Defining and Testing the Ideal University Model Using Doomy Qualitative Data and Procedures.

Introduction

The two most important university goals are the search for new knowledge(academic research) and teaching(academic instruction). All universities have specific ways on how to pursue new ideas and on how best to teach, but all of them use a system approach to maximize their goals.

University systems are based on the notion that the free interaction of faculty members and student provides the means for an unbiased environment in which new knowledge will grow and teaching excellence will prevail. The main components of these university systems are then faculty members and students, and therefore, the maximisation of the goals of the university system depend squarely in the professional abilities of both faculty members and students. To ensure high quality faculty members, universities have very strict hiring policies. To ensure high quality students, universities have very strict graduate admission policies. Hence, university screening procedures are geared to provide the best academic and non-academic environment to faculty members and students. Hence, the ideal university system must have the best faculty and the best student possible to achieve an ideal structure based on trust and unbiasedness of all sorts.

Monitoring university research and teaching conditions

Since universities are operated as ideal systems, they should be evaluated in a systematic manner as the quality of one elements of the system affects the operability of the other elements of the system. For example, bad quality faculty members affect not just the working environment of the other faculty members, but the research and learning process of students.

On the other hand, bad quality students affect the reputation of other students as well as affecting teaching conditions. However, in practice research seeking the understanding of university conditions is done in a piece wise manner or non-systematic manner. Moreover, most academic research and monitoring is focused on student conditions and abilities, which leads to skewed university planning: planning based on the information gained about one element of the system in isolation.

As shown in this paper, non-systematic university planning is incomplete and therefore, it may be incompatible with ideal university planning.

Goals of the paper

This paper has three goals. One goal is to present a qualitative comparative framework that can be used to define ideal university conditions in a holistic and systematic manner. The second goals is to show how the present or absence of ideal characteristics within ideal models can be tested using a qualitative framework and doomy qualitative data. And the third goal is to point out the potential usefulness of this simple qualitative research and testing method to address ideal university planning needs.

Methodology

First, non-optimal qualitative model structures are used to derive the ideal university model, the ideal advisor model, and the ideal student model. Then, by simple substitution procedures, the ideal university model is stated in terms of the ideal compatibilities expected between academic advisors and students. Following, possible ways to test the ideal university model are presented using doomy qualitative information. And finally, some conclusions related to the potential utility of this qualitative testing procedures to gain a better understanding of advisor/ student university conditions in a simple and holistic fashion.

Qualitative Terminology used

Table 1 in the appendix at the end contains all the terminology used and their meaning within the qualitative comparative framework presented in this paper.

Diversity of university systems

There are different types of universities(u*) depending on whether or not a particular university lacks of ideal academic advisors(p*) or ideal students(e*) or both, as represented below:

1) u* = p* + e*

According to the model above, the worse university(u₁*) would be the one that lacks both, ideal academic advisors(p*) and ideal students(e*) at the same time:

2) u₁* = p*e*

The ideal university

The ideal university(U*), on the other hand, would be the opposite extreme of the worse university, meaning that the ideal university must be made up totally of both ideal academic advisors(P*) and ideal students(E*):

3. U* = P*E*

Diversity of academic advisors

Academic advisors(p*) can be classified in different groups depending on the number of ideal characteristics that they are lacking, as stated below:

4) p* = x₁* + x₂* + x₃* +....+ x_n*

From the model above, it can be deducted that the worse academic advisor(p*) is the one that lacks all the ideal characteristics(x₁*...x_n*) at the same time.

5) p₁*= x₁*x₂* x₃*x_n*

The ideal faculty advisor

The ideal academic advisor(P*), on the contrary, must have all the ideal characteristics at the same time:

6) P* = X₁*X₂*X₃*...X_n*

Diversity of university students

Students can also be grouped into different classes depending on how many of the ideal characteristics are absent in his qualifications, as follows:

7. e* = y₁* + y₂* + y₃*+....+ y_n*

The worse student(e₁*) enrolled would be the one that lacks all the ideal characteristics at the same time:

8) e₁*= y₁*y₂*y₃*...y_n*

The ideal university student

Therefore, the ideal student(E*) is the one that possesses all the ideal characteristics at the same time:

9) E* = Y₁*Y₂*Y₃*...Y_n*

Redefining the ideal university

The ideal university model in formula 3 can be stated in terms of the ideal advisor model(P*) and the ideal student model(E*) as follows:

10) U* = P*E*

Substituting formula 6 and formula 9 in formula 10 we have:

11. U* = X₁*X₂*X₃*...X_n*.Y₁*Y₂*Y₃*...Y_n*

Rearranging terms, we have:

12) U* = X₁*Y₁*X₂*Y₂*. X₃*Y₃*...X_n*Y_n*

Hence, the ideal university model(U*) in formula 12 is expressed in terms of the ideal compatibilities between characteristics of academic advisor and students(X_iY_i).

Qualitative testing using doomy data

Formula 3 above indicates that the ideal university model(U*) can be tested at least by three different ways:

i) we can test to see whether or no the ideal faculty advisor model exist, so the model to test is:

13) U* = P* = X₁*X₂*X₃*...X_n*;

ii) we can test to see whether or not the ideal student model exist, then the model to test is:

14) U* = E* = Y₁*Y₂*Y₃*...Y_n*;

iii) we can test to see whether or not the ideal compatibilities advisor-student exist, then the test is:

15) U* = P*E* = X₁*Y₁*.X₂*Y₂*.X₃*Y₃*...X_n*Y_n*

Simplifying assumptions

For the purpose of presentation, three key ideal characteristics are used for the testing exercise: cooperation(1); academic similarity(2); and commitment(3) to complete the research, which are related to both faculty advisors and students. Faculty advisor here means the student's thesis supervisor.

Testing the ideal faculty advisor model

The model to be tested is the one that assumes that the ideal supervisor(P*) is the one that possesses three characteristics at the same time: willingness to fully cooperate(X₁*) with the student with non-academic matters; similar academic interest(X₂*) as the student; and strong commitment(X₃*) to complete the work:

16) P* = X₁*X₂*X₃*

This information can be gathered by asking graduate students a set of three specific questions about their perceptions/ experience with respect to their supervisor's personality. These questions are:

i) indicator of cooperation(X₁*): Is your supervisor showing "most of the time" a willingness to help you with non-academic issues? Yes/No;

ii) indicator of academic similarity(X₂*): Is your supervisor an expert in your area of research? Yes/No;

iii) indicator of commitment(X₃*): Do you believe that your supervisor will strive to get your research through in the even that the research approved by him/her is challenged by others? Yes/No.

Table 2 contains qualitative information that assumes that the three questions above were posted to four students. The first three entries in the four student's rows(E₁,E₂,E₃,E₄) show the perception of each students to each question related to the personality of their academic advisors. If the answer is Yes, it shows a number 1, which means that the characteristic is present. If the answer is No, it shows a number 0, which means that the characteristic is absent.

The first three entries in the total row(T) of Table 2 show the total number of students per characteristic(X_i) that indicated that this specific characteristic is present(Yes) in those supervisors. For example, the first entry at the interception of T and X₁ indicates that 3 students responded that the characteristic cooperation(X₁) was present in their supervisors. The first three entries in the row subsystem sustainability index (RSSI) in Table 2 provides the ratio representing the total number of students who indicated that their supervisor had a specific characteristic present over the total number of students interviewed. The row subsystem sustainability index(RSSI) provides information about degree of academic and non-academic sustainability within specific characteristics: the more ideal characteristics, the more sustainability. For example, the entry at the interception of RSSI and X₃ indicates that only one out of four students believes that the supervisor is committed(X₃) to his/her academic work, which may be an indication of the problems with the "trust environment". Therefore, the row subsystem sustainability index for supervisor commitment(RSSI₃) in this case can be represented as follows:

17) RSSI₃ = x₃₁x₃₂X₃₃x₃₄ = 1/4 = 0.25

The first three columns in Table 2 provide specific information about supervisor cooperation(X₁), supervisor academic similarity(X₂), and supervisor commitment(X₃) as perceived by their students. The first four entries in the column subsystem sustainability index(CSSI) provides the ratio of the number of characteristics present per supervisor over the total number of characteristics in the study. Hence, the column subsystem sustainability index(CSSI) in this four entries provides information about the degree of sustainability across characteristics per supervisor per student. For example, the interception of row E₁ and column CSSI shows that the supervisor of student E₁ has only two out of three characteristics present. This means that student E₁ does not think that his/her supervisor has the commitment to complete the work when challenged, and his column subsystem sustainability index(CSSI₁) can be represented as follows:

18) CSSI₁ = X₁X₂x₃ = 2/3 = 0.67

At the interception of Column CSSI and row T in Table 2 we have the column sustainability index(CSI₁), which is the sustainability index for the supervisor subsystem, which in this case it is equal to 0.5. This means that supervisors in the sample have over all only 50% of the ideal characteristics according to all student's perceptions across characteristics. This column sustainability index(CSI₁) is found by getting the average of the column subsystem sustainability index(CSSI) of each advisor as follows:

19) CSI₁ = (2/3 + 1/3 + 3/3 + 0/3 ) / 4 = 6/3/4 = 0.5

At the interception of column CSSI and row RSSI in Table 2 we have the over all row sustainability index(RSI₁) for supervisors within characteristics, which again it is 0.5. This again means that supervisors in the sample have over all only 50% of the ideal characteristics needed according to all student perceptions when seen within characteristics. This row sustainability index(RSI₁) is found by getting the average of the row subsystem sustainability index(RSSI) per characteristics as follows:

20) RSI₁ = ( 3/4 + 2/4 + 1/4 ) / 3 = 6/4/3 = 0.5

Notice that the column sustainability index(CSI₁) and the row sustainability index(RSI₁) in Table 2 have equal value of 0.5 for the supervisor subsystem. Notice also in Table 2 that the value of these two sustainability indices varies from 0 to 1. A sustainability index of zero means that the student considers that the supervisor does not have any of the desirable characteristics, which increases the chances of student failure. A sustainability index of 1 means that the supervisor possesses all three characteristics, which increases the graduation chances of his/her student. Hence, sustainability indices derived this way provides an insight of the academic and non-academic environment in which the student is expected to thrive at the university, and indicate specific areas of student concerns. For example, student E₄ in Table 2 believes that his/her supervisor does not have any of the ideal characteristics to support him/her while student E₃ stated the opposite. Hence, it should be expected that the academic and non-academic environment in which student E₃ operates is better than that of student E4, and therefore, student E₄ is more likely to fail.

Even though information gathered about student perceptions is important for planning, acting on this alone may be contra-productive as student perceptions are only part of the sustainability puzzle. According to the ideal university model stated in formula 3 above, addressing student's concerns is a necessary, but not sufficient condition for ideal university sustainability to exist. Hence, we need to know the supervisor side of the story.

Testing the ideal student model

The model to be tested is the one that assumes that the ideal student(E*) is the one that possesses three characteristics at the same time: willingness to fully cooperate(Y₁*) with his supervisor with non-academic matters; similar academic interest(Y₂*) as the supervisor; and strong commitment(Y₃*) to complete the work planned:

21. E* = Y₁*Y₂*Y₃*

This information can be gathered by asking thesis supervisors a set of three specific questions about their perceptions/ experience with respect to their student's personality. These questions are:

i) indicator of cooperation(Y₁*): Is student "Ei" showing "most of the time" a willingness to help you with non-academic issues? Yes/No;

ii) indicator of academic similarity(Y₂*): Do you think that student "Ei" has a strong potential in your line of academic expertise? Yes/No;

iii) indicator of commitment(Y₃*): Do you believe that student "Ei" has an acceptable level of commitment to complete his or her thesis? Yes/No.

Table 3 contains qualitative information assumed to come from posting the above three questions to the four student supervisors. The first three entries in the four supervisor rows(P₁,P₂,P₃,P₄) show the perception of each supervisor to each question related to the personality of their students. If the answer is Yes, it shows a number 1, which means that the characteristic is present. If the answer is No, it shows a number 0, which means that the characteristic is absent.

The first three entries in the total row(T) in Table 3 show the total number of supervisor per characteristic(Y_i) that indicated that this specific characteristic is present(Yes) in those students. For example, the last entry at the interception of T and Y₃ indicates that 2 supervisors responded that the characteristic commitment(Y₃) was present in their students: half of the sample of supervisors believe that their students are not committed to work hard to complete the academic work. The first three entries in the row subsystem sustainability index (RSSI) in Table 3 provides the ratio representing the total number of supervisor who indicated that their student had a specific characteristic present over the total number of supervisors interviewed. The row subsystem sustainability index(RSSI) provides information about degree of academic and non-academic sustainability within specific characteristics: the more ideal characteristics, the more sustainability. For example, the entry at the interception of RSSI and Y₃ indicates that only two out of four supervisors believe that the student is committed(Y₃) to his/her academic work, which may be too an indication of problems with the "trust environment". Therefore, the row subsystem sustainability index for student commitment(RSSI₃) in this case can be represented as follows:

22) RSSI₃ = y₃₁y₃₂Y₃₃Y₃₄ = 2/4 = 0.5

The first three columns in Table 3 provide specific information about student cooperation(Y₁), student academic similarity(Y₂), and student commitment(Y₃) as perceived by their supervisors. The first four entries in the column subsystem sustainability index(CSSI) provides the ratio of the number of characteristics present per student over the total number of characteristics in the study. Hence, the column subsystem sustainability index(CSSI) in these four entries provides information about the degree of sustainability across characteristics per student per supervisor. For example, the interception of row P₂ and column CSSI shows that the student has none of the ideal characteristics present. This means that this supervisor P₂ does not think that his/her student has the academic and non-academic qualities to complete his/her program, and his column subsystem sustainability index(CSSI₂) can be represented as follows:

23) CSSI₂ = y₁y₂y₃ = 0/3 = 0

At the interception of Column CSSI and row T in Table 3 we have the column sustainability index(CSI₂), which is the sustainability index for the student subsystem, which in this case it is equal to 0.67. This means that students in the sample have over all only 67% of the ideal characteristics needed according to all supervisor's perceptions across characteristics.

This column sustainability index(CSI₂) is found by getting the average of the column subsystem sustainability index(CSSI) of each student as follows:

24) CSI₂ = (2/3 + 0/3 + 3/3 + 3/3 ) / 4 = 8/3/4 = 0.67

At the interception of column CSSI and row RSSI in Table 3 we have the over all row sustainability index(RSI₂) for students within characteristics, which again it is 0.67. This again means that students in the sample have over all only 67% of the ideal characteristics needed according to all supervisor's perceptions when seen within characteristics. This row sustainability index(RSI₂) is found by getting the average of the row subsystem sustainability index(RSSI) per characteristics as follows:

25) RSI₂ = ( 3/4 + 3/4 + 2/4 ) / 3 = 8/4/3 = 0.67

Notice that the column sustainability index(CSI₂) and the row sustainability index(RSI₂) in Table 3 have equal value of 0.67 for the student subsystem. Notice also in Table 3 that the value of these two sustainability indices varies from 0 to 1 too. A sustainability index of zero means that the supervisor considers that the student does not have any of the desirable characteristics needed to ensure proper teaching and high quality research. A sustainability index of 1 means that the student possesses all three characteristics, which increases the chances that the supervisor will effectively fully perform his academic and non-academic duties to the student. Hence, sustainability indices derived this way provide an insight about the academic and non-academic environment in which the supervisors are expected to fulfil their duties, and uncover specific areas of supervisor's concerns. For example, in Table 3 both supervisors P₃ and P₄ indicated that their student have all the desirable characteristics present, and therefore, it is reasonable to expect that these two supervisors will be able to support their students to completion effectively.

Again, notice that even though information gathered about supervisor's perceptions is important for planning, acting on this alone too may be contra-productive as supervisor's perceptions are only part of the sustainability problem. According to the ideal university model stated in formula 3 above, addressing supervisor's concerns is a necessary, but not sufficient condition for ideal university sustainability to exist. Hence, we also need to know the student's part of the story.

Testing the compatibility of the ideal faculty-student model

In this case, the model to be tested is the one that assumes that there are ideal cooperative compatibilities between advisor and student(X₁*Y₁*); there are ideal academic compatibilities between advisor and student( X₂*Y₂*); and that there are ideal commitment compatibilities between adviser and student(X₃*Y₃*).

26) U* = X₁*Y₁*.X₂*Y₂*.X₃*Y₃*

This information can be gathered in two ways, one direct and the other indirect. The direct way would be to test each interaction directly by cross pollination of questions: asking both supervisors and students separately whether or not they think that characteristics such as cooperation, academic similarities, and commitment are present in their personalities. The indirect way is to use the information generated when testing the ideal supervisor model in formula 16 and the information generated when testing the ideal student model in formula 21, and produce from them compatibility information about cooperation, academic similarities, and commitment. Here, the indirect method is used for presentation purposes.

Table 4 contains qualitative information generated when intercepting the perceptions of students in formula 16 with he perceptions of supervisors in formula 21 following the qualitative procedures that 1.1 = 1 since Yes.Yes = Yes and that 0.0 = 0 since No.No = No.

The first three entries in the four supervisor-student rows(P₁E₁,P₂E₂,P₃E₃,P₄E₄) in Table 4 show the perception compatibilities with respect to cooperation, academic similarity, and commitment. Number 1 indicate that the perceptions expressed by supervisors and students with respect to the same characteristic are compatible. Number 0, on the other hand, shows incompatibilities. The first three entries in the total row(T) in Table 4 show the total number of supervisor-student compatibilities present per characteristic(X_iY_i). For example, the last entry at the interception of T and X₃Y₃ indicates that there is only one instance of commitment compatibility while there are 3 instances of supervisor-student commitment incompatibilities: this could be an indication of sustainability problems resulting from mutual distrust, as both supervisor and students perceive each other as lacking strong commitment to complete.

The first three entries in the row subsystem compatibility index(RSCI) in Table 4 provide the ratio representing the total number of supervisor-student compatibilities present over the total number of supervisors-students interactions. The row subsystem compatibility index(RSCI) provides information about degree of academic and non-academic compatibility within specific supervisor-student combinations: the more subsystem compatibilities, the more over all compatibility, and the more sustainability. For example, the entry at the interception of RSCI and X₃Y₃ indicates that only one out of four supervisor-student commitment compatibilities possible is present, which may signal, as indicated below, mutual distrust, a precondition for a bad academic and non-academic environment. Therefore, the row subsystem compatibility index for supervisor-student commitment(RSSI₃) in this case can be represented as follows:

27) RSCI₃ = x₃₁y₃₁.x₃₂y₃₂.X₃₃Y₃₃.x₃₄y₃₄ = 1/4 = 0.25

The first three columns in Table 4 provide specific information about supervisor-student cooperation compatibilities (X₁Y₁), supervisor-student academic similarity compatibilities (X₂Y₂), and supervisor-student commitment compatibilities

(X₃Y₃) as perceived by both of them. The first four entries in the column subsystem compatibility index(CSCI) provide the ratio of the number of supervisor-student compatibilities present over the total number of compatibilities possible in the sample. Hence, the column subsystem compatibility index(CSCI) in this four entries provides information about the degree of compatibilities across characteristics. For example, the interception of row P₂E₂ and column CSCI shows that there are not compatibilities between supervisor P₂ and student E₂, which would amount to a bad teaching and learning environment and to maximum risk for student failure. Notice that this is supported by the perceptions of student E₂ in Table 2 who believes that the supervisor does not have academic similarity(x₂) and commitment (x₃), only cooperation(X₁). In table 3 it can be seen that supervisor P₂ believes that this student does not have any of the desired characteristics of cooperation(y₁), academic similarity(y₂), and commitment(y₃).

The column subsystem compatibility index(CSCI₂) for he interaction P₂E₂ can be represented as follows:

28) CSCI₂ = x₁y₁.x₂y₂.x₃y₃ = 0/3 = 0

At the interception of Column CSCI and row T in Table 4 we have the column compatibility index(CCI), which is the compatibility index for the over all supervisor-student system. In other words, the column compatibility index(CCI) provides the degree of compatibility of the over all university system, which in this case is 0.42. This means that supervisor-students compatibilities in the sample have over all only 42% of the total possible types of compatibilities according to all perceptions across characteristics provided by supervisors and students.

This column compatibility index(CCI) is found by getting the average of the column subsystem compatibility index(CSCI) of each supervisor-student combination as follows:

29) CCI = (2/3 + 0/3 + 3/3 + 0/3 ) / 4 = 5/3/4 = 0.42

At the interception of column CSCI and row RSCI in Table 4 we have the over all row compatibility index(RCI) for supervisor-students interactions, which again it is 0.42. This again means that supervisor-student interactions in the sample have over all only 42% of the ideal interactions needed according to all supervisor-student's perceptions when seen within characteristic interactions. This row compatibility index(RCI) is found by getting the average of the row subsystem compatibility index(RSCI) per interaction as follows:

30) RCI = ( 2/4 + 2/4 + 1/4 ) / 3 = 5/4/3 = 0.42

Notice that the column compatibility index(CCI) and the row compatibility index(RCI) in Table 4 have equal value of 0.42.

Notice also in Table 4 that the value of these two compatibility indices(CI) varies from 0 to 1 too. A compatibility index of zero means that the supervisor perceptions are not compatible with student’s perceptions about the same personal qualities, which increases the changes of creating academic and non-academic unsustainability. A compatibility index of 1, on the other hand, means that both supervisors and students posses the desired characteristics producing strong compatibilities, which increases the chances for system success.

Hence, compatibility indices derived this way provides an insight of the academic and non-academic environment in which the supervisors and students are expected to fulfil their duties, and indicate specific areas of relevance to supervisor-student interactions. For example, in Table 4 shows two interactions that may be bound to be unsustainable, that of P₂E₂ and P₄E₄ as they have incompatible perceptions about their personalities which are most likely to lead to system failure. The interaction P₃E₃ in Table 4 shows an opposite situation, both supervisor and student have compatible personalities, and their interaction would most likely lead to academic success.

Notice than when compatibility information is gathered, planning focused are more likely to be efficient as we have information about the two stories around the sustainability of university systems, which can be enriched more in details by looking at subsystem specific information available. According to the ideal university model stated in formula 3 above, addressing compatibility concerns is the necessary, and sufficient condition for ideal university sustainability to exist.

Ideal university system sustainability

There are several ways to determine the over all sustainability index(SI) of the university. One is using the actual perception information in Table 2 and Table 3, and apply a simple formula:

Characteristics present in Table 2 plus in Table 3

SI = ----------------------------------------------------------

All characteristics in Table 2 plus in Table 3

SI = (6 + 8) / ( 12 + 12 ) = 14/24

SI = 0.58

Another way of determining the university sustainability index is by finding the average of the column sustainability index(CSI₁) for supervisors in Table 2 and the column sustainability index(CSI₂) for students in Table 3 as follows:

SI = ( CSI₁ in Table 2 + CSI₂ in Table 3 ) / 2

SI = (0.5 + 0.67) / 2 = 0.58

Ordering indices in ascending order

To have an over all understanding of sustainability issues and compatibility issues related to the qualitative data used in this exercise, a list of indices is presented below in ascending order:

* University compatibility index(CCI) = 0.42

* Supervisor sustainability index(CSI₁) = 0.5

* University sustainability index(SI) = 0.58

* Student sustainability index(CSI₂) = 0.67

Three aspects can be pointed out from the listing above. One aspect is that the value of the sustainability index of the university falls between the value of its two subsystems, supervisors and students. The second aspect is that the supervisor sustainability index is lower that the student sustainability index which indicates a more unstable supervisor environment. And the third aspect is that the compatibility index is in this case the lowest index, implying that a lot need to be done to improve the interaction supervisor-student to improve their academic and non-academic environment.

Conclusions

By establishing the ideal structure of university systems as the starting point of research to support planning, we can see clearly what are the necessary and sufficient conditions to maintain it sustainable. It can also be seen that non-systematic research violates the fundamental principles of the sustainability of the ideal university system as their findings while necessary, are not sufficient to maintain the sustainability of the system. There is a need to have access to the full sustainability picture to identify sources of failures and means to repair those failures. The determination of indices through systematic research and testing as shown provides a convenient way of finding out how far subsystems and systems are from ideal sustainability and compatibility conditions and also show a convenient way to identify specific strategies targeted to improve the situation and to monitoring and evaluation procedures and indices. The qualitative methodology shown is simple, applicable, systematic, and theoretically sound, and may turn out to become a standard method for analyzing system like structures.