Language for Deep Learning: Cognitive Explorative Action Games for Teacher-Learner Interactions

Volume 14
Issue 4
Sebastian Feller
This paper explores the design of what I call explorative action games for teacher-learner interactions. I argue that explorative actions games scaffold the learner’s re-representation of knowledge in ways that facilitate higher-level thinking and deep reasoning. With reference to Weigand’s (2010) Theory of Dialogic Action Games, I introduce the minimal form of the game, which consists of the explorative and the discovery speech act pair. Both speech acts are mutually related to each other. The explorative action game thereby revolves around knowledge re-representations in terms of Chi and Ohlsson’s (2005) types of changes with special emphasis on “greater complexity”, “higher level of abstraction”, and “shifted vantage point”. I illustrate explorative action games for these types and show how they are linked to deep learning and dialogic knowledge building.

Key words: explorative action games, re-representation of knowledge, teacher-learner interaction, dialogic knowledge building