Abstract
In this paper, we propose a model that personalises the learning experience of a student by automatically selecting the exercises that best suit the student’s competences and that also maintain the student’s motivation at a certain (high) level.
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Notes
- 1.
The relations between expectations, rewards and dopamine release has also been studied in the field of music. [9] discusses how we get from the perception of sound patterns and the prediction of future sound patterns to reward and valuation. They state that when listening to a new musical piece, one can expect a certain set sounds to occur, based on one’s history of listening to music. For instance, when we have heard a certain set of patterns in music, we expect a certain set of sounds to occur and we also know when they are supposed to occur in time. These expectations are related to templates derived from one’s individual history of listening. Thus, in some way we are able to decode relationships and patterns in music, such that it generates expectations about upcoming events based on past events.
- 2.
For G’s evaluation, where one essentially calculates the community’s assessments, the COMAS algorithm can be used [4]. COMAS calculates the final assessment by aggregating peer assessments in the community in such a way that more weight is given to those assessments whose assessors are more trusted by the tutor. To calculate the tutor’s trust in students, a trust graph is built based on how similar are the students’ assessments to those of the tutor.
- 3.
If probability distributions are viewed as piles of dirt, then the earth mover’s distance measures the minimum cost for transforming one pile into the other. This cost is equivalent to the ‘amount of dirt’ times the distance by which it is moved, or the distance between elements of the probability distribution’s support. The range of EMD is [0, 1], where 0 represents the minimum distance and 1 represents the maximum possible distance.
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Acknowledgments
This work is supported by the following projects: PRAISE (European Commission, grant # 388770), CollectiveMind (Spanish Ministry of Economy & Competitiveness (MINECO), grant # TEC2013-49430-EXP), and MILESS (MINECO, grant # TIN2013-45039-P).
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Gutierrez, P., Osman, N., Sierra, C. (2016). Automating Personalized Learning Through Motivation. In: Rovatsos, M., Vouros, G., Julian, V. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2015 2015. Lecture Notes in Computer Science(), vol 9571. Springer, Cham. https://doi.org/10.1007/978-3-319-33509-4_35
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