Objective: The objective of this paper is to explore the antecedents and consequents of student experience in higher education settings. Several variables such as co-creation value, social environment, physical environment and relational benefits are predicted as antecedents and consequents of student experience.
Methodology: The authors proposed the conceptual framework to identify antecedents and consequents of student experience in higher education.
Findings: Theoretically there is a possibility to create and the use of co creation in the higher education context to enhance student experience. The other factors should also be considered, such as physical environment (ambient, design and IT), social environment (Employee displayed emotion, and customer climate), and relational benefits. The findings suggest the necessary changes in how higher education institutions should be marketed with more focus on creating, communicating, and delivering value to enhance student experience.
Value Added: The author’s perspective on antecedents and consequents of student experience is a new interesting theme in higher education marketing. The paper proposed a testable propositions regarding the antecedents and consequences of student experience.
In this paper, simulation study was conducted to investigate the effect of spatial heterogeneity of multiple porosity fields on oil recovery, residual oil and microemulsion saturation. The generated porosity fields were applied into UTCHEM for simulating surfactant-polymer flooding in heterogeneous two-layered porous media. From the analysis, surfactant-polymer flooding was more sensitive than water flooding to the spatial distribution of multiple porosity fields. Residual oil saturation in upper and lower layers after water and polymer flooding was about the same with the reservoir heterogeneity. On the other hand, residual oil saturation in the two layers after surfactant-polymer flooding became more unequal as surfactant concentration increased. Surfactant-polymer flooding had higher oil recovery than water and polymer flooding within the range studied. The variation of oil recovery due to the reservoir heterogeneity was under 9.2%.
In order to plan an optimum geothermal well drilling scheme, a proper identification of drilling parameters should be well known. Information of the parameters consists of weight on bit (WOB), true vertical depth (TVD), rate of penetration (ROP), foam flowrate (FF), and rotary speed (N). The valuable information can be provided by the drilled geothermal wells. Correlation of the drilling parameters is then obtained based on the information. The application of Artificial Neural (ANN) Network is needed since the relationships among the parameters are very complex and nonlinear. Moreover, the relationships are not easily known. In this paper, Artificial Neural Network was promoted to estimate penetration rate. Data were obtained from three wells at a field in South Sumatera, Indonesia. Three ANN models were generated. Each model includes different input parameters. Based on the comparison results, the ANN-3 model has the best level of accuracy with the average values of the parameters MAE, MARE, MSE, ARMSE, and the correlation coefficients are 0.8883, 9.54%, 1.1878, 1.0825, and 0.9938 respectively. ANN models can play a role in identifying parameters that affect the characteristics of penetration rate. Keywords—Drilling, WOB, Geothermal, ROP, ANN.
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