Propiedades mecánicas de cermets basados en diboruro de titanio

Las propiedades mecánicas de los cermets basados en diboruro de titanio (TiB 2 ) dependen críticamente de la composición de la fase ligante. Se ha comprobado que tanto la tenacidad como la dureza aumentan significativamente si se evita la formación de boruros secundarios durante la sinterización en fase líquida. Las observaciones fractográficas realizadas en cermets basados en TiB 2 sin boruros secundarios confirman el comportamiento plástico de la fase ligante durante la fractura. La ruta pulvimetalúrgica aplicada a estos materiales permite la modificación intencionada de la estructura de la fase ligante desde ferrita a austenita mediante adiciones de aluminio a las mezclas de polvos. Los valores de tenacidad más elevados se han obtenido para los cermets con matriz austenítica. El análisis mediante difracción de rayos X de la superficie de fractura de estos materiales confirma que la fase ligante experimenta transformación martensítica durante la fractura, mecanismo de aumento de tenacidad ya observado en otros sistemas. Esta nueva familia de materiales duros presenta una excelente combinación de dureza y tenacidad, comparable a la obtenida con grados comerciales de carburos cementados (WC-Co).Peer reviewe

Modelling math learning on an open access intelligent tutor

This paper presents a methodology to analyze large amount of students’ learning states on two math courses offered by Global Fresh- man Academy program at Arizona State University. These two courses utilised ALEKS (Assessment and Learning in Knowledge Spaces) Arti- ficial Intelligence technology to facilitate massive open online learning. We explore social network analysis and unsupervised learning approaches (such as probabilistic graphical models) on these type of Intelligent Tu- toring Systems to examine the potential of the embedding representa- tions on students learning

PredictCS: Personalizing Programming learning by leveraging learning analytics

This paper presents a new framework to harness sources of programming learning analytics at a Higher Education Institution and how it has been progressively adopted at the classroom level to improve personalized learning. This new platform, called PredictCS, automatically detects lower-performing or “at-risk” students in computer programming modules and automatically and adaptively sends them feedback. PredictCS embeds multiple predictive models by leveraging multi-modal learning analytics of student data, including student characteristics, prior academic history, logged interactions between students and online resources, and students' progress in programming laboratory work, and their progression from introductory to advanced CS courses. Predictions are generated every week during the semester's classes. In addition, students are flexible to opt-in to receive pseudo real-time personalized feedback, which permits them to be aware of their predicted course performance. The adaptive feedback ranges from programming suggestions from top- performers in the class to resources that are suitable to bridge their programing knowledge gaps

Predicting media memorability using ensemble models

Memorability, defined as the quality of being worth remembering, is a pressing issue in media as we struggle to organize and retrieve digital content and make it more useful in our daily lives. The Predicting Media Memorability task in MediaEval 2019 tackles this problem by creating a challenge to automatically predict memorability scores building on the work developed in 2018. Our team ensembled transfer learning approaches with video captions using embeddings and our own pre-computed features which outperformed Medieval 2018’s state-of-the-art architectures

Predictive modelling of student reviewing behaviors in an introductory programming course

In this paper, we developed predictive models based on students’ reviewing behaviors in an Introductory Programming course. These patterns were captured using an educational technology that students used to review their graded paper- based assessments. Models were trained and tested with the goal of identifying students’ academic performance and those who might be in need of assistance. The results of the retrospective analysis show a reasonable accuracy. This suggests the possibility of developing interventions for students, such as providing feedback in the form of effective reviewing strategies