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RESEARCH PRODUCT
Predicting human performance in interactive tasks by using dynamic models
Francesc J. FerriMaría T. SanzMiguel Arevalillo-herráezDavid ArnauJosé Antonio González-calerosubject
business.industry05 social sciences050301 education02 engineering and technologyMachine learningcomputer.software_genreElectronic mailData modelingCorrelationDynamic models0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligenceRemainderbusiness0503 educationCompetence (human resources)computerdescription
The selection of an appropriate sequence of activities is an essential task to keep student motivation and foster engagement. Usually, decisions in this respect are made by taking into account the difficulty of the activities, in relation to the student's level of competence. In this paper, we present a dynamic model that aims to predict the average performance of a group of students at solving a given series of maths problems. The system takes into account both student- and task-related features. This model was built and validated by using the data gathered in an experimental session that involved 64 participants solving a sequence of 26 arithmetic problems. The data collected from the first 16 problems was used to build the model, and the remainder were employed to validate it. Results show a correlation with r = 0.59 between the real and predicted scores, and support the effectiveness of the model at anticipating the students' performance over a sequence of tasks.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-10-01 | 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |