Ir al contenido

Documat


On Incorporating Affective Support to an Intelligent Tutoring System: an Empirical Study

  • Cristina Cunha-Pérez [1] ; Miguel Arevalillo-Herráez [2] ; Luis Marco-Giménez [2] ; David Arnau [2]
    1. [1] Universidad Católica de Valencia San Vicente Mártir

      Universidad Católica de Valencia San Vicente Mártir

      Valencia, España

    2. [2] Universitat de València

      Universitat de València

      Valencia, España

  • Localización: Revista Iberoamericana de Tecnologías del Aprendizaje: IEEE-RITA, ISSN 1932-8540, Vol. 13, Nº. 2, 2018, págs. 63-69
  • Idioma: inglés
  • DOI: 10.1109/rita.2018.2831760
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • 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.

  • Referencias bibliográficas
    • S. Brand, T. Reimer, and K. Opwis, “How do we learn in a negative mood? Effects of a negative mood on transfer and learning,” Learn. Instruct.,...
    • M. Ainley, “Connecting with learning: Motivation, affect and cognition in interest processes,” Edu. Psychol. Rev., vol. 18, no. 4, pp. 391–405,...
    • A. M. Isen, “Positive affect and decision making,” in Handbook of emotions, M. Lewis and J. M. Haviland-Jones, Eds. New York, NY, USA: Guilford...
    • M. H. Immordino-Yang and A. Damasio, “We feel, therefore we learn: The relevance of affective and social neuroscience to education,” Mind...
    • B. Kort, R. Reilly, and R. W. Picard, “An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building...
    • O. C. Santos, “Emotions and personality in adaptive e-learning systems: An affective computing perspective,” in Emotions and Personality in...
    • R. S. J. D. Baker et al., “Towards sensor-free affect detection in cognitive tutor algebra,” in Proc. 5th Int. Conf. Edu. Data Mining (EDM),...
    • L. Paquette, “Sensor-free or sensor-full: A comparison of data modalities in multi-channel affect detection,” in Proc. 8th Int. Conf. Edu....
    • M. Arevalillo-Herráez, L. Marco-Giménez, D. Arnau, and J. A. González-Calero, “Adding sensor-free intention-based affective support to an...
    • M. Arevalillo-Herráez, D. Arnau, F. J. Ferri, O. C. Santos, “GUI-driven intelligent tutoring system with affective support to help learning...
    • S. K. D’Mello, S. D. Craig, and A. C. Graesser, “Multimethod assessment of affective experience and expression during deep learning,” Int....
    • J. R. Hill and M. J. Hannafin, “Teaching and learning in digital environments: The resurgence of resource-based learning,” Edu. Technol. Res....
    • R. S. Baker, A. T. Corbett, K. R. Koedinger, and I. Roll, “Detecting when students game the system, across tutor subjects and classroom cohorts,”...
    • B. P. Woolf et al., “The effect of motivational learning companions on low achieving students and students with disabilities,” in Proc. Int....
    • D. Carraher and A. Schliemann, “The transfer dilemma,” J. Learn. Sci., vol. 11, no. 1, pp. 1–24, Jan. 2002.
    • M. R. Lepper, M. Woolverton, D. L. Mumme, and J.-L. Gurtner, “Motivational techniques of expert human tutors: Lessons for the design of computer-based...
    • A. Kapoor and R. W. Picard, “Multimodal affect recognition in learning environments,” in Proc. 13th Annu. ACM Int. Conf. Multimedia, 2005,...
    • N. Bosch, Y. Chen, and S. K. D’Mello, “It’s written on your face: Detecting affective states from facial expressions while learning computer...
    • J. Whitehill, Z. Serpell, Y.-C. Lin, A. Foster, and J. R. Movellan, “The faces of engagement: Automatic recognition of student engagementfrom...
    • S. Mota and R. W. Picard, “Automated posture analysis for detecting learner’s interest level,” in Proc. Conf. Comput. Vis. Pattern Recognit....
    • S. K. D’Mello, “Emotional rollercoasters: Day differences in affect incidence during learning,” in Proc. 27th Int. Florida Artif. Intell....
    • P. Arnau-González, M. Arevalillo-Herráez, and N. Ramzan, “Fusing highly dimensional energy and connectivity features to identify affective...
    • I. A. Khan, W.-P. Brinkman, and R. Hierons, “Towards estimating computer users’ mood from interaction behaviour with keyboard and mouse,”...
    • O. C. Santos, S. Salmeron-Majadas, and J. G. Boticario, “Emotions detection from math exercises by combining several data sources,” in Proc....
    • R. A. Sottilare and M. D. Proctor, “Passively classifying student mood and performance within intelligent tutors,” Edu. Technol. Soc., vol....
    • S. K. D’Mello and J. Kory, “A review and meta-analysis of multimodal affect detection systems,” ACM Comput. Surv., vol. 47, no. 3, 2015, Art....
    • J. Gálvez, E. Guzmán, and R. Conejo, “A blended E-learning experience in a course of object oriented programming fundamentals,” Knowl.- Based...
    • A. Ferreira and J. Atkinson, “Designing a feedback component of an intelligent tutoring system for foreign language,” Knowl.-Based Syst.,...
    • C. R. Rakes, J. C. Valentine, M. B. McGatha, and R. N. Ronau, “Methods of instructional improvement in algebra: A systematic review and meta-analysis,”...
    • C. R. Beal, I. M. Arroyo, P. R. Cohen, and B. P. Woolf, “Evaluation of animal watch: An intelligent tutoring system for arithmetic and fractions,”...
    • K.-E. Chang, Y.-T. Sung, and S.-F. Lin, “Computer-assisted learning for mathematical problem solving,” Comput. Edu., vol. 46, no. 2, pp. 140–151,...
    • N. T. Heffernan and K. R. Koedinger, “An intelligent tutoring system incorporating a model of an experienced human tutor,” in Proc. 6th Int....
    • K. Reusser, Computers As Cognitive Tools. Hillsdale, NJ, USA: Lawrence Erlbaum Associates Inc., 1993, pp. 143–177.
    • K. R. Koedinger and J. R. Anderson, “Illustrating principled design: The early evolution of a cognitive tutor for algebra symbolization,”...
    • E. A. Croteau, N. T. Heffernan, and K. R. Koedinger, “Why are algebra word problems difficult? Using tutorial log files and the power law...
    • E. F. Yagüe, Educational Algebra: A Theoretical and Empirical Approach. Boston, MA, USA: Springer, 2008, pp. 141–161.
    • S. Ritter, J. R. Anderson, K. R. Koedinger, and A. Corbett, “Cognitive tutor: Applied research in mathematics education,” Psychonomic Bull....
    • D. Arnau, M. Arevalillo-Herráez, L. Puig, and J. A. González-Calero, “Fundamentals of the design and the operation of an intelligent tutoring...
    • D. Arnau, M. Arevalillo-Herráez, and J. A. González-Calero, “Emulating human supervision in an intelligent tutoring system for arithmetical...
    • J. A. González-Calero, D. Arnau, L. Puig, and M. Arevalillo-Herráez, “Intensive scaffolding in an intelligent tutoring system for the learning...
    • M. Arevalillo-Herráez, D. Arnau, and L. Marco-Giménez, “Domainspecific knowledge representation and inference engine for an intelligent tutoring...
    • A. Mehrabian, “Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in Temperament,” Current...
    • S. Koelstra et al., “DEAP: A database for emotion analysis; using physiological signals,” IEEE Trans. Affect. Comput., vol. 3, no. 1, pp....
    • C. Navarretta, “Predicting emotions in facial expressions from the annotations in naturally occurring first encounters,” Knowl.-Based Syst.,...
    • J. A. Russell and A. Mehrabian, “Evidence for a three-factor theory of emotions,” J. Res. Personality, vol. 11, no. 3, pp. 273–294, 1977.
    • R. S. D. Baker et al., “Towards sensor-free affect detection in cognitive tutor algebra,” Int. Edu. Data Mining Soc., 2012.
    • S. K. D’Mello, S. D. Craig, A. Witherspoon, B. Mcdaniel, and A. Graesser, “Automatic detection of learner’s affect from conversational cues,”...
    • C. Conati and H. Maclaren, “Empirically building and evaluating a probabilistic model of user affect,” User Model. User-Adapted Interact.,...
    • J. Sabourin, B. Mott, and J. C. Lester, “Modeling learner affect with theoretically grounded dynamic Bayesian networks,” in Proc. Int. Conf....
    • L. Paquette et al., “Sensor-free or sensor-full: A comparison of data modalities in multi-channel affect detection,” Int. Edu. Data Mining...
    • M. M. Bradley and P. J. Lang, “Measuring emotion: The self-assessment manikin and the semantic differential,” J. Behav. Therapy Exp. Psychiatry,...
    • C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
    • T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, Jan. 1967.
    • N. Landwehr, M. Hall, and E. Frank, “Logistic model trees,” Mach. Learn., vol. 59, no. 1, pp. 161–205, May 2005.
    • J. R. Quinlan, C4.5: Programs for Machine Learning. San Francisco, CA, USA: Morgan Kaufmann, 1993.
    • F. Rosenblatt, “Principles of neurodynamics. Perceptrons and the theory of brain mechanisms,” Cornell Aeronautival Lab Inc., Buffalo, NY,...
    • K. A. Spackman, “Signal detection theory: Valuable tools for evaluating inductive learning,” in Proc. 6th Int. Workshop Mach. Learn., San...
    • M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic, “A multimodal database for affect recognition and implicit tagging,” IEEE Trans. Affect....

Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno