Browsing by Author "Emre, Deniz"

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    The effects of ınductive and deductive approach on written output
    (2015) Emre, Deniz
    The present study explored the effects of inductive grammar instruction and deductive grammar instruction on the acquisition of conditionals and relative clauses in three aspects: written production, i. e. grammar accuracy in writing tasks, grammar test scores and students’ and the instructor’s perspectives. The study was carried out with 38 intermediate level EFL (English as a Foreign Language) students. During a four-week period, one instructor taught grammar to two groups. In the inductive group, the students worked on consciousness-raising tasks to discover the meanings and rules of the target grammatical structures. Later, they received feedback from the instructor. In the deductive group, the instructor explained the meanings and the rules of the target grammatical structures directly. The grammar pre and post-test scores did not reveal a statistically significant difference between the scores of the two groups. Furthermore, there was not a statistically significant difference between the writing tasks of the two groups in terms of grammar accuracy. The questionnaire administered in the inductive group implied that the learners generally had positive perspectives on inductive learning. The interview conducted with the instructor revealed that she regarded inductive approach as a more interactive but less practical way of teaching. Nevertheless, she preferred inductive teaching on condition that the students were motivated and the target structures were new to them. In light of these findings, teachers and material developers might consider involving both approaches in their practices and work in order to ensure variety.
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    Exact solution algorithms for biobjective mixed integer programming problems
    (2020-08) Emre, Deniz
    In this thesis, objective space based exact solution algorithms for biobjective mixed integer programming problems are proposed. The algorithms solve scalarization models in order to explore predetermined regions of the objective space called boxes, defined by two nondominated points. The initial box is defined by the two extreme nondominated points of the Pareto frontier, which includes all nondominated points. At each iteration of the algorithms, a box is explored either by a weighted sum or a Pascoletti-Serafini scalarization to determine nondominated line segments and points. The first algorithm creates new boxes immediately when it finds a nondominated point by solving Pascoletti-Serafini scalarization, whereas the second algorithm conducts additional operations after obtaining a nondominated point by this scalarization. Our computational experiments demonstrate the computational feasibility of the algorithms.
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