Facilitating student learning in Computer Science: large class sizes and interventions

Learning to program is difficult with many students struggling to master fundamental abstract concepts. This results in high dropout and failure rates on computer science and information technology degrees as programming tends to be one of the first modules taken. At Maynooth University (MU) the number of students taking introductory programming modules has increased by 300% in the last ten years. Coupled with this is a large increase in student diversity most notably in academic ability and motivation for pursing a first year course in computer science. This paper documents numerous interventions implemented at the department of Computer Science at MU in an attempt to improve engagement, performance and student learning experience. A brief overview of each intervention is provided. A longer synopsis of our most sustainable intervention to date is described. The paper concludes with recommendations for other institutions on how best to implement this intervention when faced with a similar problem

Towards risk-informed PBSHM: Populations as hierarchical systems

The prospect of informed and optimal decision-making regarding the operation and maintenance (O&M) of structures provides impetus to the development of structural health monitoring (SHM) systems. A probabilistic risk-based framework for decision-making has already been proposed. However, in order to learn the statistical models necessary for decision-making, measured data from the structure of interest are required. Unfortunately, these data are seldom available across the range of environmental and operational conditions necessary to ensure good generalisation of the model. Recently, technologies have been developed that overcome this challenge, by extending SHM to populations of structures, such that valuable knowledge may be transferred between instances of structures that are sufficiently similar. This new approach is termed population-based structural heath monitoring (PBSHM). The current paper presents a formal representation of populations of structures, such that risk-based decision processes may be specified within them. The population-based representation is an extension to the hierarchical representation of a structure used within the probabilistic risk-based decision framework to define fault trees. The result is a series, consisting of systems of systems ranging from the individual component level up to an inventory of heterogeneous populations. The current paper considers an inventory of wind farms as a motivating example and highlights the inferences and decisions that can be made within the hierarchical representation.Comment: Submitted to IMAC-XLI conference (2023), Austin, Texas, US

Predatory soil nematodes (Nematoda: Mononchida) in major land-use types across Ireland

The distribution of predatory soil nematodes (Mononchida) across Europe is well described. However, in Ireland knowledge of mononchids is limited to a single study. The CréBeo project was the first systematic survey of soil biodiversity in the major land-use types across the Republic of Ireland. Nematodes were sampled from 61 locations with an extensive geographical spread including arable, pasture, broadleaf forest, coniferous plantation, rough grazing and peatland sites. This study resulted in 11 first records of mononchid species from Ireland (of which 2 are new records for Britain and Ireland). We discuss the mononchid species found and aspects of their distribution across major land uses

An Investigation of the Role Programming Support Services Have for Mature Students

[ES] Programming support services for introductory programmers have seen a rise in popularity in recent years with third level institutions around the world providing “safe spaces” for students to practice their programming skills and get supports without the risk of being judged by anyone. These services appear in many different structures including Support Centres, Software Studios and help desks. The common trend however is that all the users of these services, in general, report that the service has helped them in their studies and garnered them with more confidence in their ability. This paper examines the role which our Computer Science Centre played for students who attended the support service during an intensive higher diploma course. The intensive course is a 3-week course tailored to students who have previously completed a degree in a field not related to CS, and covers CS1 and CS2 material. The structure and design of the support service is outlined in this paper along with the supports offered. A high-level survey was conducted to investigate the effect of the service on students programming self-efficacy. Study design and methodology are described in detail. Early findings suggest that the support services offered to these students improved their belief in their own programming ability which in turn improved their exam grade outcome. The findings provide valuable evidence to justify future research into the functions of support services with the computer science domain.Nolan, K.; Thompson, A.; Noone, M.; Mooney, A. (2020). An Investigation of the Role Programming Support Services Have for Mature Students. En 6th International Conference on Higher Education Advances (HEAd'20). Editorial Universitat Politècnica de València. (30-05-2020):625-633. https://doi.org/10.4995/HEAd20.2020.11118OCS62563330-05-202

Using Machine Learning Techniques to Predict Introductory Programming Performance

Learning to program is difficult and can result in high drop out and failure rates. Numerous research studies have attempted to determine the factors that influence programming success and to develop suitable prediction models. The models built tend to be statistical, with linear regression the most common technique used. Over a three year period a multi-institutional, multivariate study was performed to determine factors that influence programming success. In this paper an investigation of six machine learning algorithms for predicting programming success, using the predetermined factors, is described. Naïve Bayes was found to have the highest prediction accuracy. However, no significant statistical differences were found between the accuracy of this algorithm and logistic regression, SMO (support vector machine), back propagation (artificial neural network) and C4.5 (decision tree). The paper concludes with a recent epilogue study that re-validates the factors and the performance of the naïve Bayes model

NETSEARCH: Demystifying the research process of a student during their research project

Conducting research is a difficult task. Learners, typically with little domain competence, are faced with many challenges when commencing a research project, from choosing a topic, to constructing effective search queries implemented across multiple academic repositories. The 21st century learner is also expected to use technology as a tool; to research, systematise, evaluate, and communicate information effectively and seamlessly, in addition to knowledge creation. This multiplicity of demands creates a complicated tapestry of challenges for both supervisors of projects and students alike. Tools like TurnItIn, which is a plagiarism detection service, attempt to dissuade students from merely being passive researchers and move them to becoming active researchers. However, these tools work only at a surface level and a supervisor is completely unaware of the processes used to garner research articles, be this in a structured research approach or merely obtaining a bibliography list for a classmate. This paper presents an innovative research platform, NETSEARCH, which is designed to address the challenges of conducting and managing research projects in a single platform. The learners’ effective engagement with research papers is visualised through a digital ecosystem to identify learners who are struggling or disengaged with the process of conducting research, enabling early intervention, and to eliminate the possibility of plagiarism through the digitisation of the research process

A decision framework for selecting information-transfer strategies in population-based SHM

Decision-support for the operation and maintenance of structures provides significant motivation for the development and implementation of structural health monitoring (SHM) systems. Unfortunately, the limited availability of labelled training data hinders the development of the statistical models on which these decision-support systems rely. Population-based SHM seeks to mitigate the impact of data scarcity by using transfer learning techniques to share information between individual structures within a population. The current paper proposes a decision framework for selecting transfer strategies based upon a novel concept -- the expected value of information transfer -- such that negative transfer is avoided. By avoiding negative transfer, and by optimising information transfer strategies using the transfer-decision framework, one can reduce the costs associated with operating and maintaining structures, and improve safety.Comment: 12 pages, 2 figures. Author accepted manuscript in Proceedings of the 14th International Workshop on Structural Health Monitoring, Stanford University, California, USA. 202

Technical note: A bootstrapped LOESS regression approach for comparing soil depth profiles

Understanding the consequences of different land uses for the soil system is important to make better informed decisions based on sustainability. The ability to assess change in soil properties, throughout the soil profile, is a critical step in this process. We present an approach to examine differences in soil depth profiles between land uses using bootstrapped LOESS regressions (BLRs). This non-parametric approach is data-driven, unconstrained by distributional model parameters and provides the ability to determine significant effects of land use at specific locations down a soil profile. We demonstrate an example of the BLR approach using data from a study examining the impacts of bioenergy land use change on soil organic carbon (SOC). While this straightforward non-parametric approach may be most useful in comparing SOC profiles between land uses, it can be applied to any soil property which has been measured at satisfactory resolution down the soil profile. It is hoped that further studies of land use and land management, based on new or existing data, can make use of this approach to examine differences in soil profiles