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TopThe Role Of Learning Analytics In Education
The impact of the deadly coronavirus COVID-19 is likely to reverberate across global higher education long after the outbreak is eventually brought under control. The refocused health and wellbeing education should emphasizes new pathways with a view to improve pupils’ understanding on health and wellbeing. There is an urgent need to educate students when searching for information on social media sites, on whether for example vaccines are safe or have side effects, they are provided with flood of misinformation. However and as a common fact in education that emotion need to be considered before having a full cognition. While negative emotions tend to make us clearly remember data including the minutest detail, positive emotions tend to help us remember more complex things. This is where Learning Analytics can help. Learning analytics is an emergent field of research and practice that aspires to use data analysis to inform decisions made on every tier of the educational system (NMC Horizon Report, 2013). This field enables deciphering trends and patterns from educational big data, or huge sets of student-related data, to further the advancement of personalized, supportive higher education applications. Learning Analytics as a term has been significantly popularized by the EDUCAUSE International Conferences on Learning Analytics and Knowledge (LAK), where its first conference (LAK11) was in February 27-March 1, 2011 in Banff, Alberta (Cooper, 2012). LA is the third wave of developments in instructional technology that began with the advent of the learning management system (LMS) during 1991.
The second wave was on the LMS integration with the wider educational enterprise involving learners on social networks (also known as Web 2.0 wave). LA provides the capability of collecting and analyzing data from a variety of sources in the enterprise to provide information on what works and what does not with respect to teaching and learning (Brown & Analytics, 2011; Mattingly et al., 2012). Thus enabling educational institutions to improve the quality of learning and the overall competitiveness. For this reason, many research communities started to develop variety of promising LA initiatives, models and applications for improving learner success. For example, the Santa Monica College’s Glass Classroom initiative introduced during December 2012 advocates enhancing student and teacher performance through the collection and analysis of large amounts of data. Using real-time feedback, adaptive courseware adjusts based on an individual’s performance in the classroom in order to meet educational objectives. Similar initiatives other initiatives can be found elsewhere (e.g. University of Wisconsin-Madison since May 2012 is working to develop a data-driven “early-warning” system that faculty and advisors can use to support student academic success. The system will help to identify academically at-risk students, using non-traditional indicators that can be gathered very early in a student’s career, even at the beginning of a semester. The system will be able to intervene earlier with students identified by the system, improve their academic success, and bolster a campus’s retention and graduation rates). Besides these initiatives there are many research groups and societies providing excellent networks for researchers who are exploring the role and impact of analytics on teaching, learning, training and development (e.g. The Society for Learning Analytics Research (SoLAR), the Learning Analytics focus group at University of Amsterdam, University of Melbourne Learning Analytics research Group, Social Media Lab at Dalhousie University and Stanford University Transformative Learning Technologies Lab). These research groups and initiatives promoted several learning analytics models that have been developed to identify student risk level or success factors in real time to increase the students’ likelihood of success and improve learning. Table 1 provides examples of such systems. Actually higher education institutions have shown increased interest in learning analytics as they face calls for more transparency and greater scrutiny of their student recruitment and retention practices. However, no real attempt has been made to develop a rewarding learning system that utilizes learning analytics based on neuroscience and cognitive models. In this direction we can identify the SCARF model by David Rock (Rock & Ringleb, 2013) where everything we do in life is based on our brain's determination to minimize danger and increase reward. Figure 1 illustrates the notions used by the SCARF model.