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2014 » Papers » Volume 2 » Personality detection in interactive video 1. PERSONALITY DETECTION IN INTERACTIVE VIDEO Authors: Petan Sorin, Vasiu Radu, Mocofan Muguras Volume 2 | DOI: 10.12753/2066-026X-14-114 | Pages: 385-392 | Download PDF | Abstract
Kreber's 1998 study shows that an individual's personality plays a major role in the educational process, and directly determines a student's predisposition to engage in a self-directed learning process. This is particularly the case with distance education, where motivation is a key factor.
With the newly emerging field of interactive video in education, personality traits can be determined by various approaches. Filling out a personality form might be an unattractive method of profiling students. But with the case of interactive video, requiring by definition a voluntary user interaction, this information can be obtained by aligning interaction patterns to personality traits, using established psychological classification models. A key advantage is the non-intrusiveness of the process.
We first define what interactive video is and briefly present what interactions are possible with the video content. We offer a quick example of interactive video in education. Based on the way users interact with a i-video material, we outline 5 types of interactive actions: General interest, Interface Interaction, Content Interaction, Social Interaction and Contributive Interaction.
Further, we use these 5 categories of interaction to classify various types of users into psychological categories, using several personality models. A binary model first proposed by Carl Jung of extrovert/introvert is first discussed. We then discuss a four-category model of Guardian/Artisan/Idealist/Rational proposed by Keirsey in 1998, extendable to a 16-category model where each of the four primary personality traits are each further dissected in four subcategories. For each of the personality models used, we create a matrix of scores for all 5 types of interactions, using these interactions with the interactive video content to progressively determine a student's personality.
This automatic profiling model is tested on several subjects. To verify accuracy of the automatic profiling model, the participants are then asked to complete forms for personality tests, their scores being compared to. Results are discussed and conclusions are drawn, highlighting the benefits of using automatic profiling mechanisms in interactive video learning applications. | Keywords
interactive video, adaptive, personality, behavior, behavioral tracking, targeting, i-video |
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