Impact indicators of educational innovations based on active methodologies

"Think global, act locally" is one of the phrases that define the idea of any innovation. It denotes that the impact must be global and contribute to the advancement of knowledge in a specific sector, for example. The innovation applied in the classroom is known as “teaching educational innovation” and thinking in global is complicated because innovation is carried out in a specific subject. Specific contexts have needs and conditions that difficult the transference outside the subject itself. This work provides a method to consider any teaching educational innovation in global terms, even before knowing the specific innovation method to apply. In this way, transferability would be enhanced and the global impact on the change of the educational model would be improved. For this purpose, a study has been carried out with more than 85 professors from different universities. The objective of the study is to show that they have a common vision on the indicators to measure the leaning impact when they apply teaching educational innovation in their own subjects

Predicting Student Failure in an Introductory Programming Course with Multiple Back-Propagation

One of the most challenging tasks in computer science and similar courses consists of both teaching and learning computer programming. Usually this requires a great deal of work, dedication, and motivation from both teachers and students. Accordingly, ever since the first programming languages emerged, the problems inherent to programming teaching and learning have been studied and investigated. The theme is very serious, not only for the important concepts underlying computer science courses but also for reducing the lack of motivation, failure, and abandonment that result from students frustration. Therefore, early identification of potential problems and immediate response is a fundamental aspect to avoid student’s failure and reduce dropout rates. In this paper, we propose a machine-learning (neural network) predictive model of student failure based on the student profile, which is built throughout programming classes by continuously monitoring and evaluating student activities. The resulting model allows teachers to early identify students that are more likely to fail, allowing them to devote more time to those students and try novel strategies to improve their programming skills