Consolidating knowledge in teaching history with the help of the SuperMemo and similar algorithms – a proposed method of  editing  historical  content Cover Image

Utrwalanie w nauczaniu historii przy pomocy algorytmu SuperMemo i jemu podobnych – propozycja metody redagowania treści historycznych
Consolidating knowledge in teaching history with the help of the SuperMemo and similar algorithms – a proposed method of editing historical content

Author(s): Mikołaj Piotrowski
Subject(s): Social Sciences, Education, Sociology of Education
Published by: Uniwersytet Adama Mickiewicza
Keywords: algorithm; history; editing; SuperMemo; consolidation

Summary/Abstract: The historical knowledge of Poles, including Polish students, leaves much to be desired, even on a basic level. The insufficient or even the lack of consolidation is the most likely explanation of this phenomenon. The proposed solution to this problem is the SuperMemo and its similar algorithms, along with school history content specially edited for its needs. Basically an algorithm is a set of steps leading to the completion of a task. In the SuperMemo case, the task is to optimize learning, or rather to reduce forgetfulness, while reducing the time devoted to still well-remembered knowledge. Both the SuperMemo and its similar have been implemented in many applications. Quizlet or the software with the same name as the algorithm – SuperMemo, are the examples of such applications. They allow you to learn your own materials as well as those already edited, either by the producer or by other users. The previously edited school historical content for the SuperMemo algorithm and its similar cannot be considered satisfactory if important issues such as: compliance with the core curriculum, division into classes and topics, historical narrative coherence, relationships between pairs of questions and answers, or degrees of difficulty, are regarded. This article proposes a method that can improve this state of affairs. It is the result of theoretical and quasi-experimental work, corrected later on the basis of more serious editorial experience.

  • Issue Year: 2022
  • Issue No: 2
  • Page Range: 259-273
  • Page Count: 15
  • Language: Polish