An essential factor in determining the efficiency of the online education is the users' quality of interaction (QoI) with LMSs. In this chapter, the macro-meso-micro structure analysis is adopted, to examine the Fuzzy Inference System (FIS)-based approach of QoI, taking into account the LMS users' (professors' and students') interactions within a b-learning environment, in order to quantitatively estimate a normalized index of their QoI, accordingly. Additionally, for capturing the dynamics of the users interacting with the LMS, the data corresponding to a 51-week LMS Moodle usage time-period of two consequent academic years (2009/2010 and 2010/2011) at a HEI were analyzed. Finally, based on a systemic approach of the derived QoI, user-dependent/independent (group-like) (dis)similarities in LMS interaction trends, correlations, distributions and dependencies with the time-period of the LMS use are analyzed, towards an effort to contribute to a more objective interpretation of the way LMS Moodle-based b-learning functions within the HEIs.
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Education can be seen as a set of processes designed to transmit knowledge, skills and values to develop individual and collective abilities. At the same time, Learning Management Systems (LMSs) can provide educators an environment to place their online course materials and for students to receive that education, while interacting with other students/teachers; however, students’ interactions, attention and communications are seen as relatively low in the LMSs (Musbahtiti & Muhammad, 2013). Given the fact that the LMSs have been seen as an educational tool in the last few years, there is a number of benefits in using LMSs for assessing students’ performance (e.g., students’ answers can be monitored, assessments can be offered in an open-access environment, can be stored and reused, while immediate feedback along with different tasks can be given to each student). In particular, the Modular Object-Oriented Dynamic Learning Environment (Moodle) is one of the most commonly used free learning management system, enabling the creation of powerful, flexible and engaging online courses and experiences (Rice, 2006). In general, LMSs accumulate a vast amount of information which is very valuable for analyzing students’ behavior and could create a gold mine of educational data, as they can record any student’s activities involved (e.g., reading, writing, taking tests, performing various tasks) and even his/her communication with peers (Mostow et al., 2005). Additionally, they usually provide a database that stores all the system’s information, such as personal information about the users (profile) and academic results and users’ interaction data. However, due to the vast amount of data that these systems can generate daily, it is very difficult to manage manually. Although there are LMSs that offer some reporting tools, it becomes harder for an educator to extract useful information when abundant number of students uses the LMS (Dringus & Ellis, 2005).
In the context of online education, Zawacki-Richter (2009) proposed three different meta-levels of research, based on a systemic perspective (as already adopted in the fuzzy logic-based approaches of the Quality of Collaboration (QoC) in chapters 10-12). In particular: macro-level (i.e., online education systems and theories), meso-level (i.e., management, organization, and technology issues), and micro-level (i.e., teaching and learning in online education). However, thinking holistically, a macro-analysis can be useful to capture large-scale patterns (e.g., academic community interaction), a meso-analysis to explore small-group interaction (e.g., course interaction), and a micro-analysis to examine the interaction patterns of individuals (student/professor) (e.g., discipline interaction). From this perspective, the estimation of the QoI provides a scalable parameter that could reveal trends and attitudes of the LMS users at different resolution analyses.