Relationships among multiple intelligences, motor performance and academic achievement in secondary school children

This study examines the relationships between multiple intelligences, academic achievement and motor performance in a group of secondary school children. Four hundred and eighty schoolchildren participated in this study (171 female and 309 male) with an average age of 13.33 years (SD: 1.41). The Revised self-efficacy Inventory for Multiple Intelligences (IAIM-R) and the motor test Sportcomp were applied, and the average results of the academic year they had made were obtained. The analysis of the results showed how female scored significantly higher on the Linguistic, Spatial and Interpersonal intelligences, and older pupils scored significantly higher on the linguistic and naturalistic intelligences. It was the logical-mathematical intelligence which showed significant relationships with academic performance and it was the intelligence that better predicted this achievement. It was the bodily-kinesthetic intelligence that was significantly related to motor competence and the best intelligence that predicted its achievement. Finally, indicate that schoolchildren with better scores in the motor test were those who scored higher in both academic achievement and all the multiple intelligences, with the exception of musical intelligence.

Student academic performance stochastic simulator based on the Monte Carlo method

In this paper, a computer-based tool is developed to analyze student performance along a given curriculum. The proposed software makes use of historical data to compute passing/failing probabilities and simulates future student academic performance based on stochastic programming methods (MonteCarlo) according to the specific university regulations. This allows to compute the academic performance rates for the specific subjects of the curriculum for each semester, as well as the overall rates (the set of subjects in the semester), which are the efficiency rate and the success rate. Additionally, we compute the rates for the Bachelors degree, which are the graduation rate measured as the percentage of students who finish as scheduled or taking an extra year and the efficiency rate (measured as the percentage of credits of the curriculum with respect to the credits really taken). In Spain, these metrics have been defined by the National Quality Evaluation and Accreditation Agency (ANECA). Moreover, the sensitivity of the performance metrics to some of the parameters of the simulator is analyzed using statistical tools (Design of Experiments). The simulator has been adapted to the curriculum characteristics of the Bachelor in Engineering Technologies at the Technical University of Madrid(UPM).