Kember's student progress model

Abstract— The purpose of this paper is to have a quantitative analytical study and to examine the theoretical basis of the Kember’s student progress model that evaluates directly or indirectly effects of student’s opinions in online environment by recognizing the relationships between variables such as online student’s opinions, performance, cost-benefit analysis and student determination.

Many researchers like Thomson, Harlow, Houle and Porta-Merida confirmed that effectiveness and dependability of the theory, but their results are meagerly dissimilar in the degree of influence on student opinions and also indicate that it could be useful and meaningful to re-analyze various components in more present research. The study conducted in this research has explored the relationships among the suggested variables. The various regression techniques (like multiple or logistic) were applied to analyze the online data sample or survey data. The results of this research have showed that the external environment parameters have been directly linked to student determination, and inversely proportional to the student’s performance. Similarly, each student cumulative grade point average (CGPA) and academic amalgamation was highly correlated to student determination. The results of this research paper conveys the current phenomena and knowledge of online student academic determination. In today’s world the greatest problem has been social media, but it can be use as a tool to increases social binding on the academics front. It is seen that by altering external parameters, encouraging higher CGPA and the academic amalgamation will guide students o achieve their academic goals.

Keywords: Student Determination (SE); Retention; Opinions; Online Students (Og); Higher Education (HE); Higher Education Institutions (HEI’s); Cumulative Grade Point Average (CGPA); Social Binding; Academic Binding (AB); External Environment Parameters(EEP)

1. INTRODUCTION

Student determination is an important factor for both students and Higher Education Institutions (HEI’s) because it affects in achieving students’ education goals and also financially sustaining the HEI’s goals. The purpose of this paper is to test and examines the theoretical basis of Kember’s [1] student progress model which evaluated the direct or indirect effects of student determination on their successful completion of their graduation degree programs. Nichols [2] proved that student’s motivation is a crucial factor in evaluating their determination in completing their academic programs.

Spady [3] had given the empirical and theoretical model of student determination. Later on Tinto [4], in his research edited Spady’s model to explored the dropout rate behavior. Bean & Metzner [5] claimed in there conceptual model that the Tinto model is less appropriate where social binding with friends and professional educators is constraint by the time factor in a classroom and the students which are non-traditional (i.e. which are not much used to computers) are badly affected by the parameters’ governed by external environment, than by the social binding variables that affect wearing out of traditional students. Kember [1] had correlated his dropout model of student determination with the model of student progress as it relates to online student social binding (SB), academic amalgamation (AA), external environment parameters (EEP), and academic incompatibility (AI). Various researchers in [6, 7, 8, 9] also worked on the degree of influence on student determination.

We have done the qualitative study to analyze the bonding or relationships among student opinions in terms of student performance (SP), student progress factors, cost-benefits (CP), and student determination with the help of Kember’s model of student progress. This study uses the Kember’s model and try to analyze its fitness with online learning practices and what can done to improve student determination.

2. The Proposed Research Questions

There are some research questions which need to be answered in this paper and on that basis null hypotheses and their alternative hypotheses are generated.

1) Is there any linking exist between student opinions of the academic experience with the

a) social binding (SB),

b) academic binding (AB),

c) external environment parameters (EEP), and

d) academic incompatibility with student determination (within the learning environment at the institutional level)?

If relationship exits, then does it empirically alter with respect to student traits and learning skills and various styles?

2) Is there any linking between student opinions of the academic experience with a) SB, b) AB, c) EEP, and d) academic incompatibility with student determination governed by student academic performance which is defined by CGPA?

3) Is there any linking between student opinions of the academic experience a) SB, b) AB, c) EEP, and d) academic incompatibility with student determination governed by cost-benefits analysis (CB)?

3. Background Literature

3.1 Student’s Determination in Learning Environment

Kay [10] disclosed the fact that the development of distribution technology has spurred many HEI’s to offer higher education Similarly Tinto [4] had discussed various different factors related to student determination related to retention. Various other tools for the study of determination and persistence are the identification of predictors for institute ‘student achievement and retention. Many researchers like Farmer [11] studied about ‘Academic Retention Indictors’, he pointed out number of reasons given by students for non-persistence including effectiveness and quality of education. Barefoot [12] determined that the highest number of withdrawal is in the first year of graduation.

3.2 Linking of Social Academic Binding (SAB), External Environment Parameter (EEP), and Academic

Incompatibility to Student’s Determination

With the rise of in HEI’s which is directly proportional to the number of students whose performance is poor in their respective courses or attrition rate, therefore lead to wastage of student’s time and HEI’s financial loss. Tinto [13] classical model of student leaving gives a valid reason for attrition. Heyman [14] disclosed the fact that students complete their courses at higher rate if they feel connected with their institutions and students those are connected socially fell less isolated as per Senhouse [15]. Woosley [16] experimented and found that the SAB positively affect student persistence and acacdemic performance. Due to rapid increase in NET generation, there are many sources by which the social binding is possible. Kord [17] stressed on the pros and cons of online social networking on HEI’ student’s academic experiences and which will continue to help the students in more techincal way. Vuong [18] , Brown-Welty & Tracz [19], they all believed that integration of social media and faculty interaction with peers are two important factors for academic success. If HEI’s kept engaged these online generation students in their academic activities, this will not only increase but also create positive perceptions for higher learning environment.

3.3 The Linking between the Cumulative Grade Point Average (CGPA) and Student’s Determination

Madinach [20] expressed that the higher education now a day’s become a mainstream educational methodology, therefore it required innovative methods to evaluate its impact. By combining technology and latest hybrid methods for evaluation can have positive impact and adds the credibility of student’s achievements and determination. The CGPA on student determination is significantly related to continuous enrollment and student determination was given by Porta-Merida [9]. Woosley [16] indicated that high level of social binding is directly linked to lower CGPA than academic integration, therefore leading to low academic performance caused due to disturbance from social influence.

3.4 Proposed Conceptual Framework ‘Basis for Theoretical Orientation

The progress model of student as suggested by Kember [1] is the fundamental basis for this study in Figure 1. He identified four constructs of student progress i) Social Binding (SB) (it is defined as the extent to which the family and friends supports starting from the student’s registration till their persistence in the course i.e. moral support), ii) academic Binding (AB) (it includes all elements of contact between an HEI and the student’s), iii) external environment parameters (EEP) (it encompasses negative social binding, insufficient time etc. and others factors which hinder its plan of study) and iv) academic incompatibility (it includes not only marks but overall student performance in their respective course). Tinto’s model studied and discussed student’s drop-out only where as Bean-Metzer’s [22] theoretical model recommends several other direct or indirect variables that eventually effects student’s determination.

Figure 1: Kember’s [1] Student Progress Model ‘Basic

To prove research questions we alter the Kember’s framework in Figure 2 that depicts the pathway of the operational flow of the different variables in the conceptual model and shows how the measured variables affect the student determination. Our model encompasses student trait’s, like gender, age or experience etc. and also learning skills and styles..

4. Research Methodology

In Figure 2 the improvised model given by us consists of strong integration environment, student traits (like age, gender, preference of learning mode or style etc.), student performance (CGPA) , cost-benefit analysis (which is the method of choosing best option for the student by evaluating various factors like emotional, fiscal, and social benefits against the expected values) and student’s determination (we were taking into account the student opinions to study student determination in terms of enrolling in course, early drop ‘out or transfer cases etc.)

4.1 Research Design-Study Sampling and Population of HEI

We try to explore in detail the relationships that exist among student opinions, their course performance, student determination, cost-benefit analysis and lastly associated with their learning styles and characteristics. Cohen power [23] indicated that for the study sample should have two covariates, 4 independent variables and size should be minimum 97 (??=.05). The dataset [27] consist of 800 students who enrolled for the odd semester computer sciences courses like graduation program and post-graduation program students of HEI. The study is to explore the relationship among student’s geographical traits, student determinations, student’s course performance, cost-benefits and students determination in association with the student learning styles at the institutions to verify the Kember model in this environment.

Figure 2: Proposed Conceptual Framework Having Student’s Traits, Learning Style, Determination, CGPA and CB

The dataset [27] was collected and filtered. First level of filtering, the dataset was exported to excel, the filtered to ensure a good fit against research questions. Second level of filtering; provide us in detail the relationship to the mediator or exogenous variables of student traits: gender, age, class deliver mode student learning styles. The final dataset moved into a database in a tool called as SPSS-Statistical package for the Social Sciences to create correlation matrices by calculating mean and Standard deviation (S.D.) for each variable.

5. Data Analysis and Results Discussions

The various variables under this study were: i) Independent Variables: Social and Academic Binding (SAB), External Environment Parameter (EEP) and Academic Incompatibility, ii) Covariate Variable: Student’s traits and learning styles, iii) Mediator Variable: Student Performance (c) and Cost-benefit (CB) analysis and iv) Dependent variable: Student Determination.

By doing bivariate analysis (it is one of the simplest forms of quantitative (statistical) analysis and involves the analysis of two variables (often denoted as X, Y), for the purpose of calculating the empirical relationship among them), we concluded that SAB, EEP, academic incompatibility, cost-benefit analysis (CBA) , CGPA and other student’s traits like age were also highly correlated with student determination.

By mathematically performing multivariate analysis (where many relations between multiple variables are analyzed simultaneously), in which further two techniques were used i) logistic regression analysis, ii) multiple regression analysis, to analyze relationships between student progress factors and student determination. To find out the solution for the first research question, this study incorporates logistic regression analysis for dichotomous variables to verify relationships among student opinions, student traits, learning styles and student determination. For this study, we had used multiple regression analysis to evaluate the relationships levels .

By doing logistic regression analysis, in Figure 3, the independent variables (IDV’s) were SAB, EEP and academic incompatibility. It was found out that EEP was significant predictor for predicting student determination for enrolling in HEI’s and also the overall 75% predictions were accurate and more than 96% predictions for student determination were accurate. EEP and academic incompatibility were the predictors for student determination of intent to enroll.

EEP