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Examining the relationship of study approach and teaching experience with academic performance amongst graduate students of accounting (case study: Islamic Azad University, Yazd).

INTRODUCTION

Without doubt, seeking knowledge is the most important and fundamental human activity which plays a critical role in every person's life as well as in developing cultures and civilizations. Today, developed countries owe their advanced culture and civilization to their successful scientific functionalities and best practices in functionalizing youth's potential talents [13]. Education and its necessity lay amongst subjects well known to everyone as important issues. Today, the world does not consider education as expenditure anymore, but as a type of investment. Academic performance of university students is always considered as an important factor when predicting their success in the future; and success in higher education levels could be a good basis when the graduates enter the professional environment [7]. Education prepares the society for managing and applying the existing resources in an efficient and effective way aligned with development agenda. Investments in human resources could transform into positive social efficiency; and boost economic development and growth. Since academic education is considered as the highest level of education in every country, fundamental steps should be taken for enhancing qualitative academic development along with quantitative growth. Dynamism and concentration of higher education demand for paying more attention to quality; while success in this path would lead to effective, high performance and efficient society [28]. Educational programs in universities should be designed, oriented and presented in a way that can eventually enhance the knowledge, evolve the perspectives and approaches; and improve the level of skills in order to deliver the graduates to the stages of required social services as efficient outputs of the educational system. University students are the most intuitive and available information resources for evaluation efforts pertaining to teaching/learning approaches and educational best practices of academic programs. We need to assess the existing situation continuously and identify its strength and weakness points in order to improve education quality; students' opinion could be enlightening since they are to benefit from this scheme [18]. Accounting as a profession, is very demanding for the students of this field of science. In recent two decades, the necessity of individual and social skills improvement for accounting students along with professional studies has been emphasized several times. Elevating students' personal skills is based on knowing and understanding the learning paradigms and the students' concerns. It is far from trivial to determine a suitable framework for identification and better understanding of learning process within accounting discipline [28]. Therefore, education in accounting has to be customized with individual differences in learning paradigms and supplemented by appropriate teaching and learning strategies; this can enhance the students' abilities to learn how to learn. Hence, it seems necessary to investigate study paradigms and teaching experience through relationship with students' academic performance. This study aims to examine variables pertaining to study approaches and educational experiences by students; and consequently strives for answering the question: Is the academic performance of accounting graduate students related to study approaches they choose and teaching experiences they had before?

Section 2 includes research history, while section 3 is devoted to theoretical basis of this study. Then, methodology is described in section 4 followed by study findings presentation in section 5. Finally, discussions, conclusions and suggestions for future research would be presented at section 6.

Research History:

Learning approaches amongst university graduate students have been seriously examined by researchers in the field of education studies since encouraging deep learning strategy has become a big challenge in facing high work load, fading resources, lower ratio of faculty to student numbers, and increasing number of students. Important studies conducted in this field include what comes in the following.

Parsa and Saketi [5] examined how the curriculum implementation method (teaching and testing approach by faculty) and structured class approaches relate with students' learning strategies. Based on the findings, most students describe their learning approach as deep understanding while faculty assess themselves as following a teaching based approach and evaluating students based on their scientific reproduction abilities. Shokri et al. [16] studied the relationship between schools of thought; as well as learning approaches and students' academic performance. The findings suggest that different schools of thought, namely legitimate, judiciary, liberal, hierarchical, and external paradigms are positively and significantly related to deep understanding approach; while implemental, partial, and conservative schools of thought are negatively and significantly related to shallow learning approaches. Shokri et al. [14] examined the personal traits, stress in education and academic performance. The findings showed that there are positive and significant relations between the factors of loyalty, extroversion, acceptance and adaptation with academic performance; while negative and significant relations exist between psychological disorder factors and academic performance. Fathabadi and Seif [11] also investigated effects of several descriptive or four choice assessment methods on study approaches and strategies of cramming amongst students with high and low academic performance. The findings have shown that using descriptive answer exams, will guide students towards deep understanding approaches while four choice tests encourages them to use surface apathetic study methods. Shokri et al. [15] studied the gender based differences in academic performance. The results suggest that five factors of extroversion, loyalty, acceptance, neuroticism, and adaptation are positively and significantly related to academic performance. Seif and Fathabadi [12] addressed the study approach and its relations with educational improvement, gender, and study-time length in university. Research conducted on the study approaches and educational improvement, have shown that a positive correlation exists between deep understanding approach marks and educational improvement; and a negative correlation exists between surface apathetic understanding approach and educational improvement. Behpajouh et al. [3] has investigated the effect of social skills training on social adaptation and educational performance of slow-learner students. Their findings have shown that interventional programs significantly improve social adaptation abilities and educational performance amongst male slow-learner students in test group. Ghaltash et al. [20] studied the educational effects of meta-cognition strategies on educational performance and creative abilities amongst fifth elementary grade male students. Their results suggest that metacognition strategy training courses improved the educational performance of test group by 57% in comparison to control group; and improved creativity of test group by 27% in comparison to control group as well. Hejazi et al. [7] tried to determine the relation between the perception of purposeful structure of the class, schools of thought, and learning approaches and academic performance of university students. Their findings showed a positive and significant relation of structured skill-purposed variables, first type school of thought, and deep understanding approaches with academic improvement; as well as a negative and significant relation between a second school of thought and academic improvement. Shakournia [17] examined the study and learning approaches amongst nursing and medicine students of Jondishpour Medical Science University of Ahwaz during the 2010-2011 academic year. His findings showed that the extent of using deep understanding approach amongst students of medicine increased at the clinical stage and extent of usage for surface apathetic understanding approach decreased significantly.

Cheraghy et al. [6] studied the experience of learning courses at medical science programs in Hamedan Medical Science University. Obtained results using Pearson correlation coefficient pertaining to students rank in overall university entrance exam showed that there is "no" significant relation between student's rank in overall exam and his/her content regarding the learning courses in university. Pearson correlation coefficient of last academic semester average points have shown that no significant relation exists between last semester average point for each participant student and his/her general satisfaction level from the learning course. Abtahi and Nedri [1] studied the relation of creativity and social adaptation with educational performance amongst high school students of Zanjan city. Their findings showed that a positive and significant relation exists between creativity and social adaptation level on one hand, and students' educational performance on the other. Byrne et al. [26] conducted a study titled "The relation between learning approaches and learning records" which tested 95 management-accounting students for their learning approaches and learning outputs during their first year in the program. They compared those results with learning output records. Analysis showed that deep understanding approach and strategy are positively related to higher academic performance, while surface apathetic learning approach is related to week academic performance. Also a positive and significant relation was observed between deep understanding strategy and total grade points amongst the students. In a research titled "deep and shallow learning approaches in elementary accounting", Lucas [32] has assumed that students come with a presumption about accounting subject and how it is related to present society; this presumption enforces them to implement a surface apathetic approach for learning accounting subjects. Students think logical and desire for transparency in accounting. Davidson [27] examined the relation between study strategy and performance in exams amongst 211 Ph.D. students of fiscal accounting during one semester at a Canadian university. The results show that accounting instructors must encourage students to improve and use a deep approach to master complicated concepts. Byrne and Flood [25] investigated the effects of expectations amongst graduate students in Ireland universities on their academic performance. The findings suggest that a significant relation is there between expectations and academic performance amongst the students. Halabi [29] studied the academic performance of students pertaining to immediate subjects of accounting. The results showed that prior acquaintance with accounting topics, accounts for 25 percent of academic performance during the course. Sheard [34] examined the relation between devotion, gender factors and academic performance amongst university students during a two year period. Educational assessment records showed that girls perform better than boys; meanwhile educational improvement is significantly related to devotion and gender. Murphy [33] addressed the students' satisfaction level and knowledge implementation related to educational issues. His results showed that educational programs in some aspects will significantly alter academic performance amongst students; the level of effectiveness depends highly on students' incentives during the first year of study. Vitae [36] in his study titled "Meta-cognition, is awareness and control over oneself' aimed to describe meta-cognition as the state of being aware and having control over oneself; he states that students who benefit from meta-cognition strategies are more successful compared to those who don't. Sun & Richardson conducted a research titled "perspectives about quality and approaches of study in higher education" during which, he performed a comparative study on Chinese and British graduate students over six business departments in UK. The results revealed that two groups are significantly different in age conditions since Chinese students were two years older in average. However, primary analysis showed that age and gender are not significantly related to the students' marks in questionnaire. An accounting education program has to equalize learning approaches through considering personal differences and implementing appropriate educational and teaching strategies in order to enhance learning performance while improving the student's abilities to learn how to learn. Therefore, investigating study approaches and educational experiences, as well as their relations with students' academic performance looms just necessary. Thus, considering previously done research, the factors of study approach and educational experience are examined separately by local and international studies. However, those studies have not analyzed comprehensive aspects of mentioned variables along with their effects on educational performance amongst students. Hence, the variables - study approaches and educational performance--as well as their effects on students' academic performance are investigated in the present study.

Theoretical Framework:

Since accounting profession calls for a sensitive position within economic and financial firms, while considering different behavioural dimensions, future accountants need to have skills which can satisfy those expectations. Above-mentioned points, along with rapid technological developments and accounting systems getting increasingly complicated, reveal the necessity of reviewing educational curriculum for this profession. Study and learning approaches are highly important in accounting, since the main goal is to train capable and competent accountants who already possess the required knowledge, perceptions and skills in order to elevate accounting professional grade within the society. Therefore, effective learning and study methods need to be taught and formalized in order to improve learning conditions and increase accuracy, pace and quality of study along with appropriate understanding of the subjects. Understanding those learning and teaching processes naturally comes first of all.

Learning:

Since our current behaviour and reactions are the consequences of our past learning activities and experiences, learning is defined sometimes as a process which causes relatively permanent changes in our behaviour; i.e. our abilities and efficiency are consequences of our past interactions with the environment. Different people have different perceptions about learning concept. Several studies have been conducted in order to shed light on what people would deduce from learning concepts. Some perceive learning as memorizing things and using the memories when required. Some others believe that learning is not only about adding to your knowledge and known facts and recalling them in a future time, but it consists of understanding the principles and fundamental concepts which the person can use both at familiar and unfamiliar situations through the life [12]. Study and learning approaches are modal and distinct behaviour which people take towards acquiring knowledge, skills and viewpoints through study; and prefer them as methods of learning lessons. Study and learning approaches improve students' academic performance through simplification of study process [35]. Learners generally engage their task through two main forms of activities: deep learning approaches, and surface apathetic learning approaches. Those approaches depend on the behaviour initiated from the learner's interpretation of his/her educational tasks. Those interpretations lead to formation of learning incentive (the goal) and distinctive processes categorized as learning strategies [7]. Existing literature claim that people can adopt different study approaches based on perception they have about situational demands of any certain context. Past research support this statement that a person's approach towards learning is linked with his perception about learning and understanding of educational concept; those approaches eventually determine the quality of his learning activity. Accounting students at graduate level are expected to generate high quality learning results. Recently, those results not only involve robust technical competence, but also a wide understanding of the field, critical thoughts, ability to implement ideas and concepts for solving task problems, high communication skills and more of this kind. Future members of professional accounting body are expected to manifest those capabilities as well [31].

Education:

Ever-evolving developments of science and technology fields make a person's knowledge and skills to seem always shrinking. Therefore, individuals and organizations have to continuously update their information and skills in order to survive; this cannot be done but through successive and unceasing education. Education is undoubtedly of critical importance in human's life since it leads to training, constitution, improvement and eminence for human being. Higher ranks of humanity emerge through proper education; and individual's talents sprout this way [4]. Today, education as a main development mechanism for human resources at the level of organizations and economic firms, receives substantial attention by experts, authorities and education managers. Directing educational activities towards systematic perspectives and strategic views has been particularly very important amongst large organizations and firms which have understood this necessity under the new global conditions of complicated technologies, intellectual capitals, and changing job identities. Adaptation and integration of systematic view and strategic orientation with main functionalities of educational policies, planning, implementation and evaluation today leads to more targeted, logical, dynamic and flexible education cycles within organizations. Higher education system in our country has been facing many challenges and problems during past two decades. Among them are inability to produce theoretical science, consumption of fundamental and theoretical sciences produced by other countries of the world, unapplicable university education, and lack of adequate relationship between universities and other social sectors [2]; and also Increasing number of university students and applicants to universities and other higher education institutes, quantitative expansion of higher education institutes without considering the existing capacities like economical, social and cultural resources [21]. Our higher education system inevitably needs to consider maintaining, improving and enhancing the quality of higher education environment in order to cope with those challenges. To this end, all the functionalities of higher education system have to be addressed equally while the processes and their performance need to be continuously assessed using valid and appropriate tools [22]. Accounting as a profession plays an important role in economic development of countries. Evidently, this task requires providing accounting students with proper education first. Accounting students face some facts in reality, which are not well digested during university courses; and this occurs as a consequence of weak theoretical basis of accounting education as well as low importance attached to mental trainings. Old and obsolete methods of injecting information into students' brains have to be discontinued. University needs to be a place in which the students learn how to think and how to analyze the data and ratiocinate [19].

Methodology:

Achieving the goals of science or scientific understanding would not be possible without going through proper methodologies; i.e. methods, and not the subject would create validity for the research [9]. The present study is conducted through two phases: At first phase, theoretical basis are stated using library methods for collecting information; and the second phase - which is the main part of our work--aims for modeling and answering main research questions. A descriptive study method of survey type is used at this stage. The present study describes data through descriptive statistics approach while data are analyzed using SPSS software through Explorative Factor Analysis (EFA) in order to identify factors which form variables under study. Then, the identified factor structure is used as the basis to define hypotheses pertaining to relations between variables and those factors. Confirmatory Factor Analysis (CFA) and structure equation technique using LISREL software--which is well-known for executing these types of models - are used to test the hypotheses; aiming for evaluation of synchronous, direct, or indirect relations between the variables.

Statistical Population and Sample:

The statistical population is consisted of a whole set of possible recorded measurements or information taken from qualitative characteristics, over whole collection of units on which we wish to make deductions. Here, data collection means extracting the results from information about the population under study [9]. The statistical population in the present study covered all graduate students of Islamic Azad University of Yazd who majored in accounting and had finished at least two semesters of study. Their number was more than 200 at the time of study. The statistical sample consisted of graduate students of accounting from Islamic Azad University of Yazd at 2011-2012 academic year who had finished at least two semesters of study and had precise information about their total average grade points at last semester. Sample size was 108 and those students were selectively chosen.

Research Hypotheses:

Every scientific study ultimately aims for answering some questions while approving or rejecting some hypotheses. The hypotheses defined by the present study are:

Hypothesis 1: There is a relation between study approach adapted by graduate students of accounting and their academic performance.

Hypothesis 2: There is a relation between educational experience and academic performance amongst graduate students of accounting.

Study Variables:

Main variables under investigation by this study were two variables pertaining to learning approach and educational experience which measured the subject at deep, surface apathetic and strategic levels.

* Deep approach:

Deep approach covered the factors shown in Table 1:

Table 1: Coding the components of deep approach.

Item   components of deep approach   Code

1          Seeking Meaning(SM)        SM
2          Relating Ideas(RI)         RI
3         Use of Evidence(UOE)       UOE
4        Interest In Ideas(III)      III


* Strategic Approach:

The strategic approach is consisted of components shown in Table 2:

Table 2: Coding the components of strategic approach.

Item      components of strategic approach      Code

1              Organised studying(OS)            OS
2               Time Management(TM)              TM
3      Alertness to Assessment Demands(ATAD)    ATAD
4                   Achieving(A)                 A
5           Monitoring Effectiveness(ME)         ME


* Sallow Approach:

Surface Apathetic Approach consists of two components shown in Table (3):

Table 3: Coding the components of surface apathetic approach.

Item   components of Surface Apathetic Approach    Code

1                Lack of purpose(LOP)              LOP
2              Unrelated memorizing(UM)             UM
3                Syllabus-bounded(SB)               SB
4                Fear of failure(FOF)              FOF


* Course Educational Experience:

Course educational experience covers the components shown in Table 4:

Table 4: Coding the components of course educational experience.

Item   Components of educational experience    Code

1          Clear Goals and Standards(CG)        CG
2               Generic Skills(GS)              GS
3          Emphasis on Independence(EI)         EI
4                Good Teaching(GT)              GT
5            Appropriate Workload(AW)           AW
6           Appropriate Assessment(AA)          AA


Study Findings:

One of the most important aspects of every scientific study is to analyze the data collected from statistical population under examination. In this section, the findings of our study are presented:

Explorative Factor Analysis:

Generally, the Maximum Likelihood method is implemented if required conditions are met in order to ensure those dimensions fully cover variables (questions) using EFA. The Varimax rotation method is used here for identifying the factors which are possibly bases for certain tests and to identify its simple structure as well. In addition, experts highly recommend making sure about correlation between variables, as well as significance of data matrix before performing EFA. Partial correlation coefficient is an appropriate index of relation strength between variables. KMO test is usually used for this task. The value returned by KMO test which is called the "index of sampling adequacy" is a measure which compares correlation values of observations to partial values. Values near 1 suggest that data are suitable for factor analysis, otherwise (usually <0.6) the results of factor analysis would not be appropriate regarding to those data. Also the importance and significance of correlation matrix is usually evaluated using Bartlett test. Factor analysis would be suitable for identification of structure (Factor Model) if the Bartlett significance level is less than 5% [23]. The null hypothesis in Bartlett test is that variables are correlated only with themselves. Rejecting null hypothesis suggests that there are significant data inside correlation matrix and minimum required conditions for factor analysis exist. This test is also called sphericity test [10].

Exploratory Factor Analysis in Study Approach:

As stated, first step of EFA is to ensure sampling adequacy. Results obtained from KMO-Bartlett test are shown in Table (5). Those results suggest that EFA steps can be performed on data. KMO value is higher than 0.7 (i.e. 0.836) which approves the sampling adequacy; while zero confidence level obtained using Bartlett test shows the adequacy of induced factor model as well. Also, Principal Component Analysis method along with Varimax rotation is used at all study steps as the method of derivation factor analysis.

Table of total derived variance:

Table 6 shows the number of factors derived from data (primary variables). The factors with eigenvalues of higher than 1 (column "total") are identified as effective factors in this step. The last column in this table shows that all factors together explain a few percent of variability of primary variables. Those results suggest that eigenvalue is higher than 1 for three factors. Therefore, the proposed factor structure would be consisted of three factors which cover more than 61.935% of accumulated variance value.

Rotated Component Matrix:

The final factor structure throughout the rotated matrix is shown in Table 7. This table contains the stated variance contribution for variables by the factors. Variables are categorized based on the results shown in this matrix. Each factor is associated with a number of variables which had higher factor weights (at least 0.5). However, Varimax rotation method has been used for improving factor configurations, since each question regarding factor extraction should only be devoted to one factor (i.e. each question has to address only one factor with its weight higher than 0.5) and none of the factors should remain without weight (i.e. associated with no question). Also this could make interpretation, definition, and categorization of variables difficult since a variable may often have high weights associated with more than one factor. For this task, we can achieve desired interpretation through rotating coordination axes without affecting the results. Coordination rotation causes each variable to be closer only to one factor. Every element in the table indicates partial correlation coefficient between the component (row) and rotated factor (column).

As observed above, each variable in rotated matrix is associated only with a single factor with high weight. Therefore, factors can be named based on the results in rotated matrix of study approaches. The output is shown in Table (8).

Exploratory Factor Analysis of Teaching Experience:

The results of KMO-Bartlett's tests regarding teaching experience are presented in Table 9. EFA phases could be performed considering those results. The KMO statistics value of higher than 0.7 (=0.795) indicates the sampling adequacy; and zero confidence level of Bartlett's also suggests that the factor model is a suitable one.

Table of total extracted variance:

Results in Table 10 show that the eigenvalue associated to only one factor is higher than 1. Thus, the proposed factor structure would be consisted of only one factor which covers more than 51.844% of accumulated variance.

Based on the results in Table 11, and considering that teaching experience covers only one factor measured through 6 variables, rotated matrix for teaching experience has only one column:

Table 10: Results for accumulated variance of teaching experience.

Item   Eigenvalue

       total   Var. (%)    Acc.     total   Var. (%)    Acc.
                          percent                      percent

1      3.111    51.844    51.844    3.111    51.844    51.844
2      0.901    15.020    66.864
3      0.797    13.287    80.150
4      0.559    9.324     89.474
5      0.350    5.833     95.308
6      0.282    4.692       100

Results extraction method: Principal Component Analysis

Table 11: Rotated matrix of teaching experience.

Variables                        Component

                                     1

Clear Goals and Standards(CG)      0.738
Generic Skills(GS)                 0.789
Emphasis on Independence(EI)       0.781
Good Teaching(GT)                  0.864
Appropriate Workload(AW)           0.417
Appropriate Assessment(AA)         0.643


Confirmatory Factor Analysis:

Until now, only EFA analysis was performed on the data; but we need to perform Confirmatory Factor Analysis (CFA) in order to determine whether the EFA yielded correct and significant results. Researchers perform CFA in order to confirm the validity of a certain factor structure. Some explicit hypotheses are stated regarding the number of factors and factor structure's fit value is tested against covariance structure of observed variables. CFA analysis can be done with Lisrel software package. This software uses correlation and covariance between measured variables; and is able to estimate or deduce the values of factor weights, variances and errors for hidden variables as well [10]. Some indices of CFA analysis are described below.

* GFI index:

GFI index evaluates the relative value of variance and covariance commonly throughout the model. GFI value varies between zero and one. The values higher than 0.9 indicate that the model is acceptable [24].

* AGFI index:

AGFI or Adapted GFI is the Goodness of Fit Index controlled for degree of freedom. Its value also varies between 0 and 1. The GFI and AGFI which were introduced by Joreskog and Sorbom are not dependent on sample size [24].

* RMSEA index:

This index is in fact the root of average squares of estimated value. The value of RMSEA index more close to 1 is more favorable [24].

* Chi-square ([chi square]):

The Chi-square test examines the hypothesis that says proposed model is in consistency with co-scattering pattern between observed variables. The quantity of [chi square] is highly dependent to sample size, therefore a very large sample size would push [chi square] value further larger than we could ever associate it with model being wrong [24].

* NFI and CFI indices:

NFI index which is called Bentler-Buntham index, indicates that the model is fitted acceptably if yields the value of higher than 0.9. A value of higher than 0.9 for the CFI index indicates acceptable model fitness as well. The latter test also performs a comparison between the model being tested and a so called independent model which has no relations between its variables; nevertheless, it tests for the extent of improvement as well. CFI index is similar to NFI in sense of meaning, but assigns penalties according to sample size [24].

Some conventional indices exist with CFA, using Lisrel software, which render the model as significant and the proposed path as adequate when their values are at acceptable levels. CFA is usually used as a tool for testing the scale in narrative perspective. As mentioned, the evaluation model has to be tested for adaptability first. It is proposed by literature that: a model needs to meet the following conditions in order to be good fitted and adequate:

1. Chi-square level divided by degree of freedom ([chi square]/df) should yield a value less than 3,

2. Adoptability index (GFI) value should be higher than 0.8,

3. NFI and NNFI indices values should be higher than 0.9, and

4. RMSEA index value should be less than 0.1.

Confirmatory Analysis On Educational Experience Variable

* Model at non-standard estimation mode:

Based on what was said about goodness of fit indices for the models, and considering the data in Table 12, CFA output regarding the study approach indicates that the model is adequately fitted.

* Model at Standard Coefficients Mode:

Comparison between observed variables and hidden variable could only be made at standard mode. The model at standard mode shows that how much of variance associated with hidden variable is explained by any observed variable. Lisrel software calculates a t-value (estimated) for each free parameter of the model. This test indicates that which parameters can be omitted from the model without increasing [chi square] value. In an ideal case, those values are less than 2 which can then be considered as insignificant [24].

The Main Model of the Study:

It is necessary to ensure the validity of model based on observations before attempting to test hypotheses. Therefore, CFA analysis along with path analysis was performed on factors. This analysis was performed by structural equations model using LISREL statistical software. For each observed model, it is necessary to make sure about adequacy and goodness of fit.

[chi square] statistics and other goodness of fit measures need to be examined for this task. A suitable model needs to meet the following optimal criteria. The ratio of [chi square] to degree of freedom should be less than 3, lower value is even better since this test result shows the difference between data and the model. The values of less than 0.8 and near 0.5 for RMSEA index (values near zero are preferred) indicate better fitness for the model. Should those measures not indicate for good fit, the model needs to be improved based on the output data. The corrected model can be used afterwards for examining the hypotheses and questions of the study.

Examining measurement models through t-statistics, standard coefficient and error value on different factors:

Table 13: Examining coefficients and t-value.

Item   Std. Coeff.   t-value   Determination   error
                                  coeff.

SM        0.68        7.20         0.43        0.047
RI        0.62        6.71         0.38        0.051
UOE       0.76        8.89         0.58        0.044
III       0.30        3.01         0.91        0.054
OS        0.65        7.12         0.42        0.053
TM        0.63        6.97         0.40        0.056
ATAD      0.39        3.91         0.15        0.50
A         0.80        9.63         0.64        0.045
ME        0.66        7.36         0.44        0.42
LOP       -0.38       -4.04        0.15        0.062
UM        -0.26       -2.74        0.076       0.061
SB        -0.45       -4.92        0.21        0.063
FOF       -0.37       -3.77        0.14        0.066

Table 14: Examining coefficients and t-value for index
of teaching experience.

Item   Std. Coeff.   t-value   Determination   error
                                  coeff.

CG        0.64        6.98         0.41        0.043
GS        0.86        10.36        0.73        0.50
EI        0.73        8.62         0.54        0.040
G_T       0.72        8.10         0.51        0.50
AW        0.31        3.23         0.096       0.051
AA        0.40        4.15         0.16        0.059


The t-value figure is greater than 1.96 for all the variables. Also values of determination coefficients were adequate for all of them. Therefore, no item was omitted from the model. Thus, we continued our work using all items (variables) and proceeded to analyze the model further.

Estimation results based on the data in figures 3, 4, and 5 (lower part of the figure) suggest that the model is adequate. Considering LISREL output, the ratio of [chi square] to df is 1.40 which is less than 3 and an appropriate value. This index value being low indicates that little difference is there between conceptual model used by the study and the observed data. Furthermore, RMSEA = 0.061 can be seen in output results which is less than 0.8; this figure along with [chi square] value suggest that the model is fit even better.

Therefore, the model is fitted in a desired way based on data in Table 15 and above criteria. As observed, dividing Chi-square value by df yields a value less than 3, while RMSEA value equals 0.061 which is less than 0.08 and values for all indices (CFI, IFI, NNFI, NFI, AGFI, and GFI) are close to 0.90 in this case. Therefore, the model is appropriately fit and is approved by the tests. Now we examined the effect of each independent variable on dependent variables using t-value and standard coefficient according to the model.

Testing Study Hypotheses:

Following model examination and approval, the hypotheses of the study model were evaluated. This section is devoted to testing hypotheses pertaining to each main question of the study. Hypothesis (1): Study approach taken by graduate students of accounting at Islamic Azad University of Yazd is directly related to their academic performance.

H0: Study approach is not significantly related to academic performance.

H1: Study approach is significantly related to academic performance.

According to Table 16, H0 is approved if absolute t-value is less than the value in the table (1.96); while H1 is deemed true if absolute t-value is greater than 1.96 which is given by the table.

Since t-value equals to 2.17 which is greater than 1.96 given by the table, we conclude that hypothesis 1 is approved hereby, suggesting that students' approach towards study is significantly related to their academic performance; while the strength of relation is 0.34 which implies a positive (direct) relationship.

Hypothesis (2): Teaching experience of graduate students of accounting at Islamic Azad University of Yazd is directly related to their academic performance.

H0: Teaching experience is not significantly related to academic performance.

H1: Teaching experience is significantly related to academic performance.

According to Table 17, since t-value equals to -0.74 which is smaller than 1.96 given by the table, we conclude that H0 is approved hereby, suggesting that students' teaching experience is not significantly related to their academic performance.

Discussion, Conclusions and Suggestions:

Study approach is an important factor of educational activities. Understanding the approach students take towards study and learning can lead to changes in education methods and make them consistent with students' learning styles; and consequently encourage improvements in education. Educators in the field of accounting demand for new suitable educational programs and adapted to needs of the society since the number of graduated people in accounting has increased in recent years, which has given employers more freedom of choice. Recently, implementations of learning paradigms in accounting education programs have gained attention; this has allowed accounting educators to gain better understanding of how students learn. This study mainly was meant to investigate the relation between study approaches and educational course teaching experience, and academic performance amongst students. The statistical population studied hereby consisted of graduate students enrolled in Islamic Azad University of Yazd. For this task, literature and past study records were reviewed followed by examining the relations of study approach and teaching experience with academic performance. Then, statistical methods of Exploratory Factor Analysis and Confirmatory Factor Analysis were simultaneously used in order to investigate validity of those relations and approve them. Accounting knowledge and skill structures amongst students were investigated using Exploratory Factor Analysis and weighted factors as well as KMO and Bartlett's tests. The deduced factor structure was examined using Confirmatory Factor Analysis in order to ensure its veracity and accuracy. The index values obtained from CFA analysis suggested that most factor structures are appropriate and describe the study variables adequately. The results of hypotheses tests show that study approach taken by students relate to their academic performance significantly and positively; while having educational course teaching experience is not related to their academic performance. Considering those study results, a few suggestions could be proposed in order to understand the subject better in future research:

* Examining those variables amongst students of other academic fields and acquiring effective views on students' academic performance in all universities;

* Examining the intervening effect of psychological variables on the relation between study approach and teaching experience, and academic performance;

* Using more variables rather than average grade points to measure the performance of graduate students;

Considering the effect of demographic variables such as age, parents' income and education level, whether the student is studying in his/her hometown and employment status, on the relations studied hereby could potentially benefit future research as well [1]. A Study of Correlation of Serum Leptin with Trace Elements in Water Buffalo (Bubalus bubalis). Australian Journal of Basic and Applied Sciences, 31: 231-234.

ARTICLE INFO

Article history:

Received 12 October 2014

Received in revised form 26 December 2014

Accepted 1 January 2015

Available online 20 February 2015

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(1) Ali Sarafraz Ardekani, (2) Ali Fazel Yazdi, (3) Fatemeh Dehghani Firozabadi

(1) Department of Accounting, Payame Noor University, PO BOX 19395-3697 Tehran, I.R of Iran

(2) Young Researchers and Elite Club, Yazd Branch, Islamic Azad University, Yazd, Iran

(3) Department of Accounting, Yazd Branch, Islamic Azad University, Yazd, Iran

Corresponding Author: Ali Fazel Yazdi, Young Researchers and Elite Club, Yazd Branch, Islamic Azad University, Yazd, Iran, Safaieeh, Shoahadegomnam Road, Zip code: 89195/155, Yazd, Iran.

Tel: (++98)351-8211391; Fax: (++98)351-8214810

E-mail: Fazel350@iauyazd.ac.ir.

Table 5: KMO and Bartlett's test results.

KMO sampling                            0/836
adequacy index

Bartlett's        Approx. Chi-Square   544.883
Test of
Sphericity                df             78

                         Sig.           .000

Table 6: Accumulated variance results for study approaches.

             Eigenvalues

components   total   Var. percent   Acc. Percent

1            4.854      37.338         37.338
2            2.093      16.103         53.441
3            1.104      8.493          61.935
4            0.837      6.435          68.369
5            0.777      5.975          74.344
6            0.610      4.689          79.033
7            0.565      4.346          83.379
8            0.470      3.613          86.993
9            0.320      3.234          90.326
10           0.376      2.894          93.121
11           0.362      2.781          95.902
12           0.191      2.239          98.141
13           0.242      1.185           100

components   total   Var. percent   Acc. Percent

1            3.109      23.914         23.914
2            2.754      21.183         45.096
3            2.189      16.838         61.935
4
5
6
7
8
9
10
11
12
13

The method of results derivation: Principal Component Analysis

Table 7: Rotated matrix of study approach.

Variable                                           Component

                                            1        2        3

Seeking Meaning(SM)                       0.336    -0.223   0.530
Relating Ideas(RI)                        0.362    -0.104   0.727
Use of Evidence(UOE)                      0.395    -0.234   0.615
Interest In Ideas(III)                    0.010    0.160    0.764
Organised studying(OS)                    0.744    -0.170   0.217
Time Management(TM)                       0.625    -0.115   0.346
Alertness to Assessment Demands (ATAD)    0.804    0.097    -0.157
Achieving(A)                              0.587    -0.360   0.318
Monitoring Effectiveness(ME)              0.680    -0.127   0.208
Lack of purpose(LOP)                      -0.065   0.790    -0.053
Unrelated memorizing(UM)                  -0.091   0.779    0.093
Syllabus-bounded(SB)                      -0.109   0.739    -0.301
Fear of failure(FOF)                      -0.151   0.799    0.038

Table 8: Naming the factors of study approach.

components                     factor

strategic approach            Factor 1
Surface Apathetic approach    Factor 2
Deep approach                 Factor 3

Table 9: KMO and Bartlett's tests.

KMO Sampling                            0.795
Adequacy index

Bartlett Test of   Approx.Chi-Square   218.122
Sphericity
                          df             15

                         Sig.           .000

Table 12: Model goodness of fit statistics for CFA at
Non-Standard Estimation Mode.

Non-Standard        [chi square]/df   NFI    NNFI   GFI    RMSE
Estimation of
"study approach"         1.43         0.91   0.96   0.89   0.064

Table 15: Examining statistical fit.

Index                       reported value

Chi-square                      238.89
degree of freedom (df)           171
Chi-square divided by df         1.40
RMSEA                           0.061
GFI                              0.82
AGFI                             0.76
NFI                              0.87
NNFI                             0.94
IFI                              0.95
CFI                              0.95

Table 16: t-value for hypothesis 1.

t-value   value from table   conclusion   relation strength

2.17            1.96         Effective          0.34

Table 17: t-value for hypothesis 2.

t-value   value from table    conclusion     relation strength

-0.74           1.96         Not effective         -0.11
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Author:Ardekani, Ali Sarafraz; Yazdi, Ali Fazel; Firozabadi, Fatemeh Dehghani
Publication:Advances in Environmental Biology
Article Type:Report
Geographic Code:7IRAN
Date:Feb 1, 2015
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