Academic journal article Kuram ve Uygulamada Egitim Bilimleri

Learning Analytics of Student Participation and Achievement in Online Distance Education: A Structural Equation Modeling *

Academic journal article Kuram ve Uygulamada Egitim Bilimleri

Learning Analytics of Student Participation and Achievement in Online Distance Education: A Structural Equation Modeling *

Article excerpt

Truly remarkable is the ever increasing growth and impact of information and communication technologies (ICT) on human life. Through e-commerce, egovernment, e-entertainment and the like, ICT innovations have dramatically changed the way the daily activities and important tasks are undertaken, and education has not been destitute of such changes. Scholars and research groups have been investigating emerging educational technologies and their possible impacts on teaching and learning. Recent influential reviews and projects such as New Media Consortium's Horizon Reports, A Roadmap for Education Technology funded by the National Science Foundation in the USA, and the Technology Outlook for UK Tertiary Education 2011-2016 Report describe emerging technologies that are to gain dominance and significance in education along with key trends and critical challenges (Ng'ambi, 2013; Spector, 2013). Continually reported in these reports as one of the educational technologies that are most likely to affect teaching and learning in the short and medium term (one to five years) is learning analytics. In fact, learning analytics has been lauded in New Media Consortium's latest report (Johnson et al., 2016) as one of the most important emerging trends thought to accelerate technology adoption in higher education in a one-year or less time horizon. Its usage has the potential to help educational institutions in student retention, easing the burden of accountability, providing personalized learning experiences, and increasing student success (DietzUhler & Hum, 2013). This study therefore focuses on the use of learning analytics components that can be obtained from web-based distance education environments and on investigating its potential for improvement in student learning.

Learning Analytics

Although the field of learning analytics is still in its early stages, two research communities, i.e., The Society for Learning Analytics Research (SOLAR) and International Educational Data Mining Society (IEDMS), have already established with their own academic conferences andjournals. These organizations collaboratively work to share common definitions, research, methods, and tools for data mining and analytics (Baker & Siemens, 2014). SOLAR defines learning analytics as the measurement, collection, analysis, and interpretation of data related to learners' behaviors and learning contexts in order to optimize instructional processes and environments (Siemens & Gasevic, 2012). It occupies the intersection of educational research and computational techniques to capture and analyze learners' data (Fırat & Yüzer, 2016; Knight, Buckingham Shum, & Littleton, 2014). The field connects to and builds on several disciplines, including but not limited to educational sciences, instructional design and technology, computer science, user modeling, advanced statistics, and information visualization (Demirbaş & Koç, 2015; Gasevic, Dawson, Mirriahi, & Long, 2015). Although the field of education has traditionally dealt with data and analysis, the integration of ICT into education makes it now possible to record, retrieve, and store aggregate and large sets of digital data (i.e., educational data mining) in an easy and cost-effective manner. The information retrieval technologies also allow for gathering incidental, unstructured, complex, and various data that is highly authentic in terms of user behavior. Learning analytics as an emerging research domain explores the systematic use of such data to ultimately refine pedagogical strategies, regulate institutional costs, identify and help struggling students, assess factors affecting student success and teacher efficacy, and improve the assessment of student performance (Larusson & White, 2014). Since valuable educational data have become available for analysis and interpretation, institutions have started implementing data warehouses for not only improving student learning but also organizational decision making and preparation for a future in which analytics will have become a strategic asset (Stiles, Jones, & Paradkar, 2011). …

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