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.

The problem of recovering the global optimum of a system from a sample of a given data is an important one and a big topic in computer science at the present time. One important issue of this case is the computational complexity of this problem. In this paper we investigate the issue to better understand its computational and computational complexity. We analyze the properties of the solutions and propose a framework to better understand the optimal set of solutions for this problem. The framework is based on the notions of the optimality of a probability density function, a set of probability densities, and the computational complexity of the problem. We also discuss the implications of the proposed procedure for the analysis of problems beyond the realm of discrete probability densities.

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Improving Students’ Academic Success Through Strategic Search and Interactive Learning

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  • Learning Stochastic Gradient Temporal Algorithms with Riemannian Metrics

    Efficient Monte Carlo Clustering of Time-Series Data using GraphsThe problem of recovering the global optimum of a system from a sample of a given data is an important one and a big topic in computer science at the present time. One important issue of this case is the computational complexity of this problem. In this paper we investigate the issue to better understand its computational and computational complexity. We analyze the properties of the solutions and propose a framework to better understand the optimal set of solutions for this problem. The framework is based on the notions of the optimality of a probability density function, a set of probability densities, and the computational complexity of the problem. We also discuss the implications of the proposed procedure for the analysis of problems beyond the realm of discrete probability densities.


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