Traditionally, the majority of online instructors and institutional administrators rely on web-based course evaluation surveys to evaluate online courses (Hoffman, 2003). The data and information are then used to help inform online program effectiveness and generate information for program-level decision-making. While it enjoys wide use, the survey method only provides learners' self-report data, not their actual learning behaviors.
Several studies have found self-reported data were not consistent with actual learning behaviors (Hung & Crooks, 2009; Picciano, 2002). This inconsistency can potentially compound the already problematic lack of direct observation opportunities. Online program administrators need more effective tools to provide customized learning experiences, to track students' online learning activities for overseeing courses (Delavari, Phon-amnuaisuk, & Beikzadeh, 2008), to depict students' general learning characteristics (Wu & Leung, 2002), to identify struggling students (Ueno, 2006), to study trends across courses and/or years (Hung & Crooks, 2009), and to implement institutional strategies (Becker, Ghedini, & Terra, 2000). Each of these needs can be addressed by mining educational data. Nowadays, various educational data are stored in database systems. This is especially true for online programs, wherein student learning behaviors are recorded and stored in Leaning Management Systems (LMS). Program administrators can take advantage of emerging knowledge and skills by extracting and interpreting those data. The purpose of this study is to propose a program evaluation framework using Educational data mining.
Program evaluation is the means by which a program assures itself, its administration, accrediting organizations, and students that it is achieving the goals delineated in its mission statement (Nichols & Nichols, 2000). Evaluation can be done by a variety of means. The most common form of evaluation is through surveying students regarding courses/faculty/programs (e.g., Cheng, 2001; Hoffman, 2003; Spirduso & Reeve, 2011). However, making causal inferences based on a one-time assessment is risky (Astin & Lee, 2003). Nevertheless, perceptional survey data cannot accurately reflect real learning behaviors (Hung & Crooks, 2009; Picciano, 2002). Although various scholars (e.g., Grammatikopoulous, 2012; Vogt & Slish, 2011) have proposed systematic frameworks (e.g., interviews and observation) in order to obtain objective knowledge via multiple means, these methods are difficult to implement in a fully online program.
Educational data mining
Data mining (DM) is a series of data analysis techniques applied to extract hidden knowledge from server log data (Roiger & Geatz, 2003) by performing two major tasks: Pattern discovery and predictive modeling (Panov, Soldatova, & Dzeroski, 2009). Educational data mining (EDM) is a field which adopts data mining algorithms to solve educational issues (Romero & Ventura, 2010). Romero & Ventura (2010) reviewed 306 EDM articles from 1993 to 2009 and proposed desired EDM objectives based on the roles of users. For the purpose of this study, which is designed to inform administrators, the list is limited to objectives for administrators:
* Enhance the decision processes in higher learning institutions
* Streamline efficiency in the decision making process
* Achieve specific objectives
* Suggest certain courses that might be valuable for each class of learners
* Find the most course effective way of improving retention and grades
* Select the most qualified applicants for graduation
* Help to admit students who will do well in higher education settings
Based on the theory of bounded rationality, decision-making is a fully rational process of finding an optimal choice given the information available (Elster, 1983). …