Exploratory Learning Environments for Responsible Management Education
Using Lego Serious Play
- URL: http://arxiv.org/abs/2104.12539v1
- Date: Sat, 27 Mar 2021 22:28:34 GMT
- Title: Exploratory Learning Environments for Responsible Management Education
Using Lego Serious Play
- Authors: Vasilis Gkogkidis, Nicholas Dacre
- Abstract summary: We will draw on constructivist learning theories and Lego Serious Play (LSP) as a learning enhancement approach to develop a pedagogical framework.
LSP is selected due to its increasing application in learning environments to help promote critical discourse, and engage with highly complex problems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Research into responsible management education has largely focused on the
merits, attributes, and transformation opportunities to enhance responsible
business school education aims. As such, a prominent part of the literature has
occupied itself with examining if responsible management modules are inherently
considered a non-crucial element of the curriculum and determining the extent
to which business schools have introduced such learning content into their
curriculum. However, there has been scant research into how to apply novel
teaching approaches to engage students and promote responsible management
education endeavours. As such, this paper seeks to address this gap through the
development of a teaching framework to support educators in designing effective
learning environments focused on responsible management education. We will draw
on constructivist learning theories and Lego Serious Play (LSP) as a learning
enhancement approach to develop a pedagogical framework. LSP is selected due to
its increasing application in learning environments to help promote critical
discourse, and engage with highly complex problems, whether these are social,
economic, environmental, or organisational.
Related papers
- Agile Minds, Innovative Solutions, and Industry-Academia Collaboration: Lean R&D Meets Problem-Based Learning in Software Engineering Education [1.4454625330080995]
This paper aims to extend Lean R&D with skill principles, emphasizing business and software development synergy.
The educational program engaged 40 part-time students receiving lectures and mentoring while working on real problems.
Students reported increased knowledge proficiency and perceived working on real problems as contributing the most to their learning.
arXiv Detail & Related papers (2024-07-22T18:47:14Z) - LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models.
It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
arXiv Detail & Related papers (2024-04-01T16:50:54Z) - Large Language Models for Education: A Survey and Outlook [69.02214694865229]
We systematically review the technological advancements in each perspective, organize related datasets and benchmarks, and identify the risks and challenges associated with deploying LLMs in education.
Our survey aims to provide a comprehensive technological picture for educators, researchers, and policymakers to harness the power of LLMs to revolutionize educational practices and foster a more effective personalized learning environment.
arXiv Detail & Related papers (2024-03-26T21:04:29Z) - Enhancing Instructional Quality: Leveraging Computer-Assisted Textual
Analysis to Generate In-Depth Insights from Educational Artifacts [13.617709093240231]
We examine how artificial intelligence (AI) and machine learning (ML) methods can analyze educational content, teacher discourse, and student responses to foster instructional improvement.
We identify key areas where AI/ML integration offers significant advantages, including teacher coaching, student support, and content development.
This paper emphasizes the importance of aligning AI/ML technologies with pedagogical goals to realize their full potential in educational settings.
arXiv Detail & Related papers (2024-03-06T18:29:18Z) - An Effective Learning Management System for Revealing Student Performance Attributes [22.88480788156872]
This study proposes an LMS incorporated with an advanced educational mining module to mine efficiently from student performance records.
Results show increased mining efficiency of the proposed mining module without information loss compared to classic educational mining algorithms.
The design and application of such an effective LMS can enable educators to learn from past student performance experiences, empowering them to guide and intervene with students in time, and eventually improve their academic success.
arXiv Detail & Related papers (2024-03-05T03:56:49Z) - Reinforcement Learning in Education: A Multi-Armed Bandit Approach [12.358921226358133]
Reinforcement leaning solves unsupervised problems where agents move through a state-action-reward loop to maximize the overall reward for the agent.
The aim of this study was to contextualise and simulate the cumulative reward within an environment for an intervention recommendation problem in the education context.
arXiv Detail & Related papers (2022-11-01T22:47:17Z) - 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) - The Challenges of Assessing and Evaluating the Students at Distance [77.34726150561087]
The COVID-19 pandemic has caused a strong effect on higher education institutions with the closure of classroom teaching activities.
This short essay aims to explore the challenges posed to Portuguese higher education institutions and to analyze the challenges posed to evaluation models.
arXiv Detail & Related papers (2021-01-30T13:13:45Z) - 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) - 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) - 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.