527,934 research outputs found
QuizMap: Open social student modeling and adaptive navigation support with TreeMaps
In this paper, we present a novel approach to integrate social adaptive navigation support for self-assessment questions with an open student model using QuizMap, a TreeMap-based interface. By exposing student model in contrast to student peers and the whole class, QuizMap attempts to provide social guidance and increase student performance. The paper explains the nature of the QuizMap approach and its implementation in the context of self-assessment questions for Java programming. It also presents the design of a semester-long classroom study that we ran to evaluate QuizMap and reports the evaluation results. © 2011 Springer-Verlag Berlin Heidelberg
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large variety
of domains, but it relies on a small set of provided approximation features to
calculate solutions reliably. Large and rich sets of features can cause
existing algorithms to overfit because of a limited number of samples. We
address this shortcoming using regularization in approximate linear
programming. Because the proposed method can automatically select the
appropriate richness of features, its performance does not degrade with an
increasing number of features. These results rely on new and stronger sampling
bounds for regularized approximate linear programs. We also propose a
computationally efficient homotopy method. The empirical evaluation of the
approach shows that the proposed method performs well on simple MDPs and
standard benchmark problems.Comment: Technical report corresponding to the ICML2010 submission of the same
nam
Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior
The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognitive traits as predictors of performance. But these traits are static in nature, and therefore fail to encapsulate changes in a student's learning progress over the duration of a course. In this paper we present a new approach for predicting a student's performance in a programming course, based upon analyzing directly logged data, describing various aspects of their ordinary programming behavior. An evaluation using data logged from a sample of 45 programming students at our University, showed that our approach was an excellent early predictor of performance, explaining 42.49% of the variance in coursework marks - double the explanatory power when compared to the closest related technique in the literature
Hierarchical Dynamic Loop Self-Scheduling on Distributed-Memory Systems Using an MPI+MPI Approach
Computationally-intensive loops are the primary source of parallelism in
scientific applications. Such loops are often irregular and a balanced
execution of their loop iterations is critical for achieving high performance.
However, several factors may lead to an imbalanced load execution, such as
problem characteristics, algorithmic, and systemic variations. Dynamic loop
self-scheduling (DLS) techniques are devised to mitigate these factors, and
consequently, improve application performance. On distributed-memory systems,
DLS techniques can be implemented using a hierarchical master-worker execution
model and are, therefore, called hierarchical DLS techniques. These techniques
self-schedule loop iterations at two levels of hardware parallelism: across and
within compute nodes. Hybrid programming approaches that combine the message
passing interface (MPI) with open multi-processing (OpenMP) dominate the
implementation of hierarchical DLS techniques. The MPI-3 standard includes the
feature of sharing memory regions among MPI processes. This feature introduced
the MPI+MPI approach that simplifies the implementation of parallel scientific
applications. The present work designs and implements hierarchical DLS
techniques by exploiting the MPI+MPI approach. Four well-known DLS techniques
are considered in the evaluation proposed herein. The results indicate certain
performance advantages of the proposed approach compared to the hybrid
MPI+OpenMP approach
GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care
The huge wealth of data in the health domain can be exploited to create
models that predict development of health states over time. Temporal learning
algorithms are well suited to learn relationships between health states and
make predictions about their future developments. However, these algorithms:
(1) either focus on learning one generic model for all patients, providing
general insights but often with limited predictive performance, or (2) learn
individualized models from which it is hard to derive generic concepts. In this
paper, we present a middle ground, namely parameterized dynamical systems
models that are generated from data using a Genetic Programming (GP) framework.
A fitness function suitable for the health domain is exploited. An evaluation
of the approach in the mental health domain shows that performance of the model
generated by the GP is on par with a dynamical systems model developed based on
domain knowledge, significantly outperforms a generic Long Term Short Term
Memory (LSTM) model and in some cases also outperforms an individualized LSTM
model
High performance low-energy buildings
The era of legislation and creditable methods towards producing sustainable buildings is upon us. Yet, a major barrier to achieving environmental responsive design is in the lack of available information at the programming or pre-design phases of a project. The review and evaluation of climate as well as energy-efficient strategies could be difficult to consider at these preliminary stages. Until recently, introducing energy simulation tools at the design stage has been difficult and perhaps next to impossible at a pre-design or programming stage. However, analysis of this sort is essential to ‘green building rating’ or performance assessment schemes such as LEED (Leadership in Energy and Environmental Design) and BREEAM (Building Research Establishment Environment Assessment Method). This paper discusses the implementation of a particular tool, ENERGY-10, where ‘basecase’ building defaults are compared to a low-energy case which has applied multiple energy-efficient strategies automatically. An annual hour-by-hour simulation provides a daylighting calculation with a subsequent thermal evaluation. Calculation results provide energy consumption, peak load equipment sizing, a RANK feature of the energy-efficient strategies, reporting of CO2, SO2 and NOx reduction, optimum glazing type as well as excellent graphic output. Consideration is given as to the approach of how such information can be introduced into the building project brief enforcing a low-energyperformance target.<br /
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