What Satisfies Students? Mining Student-Opinion Data with Regression and Decision Tree Analysis

By E. Thomas, N. Galambos. 2002

311 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Education, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/449b06ca0cb38921a9c0bc4ff4ff5c9c5b6b5ffa

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In Computer Vision and Pattern Recognition

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In Organizational Behavior and Human Resource Management

Citations in the corpus

Dimensions Driving Business Student Satisfaction in Higher Education. (2015, background): “In view of that, their satisfaction should be important to the educational institutions (Thomas and Galambos, 2004).”
Determinants of PhD student satisfaction: the roles of supervisor, department, and peer qualities (2019, background): “…Bradley 1986; Palacio, Meneses, and Perez 2002; Schlesinger, Cervera, and PerezCabanero 2017), or they incorporate manifestly cognitive items (e.g. Thomas and Galambos 2004; Brown and Mazzarol 2009), or they have somewhat ambiguous or difficult wording (e.g. Ali et al. 2016; James and Casidy 2018).”
Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory (2015, background): “Previous studies that have documented student performance prediction models have focused on statistical modeling and data mining techniques (Gunnarsson & Alterman, 2012; Thomas & Galambos, 2004; Wolff, Zdrahal, Nikolov, & Pantucek, 2013).”
Factors influencing student satisfaction in universities in the Gulf region: does gender of students matter? (2013, methodology): “…but also to other aspects that fall within the Journal of Marketing for Higher Education 137 domain of student life such as administrative services, staff, physical characteristics of academic facilities, social environment, and advising support (DeShields et al., 2005; Thomas & Galambos, 2004).”
Educational Data Mining: A Review of the State of the Art (2010, methodology): “decision trees analysis) [258]; for predicting exam results in distance education courses (using linear regression) [188]; for predicting when a student will get a question correct and asso-”
The Contribution of Student Satisfaction to Persistence (2013, methodology): “…to employ strategic planning initiatives to address specific areas that correlate highly with overall satisfaction (Bryant, 2006; Elliott & Shin, 2002) or that differentiate between students on the most important aspects of satisfaction (Einarson & Matier, 2005; Thomas & Galambos, 2004).”
Examining student satisfaction with higher education services: using a new measurement tool (2010, background): “As students are increasingly seen as consumers of higher education services, their satisfaction should be important to institutions that want to recruit new students (Thomas and Galambos, 2004).”
Measuring business student satisfaction: a review and summary of the major predictors (2010, background): “However, it is also influenced by experiences with other aspects of university life such as administrative practices and staff, physical characteristics of academic facilities, social environment and advising support (see, for example, DeShields et al, 2005 ; LeBlanc, & Nguyen, 1997 ; Sohail, & Sh”