Promoting a Growth Mindset in CS1: Does One Size Fit All? A Pilot Study

This paper describes a pilot intervention conducted in CS1, in theacademic year of 2016-2017. The intervention was based on thework of Dweck, promoting a growth Mindset in an effort to in-crease performance in introductory programming. The study alsoexamined data from a previous year (as a control group) to compareand contrast the results. Multiple factors related to programmingperformance were recorded with the control and treatment group,which were measured at multiple intervals throughout the course,to monitor changes as the pilot intervention was implemented.This study found a significant increase in programming perfor-mance when the intervention was deployed. However, althoughperformance increased for the treatment group, the average Mindsetdid not significantly change towards a growth Mindset (replicatingthe findings of Cutts et al, 2010). To further explore this finding,a preliminary deeper investigation using k-means clustering wascarried out. The investigation found that the intervention promoteda growth Mindset for some student profiles and a fixed Mindset forothers. This finding is important for educators considering interven-tion development or implementation of Mindset, and demonstratesthat a Mindset intervention may not be suitable for all learners

Resilience and Effective Learning in First-Year Undergraduate Computer Science

Many factors have been shown to be important for supporting effective learning and teaching — and thus progression and success — in higher education. While factors such as key introductory-level (CS1) knowledge and skills, as well as pre-university learning and qualifications, have been extensively explored, the impact of measures of positive psychology are less well understood for the discipline of computer science. University study can be a period of significant transition for many students; therefore an individual’s positive psychology may have considerable impact upon their response to these challenges. This work investigates the relationships between effective learning and success (first-year performance and attendance) and two measures of positive psychology: Grit and the Nicolson McBride Resilience Quotient (NMRQ).Data was captured by integrating Grit (N=58) and Resilience (N=50) questionnaires and related coaching into the first-year of the undergraduate computer science programme at a single UK university. Analyses demonstrate that NMRQ is significantly linked to attendance and performance for individual subjects and year average marks; however, this was not the case for Grit. This suggests that development of targeted interventions to support students in further developing their resilience could support their learning, as well as progression and retention. Resilience could be used, in concert with other factors such as learning analytics, to augment a range of existing models to predict future student success, allowing targeted academic and pastoral support

Resilience and Effective Learning in First-Year Undergraduate Computer Science

Many factors have been shown to be important for supporting effective learning and teaching — and thus progression and success — in higher education. While factors such as key introductory-level (CS1) knowledge and skills, as well as pre-university learning and qualifications, have been extensively explored, the impact of measures of positive psychology are less well understood for the discipline of computer science. University study can be a period of significant transition for many students; therefore an individual’s positive psychology may have considerable impact upon their response to these challenges. This work investigates the relationships between effective learning and success (first-year performance and attendance) and two measures of positive psychology: Grit and the Nicolson McBride Resilience Quotient (NMRQ).Data was captured by integrating Grit (N=58) and Resilience (N=50) questionnaires and related coaching into the first-year of the undergraduate computer science programme at a single UK university. Analyses demonstrate that NMRQ is significantly linked to attendance and performance for individual subjects and year average marks; however, this was not the case for Grit. This suggests that development of targeted interventions to support students in further developing their resilience could support their learning, as well as progression and retention. Resilience could be used, in concert with other factors such as learning analytics, to augment a range of existing models to predict future student success, allowing targeted academic and pastoral support

Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

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

Comparing importance of knowledge and professional skill areas for engineering programming utilizing a two group Delphi survey

All engineering careers require some level of programming proficiency. However, beginning programming classes are challenging for many students. Difficulties have been well-documented and contribute to high drop-out rates which prevent students from pursuing engineering. While many approaches have been tried to improve the performance of students and reduce the dropout rate, continued work is needed. This research seeks to re-examine what items are critical for programming education and how those might inform what is taught in introductory programming classes (CS1). Following trends coming from accreditation and academic boards on the importance of professional skills, we desire to rank knowledge and professional skill areas in one list. While programming curricula focus almost exclusively on knowledge areas, integrating critical professional skill areas could provide students with a better high-level understanding of what engineering encompasses. Enhancing the current knowledge centric syllabi with critical professional skills should allow students to have better visibility into what an engineering job might be like at the earliest classes in the engineering degree. To define our list of important professional skills, we use a two-group, three-round Delphi survey to build consensus ranked lists of knowledge and professional skill areas from industry and academic experts. Performing a gap analysis between the expert groups shows that industry experts focus more on professional skills then their academic counterparts. We use this resulting list to recommend ways to further integrate professional skills into engineering programming curriculum

A molecular and cellular characterisation of the effects of neonicotinoid pesticides on the brain of the pollinator Bombus terrestris.

Bombus terrestris (L.) is one of the most important native and commercial pollinator species worldwide. Along with other pollinators their populations are in decline due to a multifactorial phenomenon that includes the extensive use of neonicotinoid insecticides. Thus, the characterisation and understanding of neonicotinoid effects on bees at the molecular level is essential to mitigate the risks of their use in the environment. This study initially characterised the brain proteomes of bumblebees in response to aging prior to assessing changes at the behavioural, cellular and molecular level as a response to neonicotinoid exposure. We demonstrated the highly catalytic nature of the developing bumblebee brain and how energy and carbohydrate metabolism increase in response to aging, while genetic information processes are downregulated. By considering differences in mode of action and mode of exposure to the neonicotinoids clothianidin and imidacloprid, the effects of acute and chronic oral exposure on bumblebee workers were determined. Neonicotinoids differentially impair energy metabolism and structural processes in the brain suggesting possible divergence of insecticide mode of action. Clothianidin and imidacloprid triggered different behavioural responses and toxicity in bees, with the former causing hyperactivity and the latter, temporal paralysis. Imidacloprid is less toxic to bumblebees and the brain physiology is differentially affected depending on chemical, dose or mode of exposure selected. The levels of the synapse associated protein synapsin increased in bumblebee brains for imidacloprid-exposed bees only, and functional annotation analysis of differential expressed proteins indicated impairment of intracellular transport, energy metabolism, translational activity, purines and pyrimidines metabolism, endocytic and exocytic activity and synaptic functioning as a whole. The pathways affected by neonicotinoid exposure vary depending on chemical and mode of exposure, which complicates the identification of biomarkers of neonicotinoid exposure in bumblebees. In addition, neonicotinoid metabolism in bees is poorly understood and these chemicals can accumulate in the bee body, which potentially contributes to long term toxicity. Overall the results presented in this thesis demonstrate individual and distinct ways by which neonicotinoids influence neuronal communication and provide novel insights into molecular aspects of bee health, through highlighting the pathways affected by aging and pesticide use on this important pollinator species

Voices of the First Women Leaders in the Federal Bureau of Investigation

This qualitative study utilized elite, semi-structured interviews of a purposive sample of the first women who became Special Agents and supervisors in the highly gendered Federal Bureau of Investigation (FBI). The historical context for their experiences is significant in light of social and legal mandates for equal opportunity and the increased interest in gender-specific research that took place during the 1970s. Literature relating to feminist perspectives, the integration of women into nontraditional occupations, and the gendered nature of bureaucracy supported the conceptual framework. Guided by educational criticism, four strategies were used recursively: typological analysis was used to define categories of data; interpretive analysis was used to identify patterns and connections in the data; evaluation was used to attach value to the data beyond the participants, and thematics were used to analyze pervasive messages within the data as a whole. Typologies included the choice of nontraditional careers, decision-making, efficacy as leaders, and efforts to negotiate the FBI’s bureaucracy. Three metaphors were used to interpret connections and patterns according to feminist standpoint theory, career self-efficacy theory, and various organizational principles. A Supergirl metaphor highlighted women’s unique knowledge and complex roles; a Target metaphor highlighted complex patterns for high achievement and response to obstacles, and a Clubhouse metaphor highlighted masculine culture, the role of rules, and changes to an organization’s equilibrium. Evaluation analysis addressed the moral obligation for women in leadership and the need for organizational diversity. Themes in the data included occupational pride, the challenge to manage multiple roles, an absence of relationship support, and inconsistency in feminist views