Abstract
Motivation is an essential element in the learning process. In computer-based tutoring environments, motivational components are as significant as cognitive ones. Previous work established that automatic affective feedback improves student motivation when he/she uses Tutoring Systems. Also, prior work examines the relationship between student’s motivation to learn and personality traits, but only from a partial point of view. The present study analyzes whole personality traits on motivation to learn by students using a Tutoring System. The work involved 30 undergraduate students in a qualitative experiment. The authors examined the results using Chi2 to determine the relationship between motivation to use the system and personality; a Naïve Bayes classifier was applied. The findings suggested that Neuroticism is a factor that influences student’s motivation to use the tutoring system. Also, the Naïve Bayes algorithm reaches an accuracy of 90% for the classification.
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Dāboliņš, J. and Grundspeņķis, J., The role of feedback in intelligent tutoring system, Appl. Comput. Syst., 2013, vol. 14, no. 1, pp. 88–93. https://doi.org/10.2478/acss-2013-0011
Gavrilova, E.A., Implementation features a university data portal using the semantic web technology, Program. Comput. Software, 2011, vol. 37, no. 1, pp. 48–55. https://doi.org/10.1134/S0361768811010026
Daineko, Y.A., Ipalakova, M.T., and Bolatov, Z.Z., Employing information technologies based on. NET XNA framework for developing a virtual physical laboratory with elements of 3D computer modeling, Program. Comput. Software, 2017, vol. 43, no. 3, pp. 161–171. https://doi.org/10.1134/S0361768817030045
Gross, S., Mokbel, B., Hammer, B., and Pinkwart, N., Learning feedback in intelligent tutoring systems, Künstliche Intelligenz, 2015, vol. 29, no. 4, pp. 413–418. https://doi.org/10.1007/s13218-015-0367-y
Hasan, M.A., Noor, N.F.M., Rahman, S.S.B.A., and Rahman, M.M., The transition from intelligent to affective tutoring system: a review and open issues, IEEE Access, 2020, vol. 8, pp. 204612–204638. https://doi.org/10.1109/access.2020.3036990
Robison, J., McQuiggan, S., Lester, J., and Carolina, N., Profiles for Affective Feedback Models,” pp. 285–295, 2010.
Hassan, L., Dias, A., and Hamari, J., How motivational feedback increases user’s benefits and continued use: a study on gamification, quantified-self and social networking, Int. J. Inf. Manag., 2019, vol. 46, pp. 151–162. https://doi.org/10.1016/j.ijinfomgt.2018.12.004
Theng, Y.L. and Aung, P., Investigating effects of avatars on primary school children’s affective responses to learning, J. Multimodal User Interfaces, 2012, vol. 5, no. 1–2, pp. 45–52. https://doi.org/10.1007/s12193-011-0078-0
Van der Meij, H., Van der Meij, J., and Harmsen, R., Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment, Educ. Technol. Res. Dev., 2015, vol. 63, no. 3, pp. 381–403. https://doi.org/10.1007/s11423-015-9378-5
Kim, C., The role of affective and motivational factors in designing personalized learning environments, Educ. Technol. Res. Dev., 2012, vol. 60, no. 4, pp. 563–584. https://doi.org/10.1007/s11423-012-9253-6
Malekzadeh, M., Mustafa, M.B., and Lahsasna, A., A review of emotion regulation in intelligent tutoring systems, Educ. Technol. Soc., 2015, vol. 18, pp. 435–445.
De Vicente, A. and Pain, H., Motivation diagnosis in intelligent tutoring systems, Proc. 4th Int. Conf. Intelligent Tutoring Systems, San Antonio, 1998, vol. 83, pp. 86–95. https://doi.org/10.1103/PhysRevB.83.121309
Jiménez, S., Juárez-Ramírez, R., Castillo, V.H., Licea, G., Ramírez-Noriega, A., and Inzunza, S., A feedback system to provide affective support to students, Comput. Appl. Eng. Educ., 2018, vol. 1, p. 1. https://doi.org/10.1002/cae.21900
Dennis, M., Masthoff, J., and Mellish, C., Adapting progress feedback and emotional support to learner personality, Int. J. Artif. Intell. Educ., 2016, vol. 26, no. 3, pp. 877–931. https://doi.org/10.1007/s40593-015-0059-7
Jiménez, S., Juárez-Ramírez, R., Castillo, V.H., and Ramírez-Noriega, A., Integrating affective learning into intelligent tutoring systems, Univers. Access Inf. Soc., 2017, vol. 1, no. 1, pp. 1–14. https://doi.org/10.1007/s10209-017-0524-1
Busato, V.V., Prins, F.J., Elshout, J.J., and Hamaker, C., Intellectual ability, learning style, personality, achievement motivation and academic success of psychology students in higher education, Pers. Individ. Differ., 2000, vol. 29, no. 6, pp. 1057–1068. https://doi.org/10.1016/S0191-8869(99)00253-6
Clark, M.H. and Schroth, C.A., Examining relationships between academic motivation and personality among college students, Learn. Individ. Differ., 2010, vol. 20, no. 1, pp. 19–24. https://doi.org/10.1016/j.lindif.2009.10.002
De Feyter, T., Caers, R., Vigna, C., and Berings, D., Unraveling the impact of the Big Five personality traits on academic performance: the moderating and mediating effects of self-efficacy and academic motivation, Learn. Individ. Differ., 2012, vol. 22, no. 4, pp. 439–448. https://doi.org/10.1016/j.lindif.2012.03.013
Goldberg, L.R., A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models, in Personality Psychology in Europe, Mervielde, I., Deary, I., De Fruyt, F., and Ostendorf, F., Eds., Tilburg: Tilburg Univ. Press, 1999, pp. 7–28.
Dennis, M., Masthoff, J., and Mellish, C., Towards a model of personality, affective state, feedback and learner motivation, Proc. CEUR Workshop, 2012, vol. 872, pp. 17–22.
Mubeen, S. and Reid, N., The measurement of motivation with science students, Eur. J. Educ. Res., 2014, vol. 3, no. 3, pp. 129–144. https://doi.org/10.12973/eu-jer.3.3.129
Keller, J.M., Using the ARCS motivational process in computer-based instruction and distance education, New Dir. Teach. Learn., 1999, no. 78, pp. 39–47. https://doi.org/10.1002/tl.7804
Dāboliņš, J. and Grundspeņķis, J., The role of feedback in intelligent tutoring system, Appl. Comput. Syst., 2013, vol. 14, no. 1, pp. 88–93. https://doi.org/10.2478/acss-2013-0011
Naghizadeh, M. and Moradi, H., A model for motivation assessment in intelligent tutoring systems, Proc. 7th Conf. on Information Knowledge Technologies (IKT), Urmia, 2015, pp. 1–6. https://doi.org/10.1109/IKT.2015.7288774
Song, D., Bonk, C.J., and English, R.M., Motivational factors in self-directed informal learning from online learning resources, Cogent Educ., 2016, vol. 3, p. 1205838. https://doi.org/10.1080/2331186X.2016.1205838
Chang, N.-C. and Chen, H.-H., A motivational analysis of the ARCS model for information literacy courses in a blended learning environment, Libri, 2015, vol. 65, no. 2, pp. 129–142. https://doi.org/10.1515/libri-2015-0010
D’Mello, S. and Graesser, A., Autotutor and affective autotutor: learning by talking with cognitively and emotionally intelligent computers that talk vack, ACM Trans. Interact. Intell. Syst., 2012, vol. 2, no. 4, pp. 1–39. https://doi.org/10.1145/2395123.2395128
Woolf, B.P., Arroyo, I., Cooper, D., Burleson, W., and Muldner, K., Affective tutors: automatic detection of and response to student emotion, in Advances in Intelligent Tutoring Systems, 2010, pp. 207–227. https://doi.org/10.1007/978-3-642-14363-2
Letzring, T.D. and Adamcik, L.A., Personality traits and affective states: relationships with and without affect induction, Pers. Individ. Differ., 2015, vol. 75, pp. 114–120. https://doi.org/10.1016/j.paid.2014.11.011
Jiménez, S., Juárez-Ramírez, R., Navarro, R., Coronel, A., and Castillo, V.H., Architecting an intelligent tutoring system with an affective dialogue module, Proc. 4th Int. Conf. in Software Engineering Research and Innovation, Puebla, 2016, pp. 122–129.
Mills, A., Durepos, G., and Wiebe, E., Encyclopedia of Case Study Research, Thousand Oaks, CA: Sage Publ., 2010.
Sampieri, R.H., Collado, C.F., and Lucio, P.B., Metodologia de la Investigación, 4th ed., McGraw-Hill, 2006.
Cohen, L., Manion, L., and Morrison, K., Research Methods in Education, 7th ed., Routledge, 2011.
Nunnally, J.C. and Bernstein, I.H., Psychometric Theory, New York: McGraw-Hill, 1994.
Neill, J., Writing up a factor analysis, Retrieved Sept., 2008, vol. 7, pp. 1–15. http://www.bwgriffin.com/gsu/courses/edur9131/content/Neill2008_WritingUpAFactorAnalysis.pdf.
Ung, P., Ngowtrakul, B., Chotpradit, R., and Thavornwong, N., Spatial ability test for upperelementary school student: confirmatory factor and normative data analysis, J. Assoc. Res., 2016, vol. 21, no. 2.
OECD, Enrolment rate (indicator), 2017.
Crouch, M. and MacKenzie, H., The logic of small samples in interview-based qualitative research, Soc. Sci. Inf., 2006, vol. 45, no. 4, p. 24.
Latham, J.R., A framework for leading the transformation to performance excellence part I: CEO perspectives on forces, facilitators, and strategic leadership systems, Qual. Manag. J., 2006, vol. 2, no. 20, p. 22.
Caine, K., Local standards for sample size at CHI, Proc. 2016 CHI Conf. Human Factors in Computing Systems, San Jose, CA, 2016, pp. 981–992. https://doi.org/10.1145/2858036.2858498
Gelbukh, A. and Sidorov, G., Comparación de los coeficientes de las leyes de Zipf y Heaps en diferentes idiomas, in Procesamiento automático del español conenfoque en recursos léxicos grandes, México: Instituto Politécnico Nacional, 2006, pp. 1–255.
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Jiménez, S., Juárez-Ramírez, R., Castillo, V.H. et al. The Role of Personality in Motivation to use an Affective Feedback System. Program Comput Soft 47, 793–802 (2021). https://doi.org/10.1134/S0361768821080156
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DOI: https://doi.org/10.1134/S0361768821080156