MKT 6009 Marketing Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S MKT 6244 Digital Marketing Strategy (2 semester credit hours) Executive Education Course. The course explores three distinct areas within marketing and sales namely, digital marketing, traditional sales prospecting, and executive sales organization and strategy. The continuing convergence of the digital marketing and sales funnels has created a strategic continuum from digital lead generation to digital sales. The course identifies the current composition of this digital continuum while providing opportunities to evaluate sales and marketing digital strategies. Prerequisites: MKT 6301 and instructor consent required. (2-0) Y MKT 6301 (SYSM 6318) Marketing Management (3 semester credit hours) Overview of marketing management methods, principles and concepts including product, pricing, promotion and distribution decisions as well as segmentation, targeting and positioning. (3-0) S MKT 6309 Marketing Data Analysis and Research (3 semester credit hours) Methods employed in market research and data analysis to understand consumer behavior, customer journeys, and markets so as to enable better decision-making. Topics include understanding different sources of data, survey design, experiments, and sampling plans. The course will cover the techniques used for market sizing estimation and forecasting. In addition, the course will cover the foundational concepts and techniques used in data visualization and \"story-telling\" for clients and management. Corequisites: MKT 6301 and OPRE 6301. (3-0) Y MKT 6310 Consumer Behavior (3 semester credit hours) An exposition of the theoretical perspectives of consumer behavior along with practical marketing implication. Study of psychological, sociological and behavioral findings and frameworks with reference to consumer decision-making. Topics will include the consumer decision-making model, individual determinants of consumer behavior and environmental influences on consumer behavior and their impact on marketing. Prerequisite: MKT 6301. (3-0) Y MKT 6321 Interactive and Digital Marketing (3 semester credit hours) Introduction to the theory and practice of interactive and digital marketing. Topics covered include: online-market research, consumer behavior, conversion metrics, and segmentation considerations; ecommerce, search and display advertising, audiences, search engine marketing, email, mobile, video, social networks, and the Internet of Things. (3-0) T MKT 6322 Internet Business Models (3 semester credit hours) Topics to be covered are: consumer behavior on the Internet, advertising on the Internet, competitive strategies, market research using the Internet, brand management, managing distribution and supply chains, pricing strategies, electronic payment systems, and developing virtual organizations. Further, students learn auction theory, web content design, and clickstream analysis. Prerequisite: MKT 6301. (3-0) Y MKT 6323 Database Marketing (3 semester credit hours) Techniques to analyze, interpret, and utilize marketing databases of customers to identify a firm's best customers, understanding their needs, and targeting communications and promotions to retain such customers. Topics

Marketing for management.

Purpose




The purpose of this study is to explain critical factors affecting student satisfaction levels in selected state universities in Sri Lanka.




Design/methodology/approach




The study has applied an quantitative survey design guided by six hypotheses. A conceptual framework has been developed to address the research questions on the basis of a literature review. The study is based on an undergraduate sample from four state universities, and it presents results of factor analytics and correlational and regression analyses.




Findings




Evidence to support construct validity and reliability of all survey-based scales measuring the key variables has been found. The quality of the academic staff, university facilities, degree program, administrative staff, university location and university image have been correlated significantly with student satisfaction levels measured at 0.45, 0.47, 0.51, 0.31, 0.39 and 0.66, respectively. The statistically significant predictors are: the quality of university facilities, the quality of the degree program and the university image, with the image being the strongest predictor.




Practical implications




The study offers a conceptual framework to guide future research and validated scales for measuring student satisfaction levels in a national higher education system in a developing region that is aspiring toward a knowledge-based economy where tertiary education is free. Five recommendations are provided for policymakers.




Originality/value




Research shows high variabilities in the models used and the findings of studies on factors affecting student satisfaction levels in universities. The study is among the first large-scale studies of student satisfaction levels in the Sri Lankan state university system, where little data exist on why students are dissatisfied and fail to complete their degrees.

Critical factors affecting students’ satisfaction with higher education in Sri Lanka

Envisioning learning sans interaction is absurd. Interaction plays a pivotal role in the efficacy and effectiveness of the present-day blended learning systems. Learner-content interaction contributes predominately towards the successful realization of the expected learning outcomes. In order to satisfy the learners and to impart them quality knowledge and education, e-learning content comprising of excellent learning and website content is of paramount importance. In the present COVID-19 outbreak challenging times the significance of e-learning system development and its application is much more pronounced. To gauge this, the study aims to examine the relationship between learner-content and the e-learning quality to determine the impact of e-learning quality on learners’ satisfaction under the moderating effect of perceived harm due to COVID-19. A structured questionnaire was used to gather data from 435 graduate and undergraduate management students (International and national) in Indian Universities. Findings indicate statistically significant relationships between the e-learning content and e-learning quality and; e-learning quality and the students’ satisfaction. The perceived harm has an insignificant moderating effect on students’ satisfaction. The results of the study further depict that the quality of e-learning has a significant positive relationship with the students’ satisfaction, and this relationship is not affected by the threat of being infected on the campus during the pandemic of COVID-19. To achieve the learners’ satisfaction, the institutions should strive for rendering the e-learning content of supreme quality. The mediating role of e-learning quality between content and students’ satisfaction is also established to be a significant one.

/pdf/learner-content-interaction-in-e-learning-the-moderating-37ks5z3shw.pdf

Learner-content interaction in e-learning- the moderating role of perceived harm of COVID-19 in assessing the satisfaction of learners

We predict university dropout using random forests based on conditional inference trees and on a broad German data set covering a wide range of aspects of student life and study courses. We model the dropout decision as a binary classification (graduate or dropout) and focus on very early prediction of student dropout by stepwise modeling students’ transition from school (pre-study) over the study-decision phase (decision phase) to the first semesters at university (early study phase). We evaluate how predictive performance changes over the three models, and observe a substantially increased performance when including variables from the first study experiences, resulting in an AUC (area under the curve) of 0.86. Important predictors are the final grade at secondary school, and also determinants associated with student satisfaction and their subjective academic self-concept and self-assessment. A direct outcome of this research is the provision of information to universities wishing to implement early warning systems and more personalized counseling services to support students at risk of dropping out during an early stage of study.

Early Prediction of University Dropouts – A Random Forest Approach

The overarching objective of this study was to assess learning satisfaction among students and to determine whether online-learning self-efficacy was associated with online learning satisfaction during the emergency transition to remote learning. This cross-sectional study involved a survey distributed to 22 Saudi Arabian universities. The survey used in this study consisted of an online learning self-efficacy (OLSE) questionnaire and an electronic learning (e-learning) satisfaction questionnaire. A total of 1,226 respondents voluntarily participated in and completed the survey. Students in medical fields made up 289 (23.6%). A Kruskal–Wallis H test and a chi-square test were used to compare the student’s satisfaction based on the educational variables. Spearman’s correlation and multiple linear regression analyses were performed to assess the association between self-efficacy and satisfaction. The findings revealed degrees of satisfaction ranging between high satisfaction and dissatisfaction. The majority of students (51%) expressed high satisfaction, and 599 students (49%) reported experiencing a low level of satisfaction with e-learning. A comparison of groups with low and high satisfaction scores revealed a significant difference in the OLSE. High satisfaction was positively correlated with the OLSE domains: time management, technology, and learning. The OLSE regression analysis model significantly predicted satisfaction. It showed that the model, corrected for education level and grade point average of the students, significantly predicted e-learning satisfaction (F = 8.04, R2 = 0.59, p = .004). The study concluded that students’ satisfaction with the e-learning experience is influenced by e-learning self-efficacy. The study’s findings lead to the practical implications and identify the need to improve the remote learning, time management and technology self-efficacy to enhance students’ satisfaction.

/pdf/exploring-the-relationship-between-students-learning-1c3iy7ozt2.pdf

Exploring the relationship between students' learning satisfaction and self-efficacy during the emergency transition to remote learning amid the coronavirus pandemic: A cross-sectional study.

Students’ satisfaction can be defined as a short-term attitude resulting from an evaluation of students’ educational experience, services and facilities Earlier it was measured by common satisfaction frameworks but later higher education specify satisfaction models were developed The objective of this review is to render all available constructive literature about students’ satisfaction with a sound theoretical and empirical background Data were collected from refereed journals and conference papers, and are constructively analyzed from different point of views to filter a sound background for future studies The first section of the paper discuss students’ satisfaction, satisfaction models and frameworks used by previous researchers around the world and second section explain the empirical findings of previous studies in real world context

/pdf/students-satisfaction-in-higher-education-literature-review-7ysehb5i4q.pdf

Students’ Satisfaction in Higher Education Literature Review

Analyzing the software development process and estimating the effort required for its completion is an essential task. In the case of Agile methodology, the values of the parameters used for estimation vary frequently as the scope of the project changes with changes in the requirements of the clients. Hence, the estimation done at the initial phase will not be appropriate until the completion of the project. Therefore, to overcome this issue, a methodology is proposed to estimate the duration of a project by applying machine learning techniques. The use-case point method is used for estimating the duration. Information about the number of use cases and values for environmental and technical factors is stored in a repository. Few values may be uncertain, and to estimate the effort for a new project with few unknown or uncertain values, the machine learning algorithm Gaussian Process Regression (GPR) is used. The repository information is taken as the training dataset, and the new project data is taken as the test dataset. The estimated value shows the accurate duration for the new project. The result is validated with a popular dataset. 

R. Lalitha

Papers

Students’ Satisfaction in Higher Education Literature Review

A methodology to analyse and estimate software development process using machine learning techniques