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Test zur Erfassung von Grundvorstellungen zu Ableitungen und Integralen (GV-AI), Empirische Erfassung von Grundvorstellungen zur ersten Ableitung einer Funktion an einer Stelle und zum bestimmten Integral

Title data

Greefrath, Gilbert ; Oldenburg, Reinhard ; Siller, Hans-Stefan ; Ulm, Volker ; Weigand, Hans-Georg:
Test zur Erfassung von Grundvorstellungen zu Ableitungen und Integralen (GV-AI), Empirische Erfassung von Grundvorstellungen zur ersten Ableitung einer Funktion an einer Stelle und zum bestimmten Integral.
In: HAL open science.
8 January 2021

Official URL: Volltext

Abstract in another language

A test is presented which measures whether and to what extent persons have developed basic mental models of the concepts of the first derivative of a function and of the definite integral.
The main idea for measuring basic mental models is to offer participants argumentations that use certain basic mental models and to ask them to what extent these argumentations are close to or consistent with their own thinking.
Each task presents a mathematical situation as a stimulus and four correct argumentations within the context of this situation as possible responses (corresponding to four basic mental models). Participants are asked to mark for each item on a five-point Likert scale to what extent the respective answer corresponds to their thinking.

Further data

Item Type: Online post
Keywords: Grundvorstellung; Ableitung; Integral
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics and Didactics
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics and Didactics > Chair Mathematics and Didactics - Univ.-Prof. Dr. Volker Ulm
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 510 Mathematics
Date Deposited: 11 Jan 2022 06:15
Last Modified: 11 Jan 2022 06:15
URI: https://eref.uni-bayreuth.de/id/eprint/68278