Investigating the Efficacy of Algorithmic Student Modelling in Predicting Students at Risk of Failing in the Early Stages of Tertiary Education: Case study of experience based on first year students at an Institute of Technology in Ireland.

The application of data analytics to educational settings is an emerging and growing research area. Much of the published works to-date are based on ever-increasing volumes of log data that are systematically gathered in virtual learning environments as part of module delivery. This thesis took a unique approach to modelling academic performance; it is a first study to model indicators of students at risk of failing in first year of tertiary education, based on data gathered prior to commencement of first year, facilitating early engagement with at-risk students. The study was conducted over three years, in 2010 through 2012, and was based on a sample student population (n=1,207) aged between 18 and 60 from a range of academic disciplines. Data was extracted from both student enrolment data maintained by college administration, and an online, self-reporting, learner profiling tool developed specifically for this study. The profiling tool was administered during induction sessions for students enrolling into the first year of study. Twenty-four factors relating to prior academic performance, personality, motivation, self-regulation, learning approaches, learner modality, age and gender were considered. Eight classification algorithms were evaluated. Cross validation model accuracies based on all participants were compared with models trained on the 2010 and 2011 student cohorts, and tested on the 2012 student cohort. Best cross validation model accuracies were a Support Vector Machine (82%) and Neural Network (75%). The k-Nearest Neighbour model, which has received little attention in educational data mining studies, achieved highest model accuracy when applied to the 2012 student cohort (72%). The performance was similar to its cross validation model accuracy (72%). Model accuracies for other algorithms applied to the 2012 student cohort also compared favourably; for example Ensembles (71%), Support Vector Machine (70%) and a Decision Tree (70%). Models of subgroups by age and by academic discipline achieved higher accuracy than models of all participants, however, a larger sample size is needed to confirm results. Progressive sampling showed a sample size \u3e 900 was required to achieve convergence of model accuracy. Results showed that factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance and self-efficacy. Kinaesthetic modality was also indicative of students at risk of failing, a factor that has not been cited previously as a significant predictor of academic performance. Models reported in this study show that learner profiling completed prior to commencement of first year of study yielded informative and generalisable results that identified students at risk of failing. Additionally, model accuracies were comparable to models reported elsewhere that included data collected from student activity in semester one, confirming the validity of early student profiling

How Can I use Learning Analytics in my Teaching Practice

In this guest lecture, Geraldine gave an overview of the genesis and practice of learning analytics in higher education in Ireland and beyond

Web Services Technology Infrastructure

Web Services using eXtensible Markup Language (XML) based standards are becoming the new archetype for enabling business to business collaborations. This paper describes the conceptual architecture and semantics of constructing and consuming Web Services. It describes how Web Services fit into the enterprise application environment. It discusses Web Services security. Finally, it outlines the flaws of Web Services in their current state

XML for Business to Business Data Exchange

This paper examines to use of XML for business to business data exchange. Starting with creating an XML document from an existing data source and transmitting that document, we explain some of the supporting standards for XML which facilitate automated processing and transformation of an XML document. Finally we look at the advantages of using XML, and why it is expected to revolutionise electronic data interchange

Using Learning Styles to Optimise Lecturer and Learner Experience and Results in an Institute of Education

The past decade of social policy making and legislative change in Ireland has led to a ‘broader range of individuals’ accessing higher education (ITB, 2006, HEA 2005, Duffin forthcoming). This means that class groups contain a greater range of diversity of learning behaviours than hitherto. The process of accommodating this range of learning behaviours within curriculum development and assessment poses a challenge for lecturers and students alike. This paper suggests how understanding the relationship of learning styles to cognitive processing can provide sound research support to the use of learning styles profiling to create conditions for optimal achievement in terms of student retention, attendance and achievement

Letter from E. Arthur Gray, Mayor of Port Jervis, to Geraldine Ferraro

Letter from E. Arthur Gray, Mayor of Port Jervis, to Geraldine Ferraro. Includes standard response letter from Ferraro.https://ir.lawnet.fordham.edu/vice_presidential_campaign_correspondence_1984_new_york/1218/thumbnail.jp

Virtual Credit Card Processing System

The virtual credit card processing system is an e-business system we have developed which provides a secure and universal mechanism for making purchases over the Internet. The system uses Remote Method Invocation (RMI), Java Server Pages (JSP), Java Servlets and Java Database Connectivity (JDBC). We also look at the possibility of implementing the system using the Web Services architecture

Learning Analytics to Inform Teaching and Learning Approaches

Learning analytics is an evolving discipline with capability for educational data analysis to enable better understanding of learning processes. This paper reports on learning analytics research at Institute of Technology Blanchardstown, Ireland, that indicated measureable factors can identify first year students at risk of failing based on data available prior to commencement of first year of study. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines (n=1,207). Data was gathered from both student enrolment data maintained by college administration, and an online, self-reporting tool administered during induction sessions for students enrolling into the first year of study. Factors considered included prior academic performance, personality, motivation, self- regulation, learning approaches, learner modality, age and gender. A k-NN classification model trained on data from the 2010 and 2011 student cohort, and tested on data from the 2012 student cohort correctly identified 74% of students at risk of failing. Some factors predictive of at-risk students are malleable, and relate to an effective learning disposition; specifically, factors relating to self-regulation and motivation. This paper discusses potential benefits of measurement of learner disposition

Workshop on methodology in learning analytics (MLA)

Learning analytics is an interdisciplinary and inclusive field, a fact which makes the establishment of methodological norms both challenging and important. This community-building workshop intends to convene methodology-focused researchers to discuss new and established approaches, comment on the state of current practice, author pedagogical manuscripts, and co-develop guidelines to help move the field forward with quality and rigor

Asynchronous Assistance: a Social Network Analysis of Influencing Peer Interactions in PeerWise

This mixed methods, investigative case study explored student patterns of use within the online PeerWise platform to identify the most influencing activities and to build a model capable of predicting performance based on these influencing activities. Peerwise is designed to facilitate student peer-to-peer engagement through creating, answering and ranking multiple choice questions; this study sought to understand the relationship between student engagement in Peerwise and learning performance. To address the research question, various usage metrics were explored, visualized and modelled, using social network analysis with Gephi, Tableau and Python. These findings were subsequently analyzed in light of the qualitative survey data gathered. The most significant activity metrics were evaluated leading to rich data visualisations and identified the activities that influenced academic performance in this study. The alignment of the key qualitative and quantitative findings converged on answering questions as having the greatest positive impact on learner performance. Furthermore, from a quantitative perspective the Average Comment Length and Average Explanation Length correlated positively with superior academic performance. Qualitatively, the motivating nature of PeerWise community also engaged learners. The key limitation of the size of the data set within the investigative case study suggests further research, with additional student cohorts as part of an action research paradigm, to broaden these findings