Improving Students’ Academic Success Through Strategic Search and Interactive Learning

Improving Students’ Academic Success Through Strategic Search and Interactive Learning – In this paper, we aim at enhancing students’ academic success through strategic search and collaborative learning. We consider the problem of assessing how students’ academic performance compares to how their parents or teachers grade scores: for each student, we aim to identify a sequence of grades, which in turn determines how much score they should attain. The resulting system is trained on a large-scale dataset collected from a social network, which we use to evaluate the performance of students. We demonstrate that the predictive ranking of the students improves with the number of grades, which increases exponentially after being aggregated together. Based on a simple and robust evaluation system, we present and evaluate several strategic search systems. Our system achieves an overall improvement of ~12.8% on average when compared to a state-of-the-arts system evaluated from the beginning, which only achieves an average ~10.2% improvement when compared to a teacher who only requires ~8.2% in grades.

Learning Bayesian networks (BNs) in a Bayesian context is a challenging problem with significant difficulty due to the high computational overhead. This work tackles this problem by learning from the input data sets and by leveraging the fact that the underlying Bayesian network representations are generated using a nonparametric, random process. We show that the network representations for both Gaussian and Bayesian networks achieve similar performance compared to the classical Bayesian network representations, including the Gaussian model and the nonparametric Bayesian model. In particular, we show that the Gaussian model performs significantly better than the nonparametric Bayesian model when the input data set includes only the Gaussian model.

Stochastic Learning of Graphical Models

Interactive Stochastic Learning

Improving Students’ Academic Success Through Strategic Search and Interactive Learning

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  • A Structural Recurrent Encoder

    A note on the lack of convergence for the generalized median classifierLearning Bayesian networks (BNs) in a Bayesian context is a challenging problem with significant difficulty due to the high computational overhead. This work tackles this problem by learning from the input data sets and by leveraging the fact that the underlying Bayesian network representations are generated using a nonparametric, random process. We show that the network representations for both Gaussian and Bayesian networks achieve similar performance compared to the classical Bayesian network representations, including the Gaussian model and the nonparametric Bayesian model. In particular, we show that the Gaussian model performs significantly better than the nonparametric Bayesian model when the input data set includes only the Gaussian model.


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