Computer Simulation-Based Learning: Student Self-Efficacy During
COVID-19 Outbreak
- URL: http://arxiv.org/abs/2201.05993v3
- Date: Fri, 11 Feb 2022 14:19:49 GMT
- Title: Computer Simulation-Based Learning: Student Self-Efficacy During
COVID-19 Outbreak
- Authors: Thaweesak Trongtirakul, Kamonnit Pusorn, Umpaporn Peerawanichkul
- Abstract summary: This paper investigates the effect of computer simulation-based learning on student self-efficacy in an electric circuit analysis course.
For the 17 participants included in this study, the students have overcome their existing achievements.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Due to the COVID-19 as a pandemic, the government has forced the nationwide
shutdown of several activities, including educational activities. It has
resulted in gigantic migration of universities with education over the internet
serving as the educational platform. Hand-on-based learning becomes a new
challenge. This paper aims to investigate the effect of computer
simulation-based learning on student self-efficacy in an electric circuit
analysis course. For the 17 participants included in this study, the students
have overcome their existing achievements indicated by a long-term average
score. Computer simulation-based learning provides positive results on student
self-efficacy. Students also perceived a valuable learning experience.
Related papers
- Enhancing Students' Learning Process Through Self-Generated Tests [0.0]
This paper describes an educational experiment aimed at the promotion of students' autonomous learning.
The main idea is to make the student feel part of the evaluation process by including students' questions in the evaluation exams.
Questions uploaded by students are visible to every enrolled student as well as to each involved teacher.
arXiv Detail & Related papers (2024-03-21T09:49:33Z) - 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) - Pandemic Pedagogy: Evaluating Remote Education Strategies during
COVID-19 [6.190511747986327]
The COVID-19 pandemic precipitated an abrupt shift in the educational landscape, compelling universities to transition from in-person to online instruction.
We present a retrospective study aimed at understanding and evaluating the remote teaching practices employed during that period.
Our findings indicate that while remote teaching practices moderately influenced students' learning outcomes, they had a pronounced positive impact on student satisfaction.
arXiv Detail & Related papers (2023-08-30T08:34:01Z) - Evaluating virtual laboratory platforms for supporting on-line
information security courses [0.30458514384586394]
Distance education has undergone a renaissance with the advent of computers and the Internet.
Online learning or e-learning introduced virtual classrooms, assessments, online tests and transformed the classroom an into an interactive online classroom.
The Covid-19 pandemic continues to impact higher education, and online learning is a forgone conclusion.
This study evaluates practical solutions for virtual labs to be used in teaching information security and ethical hacking.
arXiv Detail & Related papers (2022-08-17T05:37:42Z) - Distance Learning in Primary School During the COVID 19 Pandemic:
Results of the "SMART KIDS" Experiment [0.0]
The paper analyzes the results of the introduction of the distance learning form (DLF) using electronic educational resources (EER) and the teacher's virtual classroom in primary school.
Despite the outlined problems, the quality of distance learning of primary school students during the pandemic using EER was positively and highly assessed by teachers.
arXiv Detail & Related papers (2022-04-07T14:41:41Z) - Can in-home laboratories foster learning, self-efficacy, and motivation
during the COVID-19 pandemic? -- A case study in two engineering programs [0.0]
This study presents an educational methodology based on Problem-Based Learning (PBL) and in-home laboratories in engineering.
The methodology was carried out in two phases during 2020, in the academic programs of Industrial Engineering and Technology in Electronics with (n=44) students.
arXiv Detail & Related papers (2022-03-30T17:03:33Z) - Disadvantaged students increase their academic performance through
collective intelligence exposure in emergency remote learning due to COVID 19 [105.54048699217668]
During the COVID-19 crisis, educational institutions worldwide shifted from face-to-face instruction to emergency remote teaching (ERT) modalities.
We analyzed data on 7,528 undergraduate students and found that cooperative and consensus dynamics among students in discussion forums positively affect their final GPA.
Using natural language processing, we show that first-year students with low academic performance during high school are exposed to more content-intensive posts in discussion forums.
arXiv Detail & Related papers (2022-03-10T20:23:38Z) - TecCoBot: Technology-aided support for self-regulated learning [52.77024349608834]
Self-study activities can increase the degree of activity and the contribution of self-study activities to the achievement of learning outcomes.
Especially in times of a global pandemic, self-study activities are increasingly executed at home, where students already use technology-enhanced materials, processes, and digital platforms.
arXiv Detail & Related papers (2021-11-23T13:50:21Z) - Comparative Study of Learning Outcomes for Online Learning Platforms [47.5164159412965]
Personalization and active learning are key aspects to successful learning.
We run a comparative head-to-head study of learning outcomes for two popular online learning platforms.
arXiv Detail & Related papers (2021-04-15T20:40:24Z) - Peer-inspired Student Performance Prediction in Interactive Online
Question Pools with Graph Neural Network [56.62345811216183]
We propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools.
Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network.
We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4000 students on 1631 questions.
arXiv Detail & Related papers (2020-08-04T14:55:32Z) - Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training [118.10946662410639]
We propose a novel Self-PU learning framework, which seamlessly integrates PU learning and self-training.
Self-PU highlights three "self"-oriented building blocks: a self-paced training algorithm that adaptively discovers and augments confident examples as the training proceeds.
We study a real-world application of PU learning, i.e., classifying brain images of Alzheimer's Disease.
arXiv Detail & Related papers (2020-06-22T17:53:59Z)
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.