Related papers: Canvas Adoption Assessment and Acceptance of the Learning Management System on a Web-Based Platform

Canvas Adoption Assessment and Acceptance of the Learning Management System on a Web-Based Platform

URL: http://arxiv.org/abs/2101.12344v2
Date: Wed, 26 May 2021 15:21:28 GMT
Title: Canvas Adoption Assessment and Acceptance of the Learning Management System on a Web-Based Platform
Authors: Julius G. Garcia, Mark Gil T. Gangan, Marita N. Tolentino, Marc Ligas, Shirley D. Moraga and Amelia A. Pasilan
Abstract summary: This study aims to assess student adoption of Canvas as a new learning management system and its potential as a web-based platform in the e-learning programme of the University of the East. The students perceived ease of use has a significant effect on their perceived usefulness but has no significant effects on their attitude towards the use of Canvas.
Score: 0.0
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: The acquisition of non-proprietary and proprietary learning management system has provided a richer learning experience to users and raised interest among education providers. This study aims to assess student adoption of Canvas as a new learning management system and its potential as a web-based platform in the e-learning programme of the University of the East. This study also assessed student readiness in using Canvas. A survey was administered to 214 students of the University of the East through snowball sampling. An Exploratory Factor Analysis was conducted to examine the validity of the model. A Confirmatory Factory Analysis was used to validate the Exploratory Factor Analysis results and analyse the correlation of the constructs. A Structural Equation Modelling was conducted to analyse the relationships between the constructs, which were evaluated using fit indices. Adopted from the Technology Acceptance Model, the constructs perceived ease of use, perceived usefulness, and attitude were studied. The study reveals that students perceived usefulness and attitude towards using Canvas in a web-based platform have direct and significant effects on their intention to use Canvas. The students perceived ease of use has a significant effect on their perceived usefulness but has no significant effects on their attitude towards the use of Canvas. The students technological maturity and prior experience in using a learning management system influenced their beliefs on the adaptation of similar technology. Exploring the potential benefits of Canvas and factors affecting the students adoption amplifies access to quality education to fulfil educational directives. Furthermore, educational institutions should explore technological migration related to teaching and learning processes.

Related papers

Revolutionising Role-Playing Games with ChatGPT [0.0]
The aim of the study was to analyse the impact of AI-based simulations on students' learning experience. Based on Vygotsky's sociocultural theory, ChatGPT was used to give students a deeper understanding of strategic decision-making processes.
arXiv Detail & Related papers (2024-07-02T08:21:40Z)
Toward In-Context Teaching: Adapting Examples to Students' Misconceptions [54.82965010592045]
We introduce a suite of models and evaluation methods we call AdapT. AToM is a new probabilistic model for adaptive teaching that jointly infers students' past beliefs and optimize for the correctness of future beliefs. Our results highlight both the difficulty of the adaptive teaching task and the potential of learned adaptive models for solving it.
arXiv Detail & Related papers (2024-05-07T17:05:27Z)
Evaluating and Optimizing Educational Content with Large Language Model Judgments [52.33701672559594]
We use Language Models (LMs) as educational experts to assess the impact of various instructions on learning outcomes. We introduce an instruction optimization approach in which one LM generates instructional materials using the judgments of another LM as a reward function. Human teachers' evaluations of these LM-generated worksheets show a significant alignment between the LM judgments and human teacher preferences.
arXiv Detail & Related papers (2024-03-05T09:09:15Z)
Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis [0.9187505256430948]
The present study uses transmodal ordered network analysis to understand effective teacher practices in relationship to traditional metrics of in-system learning in a mathematics classroom working with AI tutors. Comparing teacher practices by student learning rates, we find that students with low learning rates exhibited more hint use after monitoring. Students with low learning rates showed learning behavior similar to their high learning rate peers, achieving repeated correct attempts in the tutor.
arXiv Detail & Related papers (2023-12-17T21:50:02Z)
Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education [0.30723404270319693]
This research study delves into the conceptualization, development, and deployment of an innovative learning analytics tool. This tool is designed to quantify student engagement, map learning progression, and evaluate the efficacy of diverse instructional strategies.
arXiv Detail & Related papers (2023-12-15T06:00:26Z)
Adoption of Artificial Intelligence in Schools: Unveiling Factors Influencing Teachers Engagement [5.546987319988426]
AI tools adopted in schools may not always be considered and studied products of the research community. We developed a reliable instrument to measure more holistic factors influencing teachers adoption of adaptive learning platforms in schools. Not generating any additional workload, in-creasing teacher ownership and trust, generating support mechanisms for help, and assuring that ethical issues are minimised are also essential for the adoption of AI in schools.
arXiv Detail & Related papers (2023-04-03T11:47:08Z)
A Machine Learning system to monitor student progress in educational institutes [0.0]
We propose a data driven approach that makes use of Machine Learning techniques to generate a classifier called credit score. The proposal to use credit score as progress indicator is well suited to be used in a Learning Management System.
arXiv Detail & Related papers (2022-11-02T08:24:08Z)
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution [82.89021683451432]
We propose a model-agnostic meta knowledge distillation method under the teacher-student architecture for the single image super-resolution task. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation related distillation methods.
arXiv Detail & Related papers (2022-07-18T02:41:04Z)
Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
arXiv Detail & Related papers (2021-01-19T12:23:32Z)
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition [79.75964372862279]
We propose Point Adversarial Self Mining (PASM) to improve the recognition accuracy in facial expression recognition. PASM uses a point adversarial attack method and a trained teacher network to locate the most informative position related to the target task. The adaptive learning materials generation and teacher/student update can be conducted more than one time, improving the network capability iteratively.
arXiv Detail & Related papers (2020-08-26T06:39:24Z)
Explainable Recommender Systems via Resolving Learning Representations [57.24565012731325]
Explanations could help improve user experience and discover system defects. We propose a novel explainable recommendation model through improving the transparency of the representation learning process.
arXiv Detail & Related papers (2020-08-21T05:30:48Z)

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