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

Analysis of Response of Flexible Pavements Using Finite Element Method

The characteristic response of flexible pavements under traffic load depict a delayed lateral strain relaxation (Viscoelasticity), a phenomenon that may be more accurately and expeditiously analysed using finite element (FE) viscoelastic response models. In this study a flexible pavement was modelled using ANSYS/ED finite element software suite. The pavement model was subjected to cyclic loading that simulated three levels of truck loads on 10R20 tyres at four tyre inflation pressures (viz. 350,490,630 and 770 kPa). The modelled results were in good agreement with the measured in-situ full-scale test data. Therefore, for known pavement material characteristics and tyre-pavement contact regime, finite element method could be used to efficiently estimate the fatigue life of flexible pavement with thin bituminous surfacing layers

A Computational Approach to Evaluating Curricular Alignment to the United Nations Sustainable Development Goals

The United Nations (UN) considers universities to be key actors in the pursuit of the Sustainable Development Goals (SDGs). Yet, efforts to evaluate the embeddedness of the SDGs in university curricula tend to rely on manual analyses of curriculum documents for keywords contained in sustainability lexica, with little consideration for the diverse contexts of such keywords. The efficacy of these efforts, relying on expert co-elicitation in both subject-matter contexts and sustainability, suffers from drawbacks associated with keyword searches, such as limited coverage of key concepts, difficulty in extracting intended meaning and potential for greenwashing through “keyword stuffing.” This paper presents a computational technique, derived from natural language processing (NLP), which develops a sustainability lexicon of root keywords (RKs) of relative importance by adapting the Term Frequency–Inverse Document Frequency (TF-IDF) method to a corpus of sustainability documents. Identifying these RKs in module/course descriptors offers a basis for evaluating the embeddedness of sustainability in 5,773 modules in a university\u27s curricula using classification criteria provided by the Association for the Enhancement of Sustainability in Higher Education\u27s (AASHE). Applying this technique, our analysis of these descriptors found 286 modules (5%) to be “sustainability focused” and a further 769 modules (13%) to be “sustainability inclusive,” which appear to address SDGs 1, 17, 3, 7, and 15. Whilst this technique does not exploit machine learning methods applied to large amounts of trained data, it is, nevertheless, systemic and evolutive. It, therefore, offers an appropriate trade-off, which faculty with limited analytics skills can apply. By supplementing existing approaches to evaluating sustainability in the curriculum, the developed technique offers a contribution to benchmarking curricular alignment to the SDGs, facilitating faculty to pursue meaningful curricular enhancement, whilst complying with sustainability reporting requirements. The technique is useful for first-pass analyses of any university curriculum portfolio. Further testing and validation offer an avenue for future design-science research

Corrected bladder exstrophy- Caesarean birth

Case Summary: We present a case of 26 yrs old Primigravida, who presented at the antenatal clinic of Kenyatta National Hospital at 20 weeks gestation, she was a known case of congenital bladder exstrophy. Patient was diagnosed with bladder exstrophy at birth and underwent a total of 3 bladder surgeries, has had suprapubic catheter for the last 15 years and underwent vaginoplasty and vaginal dilatation for vaginal atresia, and she has conceived naturally

An Analysis of the Impact and Efficacy of Online Emotional Intelligence Coaching as a Support Mechanism for University Students

As a consequence of the COVID-19 pandemic, many college courses have pivoted to complete online delivery and colleges are also tasked with providing student supports online. It is likely this transition will last beyond any COVID-19 specific restrictions, therefore this small-scale, exploratory study examined the efficacy and impact of the provision of a 5 week online emotional intelligence (EI) coaching programme to a cohort of Irish university students (n = 19) studying at Technological University Dublin (TU Dublin). Results revealed that the average overall level of EI increased for participants following the coaching programme. Students reported that they believed the programme provided emotional support and that it also enabled them to manage academic stress more effectively and ultimately that engagement with the programme had a positive impact on their academic engagement. Taken collectively, the results of this study suggest that whilst EI coaching can be successfully delivered online, where possible, a blended approach may be optimal. However, as this is a novel and exploratory study, further confirmatory research is recommended

Case report: corrected bladder exstrophy - caesarean birth

We present a case of 26 yrs old Primigravida, who was presented at the antenatal clinic of Kenyatta National Hospital at 20 weeks gestation. She was a known case of congenital bladder exstrophy. Patient was diagnosed with bladder exstrophy at birth and underwent a total of 3 bladder surgeries, has had suprapubic catheter for the last 15 years and underwent vaginoplasty and vaginal dilatation for vaginal atresia, and she has conceived naturally

Employer collaboration in developing graduate employability: a pilot study in Ireland

Purpose – The purpose of this paper is to evaluate the impact on student work readiness outcomes of collaboration with employers in developing and delivering tailored graduate employability workshops in socioemotional skills for work (SES4Work). Design/methodology/approach – Framed by the CareerEDGE model of graduate employability, the authors piloted a five-session module for near graduates in five disciplines. The research included an online employer survey (n 5 128), employer interviews (n 5 21) and tailored workshops for near graduates, culminating in a mock competency-based interview. Using a pre/post-test design, participants (n 5 24) also completed the CareerEDGE Employability Development Profile (EDP) and the Trait Emotional Intelligence questionnaire (TEIque). Findings – After completing the module, there was a statistically significant improvement in participant scores on the CareerEDGE EDP þ12.3%, p \u3c 0.001, effect size (Cohen’s d) 0.89, large, and the TEIque þ6.4%, p 5 0.009, effect size (Cohen’s d) 0.61, moderate. Furthermore, 70% (n=17) of participants were “hired” based on their mock interviews, with 12% (n=4) offered employer connections after graduation. Originality/value – This is the first academic research in Ireland to develop and evaluate an enterprise collaborative, discipline-specific module for enhancing graduate employability. Findings suggest that employer collaboration can enhance the efficacy of employability interventions and therefore merits further research

An evaluation of a computational technique for measuring the embeddedness of sustainability in the curriculum aligned to AASHE-STARS and the United Nations Sustainable Development Goals

Introduction: SDG 4.7 mandates university contributions to the United Nations (UN) Sustainable Development Goals (SDGs) through their education provisions. Hence, universities increasingly assess their curricular alignment to the SDGs. A common approach to the assessment is to identify keywords associated with specific SDGs and to analyze for their presence in the curriculum. An inherent challenge is associating the identified keywords as used in the diverse set of curricular contexts to relevant sustainability indicators; hence, the urgent need for more systematic assessment as SDG implementation passes its mid-cycle. Method: In this study, a more nuanced technique was evaluated with notable capabilities for: (i) computing the importance of keywords based on the term frequency-inverse document frequency (TF-IDF) method; (ii) extending this computation to the importance of courses to each SDG and; (iii) correlating such importance to a statistical categorization based on the Association for the Advancement of Sustainability in Higher Education (AASHE) criteria. Application of the technique to analyze 5,773 modules in a university’s curriculum portfolio facilitated categorization of the modules/courses to be “sustainability-focused” or “sustainability-inclusive.” With the strategic objective of systematically assessing the sustainability content of taught curricula, it is critical to evaluate the precision and accuracy of the computed results, in order to attribute text with the appropriate SDGs and level of sustainability embeddedness. This paper evaluates this technique, comparing its results against a manual and labor-intensive interpretation of expert informed assessment of sustainability embeddedness on a random sample of 306 modules/courses. Results and discussion: Except for SDGs 1 and 17, the technique exhibited a reasonable degree of accuracy in predicting module/course alignment to SDGs and in categorizing them using AASHE criteria. Whilst limited to curricular contexts from a single university, this study indicates that the technique can support curricular transformation by stimulating enhancement and reframing of module/course contexts through the lens of the SDG

Cereal Grain Combustion in Domestic Boilers

This study compared the combustion characteristics and the combustion behaviour of oats, barley, triticale and wheat to that of wood pellets. Sustained grain combustion in domestic boilers was feasible but problematic, the main impediment being clinker formation with ash agglomeration. Clinker formation was lowest for oats which burned easily with fewer operational problems. Triticale displayed reasonably good combustion characteristics and also ignited easily. In contrast, barley and wheat proved difficult to ignite while barley combustion was prone to self-extinguish. Thermal and combustion efficiency and heat output were considerably higher at a grain moisture content of 15% compared to a moisture content of 20%. The efficiency of oat combustion was similar to that of wood pellets at a moisture content of 15%. Carbon monoxide (CO) emission from cereal grains increased with increasing moisture content, but was still below limit values. Oxides of nitrogen (NOx) emissions from cereal combustion were high and would require reduction by limiting the quantity of nitrogen applied to the crop and/or the use of air staging. Oats proved superior to the other grains as a combustion feedstock with similar efficiencies to those of wood pellets but low moisture content is a prerequisite for efficient grain combustion

Sentiment Analysis: Comparative Analysis of Multilingual Sentiment and Opinion Classification Techniques

Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study