Role Modeling as a Computing Educator in Higher Education: A Focus on Care, Emotions and Professional Competencies

This paper provides insights into role modeling by educators in computing that is beyond the technical, theoretical and rational perspectives which have historically been described as dominant in computing. Surveying 199 educators in higher education, we have built on frameworks of role modeling, care, emotions, and professional competencies as a lens to see different ways of engaging in computing. Our quantitative and qualitative findings show how educators model ways of caring (for oneself, other humans and living species, technology, and the planet), emotions, professional competencies and other types of role modeling. Examples of contexts within computing and reasons why an educator can(not) model these aspects bring new light to research on care and emotions being shown in computing. This work contributes to a better understanding of computing educators as potential role models, particularly in terms of displaying emotions and various types of care. Our work can support ways of developing the professional competences of computing educators and the teaching culture of computing departments. Our findings may inspire other educators to think about their own display of emotions and care, and what this transmits to their students. Thus, the work also contributes to the discussion of ways to increase diversity among students and equitable access to computing education

What Do We Think We Think We Are Doing?: Metacognition and Self-Regulation in Programming

Metacognition and self-regulation are popular areas of interest in programming education, and they have been extensively researched outside of computing. While computing education researchers should draw upon this prior work, programming education is unique enough that we should explore the extent to which prior work applies to our context. The goal of this systematic review is to support research on metacognition and self-regulation in programming education by synthesizing relevant theories, measurements, and prior work on these topics. By reviewing papers that mention metacognition or self-regulation in the context of programming, we aim to provide a benchmark of our current progress towards understanding these topics and recommendations for future research. In our results, we discuss eight common theories that are widely used outside of computing education research, half of which are commonly used in computing education research. We also highlight 11 theories on related constructs (e.g., self-efficacy) that have been used successfully to understand programming education. Towards measuring metacognition and self-regulation in learners, we discuss seven instruments and protocols that have been used and highlight their strengths and weaknesses. To benchmark the current state of research, we examined papers that primarily studied metacognition and self-regulation in programming education and synthesize the reported interventions used and results from that research. While the primary intended contribution of this paper is to support research, readers will also learn about developing and supporting metacognition and self-regulation of students in programming courses

Computing Education Research Compiled: Keyword Trends, Building Blocks, Creators, and Dissemination

The need for organized computing education efforts dates back to the 1950s. Since then, computing education research (CER) has evolved and matured from its early initiatives and separation from mathematics education into a respectable research specialization of its own. In recent years, a number of meta-research papers, reviews, and scientometric studies have built overviews of CER from various perspectives. This paper continues that approach by offering new perspectives on the past and present state of CER: analyses of influential papers throughout the years, of the theoretical backgrounds of CER, of the institutions and authors who create CER, and finally of the top publication venues and their citation practices. The results reveal influential contributions from early curriculum guidelines to rigorous empirical research of today, the prominence of computer programming as a topic of research, evolving patterns of learning-theory usage, the dominance of high-income countries and a cluster of 52 elite institutions, and issues regarding citation practices within the central venues of dissemination.</p

Toward Competency-Based Professional Accreditation in Computing

Program accreditation in medical or religious professions has existedsince the 1800s while accreditation of business and engineeringprograms started in the early twentieth century. With this long history,these disciplines have focused on ensuring the competence oftheir graduates, as modern society demands appropriate expertisefrom doctors and engineers before letting them practice their profession.In computing, however, professional accreditation startedin the last decades of the twentieth century only after computerscience, informatics, and information systems programs becamewidespread. At the same time, although competency-based learninghas existed for centuries, its growth in computing is relativelynew, resulting from recent curricular reports such as ComputingCurricula 2020, which have defined competency comprising knowledge,skills, and dispositions. In addition, demands are being placedon university programs to ensure their graduates are ready forentering and sustaining employment in the computing profession.This work explores the role of accreditation in the formationand development of professional competency in non-computingdisciplines worldwide, building on this understanding to see howcomputing accreditation bodies could play a similar role in computing.This work explores the role of accreditation in the formationand development of professional competency in non-computingdisciplines worldwide, building on this understanding to see howcomputing accreditation bodies could play a similar role in computing.Its recommendations are to incorporate competencies inall computing programs and future curricular guidelines; create competency-based models for computing programs; involve industryin identifying workplace competencies, and ensure accreditationbodies include competencies and their assessment in their standards

Predicting programming assignment difficulty

Teaching programming is increasingly more widespread and starts at primary school level on some countries. Part of that teaching consist of students writing small programs that will demonstrate learned theory and how various things fit together to form a functional program. Multiple studies indicate that programming is difficult skill to learn and master. Some part of difficulty comes from plethora of concepts that students are expected to learn in relatively short time. Part of practicing to write programs involves feedback, which aids students’ learning of assignment’s topic, and motivation, which encourages students to continue the course and their studies. For feedback it would be helpful to know students’ opinion of a programming assignment difficulty. There are few studies that attempt to find out if there is correlation between metrics that are obtained from students’ writing a program and their reported difficulty of it. These studies use statistical models on data after the course is over. This leads to an idea if such a thing could be done while students are working on programming assignments. To do this some sort of machine learning model would be possible solution but as of now no such models exist. Due to this we will utilize idea from one of these studies to create a model, which could do such prediction. We then improve that model, which is coarse, with two additional models that are more tailored for the job. Our main results indicate that this kind of models show promise in their prediction of a programming assignment difficulty based on collected metrics. With further work these models could provide indication of a student struggling on some assignment. Using this kind of model as part of existing tools we could provide a student subtle help before his frustration grows too much. Further down the road such a model could be used to provide further exercises, if need by a student, or progress forward once he masters certain topic

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

On Designing Programming Error Messages for Novices: Readability and its Constituent Factors

The 2021 ACM CHI Virtual Conference on Human Factors in Computing Systems (CHI'21), Virtual Conference, 8-13 May 2021Programming error messages play an important role in learning to program. The cycle of program input and error message response completes a loop between the programmer and the compiler/interpreter and is a fundamental interaction between human and computer. However, error messages are notoriously problematic, especially for novices. Despite numerous guidelines citing the importance of message readability, there is little empirical research dedicated to understanding and assessing it. We report three related experiments investigating factors that influence programming error message readability. In the first two experiments we identify possible factors, and in the third we ask novice programmers to rate messages using scales derived from these factors. We find evidence that several key factors significantly affect message readability: message length, jargon use, sentence structure, and vocabulary. This provides novel empirical support for previously untested long-standing guidelines on message design, and informs future efforts to create readability metrics for programming error messages

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

Introducing Computational Thinking in K-12 Education: Historical, Epistemological, Pedagogical, Cognitive, and Affective Aspects

Introduction of scientific and cultural aspects of Computer Science (CS) (called "Computational Thinking" - CT) in K-12 education is fundamental. We focus on three crucial areas. 1. Historical, philosophical, and pedagogical aspects. What are the big ideas of CS we must teach? What are the historical and pedagogical contexts in which CT emerged, and why are relevant? What is the relationship between learning theories (e.g., constructivism) and teaching approaches (e.g., plugged and unplugged)? 2. Cognitive aspects. What is the sentiment of generalist teachers not trained to teach CS? What misconceptions do they hold about concepts like CT and "coding"? 3. Affective and motivational aspects. What is the impact of personal beliefs about intelligence (mindset) and about CS ability? What the role of teaching approaches? This research has been conducted both through historical and philosophical argumentation, and through quantitative and qualitative studies (both on nationwide samples and small significant ones), in particular through the lens of (often exaggerated) claims about transfer from CS to other skills. Four important claims are substantiated. 1. CS should be introduced in K-12 as a tool to understand and act in our digital world, and to use the power of computation for meaningful learning. CT is the conceptual sediment of that learning. We designed a curriculum proposal in this direction. 2. The expressions CT (useful to distantiate from digital literacy) and "coding" can cause misconceptions among teachers, who focus mainly on transfer to general thinking skills. Both disciplinary and pedagogical teacher training is hence needed. 3. Some plugged and unplugged teaching tools have intrinsic constructivist characteristics that can facilitate CS learning, as shown with proposed activities. 4. Growth mindset is not automatically fostered by CS, while not studying CS can foster fixed beliefs. Growth mindset can be fostered by creative computing, leveraging on its constructivist aspects

Teaching informatics to novices: big ideas and the necessity of optimal guidance

This thesis reports on the two main areas of our research: introductory programming as the traditional way of accessing informatics and cultural teaching informatics through unconventional pathways. The research on introductory programming aims to overcome challenges in traditional programming education, thus increasing participation in informatics. Improving access to informatics enables individuals to pursue more and better professional opportunities and contribute to informatics advancements. We aimed to balance active, student-centered activities and provide optimal support to novices at their level. Inspired by Productive Failure and exploring the concept of notional machine, our work focused on developing Necessity Learning Design, a design to help novices tackle new programming concepts. Using this design, we implemented a learning sequence to introduce arrays and evaluated it in a real high-school context. The subsequent chapters discuss our experiences teaching CS1 in a remote-only scenario during the COVID-19 pandemic and our collaborative effort with primary school teachers to develop a learning module for teaching iteration using a visual programming environment. The research on teaching informatics principles through unconventional pathways, such as cryptography, aims to introduce informatics to a broader audience, particularly younger individuals that are less technical and professional-oriented. It emphasizes the importance of understanding informatics's cultural and scientific aspects to focus on the informatics societal value and its principles for active citizenship. After reflecting on computational thinking and inspired by the big ideas of science and informatics, we describe our hands-on approach to teaching cryptography in high school, which leverages its key scientific elements to emphasize its social aspects. Additionally, we present an activity for teaching public-key cryptography using graphs to explore fundamental concepts and methods in informatics and mathematics and their interdisciplinarity. In broadening the understanding of informatics, these research initiatives also aim to foster motivation and prime for more professional learning of informatics