as AOI, are taken into consideration. The size of the AOI depends on both stimulus information and distance between multiple AOIs. Adequate results are ensured by not overlapping AOIs and appropriate spacing. The AOIs of the various stimuli employed in this research are illustrated in the right-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. The stimuli are presented in German language, ensuring reliable Eye Tracking measurements without any interference from language barriers. Each stimulus comprises diverse learning materials to engage students with varying learning styles, with some general information about the quantitative research cycle. Some stimuli feature identical type of material, e.g. <em>illustrations</em> or <em>key words</em>, but with different contexts and positions on the stimuli. Rearranging the identical material reduces the influence of reading style and enhances the impact of the learning style, producing a more reliable experiment. These identical types of material or AOIs on different stimuli can be grouped together, identified by the same colour and title, and referred to as AOI groupings.<br>There are ten different AOI groupings in total, as illustrated in the figure in the "[Documentation]stimuli_preview.pdf" file, where each grouping consists of several AOIs. <br>In detail, the AOI grouping regarding:</p> <ul> <li><em>table of contents and summary contain only a single AOI each,</em></li> <li><em>illustrations</em>, <em>key words</em>, <em>theory</em>, <em>exercise</em>, <em>example</em> and <em>additional material</em> contain three AOIs each,</li> <li><em>supporting text</em> and <em>multiple choice question</em> contain two AOIs each.</li> </ul> <h3>Research data management: </h3> <p>To ensure the transparency and reproducibility of this study, effective management of research data is essential. This section provides details on the management, storage and analysis of the extensive dataset collected as part of the study. Importantly, this research, the study and its processes adhered to ethical guidelines at all times, including informed consent, participant anonymity and secure data handling. The data collected will only be kept for a specific period of time as defined in the research project guidelines. The collection itself involves the recording of participants' eye movements during the ET study and the collection of their demographic data and responses to the ILS questionnaire. </p>
Patterns in the appropriation of a learning management system by instructors based on Q-methodology: Dataset
<p>Anonymized dataset</p>
Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems
<p><span>The potent greenhouse gas nitrous oxide (N</span><sub><span>2</span></sub><span>O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ~60% of the agricultural flux. Empirical models of N</span><sub><span>2</span></sub><span>O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited </span><span><span>geographic coverage;</span></span><span> process-based biogeochemical models are rarely able to predict daily to monthly emissions with > 20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured </span><span><span>soil</span></span><span> parameters. We used sub-daily N</span><sub><span>2</span></sub><span>O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper U.S. Midwest (~3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (~1100 daily fluxes), to train an ML model that explained 65-89% of daily flux variance with very few input variables –soil moisture, days after fertilization, soil texture, air temperature, soil carbon, precipitation, and N fertilizer rate. When applied to a long-term test site not used to train the model, the model explained 38% of the variation observed in weekly to biweekly manual chamber measurements from corn, and 51% upon coupling the ML model with a cropping systems model that predicted daily soil N availability. </span><span><span>This represents a 2-3 times improvement over conventional process-based models and with substantially fewer input requirements.</span></span><span> This coupled approach </span><span><span>offers promise</span></span><span> for better predictions of agricultural N</span><sub><span>2</span></sub><span>O emissions and thus more precise global models and more effective </span><span><span>agricultural mitigation interventions.</span></span></p>
Dataset of the Paper "Understanding Bugs of Learning Management Systems: An Exploratory Study on Moodle and Sakai"
<p>The datasets collected from JIRA and GitHub are used for an empirical study and data analysis of bug classification issues in learning management systems (Moodle and Sakai). A brief description of each document in the dataset is provided below:</p> <p>1. "LMS_bugs_data.sql" contains bug data items collected from JIRA for LMS (Moodle and Sakai), specifically including: BugID (D1), BugKey (D2), BugSummary (D3), BugStatus (D4), BugResolution (D5), BugDescription (D6), BugCreateAt (D7), BugCloseAt (D8), and BugComments (D9). Additional data items (e.g., fixversions, priority) are provided to support further research.</p> <p>2. "LMS_commits_data.sql" contains the commits corresponding to bug fixes in LMS (Moodle and Sakai) from GitHub, specifically including: CommitSha (D10), CommittedDate (D11), CommitMessage (D12), CommitTotal (D13), Changefiles (D14), Files (D15), and BugKey(D2). Other data items (e.g., commitauthor, additions) are provided for supporting the research.</p> <p>3. OT, LOCM, NOFM, Entropy, NODP, NOC (D16-D21) require the above data for calculation.</p> <p>4. "LMS_Moodle_Bug_Classification_Results_7898.xlsx" contains the bug classification results for Moodle, including: BugID, BugKey, Summary, ClusterId, GPT-4 Label Topic, BugType, and the mapping between BugType and ClusterId.</p> <p>5. "LMS_Sakai_Bug_Classification_Results_6089.xlsx" contains the bug classification results for Sakai, including: BugID, BugKey, Summary, ClusterId, GPT-4 Label Topic, BugType, and the mapping between BugType and ClusterId.</p>
Developing Echocardiography Image Quality Management System Based on Deep Learning
ClinicalTrials.gov study NCT05633732. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems
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