Enhancing E-Learning System Through Learning Management System (LMS) Technologies: Reshape The Learner Experience

• URL: http://arxiv.org/abs/2309.12354v1
• Date: Fri, 1 Sep 2023 02:19:08 GMT
• Title: Enhancing E-Learning System Through Learning Management System (LMS) Technologies: Reshape The Learner Experience
• Authors: Cecilia P. Abaricia (1), Manuel Luis C. Delos Santos (2), ((1)(2) Asian Institute of Computer Studies, Quezon City, Philippines)
• Abstract summary: This E-Learning System can fit any educational needs as follows: chat, virtual classes, supportive resources for the students, individual and group monitoring, and assessment using LMS as maximum efficiency.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper aims to determine how the LMS Web portal application reshapes the learner experience through the developed E-Learning Management System using Data Mining Algorithm. The methodology that the researchers used is descriptive research involving the interpretation of the meaning or significance of what is described. Gather data from questionnaires, surveys, observations concerned with the study, and the chi-square formula for the statistical treatment of data. The findings of the study, the extent that LMS Web portal application reshapes the learner experience in terms of the following variables with the Average Weighted Mean (AWM): Flexible engagement of Learners in any device is highly satisfied; Personalize learning tracker is highly satisfied; Collaborating with the Learning Expert is highly satisfied; Provides user-friendly Teaching Tools is satisfied; Evident Learner Progress and Involvement and is satisfied. In the final analysis, this E-Learning System can fit any educational needs as follows: chat, virtual classes, supportive resources for the students, individual and group monitoring, and assessment using LMS as maximum efficiency. Moreover, this platform can be used to deliver hybrid learning.

Related papers

• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System [54.71619734800526]
  GenMentor is a multi-agent framework designed to deliver goal-oriented, personalized learning within ITS. It maps learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset. GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs.
  arXiv  Detail & Related papers  (2025-01-27T03:29:44Z)
• How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments? [0.8999666725996978]
  This study introduces an approach that integrates dynamic knowledge graphs with large language models (LLMs) to offer nuanced student assistance. Central to this method is the knowledge graph's role in assessing a student's comprehension of topic prerequisites. Preliminary findings suggest students could benefit from this tiered support, achieving enhanced comprehension and improved task outcomes.
  arXiv  Detail & Related papers  (2024-12-05T04:05:43Z)
• KBAlign: Efficient Self Adaptation on Specific Knowledge Bases [73.34893326181046]
  Large language models (LLMs) usually rely on retrieval-augmented generation to exploit knowledge materials in an instant manner. We propose KBAlign, an approach designed for efficient adaptation to downstream tasks involving knowledge bases. Our method utilizes iterative training with self-annotated data such as Q&A pairs and revision suggestions, enabling the model to grasp the knowledge content efficiently.
  arXiv  Detail & Related papers  (2024-11-22T08:21:03Z)
• Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study [0.7499722271664147]
  This study explores the integration of real-world machine learning (ML) projects using human-computer interfaces (HCI) datasets in college-level courses.
  arXiv  Detail & Related papers  (2024-08-06T23:05:15Z)
• Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
  This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs) The system is into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
  arXiv  Detail & Related papers  (2023-09-15T02:42:03Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Education [15.30924350440346]
  M2LADS supports the integration and visualization of multimodal data recorded in MOOCs in the form of Web-based Dashboards. Based on the edBB platform, the multimodal data gathered contains biometric and behavioral signals. M2LADS provides opportunities to capture learners' holistic experience during their interactions with the MOOC.
  arXiv  Detail & Related papers  (2023-05-21T20:22:38Z)
• Identifying Critical LMS Features for Predicting At-risk Students [4.718094586237028]
  Learning management systems (LMSs) have become essential in higher education. We present an additional use of LMS by using its data logs to perform data-analytics and identify academically at-risk students.
  arXiv  Detail & Related papers  (2022-04-27T22:43:45Z)
• DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs [0.4397520291340695]
  Engagement plays a major role in developing intelligent educational interfaces. Non-deep learning models are based on the combination of popular algorithms such as Histogram of Oriented Gradient (HOG), Support Vector Machine (SVM), Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) The deep learning methods include Densely Connected Convolutional Networks (DenseNet-121), Residual Network (ResNet-18) and MobileNetV1.
  arXiv  Detail & Related papers  (2022-04-13T15:24:38Z)
• Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach [54.28419430315478]
  Mobile Edge Learning enables distributed training of Machine Learning models over heterogeneous edge devices. In MEL, the training performance deteriorates without the availability of sufficient training data or computing resources. We propose an incentive mechanism, where we formulate the orchestrators-learners interactions as a 2-round Stackelberg game.
  arXiv  Detail & Related papers  (2021-09-25T17:27:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.