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Rapidly changing technologies, complex business operations, huge investments on research and development for producing innovative and quality products, and the competitive pressures, organizations needs to make effective decision making to compete in the local as well as global market. A successful and aspiring manger needs to be well equipped with leadership skills and the latest quantitative techniques for effective decision making and has the ability to optimize the available resources strategically for organizational success. The quantitative techniques involve statistical analysis, developing mathematical models that help them for effectively utilization of raw material, transportation costs, effectively and efficiently utilization of human resources, and providing quality products to their customers. The quantitative techniques are essentially valuable in management decision making like planning, forecasting, control and evaluations. The strong understanding of quantitative subjects is, therefore, of fundamental importance to any management graduate. In this study the quantitative subjects include, business mathematics & statistics, quantitative techniques and research methods offered at bachelor of business administration (BBA) and master of Business administration (MBA). A large proportion of curriculum of these subjects is based on statistics and the remaining includes mathematics modeling and linear programming. Therefore, a good knowledge of statistics is a key to success in these subjects. Students must have to pass these quantitative subjects to fulfill the requirements of their degree program.
Due to the growing importance of quantitative subjects, statistics and research methods are included in almost all the curricula of management and these subjects will be useful later at their jobs (Maxwell, Wang, Kevin, 2000). JoseÂ´ Monde and Vargas-Vargas (2010) also accentuate the importance of statistics and pointed out in his studies that in most of the universities where social sciences degree programs are offered, the curriculum must contain at least one subject with statistical content. He further stated that this subject is helpful to the students for empirical findings and conclusions for their research articles/thesis, but most of the students in this discipline have strong aversion to these subjects. The students having poor aptitude and difficulties faced in learning mathematics also causes of poor learning and performance in the quantitative subjects, so mathematics is one factor that contributes in facing difficulties in quantitative subjects (Murtonen and Titterton, 2004).
In earlier studies it has been proved that the quantitative subjects are problematic and difficult to the students of social sciences in general as compare to other subjects in their disciplines (Garfield & Ahlgren, 1988; Filinson & Niklas, 1992; Pretorius & Norman, 1992; Rosenthal & Wilson, 1992; Lehtinen & Rui, 1995; Forte, 1995; Hauff & Fogarty, 1996; Wisker, Robinson, Trafford, Creighton, & Warnes, 2003). Murtonen and Lehtinen (2003) also identified that students of social sciences are facing problems in the quantitative subjects. Maxwell, Wang and Kevin (2000) explored that in USA, the students who have undergone 12 years of their schooling without taking statistics as a subject find it difficult to understand at university level and causes of anxiety towards these subjects.
Anxiety level towards quantitative subjects is high among those students came from non-mathematics oriented disciplines (Schacht & Stewart, 1991; Zeidner, 1991) and high level of anxiety among statistics and quantitative based subjects weaken student's performance in these subjects (Onwuegbuzie, 1997). In another study conducted by Onwuegbuzie and Wilson (2003) estimated that approximately 80% of the graduate students in social sciences experiences statistical anxiety while studying statistical course. The students facing difficulties in learning quantitative subjects may have to face problems in completing their degrees (Meyer, Shanahan & Laugksch, 2005; Kiley & Mullins, 2005) and may even reflected in their views in their professional life (Onwuegbuzie, 2000).
Student attitude and behavior also matters in taking interest in a subject, e.g. some students who are not 'good with numbers' assumed that these courses will be difficult to understand for them (Gal, 2000a). Therefore, student aptitude, attitude and behavior are one dimension that effect their performance and poor learning in a particular subject. Student's motivation and passion to learn a particular subject will be a one way that contributes in effective learning.
In second dimension of effective learning process, the major role performed by a teacher and he/she is one who make and create interest, enhance student motivation in the subject through his expertise, skill and knowledge. It is commonly, observed during the university/college studies that some courses are recognized by the teacher due their expertise, methodology and the way to deliver the knowledge to the students. In literature, a teacher is a person, who interact and engage with the student and his/her objective is to create change among them (Lew, 1976), and this change may be in the form of knowledge, skills and effectiveness (McNeil & Popham, 1973). The effective learning of any subject may also depend on how the knowledge is effectively delivered to the students. According to Gagne (1976), it is the teacher who creates a learning environment in the class, so that the learning process will be effectively and continuously enriched. Teacher and the method to deliver the knowledge to students assumes to be the important factor that contributes the quality of the educational program and the competency of his/her students (Dolmans, Wolfhagen, Schmidt & Vleuten ,1994). Albanese (2004) emphasizes that teacher is the main source during a learning process that can contribute in making value addition to enhance students knowledge, expertise and capability. Another important factor that contributes in learning process is the teacher behavior and off course the behavior varies from one situation to another and from one course to another course. According to WikiEducator, the most concerning factor in the learning process is teacher behavior which is divided in to four categories like, personality, methodology, expectation and competence.
The personality of a teacher is the most significant factor during the learning process which influences students during the learning environment (Lew, 1976) and its personality is said to be desirable teaching personality if he/she is successful to create and maintain a learning environment in the class room where students are motivated and feel comfortable to learn(Callahan, 1966). In another study by Khan and Weiss (1973) concluded in their studies that from theory of interpersonal perception it can be suggested that student's attitude towards the teacher will also contribute in his/her interest in the course taught and also towards Institute. Teaching methodology, expectation and competencies are the important ingredients during the learning process.
Some author considered professional competence and professional characteristics are the two distinct qualities that differentiate among successful professional teacher (Whitty, 1996). These qualities are further explained as: Professional characteristics include professional values, personal and professional development, communication and relationships as well as synthesis and application where as professional competences include knowledge and understanding of children and their learning, subject knowledge, curriculum, the education system and the teacher's role (cited in Faryal, 2010). Where as Medley and Shannon (1994) believes in three dimensions of a teacher quality: effectiveness, competence and performance. Effectiveness means to what extent a teacher accomplish desired effects upon student, teacher competence is stated in terms of his knowledge, expertise and skills and teacher performance is actually how he/she behaves during lecture delivery.
In Pakistan educational Institutes are making every possible effort to increase the quality of education and to meet the expectations of their students. For this purpose, a huge investment is made by Higher Education Commission of Pakistan (HEC) in the training and development of its faculty. Teachers are also putting their maximum efforts to enrich the curricula with practical examples, case studies and current research articles and making contribution in research and development. Effective methodologies to create interest in the lecture and computer based exercises are the common practices in most of the cases to maximize the student learning. Normala and Maimunah (2004) pointed out that in traditional teaching and learning environment students were usually taught material given in the specific course text book means it was a way of spoon feeding to students. Now the traditional role of a teacher has transformed to a facilitator that not only responsible just to deliver knowledge in the class room but also have the responsibility to identify the factor that contributes in effective learning of students. In another study by Duch, Groh and Allen (2001, p. 4) explained that traditional teaching and learning process wasâ€¦
"â€¦ content-driven, emphasizing abstract concepts over concrete examples and application rarely challenge students to perform at higher cognitive levels of understanding. This didactic instruction reinforces in students a naÃ¯ve view of learning in which the teacher is responsible for delivering content and the students are the passive receivers of knowledge."
In our education system, the students enrolled in bachelor of business administration (BBA) four years degree after 12 years of education and master in business administration (MBA) a two year degree after 14 years of education. These students are from different educational background like engineering, social sciences, pre-medical, commerce and arts. The students from other than engineering background do not study any mathematical or statistical subjects during their previous degrees mostly and thus majority of the students have a poor aptitude in these subjects. Higher education commission of Pakistan (HEC) is continuously engaged to maximize the quality of its Institutes of higher learning and their graduates so that these graduates can be easily placed and compete in the market.
The objective of this study is to identify the problem faced by management students while studying quantitative subjects especially, business mathematics, statistics, quantitative techniques and research methods during their undergraduate (BBA) and graduate (MBA) studies. The problem arises from the poor performance of students in these subjects and scoring low grades that affects their overall performance mean their CGPA. Major reason for conducting this study is to identify and highlights the key factors that contributes in poor learning. Secondly, due to poor learning and week concepts in these subjects, students also face difficulties in understanding the key concept in other subjects which involve quantitative techniques like project management, operation management, and supply chain management. Students also find it difficult to understand the research finding and statistical results in research articles and it also create problems while compiling their research analysis part during their final dissertations. In this study the key factors that contribute in poor learning due to student attitude in quantitative subjects, teacher's role in terms of its competency, behavior and methodology, and the management issues related to students that creates problem in learning process will be investigated. This study was conducted in the business schools of public and private universities located in a big city of Pakistan which is also known as the hub of educational Institutions.
Research questions and Hypothesis
Figure (1) depict the operational model that link the factors contribute student's poor learning and performance in quantitative subjects. The following four hypotheses will be tested using this model.
Figure 1 Operational Model
H1: There exist a relationship among teacher competency and poor learning
H2: There exist a relationship among administrative issues and poor learning
H3: There exist a relationship among student aptitude and poor learning
H4: There exist a relationship among student attitude and behavior and poor learning
H5: There exist a relationship among teacher professional competency and poor learning
This research was conducted in the business schools in public and private public universities located in Lahore, Pakistan. The target population in this study was the student with poor performance in these subjects. The participant of this study was the students registered in bachelor of business administration (BBA) and masters in business administration (MBA) studying in public and private institutes of Pakistan located in Lahore. In this study students with poor aptitude and lower grades in the quantitative subjects were selected. Due to limited resources the data was collected the business school located in city, Lahore of Pakistan. A simple random sampling technique was used to select the schools for responses. This study was based on the survey technique and the survey was conducted in 4 public and 6 private business schools at local level. The questionnaire was developed on the basis of interviews conducted from the students facing problems while studying these subjects and detailed interviews with the Instructors involved in teaching these subjects. The target population was the students scoring low grades and facing problems in studying quantitative subjects. On the basis of interviews, 26 major factors were identified and included in the questionnaire that contributes in poor learning and poor performance in these quantitative subjects. Out of these 26 factors, ten factors reflects the students issues regarding poor learning, 13 factors reflects teacher role which effects student performance in these subjects and 3 factors are concern with the administrative issues related to the students. A total 300 questionnaire were distributed among students and a complete 233 questionnaire were returned. So the response rate was 78%. The instrument used was a five-point Likert Scale from strongly disagrees to the strongly agrees. The observation was recorded on five Likert scale. The coding of the Likert scale was made as [1 = strongly disagree], [2 = disagree], [3 = neither agree nor disagree], [4 = agree], [5 = strongly agree].
Table.1 and 2 shows the demographic Statistics of sample comprised of gender and the student at undergraduate and graduate level in the studies. There were total 233 participants in this study. Out of which 125 participants were male representing 53.6 % participation and 108 were female representing 46.4% participation of the total population. Table 2 indicates the participation of MBA and BBA students in the studies. There are 106 participants from BBA with 45.5 % participation and 127 participants from MBA representing 54.5% participation.
The variables used in this study are represented in table 3.
Table 3 Variables used in the study
Teacher knowledge and expertise in the subject
First time teaching the subject
Course covered through make up classes
Important topics skipped
Class participation is discouraged
Office hours not allocated
Teaching through visual aid
Punctuality during semester
Registered students are more than class capacity
Administrative issues like registration in the course
Course outline, timetable
Poor attitude in quantitative subjects
Difficulties to understand derivation and word problem
In last degree Medium of education was not English
Family guidance/support in studies
Student attitude and behavior
Revision of lectures
Attending class without study material like books
Quizzes and assignments are not properly submitted
Punctuality /short attendance
Teacher professional characteristics
Teacher student relationship
Encourage and facilitate students to resolve study issues
Reliability of the data
To test the supposed hypothesis of the proposed framework the methodology, we use is 'structural equation modeling' using AMOS 16.0. Structural equation modeling is one of the effective tools for statistical analysis and specially to test the models that are path analytic with the mediating variables and include the latent constructs and further these constructs are being measured with other items included in the study (Luna-Arocas & Camps, 2008). We used Chi-square, normed-chi-square tests, the goodness of fitness (GFI) (Bentrler, 1990) should not go lower than 0.70 in case of complex models (Judge & Hulin, 1993), adjusted goodness of fit index (AGFI), the comparative fit index (CFI) and its value close to 1.00 indicate a very good fit, root mean square residual (RMR) and root mean squared error of approximation (RMSEA) (Bowne and Cudeck, 1993) and for RMSEA a value less than 0.08 represents a good approximation.
The estimated path diagram for the proposed student poor performance in quantitative subjects is generated through AMOS 16.0 in this estimated path diagram the rectangles represent exogenous or endogenous observed variables and the circles are representing the related latent variables. The light arrows indicate the observed variables that constitute the related latent variables and the bold arrows indicate the structural relationship between the corresponding variables. The numbers assigned to each arrow represents the standardized estimated coefficients. All the coefficients used in the study are positive indicating that the effect of all the variables on defined latent factor is positive. The direct effect of manifest variable like TMY on latent factor PP is easily evaluated to be (0.56 * 0.89 = 0.50). Similarly we can also calculate the direct effect of other manifest variables. The computed Chi-square of this model is 446.354 with 294 degrees of freedom. The normed Chi-square of model is 1.518 which is reasonably below the critical cut point of 3.000. This indicates that the proposed model portrays the situation fairly adequately. The GFI of model is 0.877 and CFI is 0.780. Both the values are close to subjective yardstick for these two measures. This again indicates that the proposed model fits the scenario in a good fitting manner. The RMR for fitted model is 0.085 with RMSEA of 0.045. Both of these measures are below the critical point of 0.1 indicating low predictive error by fitted model. According to the above discussion we can say that overall proposed structural model is a fair representation of poor learning performance among Overall we can say that the proposed structural model is a fair representation of poor learning performance among management students.
Similarly the Chi-square, normed Chi-square, GFI, CFI, RMR and RMSEA of each of the latent variables are represented in table 4.
Table 4 Estimated values of the Latent Variables
According to the above detail of the individual latent variables, it is clear that all the values are within the specified cut off range, so the individual construct in the proposed structural models is a fair representation. The path diagram of this study is shown in figure 2.
We must note here that before we conclude on the results presented in Figure 2 we tried all possible paths for linking controls with teacher competency, administrative issues, student aptitude, student attitude and behavior, and teacher professional characteristics. All the results are almost up to the significant level. As shown in the figure 2:
With respect to teacher competency, the teaching methodology has a direct positive effect on teacher competency (0.56*0.89 =0.50). The factors like communication (0.59*0.89=0.531), knowledge and expertise (0.56*0.89=0.50), punctuality (0.25*0.89=0.22) has direct positive effect on teacher competency. Similarly, we can calculate the other factors as all are positive so all the factors have direct impact on teacher competency. The above result indicates that there is strong effect of teacher's competency on poor performance of management students in quantitative subjects. The standardized regression weight of teacher's competency and poor learning is 0.89. Therefore, these results support the hypothesis H1 that there is a relationship between teacher competency and the poor learning.
With respect to administrative issues effect student's performance during their studies. Number of registered students more than the class capacity (0.47*0.65=0.31), registration issues (0.38*0.65=0.25) and schedule of classes (0.47*0.65=0.31),and class environment(0.45*0.65=0.29) positive effects on the administrative issues. The above results indicate that there is a strong effect of administrative issues on poor learning performance of management students. The standardized regression weight of administrative issues and poor learning is 0.65. Therefore, we can conclude that these results support our second hypothesis H2 that administrative issues effects students learning.
With respect to student aptitude about the quantitative subjects during their studies. The students enrolled in management discipline with non-mathematical background (0.64*0.31=0.20), poor aptitude in quantitative subjects (0.56*0.31=0.17), difficulties to understand the long derivations or to understand the word problems (0.46*0.31=0.14), medium of instruction in the last degree was not English (0.33*0.31=0.10), family guidance and support to help them in these subjects at home (0.19*0.31=0.06). In the light of above results we can conclude that these results support our hypothesis H3 that there is a relationship among student aptitude and poor learning in quantitative subjects.
With respect to student attitude and behavior during the learning process of quantitative subjects. Attending the classes without revising the previous lecture (0.12*0.73=0.88), attending classes without study material mean books, calculators etc. (0.29*0.73=0.21), participation in quizzes and assignments (0.56*0.73=0.41), student punctuality in the course (0.37*0.73=0.27). These results indicate a positive relationship of student attitude and behavior with student's poor learning in the quantitative subjects and therefore, it proves our hypothesis H4.
Lastly, with respect to the teacher's professional characteristics and how it causes of poor learning. Teacher personality (0.77*0.15=0.16), student teacher relationship (0.25*0.15=0.038) and to student encouragement and facilitation (0.29*0.15=0.044). The above results indicate that there is a positive relationship between teacher's professional characteristics and poor learning. It proves our hypothesis H5.
Discussion and conclusion
The results represented in figure 2 supports the proposed hypothesis. With respect to teacher competency, analysis shows that it is observed that teaching methodology, expertise in the subject, communication skills and punctuality plays an important role in the effective learning process. Therefore, to increase interest and enhance student learning in these subjects the above factors plays a significant role. Secondly, administrative issues also effect the students effective learning. As in this study the matters related to students registration in the particular course/with a particular teacher, students enrolled more than the class capacity mean sometime students love to enroll with the best teacher in that particular subject and due to the reason the class is overcrowded, time table issues like in this study particularly for those students who are repeating the course there may occur clashes mean at the same time he/she have to attend two different course, were some of the factors that causes of poor learning in these subjects. The results in the figure 2, indicates a positive relationship with the poor learning.
Another main factor which plays a significant role in student's performance in the quantitative subjects is their aptitude in these subjects. In our study most of the students enrolled in the management discipline (BBA & MBA) are without mathematical background mean these students have not studied mathematics or statistics in their previous degrees as discussed above. There is a general perception among students that mathematical subjects are difficult to learn as it involves formula's and derivations which are difficult to digest and remember, due to this reason they developed a poor aptitude in the quantitative subjects. In Pakistan most of the population is living in rural areas, where educational opportunities are very rare as compare to the urban areas. This also causes of poor aptitude in learning process. Secondly, in most of the cases the parents are uneducated and due to this reason these students find no support from their families to get help in the studies. The analysis shows that there is a positive relationship among student aptitude and poor learning.
Student's attitude and behavior is significant factor in learning of quantitative subjects. The students attending the classes without the study material like books, calculator etc. is unable to understand the concepts: especially word problems and the calculation. Another reason is that students attended the classes without revising the previous lectures and it also matters to understand the next lecture. These students also try to avoid to participate in the quizzes and to submit the assignments at the time mentioned. There is another reason that these students do not pay attention to attend the classes regularly. The analysis in figure 2 proves that students attitude and behavior contributes in poor learning of quantitative subjects.
Lastly, the teacher professional characteristics are another factor in our study that contributes in poor learning. In learning process teacher act as a role model and a good personality is one attribute. A strong student- teacher relationship helps to create a healthy working environment in the class. It creates motivation among students to increase interest in the subject and students love to actively participate in discussions which help to enhance their learning abilities. Figure 2 shows there is a positive relationship among teacher professional characteristics and learning.
From the above discussion it is concluded that teacher competency in a particular subject, administrative issues related to student, student's aptitude, student attitude and behavior and teacher's professional characteristics are the key factors that contributes in student's poor learning. To overcome these issues it may be possible to increase student interest in these subjects and better performance. There is another option that universities should offer some preliminary courses to overcome this problem.
Limitations and future research
Despite some contribution to the literature and most common factors that contributes in students poor performance in quantitative subjects. This study has some limitations that should be addressed in the future studies conducted on this topic. The first limitation of this study is that the data collected for this study is limited to only 4 public universities and 6 private universities that are located in one city (Lahore) of Pakistan. Secondly, there is a need to identify more factors to conduct a comprehensive study on this topic and it needs more inputs from students and teachers. As we have 132 public and private universities in the country. This study is conducted on personal expenses and to approach all the universities it requires funding. We hope this study serves as the basis for an effort to sharpen understanding of some of those factors that contributes in student's poor learning and performance in quantitative subjects. Another limitation of this study is that it is conducted in only few universities located in one city of Pakistan so, the results of this study cannot be generalized.