The Application of Data Mining Techniques to Learning Analytics and Its Implications for Interventions with Small Class Sizes

There 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

The Potential for Using Artificial Intelligence Techniques to Improve e-Learning Systems

There has been significant progress in the development of techniques to deliver more effective e-Learning systems in both education and commerce but our research has identified very few examples of comprehensive learning systems that exploit contemporary artificial intelligence (AI) techniques. We have surveyed existing intelligent learning/training systems and explored the contemporary AI techniques which appear to offer the most promising contributions to e-Learning. We have considered the non-technological challenges to be addressed and considered those factors which will allow step change progress. With the convergence of several of the required components for success increasingly in place we believe that the opportunity to make this progress is now much stronger. We present a description of the fundamental components of an adaptive learning system designed to fulfill the objectives of the teacher and to develop a close relationship with the learner, monitoring and adjusting the teaching based upon a wide variety of analyses of their knowledge and performance. This is an important area for future research with the opportunity to deliver significant value to both education and commerce. The development of improved learning systems in conjunction with trainers, teachers and subject matter experts will provide benefits to educational institutions and help commercial organisations to face critical challenges in the training, development and retention of the key skills required to address new, emerging technologies and business models.Final Accepted Versio

LIPID MAP: Serving the next generation of lipid researchers with tools, resources, data and training

Lipids are increasingly recognized as dynamic, critical metabolites affecting human physiology and pathophysiology. LIPID MAPS is a free resource dedicated to serving the lipid research community

Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures

A comprehensive and standardized system to report lipid structures analyzed by mass spectrometry is essentialfor the communication and storage of lipidomics data. Herein, an update on both the LIPID MAPSclassification system and shorthand notation of lipid structures is presented for lipid categories Fatty Acyls(FA), Glycerolipids (GL), Glycerophospholipids (GP), Sphingolipids (SP), and Sterols (ST). With its majorchanges, i.e. annotation of ring double bond equivalents and number of oxygens, the updated shorthandnotation facilitates reporting of newly delineated oxygenated lipid species as well. For standardized reportingin lipidomics, the hierarchical architecture of shorthand notation reflects the diverse structural resolutionpowers provided by mass spectrometric assays. Moreover, shorthand notation is expanded beyond mammalianphyla to lipids from plant and yeast phyla. Finally, annotation of atoms is included for the use of stableisotope-labelled compounds in metabolic labelling experiments or as internal standards

Quality control requirements for the correct annotation of lipidomics data.

10.1038/s41467-021-24984-yNature Communications121477

MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines

Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent

Steps Toward Minimal Reporting Standards for Lipidomics Mass Spectrometry in Biomedical Research Publications

Lipids in blood and tissues can serve as markers of normal and pathophysiological function in humans and can even reflect functions in specific tissues and organs. Lipidomics describes the analysis of large numbers of lipids using mass spectrometry (MS). The proper implementation of these methods in a manner that ensures data quality requires care and rigorous manual checking. Issues of reproducibility and overall data quality in publications and guidelines for authors submitting research are well-developed for areas that include genetics/genomics, proteomics, and clinical trials. For example, the Human Proteome Organization has developed minimum information publication guidelines for proteomics (https://www.hupo.org/HUPO-Minimum-Information-Publication-Guidelines). However, apart from specialized lipid publications, such as the Journal of Lipid Research, which adopted the Lipid Metabolites and Pathways Strategy Consortium (LIPID MAPS) classification, nomenclature, and structural drawing formats in their guidelines,1,2 there are few reporting guidelines in use for lipidomics data. This issue is particularly relevant to studies that are not focused on underpinning methodological approaches but instead cover broader issues of human health and disease. In many such articles, multiple analytical methods are applied, making it difficult to engage sufficient technical expertise to afford rigorous and comprehensive review. We developed a short set of guidelines for lipidomics submissions that we hope will contribute to improving reproducibility and standards in published work (Table). This is a living document, expected to be expanded as the field evolves. It is not intended to serve as a definitive final set of guidelines. To support this sort of activity, the Lipidomics Standard Initiative was recently established to create guidelines for major lipidomic workflows.

Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures.

A comprehensive and standardized system to report lipid structures analyzed by MS is essential for the communication and storage of lipidomics data. Herein, an update on both the LIPID MAPS classification system and shorthand notation of lipid structures is presented for lipid categories Fatty Acyls (FA), Glycerolipids (GL), Glycerophospholipids (GP), Sphingolipids (SP), and Sterols (ST). With its major changes, i.e., annotation of ring double bond equivalents and number of oxygens, the updated shorthand notation facilitates reporting of newly delineated oxygenated lipid species as well. For standardized reporting in lipidomics, the hierarchical architecture of shorthand notation reflects the diverse structural resolution powers provided by mass spectrometric assays. Moreover, shorthand notation is expanded beyond mammalian phyla to lipids from plant and yeast phyla. Finally, annotation of atoms is included for the use of stable isotope-labeled compounds in metabolic labeling experiments or as internal standards. This update on lipid classification, nomenclature, and shorthand annotation for lipid mass spectra is considered a standard for lipid data presentation