Exploring Engagement and Perceived Learning Outcomes in an Immersive Flipped Learning Context
- URL: http://arxiv.org/abs/2409.12674v1
- Date: Thu, 19 Sep 2024 11:38:48 GMT
- Title: Exploring Engagement and Perceived Learning Outcomes in an Immersive Flipped Learning Context
- Authors: Mehrasa Alizadeh,
- Abstract summary: 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.
- Score: 0.195804735329484
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The flipped classroom model has been widely acknowledged as a practical pedagogical approach to enhancing student engagement and learning. However, it faces challenges such as improving student interaction with learning content and peers, particularly in Japanese universities where digital technologies are not always fully utilized. To address these challenges and identify potential solutions, a case study was conducted in which an online flipped course on academic skills was developed and implemented in an immersive virtual environment. The primary objective during this initial phase was not to establish a causal relationship between the use of immersive flipped learning and students' engagement and perceived learning outcomes. Instead, this initiative aimed to explore the benefits and challenges of the immersive flipped learning approach in relation to students' online engagement and their perceived learning outcomes. Following a mixed-methods research approach, quantitative and qualitative data were collected through a survey (N=50) and students' reflective reports (N=80). The study revealed high levels of student engagement and perceived learning outcomes, although it also identified areas needing improvement, particularly in supporting student interactions in the target language. Despite the exploratory nature of this study, the findings suggest that a well-designed flipped learning approach, set in an engaging immersive environment, can significantly enhance student engagement, thereby supporting the learning process. When creating an immersive flipped learning course, educators should incorporate best practices from the literature on both flipped learning and immersive learning design to ensure optimal learning outcomes. The findings of this study can serve as a valuable resource for educators seeking to design engaging and effective remote learning experiences.
Related papers
- 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) - YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
This paper introduces YODA, a teacher-student progressive learning framework.
It emulates the teacher-student education process to improve the efficacy of model fine-tuning.
Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
arXiv Detail & Related papers (2024-01-28T14:32:15Z) - Leveraging generative artificial intelligence to simulate student
learning behavior [13.171768256928509]
We explore the feasibility of using large language models (LLMs) to simulate student learning behaviors.
Unlike conventional machine learning based prediction, we leverage LLMs to instantiate virtual students with specific demographics.
Our objective is not merely to predict learning outcomes but to replicate learning behaviors and patterns of real students.
arXiv Detail & Related papers (2023-10-30T00:09:59Z) - A Hierarchy-based Analysis Approach for Blended Learning: A Case Study
with Chinese Students [12.533646830917213]
This paper proposes a hierarchy-based evaluation approach for blended learning evaluation.
The results show that cognitive engagement and emotional engagement play a more important role in blended learning evaluation.
arXiv Detail & Related papers (2023-09-19T00:09:00Z) - Responsible Active Learning via Human-in-the-loop Peer Study [88.01358655203441]
We propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability.
We first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side.
During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion.
arXiv Detail & Related papers (2022-11-24T13:18:27Z) - 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) - Constructing a personalized learning path using genetic algorithms
approach [0.0]
This paper presents the possibility of constructing personalized learning paths using genetic algorithm-based model.
Experiments show that the genetic algorithm is suitable to generate optimal learning paths based on learning object difficulty level, duration, rating, and relation degree.
arXiv Detail & Related papers (2021-04-22T18:43:47Z) - 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) - Transfer Learning in Deep Reinforcement Learning: A Survey [64.36174156782333]
Reinforcement learning is a learning paradigm for solving sequential decision-making problems.
Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks.
transfer learning has arisen to tackle various challenges faced by reinforcement learning.
arXiv Detail & Related papers (2020-09-16T18:38:54Z) - Social Engagement versus Learning Engagement -- An Exploratory Study of
FutureLearn Learners [61.58283466715385]
Massive Open Online Courses (MOOCs) continue to see increasing enrolment, but only a small percent of enrolees completes the MOOCs.
This study is particularly concerned with how learners interact with peers, along with their study progression in MOOCs.
The study was conducted on the less explored FutureLearn platform, which employs a social constructivist approach and promotes collaborative learning.
arXiv Detail & Related papers (2020-08-11T16:09:10Z) - 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.