An Approach to Adaptive Microlearning in Higher Education
- URL: http://arxiv.org/abs/2205.06337v1
- Date: Wed, 11 May 2022 11:58:22 GMT
- Title: An Approach to Adaptive Microlearning in Higher Education
- Authors: Ovidiu Gherman, Cristina Elena Turcu, Corneliu Octavian Turcu
- Abstract summary: We propose a system for personalized learning using microlearning, which provides support and guidance to students based on their individual needs.
Data collected during the semester as a result of the students' behavioural analysis and their real learning motivations will be used to improve the proposed system.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Current changes in society and the education system, cumulated with the
accelerated development of new technologies, entail inherent changes in the
educational process. Numerous studies have shown that the pandemic has forced a
rapid transformation of higher education. Thus, if before the pandemic digital
technologies were used to supplement the learning process, now they are the
main means of learning delivery. In addition, as previous research has shown,
new pedagogical strategies and new ways of teaching and learning are needed for
the current generation of students, the so-called Generation Z, to acquire new
knowledge and develop new skills. In this necessary evolution of the
educational process, a possible solution to increase the effectiveness of the
learning process for the Generation Z students is to use microlearning to
extend the traditional ways of learning. Many studies have shown that
microlearning, based on how today's students learn and memorize, facilitates
learning. In recent years there has been a growing trend in their use of
microlearning in the educational process. But, in order to be effective, this
approach must allow the individual knowledge building, by indicating a guiding
direction of the optimal path to achieve the proposed objectives. We propose a
system for personalized learning using microlearning, which provides support
and guidance to students based on their individual needs, in order to increase
their interest in learning, but also to compensate for various deficiencies in
their educational background. We also present a case study from the higher
education sector. Feedback from students and data collected during the semester
as a result of the students' behavioural analysis and their real learning
motivations will be used to improve the proposed system.
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) - 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 Scalable Adaptive Learning with Graph Neural Networks and
Reinforcement Learning [0.0]
We introduce a flexible and scalable approach towards the problem of learning path personalization.
Our model is a sequential recommender system based on a graph neural network.
Our results demonstrate that it can learn to make good recommendations in the small-data regime.
arXiv Detail & Related papers (2023-05-10T18:16:04Z) - Desperately seeking the impact of learning analytics in education at
scale: Marrying data analysis with teaching and learning [0.0]
Learning analytics (LA) is argued to be able to improve learning outcomes, learner support and teaching.
There is still little empirical evidence of impact on practice that shows the effectiveness of LA in education settings.
We argue that in order to increase the impact of data-driven decision-making aimed at students' improved learning at scale, we need to better understand educators' needs.
arXiv Detail & Related papers (2021-05-14T07:33:17Z) - Student Network Learning via Evolutionary Knowledge Distillation [22.030934154498205]
We propose an evolutionary knowledge distillation approach to improve the transfer effectiveness of teacher knowledge.
Instead of a fixed pre-trained teacher, an evolutionary teacher is learned online and consistently transfers intermediate knowledge to supervise student network learning on-the-fly.
In this way, the student can simultaneously obtain rich internal knowledge and capture its growth process, leading to effective student network learning.
arXiv Detail & Related papers (2021-03-23T02:07:15Z) - 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) - 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) - 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) - Revisiting Meta-Learning as Supervised Learning [69.2067288158133]
We aim to provide a principled, unifying framework by revisiting and strengthening the connection between meta-learning and traditional supervised learning.
By treating pairs of task-specific data sets and target models as (feature, label) samples, we can reduce many meta-learning algorithms to instances of supervised learning.
This view not only unifies meta-learning into an intuitive and practical framework but also allows us to transfer insights from supervised learning directly to improve meta-learning.
arXiv Detail & Related papers (2020-02-03T06:13:01Z)
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.