FTS-SOCI 2 source code for simulating teaching strategies to predict sociometric indices in higher education

This dataset contains the source code of FTS-SOCI 2 (an advanced Framework for simulating Teaching Strategies with evolutions of SOCIograms) in Java programming language. FTS-SOCI 2 implements an agent-based simulator (ABS), in which some agents simulate the behaviors of students, while another agent simulates the behavior of the instructor.

This ABS can simulate the repercussion of the teaching strategies of several subjects concurrently, sequentially or partially overlapped by determining different starting points for each teaching strategy. This ABS simulates the repercussion of these teaching strategies on the sociometric indices of a group of students and presents the estimated sociograms. The ABS departs from a group of students determined by the user and categorized in certain initial activity roles.

FTS-SOCI 2 has been designed considering higher education illustrating their approach with strategies of several disciplines in the grades of computer science engineering, electronics engineering and psychology.

Researchers can define other teaching strategies, by simply extending the `TeacherExtended’ class and overriding the `live’ method. Developers can extend the framework by for example defining new kinds of activities, changing the user interface or amending the internal simulation behavior.

This dataset supports the research about FTS-SOCI 2, which has been submitted to a scientific journal for consideration for its publication.

Researchers that use this framework can credit its author by citing the article about the previous version of this framework called FTS-SOCI or any of the other related articles indicated below.

Reference about FTS-SOCI:

García-Magariño, I. & Plaza, I. (2015). FTS-SOCI: an agent-based framework for simulating teaching strategies with evolutions of sociograms. Simulation Modelling Practice and Theory, 57, 161-178.

Other related references:

García-Magariño, I., Medrano, C., Lombas, A. S., & Barrasa, A. (2016). A hybrid approach with agent-based simulation and clustering for sociograms. Information Sciences, 345, 81-95.

García-Magariño, I., Palacios-Navarro, G., & Lacuesta, R. (2017). TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions. Simulation Modelling Practice and Theory, 77, 84-107.

García-Magariño, I., Lombas, A. S., Plaza, I., & Medrano, C. (2017). ABS-SOCI: An Agent-Based Simulator of Student Sociograms. Applied Sciences, 7(11), 1126.

García-Magariño, I., Gómez-Rodríguez, A., González-Moreno, J. C., & Palacios-Navarro, G. (2015). PEABS: a Process for developing Efficient Agent-Based Simulators. Engineering Applications of Artificial Intelligence, 46, 104-112.