Teaching Machine Learning in K-12 Computing Education: Potential and
Pitfalls
- URL: http://arxiv.org/abs/2106.11034v1
- Date: Wed, 2 Jun 2021 10:45:47 GMT
- Title: Teaching Machine Learning in K-12 Computing Education: Potential and
Pitfalls
- Authors: Matti Tedre, Tapani Toivonen, Juho Kaihila, Henriikka Vartiainen,
Teemu Valtonen, Ilkka Jormanainen, and Arnold Pears
- Abstract summary: This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education.
A crucial step is abandoning the belief that rule-based "traditional" programming is a central aspect and building block in developing next generation computational thinking.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Over the past decades, numerous practical applications of machine learning
techniques have shown the potential of data-driven approaches in a large number
of computing fields. Machine learning is increasingly included in computing
curricula in higher education, and a quickly growing number of initiatives are
expanding it in K-12 computing education, too. As machine learning enters K-12
computing education, understanding how intuition and agency in the context of
such systems is developed becomes a key research area. But as schools and
teachers are already struggling with integrating traditional computational
thinking and traditional artificial intelligence into school curricula,
understanding the challenges behind teaching machine learning in K-12 is an
even more daunting challenge for computing education research. Despite the
central position of machine learning in the field of modern computing, the
computing education research body of literature contains remarkably few studies
of how people learn to train, test, improve, and deploy machine learning
systems. This is especially true of the K-12 curriculum space. This article
charts the emerging trajectories in educational practice, theory, and
technology related to teaching machine learning in K-12 education. The article
situates the existing work in the context of computing education in general,
and describes some differences that K-12 computing educators should take into
account when facing this challenge. The article focuses on key aspects of the
paradigm shift that will be required in order to successfully integrate machine
learning into the broader K-12 computing curricula. A crucial step is
abandoning the belief that rule-based "traditional" programming is a central
aspect and building block in developing next generation computational thinking.
Related papers
- The Landscape of Modern Machine Learning: A Review of Machine,
Distributed and Federated Learning [0.0]
We provide a high-level overview for the latest advanced machine learning algorithms, applications, and frameworks.
Our work serves as an introductory text to the vast field of modern machine learning.
arXiv Detail & Related papers (2023-12-05T20:40:05Z) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - A Survey of Deep Learning for Mathematical Reasoning [71.88150173381153]
We review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade.
Recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.
arXiv Detail & Related papers (2022-12-20T18:46:16Z) - Learning to Learn: How to Continuously Teach Humans and Machines [24.29443694991142]
We find that curriculum consistently influences learning outcomes for humans and for multiple continual machine learning algorithms.
We propose a novel algorithm, dubbed Curriculum Designer (CD), that designs and ranks curricula based on inter-class feature similarities.
arXiv Detail & Related papers (2022-11-28T15:53:44Z) - Flashlight: Enabling Innovation in Tools for Machine Learning [50.63188263773778]
We introduce Flashlight, an open-source library built to spur innovation in machine learning tools and systems.
We see Flashlight as a tool enabling research that can benefit widely used libraries downstream and bring machine learning and systems researchers closer together.
arXiv Detail & Related papers (2022-01-29T01:03:29Z) - Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling.
Deep learning has become its own subfield of machine learning.
In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
arXiv Detail & Related papers (2021-05-29T21:02:44Z) - Knowledge as Invariance -- History and Perspectives of
Knowledge-augmented Machine Learning [69.99522650448213]
Research in machine learning is at a turning point.
Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks.
This white paper provides an introduction and discussion of this emerging field in machine learning research.
arXiv Detail & Related papers (2020-12-21T15:07:19Z) - Machine Learning and Computational Mathematics [8.160343645537106]
We discuss how machine learning has already impacted and will further impact computational mathematics, scientific computing and computational science.
We describe some of the most important progress that has been made on these issues.
Our hope is to put things into a perspective that will help to integrate machine learning with computational mathematics.
arXiv Detail & Related papers (2020-09-23T23:16:46Z) - Computer-Aided Personalized Education [15.811740322935476]
The number of US students taking introductory courses has grown three-fold in the past decade.
Massive open online courses (MOOCs) have been promoted as a way to ease this strain.
Personalized education relying on computational tools can address this challenge.
arXiv Detail & Related papers (2020-07-07T18:00:04Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z) - Machine Education: Designing semantically ordered and ontologically
guided modular neural networks [5.018156030818882]
We first discuss selected attempts to date on machine teaching and education.
We then bring theories and methodologies together from human education to structure and mathematically define the core problems in lesson design for machine education.
arXiv Detail & Related papers (2020-02-07T09:43:40Z)
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.