Relative clauses and conjunctive adjuncts in Syrian University student writing in English

Initial investigations into English essays written by Syrian university students triangulated Syrian and British teachers’ evaluations of the essays and the lexico-grammatical features they identified as affecting the overall quality of writing, with text analyses of the sources, types and frequency of all grammatical errors. Following this, and a review of relevant literature, the thesis presents an in-depth study of relative clauses and conjunctive adjuncts as under-researched features in Arabic speaking university student writing that can enrich their writing syntactically and semantically. The relative clause (RC) analysis shows that the 'full' form RC occurred much more frequently than the 'reduced' form, and that confusion between these two forms was a prominent source of student error. 'Pronoun retention' errors indicating L1 interference were among the most frequent RC errors – as most studies of RC use by Arab learners find. Moreover, RC constructions with 'head noun' (or antecedent) in the non-subject position and 'gap' (or relativized NP/sentence) in the subject position were dominant, while other, and more complex, construction types were much less common. This supports the AHH and PDH hypotheses on the frequency/difficulty hierarchy of RC types. Conjunctive adjunct analysis reveals that 'additive' conjunctive adjuncts were more frequent, followed by 'causals'. Despite its informality, the resultive conjunctive adjunct 'so' was used most repeatedly, followed by 'also', 'but', and 'and'. Causal conjunctive adjuncts were most frequently misused, though in general conjunctive adjunct misuse is not a major weakness. Contrastive analysis between the L2 (Syrian) and an equivalent L1 (British) corpus of literature essays revealed no significant difference between the total frequencies of RCs, 'full' RCs and 'non-subject-subject' RCs. In contrast, the total frequencies of conjunctive adjuncts in the two corpora were significantly different, with the L2 corpus containing almost twice as many conjunctive adjuncts as the L1 corpus, particularly causals and additives, this latter category being most frequent in both corpora. The British students' employment of relative clause types and conjunctive expressions was generally more diverse than that of the Syrian students. Pedagogical implications conclude this thesis

Relative clauses and conjunctive adjuncts in Syrian University student writing in English

Initial investigations into English essays written by Syrian university students triangulated Syrian and British teachers’ evaluations of the essays and the lexico-grammatical features they identified as affecting the overall quality of writing, with text analyses of the sources, types and frequency of all grammatical errors. Following this, and a review of relevant literature, the thesis presents an in-depth study of relative clauses and conjunctive adjuncts as under-researched features in Arabic speaking university student writing that can enrich their writing syntactically and semantically. The relative clause (RC) analysis shows that the 'full' form RC occurred much more frequently than the 'reduced' form, and that confusion between these two forms was a prominent source of student error. 'Pronoun retention' errors indicating L1 interference were among the most frequent RC errors – as most studies of RC use by Arab learners find. Moreover, RC constructions with 'head noun' (or antecedent) in the non-subject position and 'gap' (or relativized NP/sentence) in the subject position were dominant, while other, and more complex, construction types were much less common. This supports the AHH and PDH hypotheses on the frequency/difficulty hierarchy of RC types. Conjunctive adjunct analysis reveals that 'additive' conjunctive adjuncts were more frequent, followed by 'causals'. Despite its informality, the resultive conjunctive adjunct 'so' was used most repeatedly, followed by 'also', 'but', and 'and'. Causal conjunctive adjuncts were most frequently misused, though in general conjunctive adjunct misuse is not a major weakness. Contrastive analysis between the L2 (Syrian) and an equivalent L1 (British) corpus of literature essays revealed no significant difference between the total frequencies of RCs, 'full' RCs and 'non-subject-subject' RCs. In contrast, the total frequencies of conjunctive adjuncts in the two corpora were significantly different, with the L2 corpus containing almost twice as many conjunctive adjuncts as the L1 corpus, particularly causals and additives, this latter category being most frequent in both corpora. The British students' employment of relative clause types and conjunctive expressions was generally more diverse than that of the Syrian students. Pedagogical implications conclude this thesis.EThOS - Electronic Theses Online ServiceJāmi'at Dimashq [University of Damascus] (JD)GBUnited Kingdo

Relative clauses and conjunctive adjuncts in Syrian University student writing in English

Initial investigations into English essays written by Syrian university students triangulated Syrian and British teachers’ evaluations of the essays and the lexico-grammatical features they identified as affecting the overall quality of writing, with text analyses of the sources, types and frequency of all grammatical errors. Following this, and a review of relevant literature, the thesis presents an in-depth study of relative clauses and conjunctive adjuncts as under-researched features in Arabic speaking university student writing that can enrich their writing syntactically and semantically. The relative clause (RC) analysis shows that the 'full' form RC occurred much more frequently than the 'reduced' form, and that confusion between these two forms was a prominent source of student error. 'Pronoun retention' errors indicating L1 interference were among the most frequent RC errors – as most studies of RC use by Arab learners find. Moreover, RC constructions with 'head noun' (or antecedent) in the non-subject position and 'gap' (or relativized NP/sentence) in the subject position were dominant, while other, and more complex, construction types were much less common. This supports the AHH and PDH hypotheses on the frequency/difficulty hierarchy of RC types. Conjunctive adjunct analysis reveals that 'additive' conjunctive adjuncts were more frequent, followed by 'causals'. Despite its informality, the resultive conjunctive adjunct 'so' was used most repeatedly, followed by 'also', 'but', and 'and'. Causal conjunctive adjuncts were most frequently misused, though in general conjunctive adjunct misuse is not a major weakness. Contrastive analysis between the L2 (Syrian) and an equivalent L1 (British) corpus of literature essays revealed no significant difference between the total frequencies of RCs, 'full' RCs and 'non-subject-subject' RCs. In contrast, the total frequencies of conjunctive adjuncts in the two corpora were significantly different, with the L2 corpus containing almost twice as many conjunctive adjuncts as the L1 corpus, particularly causals and additives, this latter category being most frequent in both corpora. The British students' employment of relative clause types and conjunctive expressions was generally more diverse than that of the Syrian students. Pedagogical implications conclude this thesis.EThOS - Electronic Theses Online ServiceJāmi'at Dimashq [University of Damascus] (JD)GBUnited Kingdo

Reasons and Motivation of Islamic Scholar for Using Code-switching as Strategy in Delivering a Speech (Da'wah)

Code-switching is a challenging phenomenon to sociolinguists. It is related to the use of two or more languages in the same utterance or conversation in a context of bilingual or multilingual setting of conversation. In giving Islamic speech (Da'wah), m

Learning action representations using kernel perceptrons

Action representation is fundamental to many aspects of cognition, including language. Theories of situated cognition suggest that the form of such representation is distinctively determined by grounding in the real world. This thesis tackles the question of how to ground action representations, and proposes an approach for learning action models in noisy, partially observable domains, using deictic representations and kernel perceptrons. Agents operating in real-world settings often require domain models to support planning and decision-making. To operate effectively in the world, an agent must be able to accurately predict when its actions will be successful, and what the effects of its actions will be. Only when a reliable action model is acquired can the agent usefully combine sequences of actions into plans, in order to achieve wider goals. However, learning the dynamics of a domain can be a challenging problem: agents’ observations may be noisy, or incomplete; actions may be non-deterministic; the world itself may be noisy; or the world may contain many objects and relations which are irrelevant. In this thesis, I first show that voted perceptrons, equipped with the DNF family of kernels, easily learn action models in STRIPS domains, even when subject to noise and partial observability. Key to the learning process is, firstly, the implicit exploration of the space of conjunctions of possible fluents (the space of potential action preconditions) enabled by the DNF kernels; secondly, the identification of objects playing similar roles in different states, enabled by a simple deictic representation; and lastly, the use of an attribute-value representation for world states. Next, I extend the model to more complex domains by generalising both the kernel and the deictic representation to a relational setting, where world states are represented as graphs. Finally, I propose a method to extract STRIPS-like rules from the learnt models. I give preliminary results for STRIPS domains and discuss how the method can be extended to more complex domains. As such, the model is both appropriate for learning data generated by robot explorations as well as suitable for use by automated planning systems. This combination is essential for the development of autonomous agents which can learn action models from their environment and use them to generate successful plans

Analysis of Students' Programming Knowledge and Error Development

Programmieren zu lernen ist für viele eine große Herausforderung, da es unterschiedliche Fähigkeiten erfordert. Man muss nicht nur die Programmiersprache und deren Konzepte kennen, sondern es erfordert auch spezifisches Domänenwissen und eine gewisse Problemlösekompetenz. Wissen darüber, wie sich die Programmierkenntnisse Studierender entwickeln und welche Schwierigkeiten sie haben, kann dabei helfen, geeignete Lehrstrategien zu entwickeln. Durch die immer weiter steigenden Studierendenzahlen wird es jedoch zunehmend schwieriger für Lehrkräfte, die Bedürfnisse, Probleme und Schwierigkeiten der Studierenden zu erkennen. Das Ziel dieser Arbeit ist es, Einblick in die Entwicklung von Programmierkenntnissen der Studierenden anhand ihrer Lösungen zu Programmieraufgaben zu gewinnen. Wissen setzt sich aus sogenannten Wissenskomponenten zusammen. In dieser Arbeit fokussieren wir uns auf syntaktische Wissenskomponen, die aus abstrakten Syntaxbäumen abgeleitet werden können, und semantische Wissenskomponenten, die durch sogenannte Variablenrollen repräsentiert werden. Da Wissen an sich nicht direkt messbar ist, werden häufig Skill-Modelle verwendet, um den Kenntnissstand abzuschätzen. Jedoch hat die Programmierdomäne ihre eigenen speziellen Eigenschaften, die bei der Wahl eines geeigneten Skill-Modells berücksichtigt werden müssen. Eine der Haupteigenschaften in der Programmierung ist, dass die Wissenskomponenten nicht unabhängig voneinander sind. Aus diesem Grund schlagen wir ein dynamisches Bayesnetz (DBN) als Skill-Modell vor, da es erlaubt, diese Abhängigkeiten explizit zu modellieren. Neben derWahl eines passenden Skill-Modells, müssen auch bestimmte Meta-Parameter wie beispielsweise die Granularität der Wissenkomponenten festgelegt werden. Daher evaluieren wir, wie sich die Wahl von Meta-Parameters auf die Vorhersagequalität von Skill-Modellen auswirkt und wie diese Meta-Parameter gewählt werden sollten. Wir nutzen das DBN, um Lernkurven für jede Wissenskomponenten zu ermitteln und daraus Implikationen für die Lehre abzuleiten. Nicht nur das Wissen von Studierenden, sondern auch deren “Falsch”-Wissen ist von Bedeutung. Deswegen untersuchen wir zunächst manuell sämtliche Programmierfehler der Studierenden und bestimmen deren Häufigkeit, Dauer und Wiederkehrrate. Wir unterscheiden dabei zwischen den Fehlerkategorien syntaktisch, konzeptuell, strategisch, Nachlässigkeit, Fehlinterpretation und Domäne und schauen, wie sich die Fehler über die Zeit entwickeln. Außerdem verwenden wir k-means-Clustering um potentielle Muster in der Fehlerentwicklung zu finden. Die Ergebnisse unserer Fallstudien sind vielversprechend. Wir können zeigen, dass die Wahl der Meta-Parameter einen großen Einfluss auf die Vorhersagequalität von Modellen hat. Außerdem ist unser DBN vergleichbar leistungsstark wie andere Skill-Modelle, ist gleichzeitig aber besser zu interpretieren. Die Lernkurven der Wissenskomponenten und die Analyse der Programmierfehler liefern uns wertvolle Erkenntnisse, die der Kursverbesserung helfen können, z.B. dass die Studierenden mehr Übungsaufgaben benötigen oder mit welchen Konzepten sie Schwierigkeiten haben.Learning to program is a hard task since it involves different types of specialized knowledge. You do not only need knowledge about the programming language and its concepts, but also knowledge from the problem domain and general problem solving abilities. Knowing how students develop programming knowledge and where they struggle, may help in the development of suitable teaching strategies. However, the ever increasing number of students makes it more and more difficult for educators to identify students’ needs, problems, and deficiencies. The goal of the thesis is to gain insights into students programming knowledge development based on their solutions to programming exercises. Knowledge is composed of so called knowledge components (KCs). In this thesis, we focus on KCs on a syntactic level, which can be derived from abstract systax trees, e.g., loops, comparison, etc., and semantic level, represented by so called roles of variables. Since knowledge is not directly measurable, skill models are an often used for the estimation of knowledge. But, the programming domain has its own characteristics which have to be considered when selecting an appropriate skill model. One of the main characteristics of the programming domain are the dependencies between KCs. Hence, we propose and evaluate a Dynamic Bayesian Network (DBN) for skill modeling which allows to model that dependencies explicitly. Besides the choice of a concrete model, also certain metaparameters like, e.g., the granularity level of KCs, has to be set when designing a skill model. Therefore, we evaluate how meta-parameterization affects the prediction performance of skill models and which meta-parameters to choose. We use the DBN to create learning curves for each KC and deduce implications for teaching from them. But not only students knowledge but also their “mal-knowledge” is of importance. Therefore, we manually inspect students’ programming errors and determine the error’s frequency, duration, and re-occurrence. We distinguish between the error categories syntactic, conceptual, strategic, sloppiness, misinterpretation, and domain and analyze how the errors change over time. Moreover, we use k-means clustering to identify different patterns in the development of programming errors. The results of our case studies are promising. We show that the correct metaparameterization has a huge effect on the prediction performance of skill models. In addition, our DBN performs as well as the other skill models while providing better interpretability. The learning curves of KCs and the analysis of programming errors provide valuable information which can be used for course improvement, e.g., that students require more practice opportunities or are struggling with certain concepts.2022-02-0