0000000000388113
AUTHOR
Luis Marco-giménez
showing 4 related works from this author
Domain-specific knowledge representation and inference engine for an intelligent tutoring system
2013
One of the most challenging steps in learning algebra is the translation of word problems into symbolic notation. This paper describes an Intelligent Tutoring System (ITS) that focuses on this stage of the problem solving process. On the one hand, a domain specific inference engine and a knowledge representation mechanism are proposed. These are based on a description language based on hypergraphs, and the idea of using conceptual schemes to represent the student's knowledge. As a result, the system is able to simultaneously: (a) represent all potential algebraic solutions to a given word problem; (b) keep track of the student's actions; (c) univocally determine the current state of the res…
Eigenexpressions: Emotion Recognition Using Multiple Eigenspaces
2013
This paper presents an appearance-based holistic method for expression recognition. A two stage supervised learning approach is used. At the first stage, training images are used to compute one subspace per expression. At the second stage, the same images are used to train a classifier. In this step, Euclidean distances from each image to each particular subspace are used as the input to the classifier. The resulting system significantly outperforms the baseline eigenfaces method on the Cohn-Kanade data set, with performance gains in the range 10%-20%.
Adding sensor-free intention-based affective support to an Intelligent Tutoring System
2017
Abstract Emotional factors considerably influence learning and academic performance. In this paper, we validate the hypothesis that learning platforms can adjust their response to have an effect on the learner’s pleasure, arousal and/or dominance, without using a specific emotion detection system during operation. To this end, we have enriched an existing Intelligent Tutoring System (ITS) by designing a module that is able to regulate the level of help provided to maximize valence, arousal or autonomy as desired. The design of this module followed a two-stage methodology. In the first stage, the ITS was adapted to collect data from several groups of students in primary education, by providi…
On Incorporating Affective Support to an Intelligent Tutoring System: an Empirical Study
2018
Previous research studies have reported strong evidence that the emotional state of students may have a considerable impact on their learning. In this paper, we present an empirical study that evidences that it is possible to influence the user’s affective state in a controlled way, by adapting the system’s response. As part of this paper, we have analyzed the affective impact of varying the level of help provided in an existing Intelligent Tutoring System. Results show that it is possible to use classification approaches to predict positive and negative variations in dominance, valence, arousal, and performance to a reasonable level of accuracy.