Abstract
Detection and visualizations of affective states of students in computer based learning environments have been proposed to support student awareness and improve learning. However, the evaluation of such visualizations with students in real life settings is an open issue. This research reports on our experiences from the use of four different types of dashboard visualizations in two user studies (n = 115). Students who participated in the studies were bachelor and master level students from two different study programs at two universities. The results indicate that usability, measured by interpretability, perceived usefulness and insight, is overall acceptable. However, the findings also suggest that interpretability of some visualizations, in terms of the capability to support emotion awareness, still needs to be improved. The level of students awareness about their emotions during learning activities based on the visualization interpretation varied depending on previous knowledge on visualization techniques. Furthermore, simpler visualizations resulted in better outcomes than more complex techniques.
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References
Ali, L., Hatala, M., Gašević, D., Jovanović, J.: A qualitative evaluation of evolution of a learning analytics tool. Comput. Educ. 58(1), 470–489 (2012)
Ashkanasy, N.M., Dasborough, M.T.: Emotional awareness and emotional intelligence in leadership teaching. J. Educ. Bus. 79(1), 18–22 (2003)
Azcarraga, J., Marcos, N., Suarez, M.T.: Modelling EEG signals for the prediction of academic emotions. In: Workshop on Utilizing EEG Input in Intelligent Tutoring Systems (2014)
Baker, R.S., D’Mello, S.K., Rodrigo, M.T., Graesser, A.C.: Better to be frustrated than bored: The incidence, persistence, and impact of learners cognitive affective states during interactions with three different computer-based learning environments. Int. J. Hum. Comput. Stud. 68(4), 223–241 (2010)
Bangor, A., Kortum, P.T., Miller, J.T.: An empirical evaluation of the system usability scale. Intl. J. Hum.-Comput. Interact. 24(6), 574–594 (2008)
Brooke, J.: Sus-a quick and dirty usability scale. Usability Eval. Ind. 189, 194 (1996)
Burleson, W.: Aective Learning Companions: strategies for empathetic agents with realtime multimodal affective sensing to foster meta-cognitive and meta-affective approaches to learning, motivation, and perseverance. Ph.D. thesis, Massachusetts Institute of Technology (2006)
Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning with autotutor. J. Educ. Media 29(3), 241–250 (2004)
DMello, S.: Monitoring affective trajectories during complex learning. In: Seel, N.M. (ed.) Encyclopedia of the Sciences of Learning, pp. 2325–2328. Springer, New York (2012)
D’Mello, S., Picard, R., Graesser, A.: Towards an affect-sensitive autotutor. IEEE Intell. Syst. 22(4), 53–61 (2007)
D’Mello, S.K., Craig, S.D., Sullins, J., Graesser, A.C.: Predicting affective states expressed through an emote-aloud procedure from autotutor’s mixed-initiative dialogue. Int. J. Artif. Intell. Educ. 16(1), 3–28 (2006)
Friesen, E., Ekman, P.: Facial action coding system: a technique for the measurement of facial movement, Palo Alto (1978)
GhasemAghaei, R., Arya, A., Biddle, R.: A dashboard for affective e-learning: data visualization for monitoring online learner emotions. In: EdMedia: World Conference on Educational Media and Technology, vol. 2016, pp. 1536–1543 (2016)
Govaerts, S., Verbert, K., Duval, E., Pardo, A.: The student activity meter for awareness and self-reflection. In: CHI 2012 Extended Abstracts on Human Factors in Computing Systems, pp. 869–884. ACM (2012)
Jaques, P.A., Vicari, R.M.: A BDI approach to infer students emotions in an intelligent learning environment. Comput. Educ. 49(2), 360–384 (2007)
Kort, B., Reilly, R., Picard, R.W.: An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion. In: ICALT, vol. 1, pp. 43–47 (2001)
Leony, D., Muñoz-Merino, P.J., Pardo, A., Delgado Kloos, C.: Modelo basado en hmm para la deteccin de emociones a partir de interacciones durante el aprendizaje de desarrollo de software. In: XI Jornadas de Ingeniera Telemtica (2013)
Leony, D., Muñoz-Merino, P.J., Pardo, A., Delgado Kloos, C.: Provision of awareness of learners’ emotions through visualizations in a computer interaction-based environment. Expert Syst. Appl. 40, 5093–5100 (2013)
Leony, D., Muñoz-Merino, P.J., Pardo, A., Ruiperez-Valiente, J., Arellano Martin-Caro, D., Delgado Kloos, C.: Detection and evaluation of emotions in massive open online courses. J. Univ. Comput. Sci. 21(5), 638–655 (2015)
Leony, D., Parada Gélvez, H.A., Muñoz-Merino, P.J., Pardo, A., Delgado Kloos, C.: A generic architecture for emotion-based recommender systems in cloud learning environments. J. Univ. Comput. Sci. 19(14), 2075–2092 (2013)
Muñoz-Merino, P.J., Fernández Molina, M., Muñoz Organero, M., Delgado Kloos, C.: Motivation and emotions in competition systems for education: an empirical study. IEEE Trans. Educ. 57(3), 182–187 (2014)
North, C.: Toward measuring visualization insight. IEEE Comput. Graph. Appl. 26(3), 6–9 (2006)
Pardos, Z.A., Baker, R.S.J.D., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M.: Affective states and state tests: investigating how affect throughout the school year predicts end of year learning outcomes. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, LAK 2013, pp. 117–124. ACM, New York (2013)
Santos, J.L., Verbert, K., Govaerts, S., Duval, E.: Addressing learner issues with stepup!: an evaluation. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 14–22. ACM (2013)
Sedrakyan, G.: Process-oriented feedback perspectives based on feedback-enabled simulation and learning process data analytics. Ph.D. thesis (2016)
Sedrakyan, G., De Weerdt, J., Snoeck, M.: Process-mining enabled feedback: tell me what i did wrong vs. tell me how to do it right. Comput. Hum. Behav. 57, 352–376 (2016)
Sedrakyan, G., Järvelä, S., Kirschner, P.: Conceptual framework for feedback automation and personalization for designing learning analytics dashboards. In: Conference EARLI SIG 27, Online Measures of Learning Processes (2016)
Sedrakyan, G., Malmberg, J., Noroozi, O., Verbert, K., Järvelä, S., Kirschner, P.: Designing a learning analytics dashboard for feedback to support learning regulation (2017, submitted)
Sedrakyan, G., Snoeck, M., De Weerdt, J.: Process mining analysis of conceptual modeling behavior of novices-empirical study using jmermaid modeling and experimental logging environment. Comput. Hum. Behav. 41, 486–503 (2014)
Trigwell, K., Ellis, R.A., Han, F.: Relations between students’ approaches to learning, experienced emotions and outcomes of learning. Stud. High. Educ. 37(7), 811–824 (2012)
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., Santos, J.L.: Learning analytics dashboard applications. Am. Behav. Sci. 57(10), 1500–1509 (2013)
Verbert, K., Govaerts, S., Duval, E., Santos, J., Van Assche, F., Parra, G., Klerkx, J.: Learning dashboards: an overview and future research opportunities. Pers. Ubiquit. Comput. 18(6), 1499–1514 (2014)
Acknowledgements
This work is partially supported by the eMadrid project (funded by the Regional Government of Madrid) under grant no S2013/ICE-2715, the Commin project (funded by the Spanish Ministry of Economy and Competitiveness) under grant no IPT-2012-0883-430000 and the RESET project (Ministry of Economy and Competiveness) under grant RESET TIN2014-53199-C3-1-R. The research has been partially financed by the SURF Foundation of the Netherlands and the KU Leuven Research Council (grant agreement no. C24/16/017).
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Sedrakyan, G., Leony, D., Muñoz-Merino, P.J., Kloos, C.D., Verbert, K. (2017). Evaluating Student-Facing Learning Dashboards of Affective States. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds) Data Driven Approaches in Digital Education. EC-TEL 2017. Lecture Notes in Computer Science(), vol 10474. Springer, Cham. https://doi.org/10.1007/978-3-319-66610-5_17
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