The importance of understanding on-line tutoring impact has increased dramatically, especially after the COVID-19 pandemic. However, deep causal concepts about on-line tutoring are still lacking, especially on economically disadvantaged students. This paper is an observational study that targets low-income high school students in Saudi Arabia with high failing risk. The paper aims at (1) finding on-line math tutoring impact on needy students who already took tutoring, and (2) identifying and characterizing students that need tutoring to pass. We use observational data collected in a student registration process to build two models: (1) a Bayesian multi-level regression causal model, then (2) a counter-factual model. Results show that the models gave statistically significant estimates. In model 1, the average causal impact of maximum tutoring minutes on the math mark was +4.9 (out of 100). In model 2, the counter-factual maximum impact on tutored students was +5.3. We also estimate that only 1.9% of students needed the tutoring to avoid failing (2.8% of the enrolled), and we show their characteristics.

Abstract- In many applications, especially signal processing and matrix computations, algorithms are in a highly regular iterative form; access patterns for most variables are highly regular and uniform. Instead of always storing the values of variables back to and retrieving them from memory or register files, it will be much more efficient and cost effective to let those variables intelligently “stay ” or “flow ” in the data path for future use. In this paper, low cost and simple structured sequencers which are best exemplified by hardware stacks and queues are introduced in the data path for efficiently implementing such a novel concept. Various algorithms are developed to map variables to sequencers and to integrate sequencers into conventional high-level synthesis procedures. Experimental results show very encouraging improvement in the performance of designs as well as significant reduction in hardware cost. I.