Lecture

Deliberate Practice in Learning: Impact and Techniques

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

In course

HUM-432: How people learn: Designing Learning Tools I

The students will understand the cognitive and social factors which affect learning - particularly in science and engineering. They will be able to use social research techniques as part of the design process to understand end users. ...

Description

This lecture discusses the impact of deliberate practice on expertise in fields like chess and music, highlighting its significance compared to other factors. It also covers topics such as interleaved practice, retrieval practice, and the differences between study methods.

Instructors (2)

Roland John Tormey

I am a sociologist and learning scientist researching and teaching on engineering education. I'm particularly focused on the roles of emotion and self-regulation in engineering teaching and learning. My profile is accessible here.

Nihat Kotluk

Dr. Kotluk is currently a post-doc at the EPFL. He focuses mainly on emotions in the decision-making process, moral reasoning, and ethics in Engineering Education. He also works on equality and diversity issues. He has an academic background in both STEM and social sciences. Dr. Kotluk has acquired advanced skills in education, inclusive pedagogy, and science teachers' training. He has 14 years of teaching experience and has trained high school students in physics and teachers in pedagogy and research methods, among others. Dr. Kotluk has strong teaching and learning communication skills. He holds a Ph.D. in Educational Science, Curriculum, and Instruction. In his thesis, he studied the perceptions and practices of educators in culturally relevant pedagogy and developed recommendations to put more emphasis on inclusion in pre-service teacher education. With his research, he opened a new discussion in the academic community in Turkey and internationally. In his recent work, he analyzes the educational challenges facing refugee communities and other marginalized groups. In the study, he focused on whether and how teachers implemented the principles of culturally relevant and sustaining pedagogies and the challenges teachers faced while trying to implement these principles with Syrian students in Turkey. In his more recent work, he has studied moral emotions, moral reasoning in engineering ethics, and equality and diversity issues in engineering education. His teaching and research interests include emotions in engineering education, culturally responsive STEM education, equity and diversity in education, and multicultural education.

Official source

Related concepts (7)

Longitudinal study

A longitudinal study (or longitudinal survey, or panel study) is a research design that involves repeated observations of the same variables (e.g., people) over long periods of time (i.e., uses longitudinal data). It is often a type of observational study, although it can also be structured as longitudinal randomized experiment.

Cross-sectional study

In medical research, social science, and biology, a cross-sectional study (also known as a cross-sectional analysis, transverse study, prevalence study) is a type of observational study that analyzes data from a population, or a representative subset, at a specific point in time—that is, cross-sectional data. In economics, cross-sectional studies typically involve the use of cross-sectional regression, in order to sort out the existence and magnitude of causal effects of one independent variable upon a dependent variable of interest at a given point in time.

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

Information retrieval

Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.

Related lectures (7)

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Introduction to Machine Learning

Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.

Text Data Analysis: Basics and Techniques

Introduces the basics of text data analysis, covering document retrieval, classification, sentiment analysis, and topic detection using preprocessing techniques and machine learning models.

Text Data Processing: Basics & Techniques

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.

Machine Learning for Physicists/Chemists: Image Classification Log in to Mediaspace to watch this video