Open Access‘Big data’ in physics education: discovering the stick-slip effect through a high sample rate
Author(s) -
Gregor Benz,
Carsten Buhlinger,
Tobias Ludwig
Publication year - 2022
Publication title -
physics education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.343
H-Index - 31
eISSN - 1361-6552
pISSN - 0031-9120
DOI - 10.1088/1361-6552/ac59cb
Subject(s) - big data , sample (material) , data set , set (abstract data type) , data acquisition , computer science , context (archaeology) , data science , physics , data mining , artificial intelligence , geography , operating system , programming language , thermodynamics , archaeology
With the availability of educational digital data acquisition systems, it has also become possible in physics education to generate ‘big’ data sets by (a) measuring multiple variables simultaneously, (b) increasing the sample rate, (c) extending the measurement duration, or (d) choosing a combination among these three options. In the context of this paper, we will use a simple acceleration experiment to show that a higher sample rate, resulting in a larger data set, quantitatively reveals the stick-slip effect. For this purpose, two variables are measured simultaneously, first with a low and then with a high sample rate. The purpose of this paper is to illustrate that dealing with ‘big’ data sets can add value to experimentation in physics labs by dealing with data sets that more accurately describe observations.