Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade

Introduction According to a recent survey conducted by Campus Computing (campuscomputing.net) and WCET (wcet.info), almost 88% of the surveyed institutions reported having used an LMS (Learning Management System) as a medium for course delivery for both on-campus and online offerings. In addition to various student information management systems (SISs), LMSs are providing the educational community with a goldmine of unexploited data about students' learning characteristics, behaviours, and patterns. The turning of such raw data into useful information and knowledge will enable institutes of higher education (HEIs) to rethink and improve students' learning experiences by using the data to streamline their teaching and learning processes, to extract and analyse students' learning and navigation patterns and behaviours, to analyse threaded discussion and interaction logs, and to provide feedback to students and to faculty about the unfolding of their students' learning experiences (Hung & Crooks, 2009; Garcia, Romero, Ventura, & de Castro, 2011). To this end, data mining has emerged as a powerful analytical and exploratory tool supported by faster multi-core 64 CPUs with larger memories, and by powerful database reporting tools. Originating in corporate business practices, data mining is multidisciplinary by nature and springs from several different disciplines including computer science, artificial intelligence, statistics, and biometrics. Using various approaches (such as classification, clustering, association rules, and visualization), data mining has been gaining momentum in higher education, which is now using a variety of applications, most notably in enrolment, learning patterns, personalization, and threaded discussion analysis. By discovering hidden relationships, patterns, and interdependencies, and by correlating raw/unstructured institutional data, data mining is beginning to facilitate the decision-making process in higher educational institutions. This interest in data mining is timely and critical, particularly as universities are diversifying their delivery modes to include more online and mobile learning environments. EDM has the potential to help HEIs understand the dynamics and patterns of a variety of learning environments and to provide insightful data for rethinking and improving students' learning experiences. This paper is focused on understanding live video streaming (LVS) students' learning behaviours, their interactions, and their learning outcomes. More specifically, this study explores how the interaction of students with each other and with their instructors predicts their learning outcomes (as measured by their final grades). By investigating these interrelated dimensions, this study aims to enrich the existing body of literature, while augmenting the understanding of effective learning strategies across a variety of new delivery modes. This paper is divided into four sections. It begins by reviewing the literature dealing with the use of data mining in administrative and academic environments, followed by a short discussion of the way in which data mining is used to understand various dimensions of learning. The second section explains the purpose and the research questions explored in this paper. The third section describes the background of the study and details its methodological approach (sampling, data collection, and analysis). The paper concludes by highlighting key findings, by discussing the study's limitations, and by proposing several recommendations for distance education administrators and practitioners. Data mining applications in administrative and academic environments At the intersection of several disciplines including computer science, statistics, psychometrics (Garcia et al., 2011), data mining has thrived in business practices as a knowledge discovery tool intended to transform raw data into highlevel knowledge for decision support (Hen & Lee, 2008). …

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