Dyslexia in higher education: Implications for maths anxiety, statistics anxiety and psychological well-being

This study examined levels of mathematics and statistics anxiety, as well as general mental health amongst undergraduate students with dyslexia (n = 28) and those without dyslexia (n = 71). Students with dyslexia had higher levels of mathematics anxiety relative to those without dyslexia, while statistics anxiety and general mental health were comparable for both reading ability groups. In terms of coping strategies, undergraduates with dyslexia tended to use planning-based strategies and seek instrumental support more frequently than those without dyslexia. Higher mathematics anxiety was associated with having a dyslexia diagnosis, as well as greater levels of worrying, denial, seeking instrumental support and less use of the positive reinterpretation coping strategy. By contrast, statistics anxiety was not predicted by dyslexia diagnosis, but was instead predicted by overall worrying and the use of denial and emotion focused coping strategies. The results suggest that disability practitioners should be aware that university students with dyslexia are at risk of high mathematics anxiety. Additionally, effective anxiety reduction strategies such as positive reframing and thought challenging would form a useful addition to the support package delivered to many students with dyslexia.

The student experience in Objective Structured Clinical Examinations (OSCEs): phenomenological analysis

Background and purpose
The dominant psychometric discourse of OSCEs may lead to unexpected problems, such as a checklist-based student performance1 which under emphasises the clinical relationship with student and standardised patient (SP). Such encounters can be dehumanising for SPs2 and have implications for what students learn about relational skills through the assessment process. In this study we explore medical students’ experiences of undertaking OSCEs using a phenomenological frame.
Methodology
Interpretative phenomenological analysis is a form of qualitative methodology which has strong resonance with existentialism and focuses on the lived experience without significant reference to external political or discursive
forces.
Six 4th year undergraduate medical students from Queen’s University Belfast were recruited in December 2013. Maximum variation sampling was used. Students were interviewed by a researcher in the week prior to the
OSCE and then again in the week following the OSCE in Jan 2014. Interviews were minimally structured in order to be open to respondents, rather than adhering to a fixed topic guide, but focussed on participants’ experiences, thoughts and feelings about taking part in OSCEs. Interviews were audio-recorded and
transcribed. Students were also asked to complete a short diary entry in the days prior to the OSCEs and another immediately following. Diary entries were written, emailed or audio-recorded at student’s preference.
Results
Transcripts are currently being analysed by interpretative phenomenological analysis. Preliminary analysis has demonstrated the significance of students’ relationships within the OSCE triad (student, SP and examiner); the effect of the immediate examination environment; realism versus roleplay; students’ perceptions of the purpose of assessment; and coping mechanisms.
Full results will be available by the time of the conference.
Conclusion and Discussion
Understanding the student experience in OSCEs is a crucial step in understanding the complex construction of relationships within the OSCE triad. The focus in OSCEs is typically on standardisation and reliability, but in exploring social interactions we may refocus attention on their inherent potential for learning and effects on both students and patients.
References
1. Hodges B. Medical education and the maintenance of incompetence. Med Teach 2006;28(8):690-6
2. Johnston JL, Lundy G, McCullough M, Gormley GJ. The view from over there: reframing the OSCE through the experience of standardised patient<br/>raters. Med Educ 2013;47(9):899-909

Development of a Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) Cell for the In Situ Analysis of Co-Electrolysis in a Solid Oxide Cell

Co-electrolysis of carbon dioxide and steam has been shown to be an efficient way to produce syngas, however further optimisation requires detailed understanding of the complex reactions, transport processes and degradation mechanisms occurring in the solid oxide cell (SOC) during operation. Whilst electrochemical measurements are currently conducted in situ, many analytical techniques can only be used ex situ and may even be destructive to the cell (e.g. SEM imaging of microstructure). In order to fully understand and characterise co-electrolysis, in situ monitoring of the reactants, products and SOC is necessary. Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) is ideal for in situ monitoring of co-electrolysis as both gaseous and adsorbed CO and CO2 species can be detected, however it has previously not been used for this purpose. The challenges of designing an experimental rig which allows optical access alongside electrochemical measurements at high temperature and operates in a dual atmosphere are discussed. The rig developed has thus far been used for symmetric cell testing at temperatures from 450[degree]C to 600[degree]C. Under a CO atmosphere, significant changes in spectra were observed even over a simple Au|10Sc1CeSZ|Au SOC. The changes relate to a combination of CO oxidation, the water gas shift reaction and carbonate formation and decomposition processes, with the dominant process being both potential and temperature dependent.

Complexity of Inferences in Polytree-shaped Semi-Qualitative Probabilistic Networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the Bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that inferences can be performed in linear time if there is a single observed node, which is a relevant practical case. Because our proof is constructive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynomial-time algorithm for SQPNs. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.