Abstract. Learning analytics are being used in many educational applications in order to help students and Faculty. In our work we use predictive analytics, using student behaviour to predict the likely performance of end of semester final grades with a system we call PredictED. The main contribution of our approach is that our intervention automatically emailed students on a regular basis, with our prediction for the outcome of their exam performance. We targeted first year, first semester University students who often struggle with making the transition into University life where they are given much more responsibility for things like attending class, completing assignments, etc. The form of student behaviour that we used is students' levels and types of engagement with the University's Virtual Learning Environment (VLE), Moodle. We mined the Moodle access log files for a range of parameters based on temporal as well as content access, and use machine learning techniques to predict likely pass/fail, on a weekly basis throughout the semester using logs and outcomes from previous years as training material. We chose ten first-year modules with reasonably high failure rates, large enrolments and stability of module content across the years to implement an early warning system on. From these modules 1,558 students were registered for one of these modules. They were offered the chance to opt into receiving weekly email alerts warning them about their likely outcome. Of these 75% or 1,181 students opted into this service. Pre-intervention there were no differences between participants and non-participants on a number of measures related to previous academic record. However, postintervention the first-attempt final grade performance yielded nearly 3% improvement (58.4% to 61.2%) on average for those who opted in. This tells us that providing weekly guidance and personalised feedback to vulnerable first year students, automatically generated from monitoring of their online behaviour, has a significant positive effect on their exam performance.
In March 2020, Irish universities closed their doors and teaching, learning and assessment moved online, due to the Covid-19 pandemic. This paper arises from a Dublin City University (DCU) research study with a three-fold purpose:(1) Examine the sudden transition of large classes (100+ students) from faceto-face teaching and learning to remote online provision from the perspectives of staff and students.(2) Contextualise these perspectives by reviewing relevant literature.(3) Inform the work of teaching large classes in future online teaching and learning spaces.Presented here, are findings informed by; a rapid, systematised literature review; analysis of student and staff survey responses who experienced the swift move online; and academic support data mined by the Teaching Enhancement Unit (TEU) between March and June 2020. This paper will contribute to prompt, timely reflection on the lived student experience of engagement and isolation during the sudden pivot for large class cohorts. These reflections and analysis will add to the growing body of literature on large class teaching and online pedagogy, specifically in the context of the Covid-19 pandemic. The authors provide conceptualised solutions as we move out of the emergency pivot and into more considered, planned approaches to better engage students in online, hybrid and face-to-face contexts.
E-Learning is becoming an integral part of undergraduate medicine, with many curricula incorporating a number of online activities and resources, in addition to more traditional teaching methods. This study examines physical attendance, online activity, and examination outcomes in a first-year undergraduate medical program. All 358 students who completed the Alimentary System module within the first semester of the program were included, 30 of whom were repeating the year, and thus the module. This systems-based, multidisciplinary module incorporated didactic lectures, cadaveric small group tutorials and additional e-Learning resources such as online histology tutorials. Significant differences were demonstrated in physical attendance and utilization of online resources between repeating students and those participating in the module for the first time. Subsequent analyses confirmed that physical attendance, access of online lecture resources, and utilization of online histology tutorials were all significantly correlated. In addition, both physical attendance and utilization of online resources significantly correlated with summative examination performance. While nonattendance may be due to a variety of factors, our data confirm that significant differences exist in both physical attendance and online activity between new entrants and repeating students, such that all students repeating a module or academic year should be routinely interviewed and offered appropriate supports to ensure that they continue to engage with the program. While the development of complex algorithmic models may be resource intensive, using readily available indices from virtual learning environments is a straightforward, albeit less powerful, means to identify struggling students prior to summative examinations. Anat Sci Educ 9: 555-564. © 2016 American Association of Anatomists.
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