Can in-home laboratories foster learning, self-efficacy, and motivation
during the COVID-19 pandemic? -- A case study in two engineering programs
- URL: http://arxiv.org/abs/2203.16465v1
- Date: Wed, 30 Mar 2022 17:03:33 GMT
- Title: Can in-home laboratories foster learning, self-efficacy, and motivation
during the COVID-19 pandemic? -- A case study in two engineering programs
- Authors: Jonathan \'Alvarez Ariza
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The COVID-19 pandemic has represented a challenge for higher education in
terms to provide quality education despite the lockdown periods, the
transformation of the in-person classes to virtual classes, and the
demotivation and anxiety that are experimented by the students. Because the
basis of engineering is the experimentation through hands-on activities and
learning by doing, the lockdown periods and the temporary suspension of the
in-person classes and laboratories have meant a problem for educators that try
to teach and motivate the students despite the situation. In this context, 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. The in-home laboratories were
sent to the students as part of "kits" with the devices needed in each subject.
Besides, due to the difficulties in monitoring the learning process, the
students made videos and blogs as a strategy to reinforce their learning and
evidence the progress in the courses. The outcomes of the methodology show
mainly the following points: (1) An improvement of the academic performance and
learning of the students in the courses. (2) A positive influence of the usage
of in-home laboratories in motivation, self-efficacy, and reduction of anxiety.
(3) Positive correlations between the usage of in-home laboratories, the blogs
and videos, and the teacher's feedback for learning, motivation, and
self-efficacy. Thus, these results evidence that other alternatives that gather
the cognitive and affective learning domains can emerge from engineering to
deal with the educational problems produced by the crisis periods.
Related papers
- Bringing active learning, experimentation, and student-created videos in engineering: A study about teaching electronics and physical computing integrating online and mobile learning [0.0]
The main aim of this study was to create an AL methodology to learn electronics, physical computing (PhyC), programming, and basic robotics in engineering through hands-on activities and active experimentation in online environments.
The methodology was conceived using the guidelines of the Integrated Course Design Model (ICDM) and in some courses combining mobile and online learning with an Android app.
The outcomes indicate a good perception of the PhyC and programming activities by the students and suggest that these influence motivation, self-efficacy, reduction of anxiety, and improvement of academic performance in the courses.
arXiv Detail & Related papers (2024-06-02T23:26:27Z) - A Review of Neuroscience-Inspired Machine Learning [58.72729525961739]
Bio-plausible credit assignment is compatible with practically any learning condition and is energy-efficient.
In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks.
We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.
arXiv Detail & Related papers (2024-02-16T18:05:09Z) - Learn to Teach: Improve Sample Efficiency in Teacher-student Learning
for Sim-to-Real Transfer [5.731477362725785]
We propose a sample efficient learning framework termed Learn to Teach (L2T) that recycles experience collected by the teacher agent.
We show that a single-loop algorithm can train both the teacher and student agents under both Reinforcement Learning and Inverse Reinforcement Learning contexts.
arXiv Detail & Related papers (2024-02-09T21:16:43Z) - Digital Distractions from the Point of View of Higher Education Students [0.0]
The aim of this study was to identify the main digital distractions from the point of view of students.
Students considered digital distractions to have a significant impact on their performance in lab sessions.
Professors should implement strategies to raise students' awareness of the significant negative effects of digital distractions on their performance.
arXiv Detail & Related papers (2024-02-04T19:28:20Z) - Continual Learning: Applications and the Road Forward [119.03464063873407]
Continual learning aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past.
This work is the result of the many discussions the authors had at the Dagstuhl seminar on Deep Continual Learning, in March 2023.
arXiv Detail & Related papers (2023-11-20T16:40:29Z) - Distantly-Supervised Named Entity Recognition with Adaptive Teacher
Learning and Fine-grained Student Ensemble [56.705249154629264]
Self-training teacher-student frameworks are proposed to improve the robustness of NER models.
In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks.
Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
arXiv Detail & Related papers (2022-12-13T12:14:09Z) - Dynamic Diagnosis of the Progress and Shortcomings of Student Learning
using Machine Learning based on Cognitive, Social, and Emotional Features [0.06999740786886534]
Student diversity can be challenging as it adds variability in the way in which students learn and progress over time.
A single teaching approach is likely to be ineffective and result in students not meeting their potential.
This paper discusses a novel methodology based on data analytics and Machine Learning to measure and causally diagnose the progress and shortcomings of student learning.
arXiv Detail & Related papers (2022-04-13T21:14:58Z) - Understanding the role of single-board computers in engineering and
computer science education: A systematic literature review [0.0]
Single-Board Computers (SBCs) have been employed more frequently in engineering and computer science both to technical and educational levels.
This systematic literature review explores how the SBCs are employed in engineering and computer science.
arXiv Detail & Related papers (2022-03-30T18:34:03Z) - 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) - SARS-CoV-2 Impact on Online Teaching Methodologies and the Ed-Tech
Sector: Smile and Learn Platform Case Study [50.591267188664666]
The study analyzes the importance of online methodologies and usage tendency of an educational resource example: The Smile and Learn platform.
Thereby, the study presents the different models implemented to support education and its impact in the use of the platform.
arXiv Detail & Related papers (2020-07-15T10:06:51Z) - Dual Policy Distillation [58.43610940026261]
Policy distillation, which transfers a teacher policy to a student policy, has achieved great success in challenging tasks of deep reinforcement learning.
In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment.
The key challenge in developing this dual learning framework is to identify the beneficial knowledge from the peer learner for contemporary learning-based reinforcement learning algorithms.
arXiv Detail & Related papers (2020-06-07T06:49:47Z)
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.