Learn-Apply-Reinforce/Share Learning: Hackathons and CTFs as General Pedagogic Tools in Higher Education, and Their Applicability to Distance Learning

• URL: http://arxiv.org/abs/2006.04226v1
• Date: Sun, 7 Jun 2020 18:41:39 GMT
• Title: Learn-Apply-Reinforce/Share Learning: Hackathons and CTFs as General Pedagogic Tools in Higher Education, and Their Applicability to Distance Learning
• Authors: Tom Goodman and Andreea-Ina Radu
• Abstract summary: This paper lays out two teaching/learning methods that are becoming increasingly prevalent in computer science. A case study of each is analysed, and the underpinning similarities extracted. The frameworks are generalised to Learn-Apply-Reinforce/Share Learning - a social constructivistic method that can be used subject-independently.
• Score: 0.26651200086513094
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper lays out two teaching/learning methods that are becoming increasingly prevalent in computer science - hackathons, and Capture the Flag (CTF) competitions - and the pedagogic theory that underpins them. A case study of each is analysed, and the underpinning similarities extracted. The frameworks are then generalised to Learn-Apply-Reinforce/Share Learning - a social constructivistic method that can be used subject-independently. The applicability of this new method to distance learning is then investigated - with a mind to potential necessity to work from home - both due to increasing demand in the Higher Education sector, but also the devastating impact of crises such as the ongoing COVID-19 pandemic. Finally, a few potential extensions and future applications are discussed - including the possibilities of pivoting the method to be more research-driven, or indeed, to drive research.

Related papers

• Let Students Take the Wheel: Introducing Post-Quantum Cryptography with Active Learning [4.804847392457553]
  Post-quantum cryptography (PQC) has been identified as the solution to secure existing software systems. This research proposes a novel active learning approach and assesses the best practices for teaching PQC to undergraduate and graduate students.
  arXiv  Detail & Related papers  (2024-10-17T01:52:03Z)
• Towards a Categorical Foundation of Deep Learning: A Survey [0.0]
  This thesis is a survey that covers some recent work attempting to study machine learning categorically. acting as a lingua franca of mathematics and science, category theory might be able to give a unifying structure to the field of machine learning.
  arXiv  Detail & Related papers  (2024-10-07T13:11:16Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations [1.1060425537315088]
  Federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. In times of crisis, when real-time decision-making is critical, federated learning allows multiple entities to work collectively without sharing sensitive data. This paper is a systematic review of the literature on privacy-preserving machine learning in the last few years.
  arXiv  Detail & Related papers  (2023-11-17T19:23:21Z)
• Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions [1.8259323824078306]
  Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in Internet of Things (IoT) We review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment.
  arXiv  Detail & Related papers  (2022-07-20T01:31:22Z)
• A Survey of Knowledge Tracing: Models, Variants, and Applications [70.69281873057619]
  Knowledge Tracing is one of the fundamental tasks for student behavioral data analysis. We present three types of fundamental KT models with distinct technical routes. We discuss potential directions for future research in this rapidly growing field.
  arXiv  Detail & Related papers  (2021-05-06T13:05:55Z)
• Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums [58.221459787471254]
  Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. Due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support. With the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention. This paper explores for the first time Bayesian deep learning on learner-based text posts with two methods: Monte Carlo Dropout and Variational Inference.
  arXiv  Detail & Related papers  (2021-04-26T15:12:13Z)
• Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning [65.06445195580622]
  Federated learning is a new paradigm that decouples data collection and model training via multi-party computation and model aggregation. We conduct a focused survey of federated learning in conjunction with other learning algorithms.
  arXiv  Detail & Related papers  (2021-02-25T15:18:13Z)
• 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)
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey [53.73359052511171]
  Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. We present a framework for curriculum learning (CL) in RL, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals.
  arXiv  Detail & Related papers  (2020-03-10T20:41:24Z)

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.