8,867 research outputs found
Investigating novice programming mistakes: educator beliefs vs. student data
Educators often form opinions on which programming mistakes novices make most often - for example, in Java: "they always confuse equality with assignment", or "they always call methods with the wrong types". These opinions are generally based solely on personal experience. We report a study to determine if programming educators form a consensus about which Java programming mistakes are the most common. We used the Blackbox data set to check whether the educators' opinions matched data from over 100,000 students - and checked whether this agreement was mediated by educators' experience. We found that educators formed only a weak consensus about which mistakes are most frequent, that their rankings bore only a moderate correspondence to the students in the Blackbox data, and that educators' experience had no effect on this level of agreement. These results raise questions about claims educators make regarding which errors students are most likely to commit
37 Million Compilations: Investigating Novice Programming Mistakes in Large-Scale Student Data
Previous investigations of student errors have typically focused on samples of hundreds of students at individual institutions. This work uses a year's worth of compilation events from over 250,000 students all over the world, taken from the large Blackbox data set. We analyze the frequency, time-to-fix, and spread of errors among users, showing how these factors inter-relate, in addition to their development over the course of the year. These results can inform the design of courses, textbooks and also tools to target the most frequent (or hardest to fix) errors
Towards Closed World Reasoning in Dynamic Open Worlds (Extended Version)
The need for integration of ontologies with nonmonotonic rules has been
gaining importance in a number of areas, such as the Semantic Web. A number of
researchers addressed this problem by proposing a unified semantics for hybrid
knowledge bases composed of both an ontology (expressed in a fragment of
first-order logic) and nonmonotonic rules. These semantics have matured over
the years, but only provide solutions for the static case when knowledge does
not need to evolve. In this paper we take a first step towards addressing the
dynamics of hybrid knowledge bases. We focus on knowledge updates and,
considering the state of the art of belief update, ontology update and rule
update, we show that current solutions are only partial and difficult to
combine. Then we extend the existing work on ABox updates with rules, provide a
semantics for such evolving hybrid knowledge bases and study its basic
properties. To the best of our knowledge, this is the first time that an update
operator is proposed for hybrid knowledge bases.Comment: 40 pages; an extended version of the article published in Theory and
Practice of Logic Programming, 10 (4-6): 547 - 564, July. Copyright 2010
Cambridge University Pres
05171 Abstracts Collection -- Nonmonotonic Reasoning, Answer Set Programming and Constraints
From 24.04.05 to 29.04.05, the Dagstuhl Seminar
05171 ``Nonmonotonic Reasoning, Answer Set Programming and Constraints\u27\u27
was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Cross Temporal Recurrent Networks for Ranking Question Answer Pairs
Temporal gates play a significant role in modern recurrent-based neural
encoders, enabling fine-grained control over recursive compositional operations
over time. In recurrent models such as the long short-term memory (LSTM),
temporal gates control the amount of information retained or discarded over
time, not only playing an important role in influencing the learned
representations but also serving as a protection against vanishing gradients.
This paper explores the idea of learning temporal gates for sequence pairs
(question and answer), jointly influencing the learned representations in a
pairwise manner. In our approach, temporal gates are learned via 1D
convolutional layers and then subsequently cross applied across question and
answer for joint learning. Empirically, we show that this conceptually simple
sharing of temporal gates can lead to competitive performance across multiple
benchmarks. Intuitively, what our network achieves can be interpreted as
learning representations of question and answer pairs that are aware of what
each other is remembering or forgetting, i.e., pairwise temporal gating. Via
extensive experiments, we show that our proposed model achieves
state-of-the-art performance on two community-based QA datasets and competitive
performance on one factoid-based QA dataset.Comment: Accepted to AAAI201
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