User modelling in Exploratory Learning Environments (ELEs) is an emerging field with several challenges to be addressed. Due to the freedom given to learners, the amount of information generated is very large, making the modelling process very challenging. Consequently, only relevant information should be used in the user modelling process. This, however, leads to other challenges such as identification of relevant information, finding an optimal knowledge representation and defining an inference mechanism by which this knowledge is used in diagnosing the learner. This thesis addresses the challenges of user modelling in ELEs by monitoring learners' behaviour and taking into account only relevant actions in the context of an ELE for the domain of mathematical generalisation. An iterative approach was used, in line with the iterative design of the ELE. The modelling mechanism employed a modified version of Case-based Reasoning (CBR) and was evaluated using pedagogical scenarios and data from simulated and real students. This approach has the advantage of storing only relevant information and allows learner diagnosis during as well as at the end of a task. The user model was further exploited to support learning related activities, such as prioritising feedback and grouping for collaboration. For feedback prioritisation, a mechanism based on Multi-criteria Decision Making was developed and tested with the help of educational experts. The grouping for collaboration approach was inspired from Group Technology, a method from cellular manufacturing systems, and its testing showed it produces meaningful groups. Both the feedback prioritisation and the grouping for collaboration mechanisms propose solutions that are particularly relevant for ELEs by considering pertinent criteria for this type of learning. To ensure optimal coverage of the knowledge base, the user modelling approach was enhanced with adaptive mechanisms for expanding the knowledge base, which was tested on real and simulated data. This approach ensures that learner diagnostic is possible when the initial knowledge base is small and/or new behaviours are encountered over time.

Thesis advisor: C. Patrick . Proctor / The demand for talent in the technology sector and the notion of computational thinking as everyday skills propel computing to enter middle school classrooms. The growing popularity of physical computing in educational spaces also infuses computing with elements of creativity and joy. Despite these recent movements, computing remains primarily in informal spaces due to a shortage of computer science teachers and the increasing focus on standardized testing. Arguing that computing and science share practices, this study views computing as problem-solving tools for science and proposes an integrated approach to teaching computing in science classrooms that takes advantage of the affordances of modern physical computing devices. Based on this perspective, a set of physical computing tools was developed to de-emphasize the mechanisms of computer science and shift focus to problem-solving and authentic scientific practices. This study aims to investigate the experiences of two science teachers and 16 students who learned to build self-regulated smart tabletop greenhouses with these tools as complete novices and critically evaluate the principles that undergird the design of the tools. With a qualitative, multiple case study design, this study answers two questions: 1) how did the teachers implement and reflect on their instruction? 2) how did the students engage with computing and science? Data from interviews and observations suggest that although the teachers shared similar instructional practices, their conceptualizations of the interplay between computing and science differed initially. They also had different instructional focuses and followed different trajectories in teaching, which may have produced subtly different understandings of computing-science relationships from their students. Despite these differences, all participants’ understandings of computing-science relationship conformed to a reciprocal pattern, which augmented the shared-practice argument for the integrated approach found in the literature. The challenges that the participants experienced contributed to the revision of the design of the computing tools. Based on these findings, the study recommends future directions in disambiguating the role of computing in middle school classrooms and in working with science teachers who are often simultaneously content experts and computing novices. / Thesis (PhD) — Boston College, 2019. / Submitted to: Boston College. Lynch School of Education. / Discipline: Teacher Education, Special Education, Curriculum and Instruction.

Graduating CS students face well-documented difficulties upon entering the workforce, with reports of a gap between what they learn and what is expected of them in industry. Project management, software testing, and debugging have been repeatedly listed as common "knowledge deficiencies" among newly hired CS graduates. Similar difficulties manifest themselves on a smaller scale in upper-level CS courses, like the Data Structures and Algorithms course at Virginia Tech: students are required to develop large and complex projects over a three to four week lifecycle, and it is common to see close to a quarter of the students drop or fail the course, largely due to the difficult and time-consuming nature of the projects. My research is driven by the hypothesis that regular feedback about the software development process, delivered during development, will help ameliorate these difficulties. Assessment of software currently tends to focus on qualities like correctness, code coverage from test suites, and code style. Little attention or tooling has been developed for the assessment of the software development process. I use empirical software engineering methods like IDE-log analysis, software repository mining, and semi-structured interviews with students to identify effective and ineffective software practices to formulate. Using the results of these analyses, I have worked on assessing students' development in terms of time management, test writing, test quality, and other "self-checking" behaviours like running the program locally or submitting to an oracle of instructor-written test cases. The goal is to use this information to formulate formative feedback about the software development process. In addition to educators, this research is relevant to software engineering researchers and practitioners, since the results from these experiments are based on the work of upper-level students who grapple with issues of design and work-flow that are not far removed from those faced by professionals in industry. / Doctor of Philosophy / Graduating CS students face well-documented difficulties upon entering the workforce, with reports of a gap between what they learn and what is expected of them as professional soft-ware developers. Project management, software testing, and debugging have been repeatedly listed as common "knowledge deficiencies" among newly hired CS graduates. Similar difficulties manifest themselves on a smaller scale in upper-level CS courses, like the DataStructures and Algorithms course at Virginia Tech: students are required to develop large and complex software projects over a three to four week lifecycle, and it is common to see close to a quarter of the students drop or fail the course, largely due to the difficult and time-consuming nature of the projects. The development of these projects necessitates adherence to disciplined software process, i.e., incremental development, testing, and debugging of small pieces of functionality. My research is driven by the hypothesis that regular feedback about the software development process, delivered during development, will help ameliorate these difficulties. However, in educational contexts, assessment of software currently tends to focus on properties of the final product like correctness, quality of automated software tests, and adherence to code style requirements. Little attention or tooling has been developed for the assessment of the software development process. In this dissertation, I quantitatively characterise students' software development habits, using data from numerous sources: us-age logs from students' software development environments, detailed sequences of snapshots showing the project's evolution over time, and interviews with the students themselves. I analyse the relationships between students' development behaviours and their project out-comes, and use the results of these analyses to determine the effectiveness or ineffectiveness of students' software development processes. I have worked on assessing students' development in terms of time management, test writing, test quality, and other "self-checking"behaviours like running their programs locally or submitting them to an online system that uses instructor-written tests to generate a correctness score. The goal is to use this information to assess the quality of one's software development process in a way that is formative instead of summative, i.e., it can be done while students work toward project completion as opposed to after they are finished. For example, if we can identify procrastinating students early in the project timeline, we could intervene as needed and possibly help them to avoid the consequences of bad project management (e.g., unfinished or late project submissions).In addition to educators, this research is relevant to software engineering researchers and practitioners, since the results from these experiments are based on the work of upper-level students who grapple with issues of design and work-flow that are not far removed from those faced by professionals in industry.

This thesis is about students’ approaches to learning (SAL) in computer science education. Since the initial development of SAL instruments and inventories in the 70’s, they have been used as a means to understand students’ approaches to learning better, as well as to measure and predict academic achievement (such as retention, grades and credits taken) and other correlating factors. It is an instrument to measure a student’s study strategies – not how “good” a student is. A Swedish short version of Approaches and Study Skills Inventory for Students (ASSIST) was used to gather information on whether we, through context and content, encouraged sustainable study behaviour among our students. ASSIST was used in two distinct situations: 1) Evaluation and evolvement of an online programming course design, and 2) Engineering education in media technology and computer science in a campus environment where approaches to learning has been evaluated and studied over time during the five year long programmes. Repeated measurements have been analysed against factors predicting academic achievement, and have been evaluated on a cohort level (not individual) in order to clarify patterns rather than individual characteristics. Significant for both projects was that a surface approach to learning correlated negatively with retention. Students who adopted a combination of deep and strategic approach to learning performed better in terms of grades, ECTS credits completed and perceived value of the education. As part of developmental tools it can be beneficial to use ASSIST at a group level in order to see what kind of approach a course design or a programme supports among the students. / <p>QC 20161028</p>

Computer Science is a complex field, and even experts do not always agree how the field should be defined. Though a moderate amount is known about how precollege students think about the field of CS, less is known about how CS majors' conceptions of the field develop during the undergraduate curriculum. Given the difficulty of understanding CS, how do students make educational decisions like what electives or specializations to pursue? This work presents a theory of student conceptions of CS, based on 37 interviews with students and student advisers and analyzed with a grounded theory approach. Students tend to have one of three main views about CS: CS as an academic discipline focused on the mathematical study of algorithms, CS as mostly about programming but also incorporating supporting subfields, and CS as a broad discipline with many different (programming and non-programming) subfields. I have also developed and piloted a survey instrument to determine how prevalent each kind of conception is in the undergraduate population. I also present a theory of student educational decisions in CS. Students do not usually have specific educational goals in CS and instead take an exploratory approach to their classes. Particularly enjoyable or unenjoyable classes cause them to narrow their educational focus. As a result, students do not reason very deeply about the CS content of their classes when they make educational decisions. This work makes three main contributions: the theory of student conceptions, the theory of student educational decisions, and the preliminary survey instrument for evaluating student conceptions. This work has applications in CS curriculum design as well as for future research in the CS education community.

abstract: This study explores experiences of women as they pursue post-secondary computing education in various contexts. Using in-depth interviews, the current study employs qualitative methods and draws from an intersectional approach to focus on how the various barriers emerge for women in different types of computing cultures. In-depth interviews with ten participants were conducted over the course of eight months. Analytical frameworks drawn from the digital divide and explorations of the role of hidden curricula in higher education contexts were used to analyze computing experiences in earlier k-12, informal, workplace, and post-secondary educational contexts to understand how barriers to computing emerge for women. Findings suggest several key themes. First, early experiences in formal education contexts are alienating women who develop an interest in computing. Opportunities for self-guided exploration, play, and tinkering help sustain interest in computing for women of color to engage in computing at the post-secondary level. Second, post-secondary computing climates remain hostile places for women, and in particular, for women of color. Thirdly, women employ a combination of different strategies to navigate these post-secondary computing cultures. Some women internalized existing dominant cultures of computing programs. Others chose exclusively online programs in computing to avoid negative interactions based on assumptions about their identity categories. Some women chose to forge their own pathways through computing to help diversify the culture via teaching, creating their own businesses, and through social programs. / Dissertation/Thesis / Ph.D. Justice Studies 2012

Studies assessing novice programming proficiency have often found that many students coming out of introductory-level programming courses still struggle with programming. To address this, some researchers have attempted to find and develop ways to better help students succeed in learning to program. This dissertation research contributes to this area by studying the programming processes of students trained through a specific program design curriculum, How to Design Programs (HTDP). HTDP is an introductory-level curriculum for teaching program design that teaches a unique systematic process called the design recipe that leverages the structure of input data to design programs. The design recipe explicitly scaffolds learners through the program design process by asking students to produce intermediate artifacts that represent a given problem in different ways up to a program solution to the problem. Although HTDP is used in several higher-education institutions and some K-12 programs, how HTDP-trained students design programs towards problems, particularly ones with multiple task-components, has not been thoroughly studied. The overarching goal of this dissertation is to gain an understanding and insight into how students use the techniques put forth by the design recipe towards designing solutions for programming problems. I conducted a series of exploratory user studies with HTDP-trained student cohorts from HTDP course instances across two different universities to collect and analyze students’ programming process data in situ. I synthesized findings from each study towards an overall conceptual framework, which serves as a data-grounded theory that captures several facets of HTDP-trained students’ program design process. The main contribution of this work is this theory, which describes: (1) the program design-related skills that students used and the levels of complexity at which they applied these skills, (2) how students’ use of design skills evolve during a course, (3) the interactions between program design skills and course contexts that influenced how students applied their skills, and (4) the programming process patterns by which students approached the programming problems we gave and how these approaches relate towards students’ success with the problems. Using insights from the theory, I describe recommendations toward pedagogical practices for teaching HTDP-based courses, as well as broader reflections towards teaching introductory CS.

As the field of computer security continues to grow, it becomes increasingly important to educate the next generation of security professionals. However, much of the current education landscape primarily focuses on teaching defensive skills. Teaching offensive security, otherwise known as ethical hacking, is an important component in the education of all students who hope to contribute to the field of cybersecurity. Doing so requires a careful consideration of what ethical, legal, and practical issues arise from teaching students skills that can be used to cause harm. In this thesis, we first examine the current state of cybersecurity education in the United States through a holistic view of funding, certifications, and course offerings. We then offer a framework to navigate the ethical and legal issues of teaching offensive security, as well as serve as a technical reference of useful tools for configuring and conducting a course in ethical hacking. Together, these contributions can be a baseline for educators looking to create courses on ethical hacking topics.

One of the greatest achievements of Scratch as an educational tool is the eager willingness of programmers to use existing projects as the starting point for their own projects, a practice known as remixing. Despite the importance of remixing as a foundation of collaborative and communal learning, the practice remains poorly understood. Without a clear picture of how and why Scratch programmers remix a project as a starting point of their own projects, this programming community would remain in the dark about which programming practices encourage and facilitate remixing. The designers of programming environments for blocks lack feedback on how the remixing facility is used in the wild. To gain a deeper insight into remixing, this thesis presents the results of a comprehensive study of this practice in Scratch that investigates the following heretofore unexplored dimensions of remixing: (1) the prevailing modifications that remixes perform on existing projects, (2) the impact of the original project's code quality on the granularity, extent, and development time of the modifications in the remixes, and (3) the propensity of the dominant programming practices in the original project to remain so in the remixes. Our findings can be used to promote those programming practices in the Scratch community that encourage remixing while also improving this practice's effectiveness, thus benefiting the educational and end-user programming communities. / Master of Science / The Scratch programming language has become an intrinsically important tool in introductory CS education. A visual, block-based language, Scratch is web-based, featuring an enormous online programming community, through which projects are eagerly shared. One of the unique learning provisions of Scratch is the ability to easily start a project by modifying someone else’s project, a practice referred to as remixing. Despite the central role that remixing plays in enabling the communal and collaborative learning styles in the Scratch community, the practice of remixing remains inadequately understood. This knowledge gap leaves the Scratch community in the dark about which programming practices encourage and facilitate remixing, as well as deprives Scratch environment designers from actionable feedback on how the remixing facility is used in the wild. To address this problem, this thesis reports on the results of an exploratory study of remixing in Scratch that investigates three heretofore unexplored dimensions of this practice. First, we study the general remixing trends in terms of how remixes modify the original projects. Second, we infer the impact of a project’s code quality on the modifications in its remixes and the development time. Finally, we investigate whether programmers adopt the techniques and practices of the remixed projects. Computing educators can apply our findings to enhance the educational effectiveness of Scratch by encouraging the practice and magnitude of remixing.

Much of software engineering research and practice is concerned with improving software quality. While enormous prior efforts have focused on improving the quality of programs, this dissertation instead provides the means to educate the next generation of programmers who care deeply about software quality. If they embrace the culture of quality, these programmers would be positioned to drastically improve the quality of the software ecosystem. This dissertation describes novel methodologies, techniques, and tools for introducing novice programmers to software quality and its systematic improvement. This research builds on the success of Scratch, a popular novice-oriented block-based programming language, to support the learning of code quality and its improvement. This dissertation improves the understanding of quality problems of novice programmers, creates analysis and quality improvement technologies, and develops instructional approaches for teaching quality improvement. The contributions of this dissertation are as follows. (1) We identify twelve code smells endemic to Scratch, show their prevalence in a large representative codebase, and demonstrate how they hinder project reuse and communal learning. (2) We introduce four new refactorings for Scratch, develop an infrastructure to support them in the Scratch programming environment, and evaluate their effectiveness for the target audience. (3) We study the impact of introducing code quality concepts alongside the fundamentals of programming with and without automated refactoring support. Our findings confirm that it is not only feasible but also advantageous to promote the culture of quality from the ground up. The contributions of this dissertation can benefit both novice programmers and introductory computing educators. / Doctor of Philosophy / Software remains one of the most defect-prone artifacts across all engineering disciplines. Much of software engineering research and practice is concerned with improving software quality. While enormous prior efforts have focused on improving the quality of programs, this dissertation instead provides the means to educate the next generation of programmers who care deeply about software quality. If they embrace the culture of quality, these programmers would be positioned to drastically improve the quality of the software ecosystem, akin to professionals in traditional engineering disciplines. This dissertation describes novel methodologies, techniques, and tools for introducing novice programmers to software quality and its systematic improvement. This research builds on the success of Scratch, a popular visual programming language for teaching introductory students, to support the learning of code quality and its improvement. This dissertation improves the understanding of quality problems of novice programmers, creates analysis and quality improvement technologies, and develops instructional approaches for teaching quality improvement. This dissertation contributes (1) a large-scale study of recurring quality problems in Scratch projects and how these problems hinder communal learning, (2) four new refactorings, quality improving behavior-preserving program transformations, as well as their implementation and evaluation, (3) a study of the impact of introducing code quality concepts alongside the fundamentals of programming with and without automated refactoring support. Our findings confirm that it is not only feasible but also advantageous to promote the culture of quality from the ground up. The contributions of this dissertation can benefit both novice programmers and introductory computing educators.