Adaptive and Gamified Learning Paths with Polyglot and .NET Interactive
        - URL: http://arxiv.org/abs/2310.07314v1
 - Date: Wed, 11 Oct 2023 09:00:36 GMT
 - Title: Adaptive and Gamified Learning Paths with Polyglot and .NET Interactive
 - Authors: Tommaso Martorella, Antonio Bucchiarone
 - Abstract summary: Growing demand for general and specialized education inside and outside classrooms is at the heart of this rising trend.
In modern, heterogeneous learning environments, the one-size-fits-all approach is proven to be fundamentally flawed.
We aim to define and implement an open, content-agnostic, and platform to design and consume adaptive and gamified learning experiences.
 - Score: 3.720289971260197
 - License: http://creativecommons.org/licenses/by/4.0/
 - Abstract:   The digital age is changing the role of educators and pushing for a paradigm
shift in the education system as a whole. Growing demand for general and
specialized education inside and outside classrooms is at the heart of this
rising trend. In modern, heterogeneous learning environments, the
one-size-fits-all approach is proven to be fundamentally flawed.
Individualization through adaptivity is, therefore, crucial to nurture
individual potential and address accessibility needs and neurodiversity. By
formalizing a learning framework that takes into account all these different
aspects, we aim to define and implement an open, content-agnostic, and
extensible platform to design and consume adaptive and gamified learning
experiences.
 
       
      
        Related papers
        - A Survey of Self-Evolving Agents: On Path to Artificial Super   Intelligence [87.08051686357206]
Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
arXiv  Detail & Related papers  (2025-07-28T17:59:05Z) - Bridging MOOCs, Smart Teaching, and AI: A Decade of Evolution Toward a   Unified Pedagogy [4.943165921136573]
We propose a three-layer instructional framework that combines the scalability of MOOCs, the responsiveness of Smart Teaching, and the adaptivity of AI.<n>The findings highlight the framework's potential to enhance learner engagement, support instructors, and enable personalized yet scalable learning.
arXiv  Detail & Related papers  (2025-07-18T14:57:20Z) - Unveiling the Learning Mind of Language Models: A Cognitive Framework   and Empirical Study [50.065744358362345]
Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning.<n>Yet their learning ability, which is crucial for adapting to dynamic environments and acquiring new knowledge, remains underexplored.
arXiv  Detail & Related papers  (2025-06-16T13:24:50Z) - A Computational Model of Inclusive Pedagogy: From Understanding to   Application [1.2058600649065616]
Human education transcends mere knowledge transfer, it relies on co-adaptation dynamics.<n>Despite its centrality, computational models of co-adaptive teacher-student interactions (T-SI) remain underdeveloped.<n>We present a computational T-SI model that integrates contextual insights on human education into a testable framework.
arXiv  Detail & Related papers  (2025-05-02T12:26:31Z) - Education in the Era of Neurosymbolic AI [0.6468510459310326]
We propose a system that leverages the unique affordances of pedagogical agents as critical components of a hybrid NAI architecture.
We conclude that education in the era of NAI will make learning more accessible, equitable, and aligned with real-world skills.
arXiv  Detail & Related papers  (2024-11-16T19:18:39Z) - Guiding Empowerment Model: Liberating Neurodiversity in Online Higher   Education [2.703906279696349]
We address the equity gap for neurodivergent and situationally limited learners by identifying the spectrum of dynamic factors that impact learning and function.
We suggest that by applying the mode through technology-enabled features such as customizable task management, guided varied content access, and guided multi-modal collaboration, major learning barriers will be removed.
arXiv  Detail & Related papers  (2024-10-24T16:05:38Z) - From MOOC to MAIC: Reshaping Online Teaching and Learning through   LLM-driven Agents [78.15899922698631]
MAIC (Massive AI-empowered Course) is a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom.
We conduct preliminary experiments at Tsinghua University, one of China's leading universities.
arXiv  Detail & Related papers  (2024-09-05T13:22:51Z) - 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) - Adapting Large Language Models for Education: Foundational Capabilities,   Potentials, and Challenges [60.62904929065257]
Large language models (LLMs) offer possibility for resolving this issue by comprehending individual requests.
This paper reviews the recently emerged LLM research related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering.
arXiv  Detail & Related papers  (2023-12-27T14:37:32Z) - Incorporating Neuro-Inspired Adaptability for Continual Learning in
  Artificial Intelligence [59.11038175596807]
Continual learning aims to empower artificial intelligence with strong adaptability to the real world.
Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting.
We propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity.
arXiv  Detail & Related papers  (2023-08-29T02:43:58Z) - 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) - 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) - A Network Science Perspective to Personalized Learning [0.0]
We examine how learning objectives can be achieved through a learning platform that offers content choices and multiple modalities of engagement to support self-paced learning.
This framework brings the attention to learning experiences, rather than teaching experiences, by providing the learner engagement and content choices supported by a network of knowledge.
arXiv  Detail & Related papers  (2021-11-02T01:50:01Z) - Importance Weighted Policy Learning and Adaptation [89.46467771037054]
We study a complementary approach which is conceptually simple, general, modular and built on top of recent improvements in off-policy learning.
The framework is inspired by ideas from the probabilistic inference literature and combines robust off-policy learning with a behavior prior.
Our approach achieves competitive adaptation performance on hold-out tasks compared to meta reinforcement learning baselines and can scale to complex sparse-reward scenarios.
arXiv  Detail & Related papers  (2020-09-10T14:16:58Z) - Learning with AMIGo: Adversarially Motivated Intrinsic Goals [63.680207855344875]
AMIGo is a goal-generating teacher that proposes Adversarially Motivated Intrinsic Goals.
We show that our method generates a natural curriculum of self-proposed goals which ultimately allows the agent to solve challenging procedurally-generated tasks.
arXiv  Detail & Related papers  (2020-06-22T10:22:08Z) 
        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.