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Predicting academic performance of master's students in engineering management.

Fethi Calisir

Ecem Basak

Sevinc Comertoglu

Istanbul Technical University


The purpose of this study is to investigate the factors affecting academic achievement of the master's students who are enrolling in the executive engineering management master's programs in Turkey. These factors include admission requirements (entrance examination, undergraduate grade point average, English proficiency) and demographic attributes (gender, concurrent employment while studying).The sample for this study consisted of 105 students who graduated from the executive engineering management master's program at Istanbul Technical University between 2011 and 2014. A stepwise logistic regression method (Forward LR) was used for the analysis. The overall graduate grade point average score was modelled as a binary decision variable. The probability of excellent academic standing in graduate education was modelled as a function of Entrance Examination for Academic Personnel and Graduate Education (ALES) score, English proficiency exam score, undergraduate grade point average, gender, and concurrent employment while studying. The variables predicting probability of excellent academic standing in graduate education are ALES score (positively related), English proficiency exam score (positively related), undergraduate grade point average (positively related), and concurrent employment while studying (negatively related).

Keywords: Academic achievement, Admission requirements, Concurrent employment, Gender, Logistic regression


Engineering schools are successful institutions that educate excellent engineers who are capable of designing complex systems, whereas business schools are very good at teaching how to handle business problems. However, neither the engineers, nor the business managers do not have sufficient qualifications to manage technology that is the driving force behind the industrial power. Therefore, the concept of Engineering Management has been risen to combine these two areas (Kocaoglu 2009). Engineering management is "the discipline addressed in making and implementing decisions for strategic and operational leadership in current and emerging technologies and their impacts on the interrelated systems" (Kocaoglu 1990). There is a dramatic increase of Engineering Management programs since 1965 (Kocaoglu 1994). Especially, in the last decades, educational and research programs in Engineering Management have radically increased (Srour et al. 2013). Even though Engineering Management programs have sometimes different titles such as Technology Management, or Engineering and Technology Management, the common point of all programs is to teach how to manage innovation, creativity, basic and applied research, development, design, implementation, marketing and transfer of technology that are required in both the strategic and operational levels of the organizations (Kocaoglu 1994). Moreover, the central point in the Engineering Management Programs is to make students successful managers because organizations need engineering managers who are very competent in analytical and managerial thinking and decision-making.

While academic success is the most important element of students' education, it is also the main ingredient of educational institution's reputation which they keep protecting along their existence. The desire of education institutions' been the focus of successful students and enthusiasm of attracting them is increasing day by day. When evaluated in a larger perspective, it is seen that students' academic achievement within the institution is the most important criteria. So, the question that must be asked is how to measure the success of students. Academic achievement is particularly valued by their overall Grade Point Average (GPA) (Yang and Lu 2001). Therefore, GPA is seen as the indicator of the academic performance of the students in many studies (Ali et al. 2013; Fish and Scott 2009; Grayson 2008; Hill et al. 2011; McKenzie and Scheweiter 2001; Oliver et al. 2012; Sebok 1971; Talento-Miller et al. 2011; Ragothaman et al. 2009; Reed et al. 2009; Vu and Vu 2013).

The purpose of this study is to investigate the factors affecting academic achievement of the graduate students who enroll in Master of Executive Engineering Management Programs in Turkey. These factors include admission requirements (entrance examination, undergraduate GPA, English proficiency), and demographic attributes (gender, concurrent employment during master program). Entrance Examination for Academic Personnel and Graduate Education (ALES) is one of the admission requirements for applying to graduate schools in Turkey, and is the primary determinant of the candidate's admission score. It is given twice a year by Student Selection and Placement Center (OSYM) (Yuksek Ogretim Kurumu [YOK] 1996). ALES is similar to Graduate Record Examinations (GRE) and Graduate Management Admission Test (GMAT) which are required for many graduate schools in the United States (Educational Testing Service 2009; Graduate Management Admission Council [GMAC] n.d.). Graduate Studies Regulation which was published in 1996 by YOK in Turkey notes that an individual must receive a minimum standard ALES score which is specified by the University Senate, and while calculating individual's admission scores, at least 50% of ALES score must be taken into account. This percentage is also determined by the University Senate. Therefore, it has an important role for any applicant to be accepted to a graduate program in Turkey.

Another admission requirement is the undergraduate GPA (UGPA) score. A strong academic background is essential at the each stage of engineering programs (French et al. 2005). The students with higher UGPAs are expected to have higher graduate GPAs (GGPA). Therefore, each program sets its own minimum acceptable UGPA score as one of the application requirements. Later, UGPA scores of an applicant are taken into consideration to determine his or her admission score.

The weight of the UGPA in total admission score also changes depending on the University Senate's regulation.

English proficiency (ENG) is another important requirement when applying to Master of Engineering Management Programs in Turkey. Language of instruction in Engineering Management programs in Turkey is both Turkish and English. Hence, a candidate must prove that he or she is proficient in English by taking a test such as university's own English proficiency test, Interuniversity Foreign Language Exam (UDS), and Public Personnel Foreign Language Placement Exam (KPDS) which are carried out by OSYM (YOK 1996). Test of English as a Foreign Language (TOEFL) and International English Language Testing System (IELTS) are also acceptable and equivalent to the previous mentioned exams. In order to apply an Engineering Management program, an applicant must receive a minimum score which is specified by the University Senate, and then this score is used to calculate an admission score of the candidate. Similar to ALES, the weight of the English test score is also determined by the University Senate.

Aside from the admission requirements, demographic attributes may play a role in the academic achievement of the students enrolling Master of Executive Engineering Management Programs. Gender is one of these attributes. The gender (GEN) difference in academic performance has been investigated for decades. It is argued that female students show better performance than male students because they attend class more frequently, work harder, and have better study skills (Dayioglu and Turut-Asik 2007).

Concurrent employment (EMP) is another demographic attribute that may affect the student's academic performance during the graduate program. Employed students have less time for studying outside of class than unemployed ones. Therefore, working more hours rather than studying causes students to be in academic trouble (Nonis and Hudson 2006).

The findings of the study make several contributions to the literature in several ways. First, the effect of admission requirements of the Master of Executive Engineering Management in Turkey on the academic achievement of students was tested. There are several studies examining admission requirements of different graduate programs in different countries (Kolluri et al. 2010; Fish and Scott 2009; Hill et al. 2011; Oliver et al. 2012; Ragothaman et al. 2009; Talento-Miller et al. 2011; Weit and Gressel 2009) but to our knowledge there is no study focused on any graduate program in Turkey. Second, gender has been a critical factor about student performance. There are different empirical evidences supporting significant effect (Dayioglu and Turut-Asik 2007; Kolluri et al. 2010) and insignificant effect of gender (Mcmillan-Capehart and Adeyemi-Bello 2011; Sulaiman and Mohezar 2006). According to us, the effect of gender differences may depend on level of education, majors, age, or ethnicity, so that its effect on Execuive Engineering Management Graduate Program in Turkey is a specified area to be questioned. Another contribution of the study is to show the effect of concurrent employment. In the literature, there are few studies related to concurrent employment (Bradley 2006; Hunt et al. 2004). We believe that concurrent employment has an effective role influencing students' success because of letting the students have less time to study. So, this study will fill the gaps of Engineering Management programs administered in Turkey regarding these factors.

The next section of this paper discusses the research model and the hypotheses. This is followed by the methodology and the analysis. Then, the results of the analysis are presented and, this paper concludes with a discussion of the findings.

Literature Review and Research Hypotheses

Our research model includes the factors ALES, UGPA, English proficiency, gender, and concurrent employment during master's program. The following hypotheses were developed based on the findings of previous research on this subject.

"Effective selection and training of graduate students is of critical importance for all fields requiring graduate training" (Kuncel et al. 2001). Therefore, entrance examination and UGPA have a vital role in the admission process to select promising students. Kuncel et al. (2001) asserted that GRE-Verbal (GRE-V), GRE-Quantitative (GRE-Q) and GRE-Analytical (GRE-A) were valid predictors of GGPA. They collected the data from different studies which focused on the prediction of graduate school performance. Another study that GRE and UGPA were also tested together to predict graduate school success, was carried out with the data collected from 157 accredited institutions across the United States. The results showed that even though GRE was found to be the least effective factor, it had still impact on student's GGPA(Leverett-Main 2004). In the study of Reisig and DeJong (2005), academic performance of criminal justice graduate students was investigated. GRE and UGPA were used in the research model. In their study, two data sets were analyzed. The first data set included the students who graduated from Master of Criminal Justice in Michigan State University between spring semester 1996 and fall semester 2002, whereas the second data set was collected from the students who received their doctoral degree from the Criminal Justice program which was administered in the same university. The findings of the study pointed out that GRE scores and UGPA were the valid predictors of GGPA among students, and the students with higher GRE scores and UGPAs were more likely to perform better, and graduate with higher GGPAs.

Ragothaman et al. (2009) also found the significant positive effect of GMAT scores and UGPAs on GGPAs of students who graduated from Master of Professional Accountancy (MPA) Program of Midwestern University. In their study, the sample comprised 196 graduates of the MPA program over a period of ten years. They also questioned the effect of age, campus location, and undergraduate institution from which obtained their bachelor's degree. Age was found to be positively influencing student's academic performance, whereas campus location had no impact on, and undergraduate institution was negatively correlated with GGPA. In the study of Fish and Scott (2009), the One-Year MBA and the Part-Time evening MBA programs of Northeastern University in the United-States Canadian border was compared analyzing the factors which were considered as the determinants of exiting graduate quality point average (GQPA). The factors were GMAT-Verbal percentile (GMAT-V), GMAT-Quantitative percentile (GMAT-Q), UGPA, Canadian student (Canadian), undergraduate of the particular school (School), business undergraduate degree (Business), and age. According to the results, GMAT-V and UGPA were found statistically significant in predicting students' MBA performance for both programs. On the other hand, School and Business were found that they did not predict the GQPA in both programs. Furthermore, there are also several studies examining the significant effect of entrance examination and UGPA (Hill et al. 2011, Holt et al. 2006; McClain et al. 1984; Sobol 1984; Sulaiman and Mohezar 2006; Talento-Miller et al. 2011). Therefore, we hypothesize as follows:

H1: ALES score is a predictor of GGPA.

H2: UGPA is a predictor of GGPA.

English proficiency is another important factor for the student success where English is used for teaching and learning (Cho and Bridgeman 2012). In the study of Oliver et al. (2012), the impact of English proficiency on students' academic achievement was investigated. The data included the English proficiency scores of international and domestic students who enrolled in undergraduate and postgraduate programs of one Australian university between 2006 and 2008. The finding of their study showed that students who had higher English proficiency score performed better academic progress. The results also indicated that standardized test such as TOEFL or IELTS provided more sufficient evidence for the potential academic success than university's own English proficiency test. Simner and Mitchell (2007) conducted a study at the University of Western Ontario in Canada. The paper-based TOEFL score of the 345 students whose native language was not English were collected. It was found out that students with a higher TOEFL score outperformed than the students with lower TOEFL scores. Vu and Vu (2013) have also examined the relationship between TOEFL scores and international graduate students' GGPAs. The sample for their study comprised 464 international graduate students at a Midwestern Public University. According to the results, even though the English score was not to be found to be a significant predictor of GGPA, students expressed that their TOEFL scores were related to their academic performance. Weit and Gressel (2009) investigated the relationship between TOEFL score and overall UGPA for international engineering students and they also questioned the effect of high school GPA, gender, and nationality on estimating the overall UGPA. They used linear and logistic regression to evaluate TOEFL score relative to overall GPA. The findings of their study revealed that there was a positive statistically significant relationship between TOEFL score and UGPA. Therefore, we hypothesize as follows:

H3: English proficiency score is a predictor of GGPA.

In the study of Sheard (2009), the impact of demographic variables, age and gender, on academic achievement was examined. It was suggested that female students outperformed their male counterparts because of studying harder and more consistently, being motivated to reach their academic goals and activities, and adapting more easily to the courses. The results of the study that was conducted with 134 undergraduates from an urban university in the northeast of United Kingdom showed that female students achieved better GPA scores than male students. Dayioglu and Turut-Asik (2007) investigated the significant gender differences in academic success of undergraduates studied at Middle East Technical University in Turkey. The results indicated that female students were expected to achieve 0.12--0.13 points higher than their male counterparts. In another study, the significant effect of gender differences in academic performance of the students graduated from the MBA Program of the Barney School of Business of the University of Hartford was explored as UGPA was also found to be a predictor of GGPA, whereas GMAT was not (Kolluri et al. 2010). Therefore, we hypothesize as follows:

H4: Gender differences are a predictor of GGPA.

Unemployed students are expected to outperform than employed students. They are more likely to devote themselves to their studies, because they have more time, energy, and other resources. Therefore, they were more engaged with their studies in the university (Bradley 2006). In the study of Hunt et al. (2004), the effect of concurrent employment on academic achievement was investigated. The data collected from 2737 full-time undergraduate students at Northumbria University. According to the results of their study, Empirical evidence that concurrent employment negatively influenced academic performance was provided. Bradley (2006) also examined the relationship between concurrent employment and GPA. The data set for his study included 246 full-time students at a large public university in Queensland, Australia. However, his findings poorly supported that unemployed students had significantly higher GPAs than employed students. Apart from the studies above, Moro-Egido and Parades (2009) researched the impact of employment status on student's satisfaction related to the undergraduate program. The data collected from the students who graduated from Computing (BPC) at the Autonomous University in Spain between 2001 and 2004. It was found out that working students felt less satisfaction about their academic experience. This finding may be interpreted that satisfied students will be more likely to achieve higher GPAs than less satisfied ones. Therefore, employment status may also indirectly affect the students' GPAs. In the study of McKenzie and Schweitzer (2001), it was also found that the students with employment responsibilities had lower UGPA than the students with no employment responsibilities. Therefore, we hypothesize as follows:

H5: Concurrent employment is a predictor of GGPA.

Sample and Data Collection

The study was conducted in the executive engineering management master's program at Istanbul Technical University (ITU). Differences between the executive engineering management master's program and the regular engineering management master's program at ITU are the admission requirements of the programs, the percentage of courses taught in English, and the number of courses required to take in the program. The most important difference between these programs is that students of the executive engineering management master's program are required to complete a semester project, not a thesis. In order to apply to this program, an applicant must obtain a minimum ALES score of 70 out of 100 and a minimum UGPA of 2.3 out of 4.0 and a minimum ITU English proficiency exam score of 70 out of 100 or a minimum TOEFL IBT score of 65 out of 120 or a minimum TOEFL CBT score of 183 out of 300. In the engineering management master's program, 50% of the courses are taught in English and 50% are taught in Turkish. Students must complete 12 courses (36 credit hours), take a term project (uncredited), and obtain a minimum GGPA of 3.00 to graduate from the program. According to senate principles of ITU, GGPA greater than 3.50 refers to the excellent academic standing, whereas GGPA between 3.00 and 3.50 refers to the good academic standing. The performance of the program is evaluated based on the GGPAs greater than 3.50. The maximum duration of the executive engineering management program is three years and students who are not able to graduate in three years are considered unsuccessful and they are expelled from the program; that is why the sample for this study consisted of all 105 students who graduated between 2011 and 2014. Out of the 105, 66 graduates were male and 71 graduates were employed while studying and out of the 105, 35 graduates had a GGPA greater than 3.50 whereas 70 graduates had a GGPA less than 3.50. The summary of profiles of students is given in Table 1.

Data Analysis

In our study, a binary logistic regression method (Forward LR) which is a suitable method for predicting the dependent variable that is categorical was used for the analysis instead of discriminant analysis because of not meeting the basic assumptions. The model was tested using the IBM SPSS 20.0. The overall GGPA score was modelled as a binary decision variable. The reason behind dichotomization of GGPA was to evaluate graduates whose academic standing was either excellent or good. A cumulative GGPA score which is between 3.5 and 4.0 indicates excellent academic standing whereas GGPA score which referring between 3.0 and 3.5 to good academic standing. In this case, GGPA score over 3.5 = 1; and GGPA score below 3.5 = 0 were included in the binary logistic regression analysis as dependent variables. ALES (on a scale of 100), undergraduate GGPA (on a scale of 4.0), and English proficiency (ENG) (on a scale of 100) and were taken as scale independent variables while gender (GEN), and concurrent employment (EMP) were taken as categorical independent variables. The categorical independent variables were discrete dummy variables (female = 1, and male = 0; employed = 1, and unemployed = 0.


The chi-square statistics is used to see if the model reasonably fits to the data (Hair et al. 1995). A test of the full model with all predictors against the null model was found statistically significant, = 30.338, p<0.05, indicating that the predictors selected reliably distinguished between students with excellent academic standing and those with good academic standing. Another test, Hosmer and Lemoshow, is used to make an assessment of the Goodness-of-Fit of the logistic regression model. It explains the correspondence between actual and predicted values of the dependent variable. A non-significant test indicates that the model fit is acceptable (Hosmer and Lemeshow 2004). The result shows that the model adequately fits the data with a p-value of 0.672 (p>0.05). Cox and Snell [R.sup.2] and Nagelkerke [R.sup.2] show the percent of variance explained in the dependent variable (Hosmer and Lemeshow, 2004). The Cox and Snell measure was found to be 0.25, whereas the Nagelkerke measure (an adjustment of Cox and Snell [R.sup.2]) was found at 0.355. Of the students who achieved excellence academic standing, 86.3% are correctly classified by the model; of those who achieved good academic standing, 50% are correctly classified. An overall success rate of % 75.2 was noted, which is quite impressive. In Table 2, classification summary can be seen.

Table 3 below shows regression coefficients (B), standard errors, Wald statistics, and odds ratios (Exp(B)) for each predictor. According to the Wald criterion, which is used to assess the significance of each regression coefficient (Hair et al., 1995), ALES, ENG, UGPA, and EMP predicted GGPA at a=0.05, whereas gender was found to have no effect on GGPA (p<0.892).

The sign of the unstandardized logistic coefficients (B) indicates the direction of the relationship between dependent variables and the predictors (Hair et al. 1995). ALES, ENG, and UGPA have positive B values indicating that an increase in these predictors will increase the probability of achieving higher GGPA. On the other hand, EMP has a negative B value which means than working students are more likely to have a lower GGPA at the end of the program. B values also show the change in the log odds of the dependent variable for every unit change in a predictor (Hair et al. 1995). For example, the log odds of GGPA increases by 0.067 for every one unit change in ALES; however, unstandardized logistic coefficients are not adequate to determine the probability change given a one unit change in the predictor (Hair et al. 1995). Therefore, exponential logistic coefficients, marginal effects, are used to assess the magnitude of a change in the odds and to calculate the percentage change. Exp (B) is the change in the odds of the dependent variable for every unit change in independent variable (Hair et al. 1995). For example, the probability of excellent academic standing in graduate education increases (decreases) by a multiplicative factor of 1.12 (0.33) as UGPA (EMP) increases by 1 unit. Percentage change in odds is calculated by the multiplication of (Exponential coefficient--1) and 100. It gives us the probability change due to a one unit change in the independent variable (Hair et al. 1995). For example, a one unit change in ALES will increase the odds of GGPA by 7%. However, exponential logistic coefficient of a nonmetric is interpreted differently from a metric variable (Hair et al. 1995). It is known that employed students are coded as 1. Given that Exp(B) of EMP is 0.33, then employed students have 67% (0.33-1.00=-0.67) less odds than unemployed students. Put differently, students who are not employed during school had three times greater odds of having excellent academic standing.


In this study, the key determinants of successful academic achievement in the Executive Engineering Management Master's program were investigated using Binary Logistic Regression method. For this, data were collected from all 105 students who graduated from the Executive Engineering Management Master's Program at Istanbul Technical University.

The probability of excellent academic standing in graduate education was modeled as a function of Entrance Examination for Academic Personnel and Graduate Education score (ALES), English proficiency exam score (ENG), undergraduate grade point average (UGPA), gender (GEN), and concurrent employment (EMP). The variables predicting probability of excellent academic standing in graduate education are ALES (positively related), ENG (positively related), UGPA (positively related), and EMP (negatively related).

The results contribute to the literature of academic achievement of the students indicating that concurrent employment is the most potent predictor of GGPA. If students do not work during their education, they will be more likely to receive an excellent academic standing. Consistent with the findings of Hunt et al. (2004), employment status plays a vital role among other factors in academic success of students, because non-working students have more time and energy to study.

This study also contributes to the literature confirming the effects of admission requirements, ALES, UGPA, and English proficiency on GGPA. The results indicate that admission requirements are the significant predictors of GGPA. Therefore, graduate programs should continue to consider ALES, UGPA, and English Proficiency scores to find the best candidates for the program. Especially, UGPA is found to have the most important effect among the factors related admission requirements. A strong undergraduate background is a precursor of excellence in graduate programs. Similar to findings of Ragothaman et al. (2009), GGPA is mostly predicted by UGPA. Therefore, in order to select adequate students for the program, the weight of the UGPA in calculating admission scores can be increased or the minimum level of UGPA for the entry requirements can be reordered considering the program committee's suggestions. Moreover, Hill et al. (2011) also confirm the significant effect of UGPA on GGPA.

English is another important predictor of GGPA. Similar to findings of Oliver et al. (2012), it is important that students whose native language is not English must have sufficient English skills to be successful in English related programs. Because 50% of the courses are taught in English in the Engineering Management program, Turkish students must confirm that they have adequate English skills. Consistent with the findings of Weit and Gressel (2009), English proficiency exam score plays a role in estimating GGPA. ALES is the final predictor related admission requirements. It was mentioned that ALES is a similar entry requirements such as GRE and GMAT. Consistent with the findings of the studies (Fish and Scott, 2009; Reisig and DeJong 2005), the students with higher entrance examination scores will be more likely to have a better academic performance. Holloway et al. (2014) also state that universities are using standardized tests scores as a part of admission process to estimate applicants' academic performance.

The other result of the study that gender is found to have no effect on GGPA. It is seen that a demographic attribute does not play a role in students' success. Inconsistent with the findings of Sheard (2009), gender differences do not affect the academic success of students, however similar to findings of Sulaiman and Mohezar (2006), there is no relation between gender and excellent academic standing. It is found that female and male students show the same performance on the Engineering Management program. Stump et al. (2011) also assert that there is no statistical difference between the performance of women and men who are enrolled in engineering courses.

Limitations and Directions for Future Research

The findings of this study provide an understanding of the factors affecting GGPAs of the students enrolled in the executive engineering management program at ITU, but we should also consider its several limitations. The sample of this study was chosen from the graduates of the executive engineering management master's program at Istanbul Technical University. Therefore, guarded generalizations to wider populations may be made with due caution.

Second, with a larger sample size, some additional factors such as the university they graduated from and the subject of their undergraduate degree that may be important in predicting the performance of students can be included in a model of further study.

Third, by increasing the size of the data collected, group differences among female and male students may be analyzed as a further study.

Finally, the results of the current study may be shared with the engineering management program coordinators to deliver a further understanding about the findings. With the opinions obtained from them, a qualitative analysis may be merged with the current quantitative analysis' results.


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Table 1 Profiles of the students

Gender (%)
                                   Female:37.14       Male:62.86

ALES (on a scale of 100)
Max:97.14                          Min:55.42          Average:80.92

Proficiency (on a scale of 100)
Max:96                             Min:57.50          Average:73.06

UGPA (on a scale of 4.0)
Max:3.67                           Min:2.10           Average:2.69

GGPA (on a scale of 4.0)
Max:4.00                           Min:3.00           Average:3.63

Employment (%)
                                   Unemployed:32.38   Employed:67.62

Undergraduate University (%)
YTU:32.38                          ITU:24.76          Marmara:5.71
Bahcesehir:3.81                    Kocaeli:3.81       Others:29.53

Undergraduate Engineering Discipline (%)
Mechanical: 26.67                  Computer: 15.27    Electrical:11.43
Chemical: 11.43                    Metallurgy:7.62    Others:27.58

Table 2 Classification table

                                      Predicted GGPA
                                  Good   Excellent    Correct

Observed GGPA         Good         63       10          86.3
                      Excellent    16       16          50.0
Overall Percentage                                      75.2

Table 3 Variables in the equation

                                                      95% C.I.for

Variable      B     S.E.    Wald   df  Sig.   Exp(B)  Lower   Upper

ALES        0.067   0.035  3.744   1   0.053  1.069   0.999   1.144
ENG         0.091   0.030  9.093   1   0.003  1.096   1.032   1.162
EMP        -1.109   0.535  4.304   1   0.038  0.330   0.116   0.941
UGPA        0.110   0.032  11.657  1   0.001  1.117   1.048   1.190
Constant   -19.875  4.885  16.554  1   0.000  0.000
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Author:Calisir, Fethi; Basak, Ecem; Comertoglu, Sevinc
Publication:College Student Journal
Article Type:Report
Geographic Code:7TURK
Date:Dec 1, 2016
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