Comparison of Time Metrics in Programming

Research on the indicators of student performance in introductory programming courses has traditionally focused on individual metrics and specific behaviors. These metrics include the amount of time and the quantity of steps such as code compilations, the number of completed assignments, and metrics that one cannot acquire from a programming environment. However, the differences in the predictive powers of different metrics and the cross-metric correlations are unclear, and thus there is no generally preferred metric of choice for examining time on task or effort in programming. In this work, we contribute to the stream of research on student time on task indicators through the analysis of a multi-source dataset that contains information about students' use of a programming environment, their use of the learning material as well as self-reported data on the amount of time that the students invested in the course and per-assignment perceptions on workload, educational value and difficulty. We compare and contrast metrics from the dataset with course performance. Our results indicate that traditionally used metrics from the same data source tend to form clusters that are highly correlated with each other, but correlate poorly with metrics from other data sources. Thus, researchers should utilize multiple data sources to gain a more accurate picture of students' learning.Peer reviewe

Code Complexity in Introductory Programming Courses

Instructors of introductory programming courses would benefit from having a metric for evaluating the sophistication of student code. Since introductory programming courses pack a wide spectrum of topics in a short timeframe, student code changes quickly, raising questions of whether existing software complexity metrics effectively reflect student growth as reflected in their code. We investigate code produced by over 800 students in two different Python-based CS1 courses to determine if frequently used code quality and complexity metrics (e.g., cyclomatic and Halstead complexities) or metrics based on length and syntactic complexity are more effective as a heuristic for gauging students' progress through a course. We conclude that the traditional metrics do not correlate well with time passed in the course. In contrast, metrics based on syntactic complexity and solution size correlate strongly with time in the course, suggesting that they may be more appropriate for evaluating how student code evolves in a course context.Instructors of introductory programming courses would benefit from having a metric for evaluating the sophistication of student code. Since introductory programming courses pack a wide spectrum of topics in a short timeframe, student code changes quickly, raising questions of whether existing software complexity metrics effectively reflect student growth as reflected in their code. We investigate code produced by over 800 students in two different Python-based CS1 courses to determine if frequently used code quality and complexity metrics (e.g., cyclomatic and Halstead complexities) or metrics based on length and syntactic complexity are more effective as a heuristic for gauging students' progress through a course. We conclude that the traditional metrics do not correlate well with time passed in the course. In contrast, metrics based on syntactic complexity and solution size correlate strongly with time in the course, suggesting that they may be more appropriate for evaluating how student code evolves in a course context.Peer reviewe

Morning or Evening? An Examination of Circadian Rhythms of CS1 Students

Circadian rhythms are the cycles of our internal clock that play a key role in governing when we sleep and when we are active. A related concept is chronotype, which is a person's natural tendency toward activity at certain times of day and typically governs when the individual is most alert and productive. In this work we investigate chronotypes in the setting of an Introductory Computer Programming (CS1) course. Using keystroke data collected from students we investigate the existence of chronotypes through unsupervised learning. The chronotypes we find align with those of typical populations reported in the literature and our results support correlations of certain chronotypes to academic achievement. We also find a lack of support for the still-popular stereotype of a computer programmer as a night owl. The analyses are conducted on data from two universities, one in the US and one in Europe, that use different teaching methods. In comparison of the two contexts, we look into programming assignment design and administration that may promote better programming practices among students in terms of procrastination and effort.Peer reviewe

Entendiendo el impacto de los horarios de estudio en el rendimiento académico universitario en línea

Time dedication is key to the academic performance of students. Similarly, the time spent in different time slots also directly intervenes in academic performance. The existing scientific literature shows that more nocturnal students see their circadian rhythm interfered with and achieve lower academic performance. Even the most nocturnal students sometimes see their quality of life and health diminished. The aim of the study, to improve the well-being of students, is to find a possible relationship between the time slots of the time dedication of students in virtual learning environments, with online learning methodology and academic performance. The method of the work is comparative through a mainly quantitative approach with a sample based on university degree students in a specific cultural context such as Mexico. Contrary to expectations, the results show that the nocturnal students achieve a higher performance than students with supposed appropriate circadian rhythm considering the scientific literature. We conclude that cultural aspects explain the results.El tiempo de dedicación es clave en el rendimiento académico de los estudiantes. De igual manera, el estado del arte que el tiempo de dedicación realizado en distintas franjas horarias también interviene directamente en el rendimiento académico. Tras una revisión sistemática de la literatura científica existente ponemos de manifiesto que los estudiantes que son más nocturnos ven interferido su ritmo circadiano y consiguen un menor rendimiento académico. Incluso los alumnos más nocturnos en ocasiones ven mermadas su calidad de vida y salud, pudiéndose generar trastornos de ansiedad o depresión. El objetivo del estudio, con fines de mejorar el bienestar de los estudiantes, consiste en analizar la relación entre las franjas horarias de dedicación de los estudiantes en entornos virtuales de aprendizaje, con metodología de aprendizaje en línea, y el rendimiento académico. La metodología del trabajo es descriptiva con un enfoque mixto cualitativo-cuantitativo con una muestra basada en estudiantes de grados universitarios en un contexto cultural concreto como es México. A contra pronóstico, los resultados muestran que los alumnos mayoritariamente nocturnos consiguen un rendimiento superior a diferencia de los alumnos con un ritmo circadiano más apropiado según la literatura científica. Tras discutir los resultados concluimos que distintos aspectos culturales explican los resultados

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

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