State anxiety modulates the return of fear

Current treatments for anxiety disorders are effective but limited by the high frequency of clinical relapse. Processes underlying relapse are thought to be experimentally modeled in fear conditioning experiments with return fear (ROF) inductions. Thereby reinstatement-induced ROF might be considered a model to study mechanisms underlying adversity-induced relapse. Previous studies have reported differential ROF (i.e. specific for the danger stimulus) but also generalized ROF (i.e. for safe and danger stimuli), but reasons for these divergent findings are not clear yet. Hence, the response pattern (i.e. differential or generalized) following reinstatement may be of importance for the prediction of risk or resilience for ROF. The aim of this study was to investigate state anxiety as a potential individual difference factor contributing to differentiability or generalization of return of fear. Thirty-six participants underwent instructed fear expression, extinction and ROF induction through reinstatement while physiological (skin conductance response, fear potentiated startle) and subjective measures of fear and US expectancy were acquired. Our data show that, as expected, high state anxious individuals show deficits in SCR discrimination between dangerous and safe cues after reinstatement induced ROF (i.e. generalization) as compared to low state anxious individuals. The ability to maintain discrimination under aversive circumstances is negatively associated with pathological anxiety and predictive of resilient responding while excessive generalization is a hallmark of anxiety disorders. Therefore, we suggest that experimentally induced ROF might prove useful in predicting relapse risk in clinical settings and might have implications for possible interventions for relapse prevention

Predicting and Improving Performance on Introductory Programming Courses (CS1)

This thesis describes a longitudinal study on factors which predict academic success in introductory programming at undergraduate level, including the development of these factors into a fully automated web based system (which predicts students who are at risk of not succeeding early in the introductory programming module) and interventions to address attrition rates on introductory programming courses (CS1). Numerous studies have developed models for predicting success in CS1, however there is little evidence on their ability to generalise or on their use beyond early investigations. In addition, they are seldom followed up with interventions, after struggling students have been identified. The approach overcomes this by providing a web-based real time system, with a prediction model at its core that has been longitudinally developed and revalidated, with recommendations for interventions which educators could implement to support struggling students that have been identified. This thesis makes five fundamental contributions. The first is a revalidation of a prediction model named PreSS. The second contribution is the development of a web-based, real time implementation of the PreSS model, named PreSS#. The third contribution is a large longitudinal, multi-variate, multi-institutional study identifying predictors of performance and analysing machine learning techniques (including deep learning and convolutional neural networks) to further develop the PreSS model. This resulted in a prediction model with approximately 71% accuracy, and over 80% sensitivity, using data from 11 institutions with a sample size of 692 students. The fourth contribution is a study on insights on gender differences in CS1; identifying psychological, background, and performance differences between male and female students to better inform the prediction model and the interventions. The final, fifth contribution, is the development of two interventions that can be implemented early in CS1, once identified by PreSS# to potentially improve student outcomes. The work described in this thesis builds substantially on earlier work, providing valid and reliable insights on gender differences, potential interventions to improve performance and an unsurpassed, generalizable prediction model, developed into a real time web-based system

What’s Motivation Got to Do with It? A Survey of Recursion in the Computing Education Literature

One of the most challenging topics for both computing educators and students is recursion. Pedagogical approaches for teaching recursion have appeared in the computing education literature for over 30 years, and the topic has generated a significant body of work. Given its persistence, relatively little attention has been paid to student motivation. This article summarizes results on teaching and learning recursion explored by the computing education community, noting the relative lack of interest in motivation. It concludes by briefly discussing an approach to teaching recursion is appealing for students interested in web development

Education Research Using Data Mining and Machine Learning with Computer Science Undergraduates

In recent decades, we are witness to an explosion of technology use and integration of everyday life. The engine of technology application in every aspect of life is Computer Science (CS). Appropriate CS education to fulfill the demand from the workforce for graduates is a broad and challenging problem facing many universities. Research into this ‘supply–chain’ problem is a central focus of CS education research. As of late, Educational Data Mining (EDM) emerges as an area connecting CS education research with the goal to help students stay in their program, improve performance in their program, and graduate with a degree. We contribute to this work with several research studies and future work focusing on CS undergraduate students relating to their program success and course performance analyzed through the lens of data mining. We perform research into student success predictors beyond diversity and gender. We examine student behaviors in course load and completion. We study workforce readiness with creation of a new teaching strategy, its deployment in the classroom, and the analysis shows us relevant Software Engineering (SE) topics for computing jobs. We look at cognitive learning in the beginning CS course its relations to course performance. We use decision trees in machine learning algorithms to predict student success or failure of CS core courses using performance and semester span of core curriculum. These research areas refine pathways for CS course sequencing to improve retention, reduce time-to–graduation, and increase success in the work field

Participatory Design for sustainable social innovation in developing countries: Design experiments towards a model to deploy interventions with marginalised youth

An increasing number of practitioners are engaging in the consideration of Participatory Design (PD) as a strategic modus operandi to attain socially progressive ends among marginalised communities in developing countries. However, the structures, methods and objectives of this type of work constitute an ongoing debate. A scattered body of resources in this area tend to focus on either theory (such as journal papers) or practice (such as design toolkits). To fill this gap, this research develops a model of practice that links these two dimensions through a collection of elements drawn upon contemporary approaches to design and development. The model considers three layers of ethos, methods and outputs to guide the design and undertaking of social-entrepreneurially oriented PD interventions with a focus on problem identification. Two case studies are undertaken with communities of marginalised youth in South Africa to evaluate the model and its inherent flexibility respectively. The evaluation found that the model enabled the researcher to build capacity and empower participants to gain leadership and ownership over the intervention, ultimately developing their sense of activism and aspiration for change. On this basis, a final version of the model is put forward to help prepare and guide design practitioners to deploy PD interventions with marginalised youth in developing countries for responsible and sustainable social innovation. In addition, the research reflects on the various roles that design practitioners take on while deploying the intervention and on the use of a cross-paradigm to undertake the type of design research approached in this thesis

Developing an Open-Book Online Exam for Final Year Students

Like many others, our institution had to adapt our traditional proctored, written examinations to open-book online variants due to the COVID-19 pandemic. This paper describes the process applied to develop open-book online exams for final year (undergraduate) students studying Applied Machine Learning and Applied Artificial Intelligence and Deep Learning courses as part of a four-year BSc in Computer Science. We also present processes used to validate the examinations as well as plagiarism detection methods implemented. Findings from this study highlight positive effects of using open-book online exams, with 85% of students reporting that they either prefer online open-book examinations or have no preference between traditional and open-book exams. There were no statistically significant differences reported comparing the exam results of student cohorts who took the open-book online examination, compared to previous cohorts who sat traditional exams. These results are of value to the CSEd community for three reasons. First, it outlines a methodology for developing online open-book exams (including publishing the open-book online exam papers as samples). Second, it provides approaches for deterring plagiarism and implementing plagiarism detection for open-book exams. Finally, we present feedback from students which may be used to guide future online open-book exam development

Identification and Evaluation of Predictors for Learning Success and of Models for Teaching Computer Programming in Contemporary Contexts

Introductory undergraduate computer programming courses are renowned for higher than average failure and withdrawal rates when compared to other subject areas. The closer partnership between higher education and the rapidly expanding digital technology industry, as demonstrated by the establishment of new Degree Apprenticeships in computer science and digital technologies, requires efficient and effective means for teaching programming skills. This research, therefore, aimed to identify reliable predictors of success in learning programming or vulnerability to failure. The research also aimed to evaluate teaching methods and remedial interventions towards recommending a teaching model that supported and engaged learners in contemporary contexts that were relevant to the workplace. Investigation of qualifications designed to prepare students for undergraduate computer science courses revealed that A-level entrants achieved significantly higher programming grades than BTEC students. However, there was little difference between the grades of those with and those without previous qualifications in computing or ICT subjects. Analysis of engagement metrics revealed a strong correlation between extent of co-operation and programming grade, in contrast to a weak correlation between programming grade and code understanding. Further analysis of video recordings, interviews and observational records distinguished between the type of communication that helped peers comprehend tasks and concepts, and other forms of communication that were only concerned with completing tasks. Following the introduction of periodic assessment, essentially converting a single final assessment to three staged summative assessment points, it was found that failing students often pass only one of the three assignment parts. Furthermore, only 10% of those who failed overall had attempted all three assignments. Reasons for failure were attributed to ‘surface’ motivations (such as regulating efforts to achieve a minimum pass of 40%), ineffective working habits or stressful personal circumstances rather than any fundamental difficulty encountered with subject material. A key contribution to pedagogical practice made by this research is to propose an ‘incremental’ teaching model. This model is informed by educational theory and empirical evidence and comprises short cycles of three activities: presenting new topic information, tasking students with a relevant exercise and then demonstrating and discussing the exercise solution. The effectiveness of this model is evidenced by increased engagement, increased quiz scores at the end of each teaching session and increased retention of code knowledge at the end of the course

A study to explore how interventions support the successful transition of Overseas Medical Graduates to the NHS: Developing and refining theory using realist approaches

Background: The UK’s National Health Service (NHS) currently relies on overseas doctors to ensure effective healthcare delivery. However, concern has grown around their regulation and practice and there is a recognition of the need to support overseas qualified doctors to make a successful transition to the NHS. Interventions have been implemented to address transitional issues without sufficient exploration of what is likely to work or how much training and support are appropriate. The absence of a supportive framework, targeting social, cultural and work related issues, has led to overseas graduates feeling stressed, being isolated and experiencing mental health issues. Difficulties in career progression, retention and performance are also evident. This thesis explores and evaluates interventions that have been developed to support the transition of overseas medical graduates to the UK. Method: A realist approach was adopted. A realist synthesis (exploration of literature and development of initial theory) was conducted. A realist evaluation was then completed to test and refine theory. The main intervention subject was the Programme for Overseas Doctors (POD) developed within one North East Trust. A comparative case study design, using mixed methods, was used (including interviews, questionnaires, researcher observation and analysis of performance data). Findings: A synthesis of the findings, including 123 interviews, illustrated that three key contextual levels; organisational, training and individual, will likely impact on the adjustment of overseas doctors (including performance, retention, career progression and wellbeing). One of the main outcomes of this thesis is a transferable, theoretical explanation of how interventions can successfully support the transition of overseas medical graduates to the NHS. Conclusions: In order to successfully support the transition of overseas doctors, interventions need to be more comprehensive and broad ranging than a simple induction or one-off training programme. Interventions must focus on building an open and supportive culture, address individual needs, and include ongoing support from all staff beyond the initial intervention. This work has reviewed factors that contribute to a successful intervention and has put forward recommendations for future policy, interventions and future research