Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Learning Strategies and Rewards

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 covers the use of Cohen's d and Pearson's Correlation coefficient for hypothesis testing in different data types, the impact of mastery goals on learning outcomes, the influence of rewards on student motivation, and the effects of extrinsic rewards on intrinsic motivation based on a meta-analysis of 128 studies.

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 (18)

Motivation

Motivation is the reason for which humans and other animals initiate, continue, or terminate a behavior at a given time. Motivational states are commonly understood as forces acting within the agent that create a disposition to engage in goal-directed behavior. It is often held that different mental states compete with each other and that only the strongest state determines behavior. This means that we can be motivated to do something without actually doing it. The paradigmatic mental state providing motivation is desire.

Pearson correlation coefficient

In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations.

Correlation coefficient

A correlation coefficient is a numerical measure of some type of correlation, meaning a statistical relationship between two variables. The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution. Several types of correlation coefficient exist, each with their own definition and own range of usability and characteristics. They all assume values in the range from −1 to +1, where ±1 indicates the strongest possible agreement and 0 the strongest possible disagreement.

Statistical hypothesis testing

A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see .

Spearman's rank correlation coefficient

In statistics, Spearman's rank correlation coefficient or Spearman's ρ, named after Charles Spearman and often denoted by the Greek letter (rho) or as , is a nonparametric measure of rank correlation (statistical dependence between the rankings of two variables). It assesses how well the relationship between two variables can be described using a monotonic function. The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships (whether linear or not).

Related lectures (35)

Describing Data: Statistics and Hypothesis Testing

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Dependence Concepts and Copulas

Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.

Describing Data: Statistics & Uncertainty

Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Dependence Measures: Rank Correlations