Patterns of Game Playing Behavior as Indicators of Mastery

Jantke, Klaus P.

doi:10.1007/978-1-4614-3546-4_6

Cited by 12 publications

(13 citation statements)

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“…What the system "knows" about its human user comes from an analysis of interaction data. [3] describes a study based on a commercial digital game. When playing the game, players may learn about pieces of legerdemain.…”

Section: Data Mining As Theory Induction Based On Hci Datamentioning

confidence: 99%

“…In game studies such as [3,5], interaction is abstractly represented by finite sequences over an alphabet A that contains identifiers of all possible activities of all engaged agents such as human players, non-player characters (NPCs), and other computerized components. A + denotes the set of all those strings, and given any particular game G, Π(G) is the subset of all strings that can occur according to the rules and mechanics of G. Angluin's pattern concept describes string properties of a certain type.…”

Section: Data Mining As Theory Induction Based On Hci Datamentioning

confidence: 99%

“…An emergence over time is beyond the limits of conventional perspectives. In contrast, humancomputer interaction data emerge over time [3,5,7]. This leads to the learning task of processing sequences Ω 1 ⊆ Ω 2 ⊆ … ⊆ Ω n ⊆ … of growing finite data sets of observations.…”

Section: Data Mining As Theory Induction Based On Hci Datamentioning

confidence: 99%

“…Every string π ∈ Π abstractly represents some process of human-computer interaction such as a game play [3] or a session with a data analysis tool [7]. When trying "to understand the human user," π is subject to investigation.…”

Section: Formalization and Operationalization Of Theory Induction On mentioning

confidence: 99%

“…The present work originates from the authors' earlier work in the field of digital games with emphasis on the impact of game play, in general, and on game-based learning, in particular [1][2][3][4][5]. The original approach has been generalized, and algorithms have been extended toward business applications far beyond the limits of gaming [6].…”

Section: Introductionmentioning

confidence: 99%

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Mining HCI Data for Theory of Mind Induction

Arnold¹,

Jantke²

2018

Data Mining

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Human-computer interaction (HCI) results in enormous amounts of data-bearing potentials for understanding a human user's intentions, goals, and desires. Knowing what users want and need is a key to intelligent system assistance. The theory of mind concept known from studies in animal behavior is adopted and adapted for expressive user modeling. Theories of mind are hypothetical user models representing, to some extent, a human user's thoughts. A theory of mind may even reveal tacit knowledge. In this way, user modeling becomes knowledge discovery going beyond the human's knowledge and covering domain-specific insights. Theories of mind are induced by mining HCI data. Data mining turns out to be inductive modeling. Intelligent assistant systems inductively modeling a human user's intentions, goals, and the like, as well as domain knowledge are, by nature, learning systems. To cope with the risk of getting it wrong, learning systems are equipped with the skill of reflection.

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Section: Data Mining As Theory Induction Based On Hci Datamentioning

confidence: 99%

Section: Data Mining As Theory Induction Based On Hci Datamentioning

confidence: 99%