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Register a new userToward Predicting Success and Failure in CS2: A Mixed-Method Analysis
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Added by
Lucas Layman
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Factors driving success and failure in CS1 are the subject of much study but less so for CS2. This paper investigates the transition from CS1 to CS2 in search of leading indicators of success in CS2. Both CS1 and CS2 at the University of North Carolina Wilmington (UNCW) are taught in Python with annual enrollments of 300 and 150 respectively. In this paper, we report on the following research questions: 1) Are CS1 grades indicators of CS2 grades? 2) Does a quantitative relationship exist between CS2 course grade and a modified version of the SCS1 concept inventory? 3) What are the most challenging aspects of CS2, and how well does CS1 prepare students for CS2 from the students perspective? We provide a quantitative analysis of 2300 CS1 and CS2 course grades from 2013--2019. In Spring 2019, we administered a modified version of the SCS1 concept inventory to 44 students in the first week of CS2. Further, 69 students completed an exit questionnaire at the conclusion of CS2 to gain qualitative student feedback on their challenges in CS2 and on how well CS1 prepared them for CS2. We find that 56% of students grades were lower in CS2 than CS1, 18% improved their grades, and 26% earned the same grade. Of the changes, 62% were within one grade point. We find a statistically significant correlation between the modified SCS1 score and CS2 grade points. Students identify linked lists and class/object concepts among the most challenging. Student feedback on CS2 challenges and the adequacy of their CS1 preparations identify possible avenues for improving the CS1-CS2 transition.
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Spin Transfer Torque MRAMs are attractive due to their non-volatility, high density and zero leakage. However, STT-MRAMs suffer from poor reliability due to shared read and write paths. Additionally, conflicting requirements for data retention and write-ability (both related to the energy barrier height of the magnet) makes design more challenging. Furthermore, the energy barrier height depends on the physical dimensions of the free layer. Any variations in the dimensions of the free layer lead to variations in the energy barrier height. In order to address poor reliability of STT-MRAMs, usage of Error Correcting Codes (ECC) have been proposed. Unlike traditional CMOS memory technologies, ECC is expected to correct both soft and hard errors in STT_MRAMs. To achieve acceptable yield with low write power, stronger ECC is required, resulting in increased number of encoded bits and degraded memory efficiency. In this paper, we propose Failure aware ECC (FaECC), which masks permanent faults while maintaining the same correction capability for soft errors without increased encoded bits. Furthermore, we investigate the impact of process variations on run-time reliability of STT-MRAMs. We provide an analysis on the impact of process variations on the life-time of the free layer and retention failures. In order to analyze the effectiveness of our methodology, we developed a cross-layer simulation framework that consists of device, circuit and array level analysis of STT-MRAM memory arrays. Our results show that using FaECC relaxes the requirements on the energy barrier height, which reduces the write energy and results in smaller access transistor size and memory array area. Keywords: STT-MRAM, reliability, Error Correcting Codes, ECC, magnetic memory
Structural analysis is a method for verifying equation-oriented models in the design of industrial systems. Existing structural analysis methods need flattening the hierarchical models into an equation system for analysis. However, the large-scale equations in complex models make the structural analysis difficult. Aimed to address the issue, this study proposes a hierarchical structural analysis method by exploring the relationship between the singularities of the hierarchical equation-oriented model and its components. This method obtains the singularity of a hierarchical equation-oriented model by analyzing the dummy model constructed with the parts from the decomposing results of its components. Based on this, the structural singularity of a complex model can be obtained by layer-by-layer analysis according to their natural hierarchy. The hierarchical structural analysis method can reduce the equation scale in each analysis and achieve efficient structural analysis of very complex models. This method can be adaptively applied to nonlinear algebraic and differential-algebraic equation models. The main algorithms, application cases, and comparison with the existing methods are present in the paper. Complexity analysis results show the enhanced efficiency of the proposed method in structural analysis of complex equation-oriented models. As compared with the existing methods, the time complexity of the proposed method is improved significantly.
Electrical transport in semiconductor superlattices is studied within a fully self-consistent quantum transport model based on nonequilibrium Green functions, including phonon and impurity scattering. We compute both the drift velocity-field relation and the momentum distribution function covering the whole field range from linear response to negative differential conductivity. The quantum results are compared with the respective results obtained from a Monte Carlo solution of the Boltzmann equation. Our analysis thus sets the limits of validity for the semiclassical theory in a nonlinear transport situation in the presence of inelastic scattering.
In this paper we use a time-evolving graph which consists of a sequence of graph snapshots over time to model many real-world networks. We study the path classification problem in a time-evolving graph, which has many applications in real-world scenarios, for example, predicting path failure in a telecommunication network and predicting path congestion in a traffic network in the near future. In order to capture the temporal dependency and graph structure dynamics, we design a novel deep neural network named Long Short-Term Memory R-GCN (LRGCN). LRGCN considers temporal dependency between time-adjacent graph snapshots as a special relation with memory, and uses relational GCN to jointly process both intra-time and inter-time relations. We also propose a new path representation method named self-attentive path embedding (SAPE), to embed paths of arbitrary length into fixed-length vectors. Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.
We investigate the nature of the plasma analogy for the Laughlin wave function on a torus describing the quantum Hall plateau at $ u=frac{1}{q}$. We first establish, as expected, that the plasma is screening if there are no short nontrivial paths around the torus. We also find that when one of the handles has a short circumference -- i.e. the thin-torus limit -- the plasma no longer screens. To quantify this we compute the normalization of the Laughlin state, both numerically and analytically. For the numerical calculation we expand the Laughlin state in a Fock basis of slater-determinants of single particle orbitals, and determine the Fock coefficients of the expansion as a function of torus geometry. In the thin torus limit only a few Fock configurations have non-zero coefficients, and their analytical forms simplify greatly. Using this simple limit, we can reconstruct the normalization and analytically extend it back into the 2D regime. We find that there are geometry dependent corrections to the normalization, and this in turn implies that the plasma in the plasma analogy is not screening when in the thin torus limit. Further we obtain an approximate normalization factor that gives a good description of the normalization for all tori, by extrapolating the thin torus normalization to the thick torus limit.
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