Mathematics teacher quality: its distribution and relationship with student achievement in Turkey

A main purpose of the present study was to investigate the distribution of qualified mathematics teachers in relation to students’ socioeconomic status (SES), as measured by parental education, among Turkish middle schools. Further, relationships between mathematics teacher quality indicators and students’ mathematics achievement were explored. Data from the 2007 Trends in International Mathematics and Science Study were used. Relationships between mathematics teacher quality and students’ mathematics achievement were investigated using hierarchical linear modeling. The current study identified SES-related inequities in access to qualified mathematics teachers in Turkey. Low-SES students were more likely to be taught by mathematics teachers who had less than 3 years of experience or who did not hold a degree in mathematics or mathematics education. On the other hand, years of experience and a degree in mathematics or mathematics education were found to be substantially related to Turkish eighth-grade students’ mathematics achievement. Low-SES students’ mathematics teachers were also more likely to report a lack of confidence in their preparation to teach various mathematics contents.     

PISA mathematics and reading performance differences of mainstream European and Turkish immigrant students

Lower reading and mathematics performance of Turkish immigrant students as compared to mainstream European students could reflect differential learning outcomes, differential socioeconomic backgrounds of the groups, differential mainstream language proficiency, and/or test bias. Using PISA reading and mathematics scores of these groups, we examined the role of bias and various measures related to immigrant integration policies of the host societies. Results of a multilevel analysis of reading and mathematics tests demonstrated that at individual level, students with higher scores on an index of economic, social, and cultural status obtained higher achievement scores. At country level, MIPEX scores of education and the human development index of participating countries could predict differences in reading results but not in mathematics. After correction for background characteristics, effect sizes showed a difference of .65 SD (down from a value of .96 before correction) for reading and .58 SD (down from .78) for mathematics. However, a similar correction for background variables increased the score differences between Turkish immigrants and mainstreamers.     

A Regression Model With a New Tool： IDB Analyzer for Identifying Factors Predicting Mathematics Performance Using PISA 2012 Indices

There are many studies that focus on factors affecting achievement. However, there is limited research that used student characteristics indices reported by the Programme for International Student Assessment （PISA）. Therefore, this study investigated the predictive effects of student characteristics on mathematics performance of Turkish students. In PISA studies, sampling design, sampling weights, and plausible values have to be taken into consideration in order not to have biased multiple regression results. In order to conduct multiple regression analyses in PISA, software called the International Association for the Evaluation of Educational Achievement （lEA） International Database （IDB） Analyzer is required to be used because the dependent variable consists of several plausible values. This study aims to identify student characteristics that are significant in predicting mathematics performance in Turkey. Results showed that being successful in mathematics is a combination of several factors in which students＇ beliefs, motivation, and other factors must be organized to achieve mathematics. Among these beliefs and motivation, strong self-efficacy, positive self-concept, and minimum level of anxiety seem to be the key for success.     

The impact of preprocessing on text classification

Preprocessing is one of the key components in a typical text classification framework. This paper aims to extensively examine the impact of preprocessing on text classification in terms of various aspects such as classification accuracy, text domain, text language, and dimension reduction. For this purpose, all possible combinations of widely used preprocessing tasks are comparatively evaluated on two different domains, namely e-mail and news, and in two different languages, namely Turkish and English. In this way, contribution of the preprocessing tasks to classification success at various feature dimensions, possible interactions among these tasks, and also dependency of these tasks to the respective languages and domains are comprehensively assessed. Experimental analysis on benchmark datasets reveals that choosing appropriate combinations of preprocessing tasks, rather than enabling or disabling them all, may provide significant improvement on classification accuracy depending on the domain and language studied on.     

Validity and Reliability of Turkish Version of the Balanced Index of Psychological Mindedness

The purpose of current study was to explore psychometric properties of the Turkish version of the Balanced Index of Psychological Mindedness (BIPM). Studies regarding psychological mindedness (PM) are still limited in Turkey; and there are few valid instruments to measure PM. The Turkish version of the BIPM and the 20-Item Toronto Alexithymia Scale (TAS) was administered to 654 voluntary undergraduate students. Confirmatory factor analysis indicated a good fit for the model including two subscales (interest and ability dimensions of PM) with a total of 14 items (x²/df?=?3.11, p?<?.001, RMSEA?=?0.060, S-RMR?=?0.059, GFI?=?0.95, AGFI?=?.0.93, CFI?=?0.94). McDonald’s Omega (ω) for scores on the overall scale was .85, .73 for scores on the Interest Subscale and .76 for scores on the Insight Subscale. Test–retest reliability coefficients were found as .61 for the scores on the BIPM total score, .55 for scores on the Interest subscale and .56 for scores on the Insight subscale (p?<?.01). As expected, the BIPM total scale score were negatively correlated with TAS total score (r?=?-.59).

     

Programming education in the frameworks of reverse engineering and theory of didactical situations

Education and Information Technologies - This study aims to evaluate the teaching of the programming process carried out through scenarios related to daily life within the framework of Reverse...     

The effects of constructivist learning environment on prospective mathematics teachers＇ opinions

To explore the effects of constructivist learning environment on prospective teachers＇ opinions about ＂＇mathematics, department of mathematics, discrete mathematics, countable and uncountable infinity＂ taught under the subject of Cantorian Set Theory in discrete mathematics class, 60 first-year students in the Division of Mathematics Education at the Department of Science and Mathematics in Buca Education Faculty at Dokuz Eylul University were divided into two homogenous groups. In order to do this segmentation, Minimum Requirements Identification Test was developed and used by the researchers. This test includes concepts like ＂＇set＂, ＂correlation＂ and ＂function＂, which are required to understand Cantorian Set Theory. While the control group was taught by traditional methods, a teaching method based on a constructivist approach was applied to the experimental group. Data were gathered by an open-ended questionnaire administered to total 40 students, 20 from the each group. Collected data were evaluated through content analysis. In the end, despite the minor differences, no statistically significant difference was found between the opinions of control and experimental groups about mathematics （χ^2calculation=2.578, SD=3, p〉0.05）, department of mathematics （χ^2calculation=3.185, SD=3, p〉0.05） and discrete mathematics （χ^2calculation=4.935, SD=3, p〉0.05） after the instruction. However, opinions about Cantorian Set Theory were significantly differentiated between experimental and control groups after the instruction （χ^2 calculation= 13.486, SD=2, p〈0.05）.     

In the context of multiple intelligences theory, intelligent data analysis of learning styles was based on rough set theory

The present study aims to identify the relationship between individuals' multiple intelligence areas and their learning styles with mathematical clarity using the concept of rough sets which is used in areas such as artificial intelligence, data reduction, discovery of dependencies, prediction of data significance, and generating decision (control) algorithms based on data sets. Therefore, first multiple intelligence areas and learning styles of 243 mathematics prospective teachers studying at a state university were identified using the “Multiple Intelligence Inventory for Educators” developed by Armstrong and the “Learning Styles Scale” developed by Kolb. Second, the data was appropriated for rough set analysis and we identified potential learning styles that a student can have based on the learning style s/he already has. Certainty degrees of the learning style sets were α_R(D) ≅ 0.717, α_R(C) ≅ 0.618, α_R(AS) ≅ 0.699, α_R(AC) ≅ 0.461, and these sets were found to be rough sets. Finally, decision rules were identified for multiple intelligences and learning styles.     

On constituent chunking for Turkish

Chunking is a task which divides a sentence into non-recursive structures. The primary aim is to specify chunk boundaries and classes. Although chunking generally refers to simple chunks, it is possible to customize the concept. A simple chunk is a small structure, such as a noun phrase, while constituent chunk is a structure that functions as a single unit in a sentence, such as a subject. For an agglutinative language with a rich morphology, constituent chunking is a significant problem in comparison to simple chunking. Most of Turkish studies on this issue use the IOB tagging schema to mark the boundaries.In this study, we proposed a new simpler tagging schema, namely OE, in constituent chunking for Turkish. “E” represents the rightmost token of a chunk, while “O” stands for all other items. In reference to OE, we also used a schema called OB, where “B” represents the leftmost token of a chunk. We aimed to identify both chunk boundaries and chunk classes using the conditional random fields (CRF) method. The initial motivation was to employ the fact that Turkish phrases are head-final for chunking. In this context, we assumed that marking the end of a chunk (OE) would be more advantageous than marking the beginning of a chunk (OB). In support of the assumption, the test results reveal that OB has the worst performance and OE is significantly a more successful schema in many cases. Especially in long sentences, this contrast is more obvious. Indeed, using OE means simply marking the head of the phrase (chunk). Since the head and the distinctive label “E” are aligned, CRF finds the chunk class more easily by using the information contained in the head. OE also produced more successful results than the schemas available in the literature.In addition to comparing tagging schemas, we performed four analyses. Along with the examination of window size, which is a parameter of CRF, it is adequate to select and accept this value as 3. A comparison of the evaluation measures for chunking revealed that F-score was a more balanced measure in contrast to token accuracy and sentence accuracy. As a result of the feature analysis, syntactic features improves chunking performance significantly under all conditions. Yet when withdrawing these features, a pronounced difference between OB and OE is forthcoming. In addition, flexibility analysis shows that OE is more successful in different data.