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dc.contributor.authorWakelam, Edward
dc.description.abstractThere has been significant progress in the development of techniques to deliver effective technology enhanced learning systems in education, with substantial progress in the field of learning analytics. These analyses are able to support academics in the identification of students at risk of failure or withdrawal. The early identification of students at risk is critical to giving academic staff and institutions the opportunity to make timely interventions. This thesis considers established machine learning techniques, as well as a novel method, for the prediction of student outcomes and the support of interventions, including the presentation of a variety of predictive analyses and of a live experiment. It reviews the status of technology enhanced learning systems and the associated institutional obstacles to their implementation and deployment. Many courses are comprised of relatively small student cohorts, with institutional privacy protocols limiting the data readily available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. I present an experiment conducted on a final year university module, with a student cohort of 23, where the data available for prediction is limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. I apply and compare a variety of machine learning analyses to assess and predict student performance, applied at appropriate points during module delivery. Despite some mixed results, I found potential for predicting student performance in small student cohorts with very limited student attributes, with accuracies comparing favourably with published results using large cohorts and significantly more attributes. I propose that the analyses will be useful to support module leaders in identifying opportunities to make timely academic interventions. Student data may include a combination of nominal and numeric data. A large variety of techniques are available to analyse numeric data, however there are fewer techniques applicable to nominal data. I summarise the results of what I believe to be a novel technique to analyse nominal data by making a systematic comparison of data pairs. In this thesis I have surveyed existing intelligent learning/training systems and explored the contemporary AI techniques which appear to offer the most promising contributions to the prediction of student attainment. I have researched and catalogued the organisational and non-technological challenges to be addressed for successful system development and implementation and proposed a set of critical success criteria to apply. This dissertation is supported by published work.en_US
dc.rightsAttribution 3.0 United States*
dc.subjectLearning Analyticsen_US
dc.subjectEducational Data Miningen_US
dc.subjectEducational Interventionen_US
dc.subjectSmall Datasetsen_US
dc.subjectDecision Treesen_US
dc.subjectRandom Foresten_US
dc.subjectK-Nearest Neighboursen_US
dc.subjectAdaptive learning systemsen_US
dc.subjectNumeric and Nominal Data Analysisen_US
dc.subjectDimensionality reductionen_US
dc.titleThe Application of Data Mining Techniques to Learning Analytics and Its Implications for Interventions with Small Class Sizesen_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US

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