From Paper to Platform: Evolution of a Novel Learning Environment for Tabletop Exercises
- URL: http://arxiv.org/abs/2404.10988v1
- Date: Wed, 17 Apr 2024 01:52:48 GMT
- Title: From Paper to Platform: Evolution of a Novel Learning Environment for Tabletop Exercises
- Authors: Valdemar Švábenský, Jan Vykopal, Martin Horák, Martin Hofbauer, Pavel Čeleda,
- Abstract summary: This paper presents data and teaching experience from a cybersecurity course that introduces tabletop exercises in classrooms.
InJECT Exercise Platform (IXP) is a web-based learning environment for delivering and evaluating the exercises.
Unlike in traditional tabletop exercises, which are difficult to evaluate manually, IXP provides insights into students' behavior and learning.
- Score: 0.2796197251957245
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: For undergraduate students of computing, learning to solve complex practical problems in a team is an essential skill for their future careers. This skill is needed in various fields, such as in cybersecurity and IT governance. Tabletop exercises are an innovative teaching method used in practice for training teams in incident response and evaluation of contingency plans. However, tabletop exercises are not yet widely established in university education. This paper presents data and teaching experience from a cybersecurity course that introduces tabletop exercises in classrooms using a novel technology: INJECT Exercise Platform (IXP), a web-based learning environment for delivering and evaluating the exercises. This technology substantially improves the prior practice, since tabletop exercises worldwide have usually been conducted using pen and paper. Unlike in traditional tabletop exercises, which are difficult to evaluate manually, IXP provides insights into students' behavior and learning based on automated analysis of interaction data. We demonstrate IXP's capabilities and evolution by comparing exercise sessions hosted throughout three years at different stages of the platform's readiness. The analysis of student data is supplemented by the discussion of the lessons learned from employing IXP in computing education contexts. The data analytics enabled a detailed comparison of the teams' performance and behavior. Instructors who consider innovating their classes with tabletop exercises may use IXP and benefit from the insights in this paper.
Related papers
- Dynamic Skill Adaptation for Large Language Models [78.31322532135272]
We present Dynamic Skill Adaptation (DSA), an adaptive and dynamic framework to adapt novel and complex skills to Large Language Models (LLMs)
For every skill, we utilize LLMs to generate both textbook-like data which contains detailed descriptions of skills for pre-training and exercise-like data which targets at explicitly utilizing the skills to solve problems for instruction-tuning.
Experiments on large language models such as LLAMA and Mistral demonstrate the effectiveness of our proposed methods in adapting math reasoning skills and social study skills.
arXiv Detail & Related papers (2024-12-26T22:04:23Z) - Learner Attentiveness and Engagement Analysis in Online Education Using Computer Vision [3.449808359602251]
This research presents a computer vision-based approach to analyze and quantify learners' attentiveness, engagement, and other affective states within online learning scenarios.
A machine learning-based algorithm is developed on top of the classification model that outputs a comprehensive attentiveness index of the learners.
An end-to-end pipeline is proposed through which learners' live video feed is processed, providing detailed attentiveness analytics of the learners to the instructors.
arXiv Detail & Related papers (2024-11-30T10:54:08Z) - FlashHack: Reflections on the Usage of a Micro Hackathon as an Assessment Tool in a Machine Learning Course [0.0]
Group project-based learning is an increasingly popular form of experiential learning in CS education.
To tackle these issues, we introduced FlashHack: a monitored, incremental, in-classroom micro Hackathon.
Our results indicate high student engagement and satisfaction, alongside simplified assessment processes for instructors.
arXiv Detail & Related papers (2024-10-07T11:21:11Z) - Research and Practice of Delivering Tabletop Exercises [0.2796197251957245]
Since tabletop exercises train competencies required in the workplace, they have been introduced into computing courses at universities as an innovation.
To help computing educators adopt this innovative method, we surveyed academic publications that deal with tabletop exercises.
Our review provides researchers, tool developers, and educators with an orientation in the area, a synthesis of trends, and implications for further work.
arXiv Detail & Related papers (2024-04-16T01:12:20Z) - PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [65.57123249246358]
This paper introduces a pre-trained model-based continual learning toolbox known as PILOT.
On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt.
On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
arXiv Detail & Related papers (2023-09-13T17:55:11Z) - UKP-SQuARE: An Interactive Tool for Teaching Question Answering [61.93372227117229]
The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course.
We introduce UKP-SQuARE as a platform for QA education.
Students can run, compare, and analyze various QA models from different perspectives.
arXiv Detail & Related papers (2023-05-31T11:29:04Z) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z) - Scalable Learning Environments for Teaching Cybersecurity Hands-on [0.4893345190925178]
This paper describes a technical innovation for scalable teaching of cybersecurity hands-on classes using interactive learning environments.
We present our research effort and practical experience in designing and using learning environments that scale up hands-on cybersecurity classes.
arXiv Detail & Related papers (2021-10-19T14:18:54Z) - Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A
Stackelberg Game Approach [54.28419430315478]
Mobile Edge Learning enables distributed training of Machine Learning models over heterogeneous edge devices.
In MEL, the training performance deteriorates without the availability of sufficient training data or computing resources.
We propose an incentive mechanism, where we formulate the orchestrators-learners interactions as a 2-round Stackelberg game.
arXiv Detail & Related papers (2021-09-25T17:27:48Z) - Graph-based Exercise- and Knowledge-Aware Learning Network for Student
Performance Prediction [8.21303828329009]
We propose a Graph-based Exercise- and Knowledge-Aware Learning Network for accurate student score prediction.
We learn students' mastery of exercises and knowledge concepts respectively to model the two-fold effects of exercises and knowledge concepts.
arXiv Detail & Related papers (2021-06-01T06:53:17Z) - 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)
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.