Hybrid Active Teaching Methodology for Learning Development: A
Self-assessment Case Study Report in Computer Engineering
- URL: http://arxiv.org/abs/2402.06020v1
- Date: Thu, 8 Feb 2024 19:42:37 GMT
- Title: Hybrid Active Teaching Methodology for Learning Development: A
Self-assessment Case Study Report in Computer Engineering
- Authors: Renan Lima Baima (1 and 4), Tiago Miguel Barao Caetano (2), Ana
Carolina Oliveira Lima (3 and 4), Emilia Oliveira Lima Leal (5), Tiago Miguel
Pereira Candeias (3) and Silvia Maria Dias Pedro Rebou\c{c}as (3 and 6) ((1)
SnT - Interdisciplinary Centre for Security, Reliability and Trust / FINATRAX
- Digital Financial Services and Cross-Organisational Digital
Transformations, University of Luxembourg, (2) Instituto Superior Manuel
Teixeira Gomes, (3) COPELABS, Lusofona University, (4) CICARI - Innovation
Centre for Industrial Control, Automation and Robotics, (5) Facultad de
Humanidades y Artes - Escuela de Posgrado, Universidad Nacional de Ros\'ario,
(6) CEAUL, University of Lisbon)
- Abstract summary: The primary objective is to emphasize the merits of active methodologies and cross-disciplinary curricula in Requirement Engineering.
This direction promises a holistic and applied trajectory for Computer Engineering education.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The primary objective is to emphasize the merits of active methodologies and
cross-disciplinary curricula in Requirement Engineering. This direction
promises a holistic and applied trajectory for Computer Engineering education,
supported by the outcomes of our case study, where artifact-centric learning
proved effective, with 73% of students achieving the highest grade.
Self-assessments further corroborated academic excellence, emphasizing
students' engagement in skill enhancement and knowledge acquisition.
Related papers
- Exploring Engagement and Perceived Learning Outcomes in an Immersive Flipped Learning Context [0.195804735329484]
The aim of this study was to explore the benefits and challenges of the immersive flipped learning approach in relation to students' online engagement and perceived learning outcomes.
The study revealed high levels of student engagement and perceived learning outcomes, although it also identified areas needing improvement.
The findings of this study can serve as a valuable resource for educators seeking to design engaging and effective remote learning experiences.
arXiv Detail & Related papers (2024-09-19T11:38:48Z) - 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) - The Perceived Learning Behaviors and Assessment Techniques of First-Year Students in Computer Science: An Empirical Study [0.0]
Students believe that in-person instruction is the most effective way to learn.
For evaluation methods, there is a preference for practical and written examinations.
arXiv Detail & Related papers (2024-05-10T08:45:32Z) - Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach [50.36650300087987]
This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism.
We have found that integrating the forgetting mechanisms significantly enhances the models' performance in acquiring new knowledge.
arXiv Detail & Related papers (2024-03-27T05:10:38Z) - Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
We shed light on the intersectional studies of generative AI and adaptive learning.
We argue that this union will contribute significantly to the development of the next-stage learning format in education.
arXiv Detail & Related papers (2024-02-02T23:54:51Z) - Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS)
GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
arXiv Detail & Related papers (2023-12-03T12:37:16Z) - 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) - From Teaching to Coaching: A Case Study of a Technical Communication
Course [0.0]
Development of non-technical skills like self-esteem, life-long learning among students is vital for a successful career.
The approach adopted in this research work is to transform the delivery of a faculty-wide course from teaching to coaching.
arXiv Detail & Related papers (2021-07-20T14:44:38Z) - Learning Student-Friendly Teacher Networks for Knowledge Distillation [50.11640959363315]
We propose a novel knowledge distillation approach to facilitate the transfer of dark knowledge from a teacher to a student.
Contrary to most of the existing methods that rely on effective training of student models given pretrained teachers, we aim to learn the teacher models that are friendly to students.
arXiv Detail & Related papers (2021-02-12T07:00:17Z) - 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) - A Review on Intelligent Object Perception Methods Combining
Knowledge-based Reasoning and Machine Learning [60.335974351919816]
Object perception is a fundamental sub-field of Computer Vision.
Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
arXiv Detail & Related papers (2019-12-26T13:26:49Z)
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.