Among other things, learning to write entails learning how to use complex sentences effectively in discourse. Some research has therefore focused on relating measures of syntactic complexity to text quality. Apart from the fact that the existing research on this topic appears inconclusive, most of it has been conducted in English L1 contexts. This is potentially problematic, since relevant syntactic indices may not be the same across languages. The current study is the first to explore which syntactic features predict text quality in Dutch secondary school students’ argumentative writing. In order to do so, the quality of 125 argumentative essays written by students was rated and the syntactic features of the texts were analyzed. A multilevel regression analysis was then used to investigate which features contribute to text quality. The resulting model (explaining 14.5% of the variance in text quality) shows that the relative number of finite clauses and the ratio between the number of relative clauses and the number of finite clauses positively predict text quality. Discrepancies between our findings and those of previous studies indicate that the relations between syntactic features and text quality may vary based on factors such as language and genre. Additional (cross-linguistic) research is needed to gain a more complete understanding of the relationships between syntactic constructions and text quality and the potential moderating role of language and genre.
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Untrained listeners demonstrate implicit knowledge of syntactic patternsand principles. Untrained generative music ability, for example singing,humming, and whistling, is a largely unconscious or intuitive applicationof these patterns and principles. From the viewpoint of embodied cognition,listening to music should evoke an internal representation or motorimage which, together with the perception of organized music, shouldform the basis of musical cognition. Indeed, that is what listeners demonstratewhen they sing, hum, or whistle familiar and unfamiliar tunes orwhen they vocally or orally improvise continuations to interruptedphrases. Research on vocal improvisation using continuations sung to aninterrupted musical phrase, has shown that one’s cultural backgroundinfluences the music generated. That should be the case for instrumentalistsas well: when they play familiar or unfamiliar tunes by ear in differentkeys (transposition) or when they improvise variations,accompaniments, or continuations to interrupted phrases, the music theygenerate should reflect the same cognitive structures as their oral improvisations.This study is attempting to validate a test of (non) scoredependencythat will enable assessment of the music student’s implicitknowledge of these structures during performance on the principal instrument.
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Untrained listeners demonstrate implicit knowledge of syntactic patternsand principles. Untrained generative music ability, for example singing,humming, and whistling, is a largely unconscious or intuitive applicationof these patterns and principles. From the viewpoint of embodied cognition,listening to music should evoke an internal representation or motorimage which, together with the perception of organized music, shouldform the basis of musical cognition. Indeed, that is what listeners demonstratewhen they sing, hum, or whistle familiar and unfamiliar tunes orwhen they vocally or orally improvise continuations to interruptedphrases. Research on vocal improvisation using continuations sung to aninterrupted musical phrase, has shown that one’s cultural backgroundinfluences the music generated. That should be the case for instrumentalistsas well: when they play familiar or unfamiliar tunes by ear in differentkeys (transposition) or when they improvise variations,accompaniments, or continuations to interrupted phrases, the music theygenerate should reflect the same cognitive structures as their oral improvisations.This study is attempting to validate a test of (non) scoredependencythat will enable assessment of the music student’s implicitknowledge of these structures during performance on the principal instrument.
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Abstract-Architecture Compliance Checking (ACC) is useful to bridge the gap between architecture and implementation. ACC is an approach to verify conformance of implemented program code to high-level models of architectural design. Static ACC focuses on the modular software architecture and on the existence of rule violating dependencies between modules. Accurate tool support is essential for effective and efficient ACC. This paper presents a study on the accuracy of ACC tools regarding dependency analysis and violation reporting. Seven tools were tested and compared by means of a custom-made test application. In addition, the code of open source system Freemind was used to compare the tools on the number and precision of reported violation and dependency messages. On the average, 74 percent of 34 dependency types in our custom-made test software were reported, while 69 percent of 109 violating dependencies within a module of Freemind were reported. The test results show large differences between the tools, but all tools could improve the accuracy of the reported dependencies and violations.
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SUMMARY Architecture compliance checking (ACC) is an approach to verify conformance of implemented program code to high-level models of architec tural design. Static ACC focuses on the modular software architecture and on the existence of rule violating dependencies between modules. Accurate tool support is essential for effective and efficient ACC. This paper presents a study on the accuracy of ACC tools regarding dependency analysis and violation reporting. Ten tools were tested and compare d by means of a custom-made benchmark. The Java code of the benchmark testware contains 34 different types of dependencies, which are based on an inventory of dependency types in object oriented program code. In a second test, the code of open source system FreeMind was used to compare the 10 tools on the number of reported rule violating dependencies and the exactness of the dependency and violation messages. On the average, 77% of the dependencies in our custom-made test software were reported, while 72% of the dependencies within a module of FreeMind were reported. The results show that all tools in the test could improve the accuracy of the reported dependencies and violations, though large differences between the 10 tools were observed. We have identified10 hard-to-detect types of dependencies and four challenges in dependency detection. The relevance of our findings is substantiated by means of a frequency analysis of the hard-to-detect types of dependencies in five open source systems. DOI: 10.1002/spe.2421
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Information structure facilitates communication between interlocutors by highlighting relevant information. It has previously been shown that information structure modulates the depth of semantic processing. Here we used event-related potentials to investigate whether information structure can modulate the depth of syntactic processing. In question-answer pairs, subtle (number agreement) or salient (phrase structure) syntactic violations were placed either in focus or out of focus through information structure marking. P600 effects to these violations reflect the depth of syntactic processing. For subtle violations, a P600 effect was observed in the focus condition, but not in the non-focus condition. For salient violations, comparable P600 effects were found in both conditions. These results indicate that information structure can modulate the depth of syntactic processing, but that this effect depends on the salience of the information. When subtle violations are not in focus, they are processed less elaborately. We label this phenomenon the Chomsky illusion.
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Particle verbs (e.g., look up) are lexical items for which particle and verb share a single lexical entry. Using event-related brain potentials, we examined working memory and long-term memory involvement in particle-verb processing. Dutch participants read sentences with head verbs that allow zero, two, or more than five particles to occur downstream. Additionally, sentences were presented for which the encountered particle was semantically plausible, semantically implausible, or forming a non-existing particle verb. An anterior negativity was observed at the verbs that potentially allow for a particle downstream relative to verbs that do not, possibly indexing storage of the verb until the dependency with its particle can be closed. Moreover, a graded N400 was found at the particle (smallest amplitude for plausible particles and largest for particles forming non-existing particle verbs), suggesting that lexical access to a shared lexical entry occurred at two separate time points.
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Many quality aspects of software systems are addressed in the existing literature on software architecture patterns. But the aspect of system administration seems to be a bit overlooked, even though it is an important aspect too. In this work we present three software architecture patterns that, when applied by software architects, support the work of system administrators: PROVIDE AN ADMINISTRATION API, SINGLE FILE LOCATION, and CENTRALIZED SYSTEM LOGGING. PROVIDE AN ADMINISTRATION API should solve problems encountered when trying to automate administration tasks. The SINGLE FILE LOCATION pattern should help system administrators to find the files of an application in one (hierarchical) place. CENTRALIZED SYSTEM LOGGING is useful to prevent coming up with several logging formats and locations. Abstract provided by the authors. Published in PLoP '13: Proceedings of the 20th Conference on Pattern Languages of Programs ACM.
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When teaching grammar, one of the biggest challenges teachers face is how to make their students achieve conceptual understanding. Some scholars have argued that metaconcepts from theoretical linguistics should be used to pedagogically and conceptually enrich traditional L1 grammar teaching, generating more opportunities for conceptual understanding. However, no empirical evidence exists to support this theoretical position. The current study is the first to explore the role of linguistic metaconcepts in the grammatical reasoning of university students of Dutch Language and Literature. Its goal was to gain a better understanding of the characteristics of students’ grammatical conceptual knowledge and reasoning and to investigate whether students’ reasoning benefits from an intervention that related linguistic metaconcepts to concepts from traditional grammar. Results indicate, among other things, that using explicit linguistic metaconcepts and explicit concepts from traditional grammar is a powerful contributor to the quality of students’ grammatical reasoning. Moreover, the intervention significantly improved students’ use of linguistic metaconcepts.
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Preprint submitted to Information Processing & Management Tags are a convenient way to label resources on the web. An interesting question is whether one can determine the semantic meaning of tags in the absence of some predefined formal structure like a thesaurus. Many authors have used the usage data for tags to find their emergent semantics. Here, we argue that the semantics of tags can be captured by comparing the contexts in which tags appear. We give an approach to operationalizing this idea by defining what we call paradigmatic similarity: computing co-occurrence distributions of tags with tags in the same context, and comparing tags using information theoretic similarity measures of these distributions, mostly the Jensen-Shannon divergence. In experiments with three different tagged data collections we study its behavior and compare it to other distance measures. For some tasks, like terminology mapping or clustering, the paradigmatic similarity seems to give better results than similarity measures based on the co-occurrence of the documents or other resources that the tags are associated to. We argue that paradigmatic similarity, is superior to other distance measures, if agreement on topics (as opposed to style, register or language etc.), is the most important criterion, and the main differences between the tagged elements in the data set correspond to different topics
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