Literature on employability signifies "enabling" learning environments where students encounter ill-formed and open-ended problems and are required to adapt and be creative. Varying forms of "projects" have populated computing curricula for decades and are generally deemed an answer to this call. We performed a qualitative study to describe if and how project course students are able to capitalize on the promise of enabling learning environments. This critical perspective was motivated by the circumstance of the present-day education systems being heavily regulated for the precipitated production of human capital. The students involved in our study described education system-imposed and group-imposed narratives of lost opportunities, as well as many self-related challenges. On the other hand, students welcomed autonomy as an enjoyable condition and linked it with motivation. Self-related attitudinal attributes such as taking care of one's own learning and whole-group commitment appeared as important conditions. The results highlight targets for interventions that can counteract unfruitful behaviors and continue the march of projects as a means to foster professionalism in global software engineering.
Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task which is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers. This paper focuses only on plagiarism detection and presents a detailed systematic review of the field of source-code plagiarism detection in academia. This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types. Perspectives on the meaning of source-code plagiarism detection in academia are presented, together with categorisations of the available detection tools and analyses of their effectiveness. While writing the review some interesting insights have been found about metrics and datasets for quantitative tool comparison, and categorisation of detection algorithms. Also, existing obfuscation methods classifications have been expanded together with a new definition of source-code plagiarism detection in academia.
High school computing education reform efforts have been ongoing across the United States, particularly in the past decade. Although national computer science (CS) for All initiatives are promising, states retain control over education policies. Recent computing education reform efforts in Maryland focused on providing every public high school student with access to high quality high school computing courses. Such access provides exposure to computing careers and better prepares a diverse pool of students for computing majors in college. This comprehensive embedded multi-level case study examines the computing education reform efforts from 2010 through 2016. The expansion of computing education indicates that while there was positive growth, the growth is not the same for all public high school students. Despite successes, barriers at the state, Local Education Agencies (LEA), school, and classroom levels persist and are discussed. The findings in this study are applicable to other states with similar policy structures.
Our work is situated in research on Computer Science (CS) learning in informal learning environments and literature on the factors that influence girls to enter CS. In this paper, we outline design choices around the creation of a summer programming camp for middle school youth. In addition, we describe a near-peer mentoring model we used that was influenced by Bandura?s self-efficacy theory. The purpose of this paper, apart from promoting transparency of program design, was to evaluate the effectiveness of our camp design in terms of increasing youths? interest, self-efficacy beliefs, and perceptions of parental support. We found significant gains for all three of these concepts. Additionally, we make connections between our design choices (e.g. videos, peer support, mentor support) and the affective gains by thematically analyzing interview data concerning the outcomes found in our camps.
This article aims to lay a foundation for the research and practice of ML education for creative practitioners. We begin by motivating the usefulness of ML for creative practice, and briefly describing the long history of ML's use in art, music, dance, and related fields. We propose a set of learning objectives for creative practitioners learning about ML, and we describe where these objectives likely differ from non-creative contexts. We describe a curriculum---including lecture topics and assignments, as well as underpinning abstractions and illustrative examples---developed to support these learning objectives in an introductory supervised learning class aimed at musicians, artists, and other creative practitioners. We also describe a set of scaffolding technologies to support constructivist experimentation and creative project development. These resources are aimed at students who may not be programmers, who may lack strong mathematics backgrounds, and who have an interest in creative work with technology (e.g., in music, art, game design). We describe our use of this curriculum and toolset an undergraduate course, a masters-level course, and an online MOOC. We use data collected from these classes to explore the following questions: (1) How successful were this curriculum and toolset in enabling students to meet the proposed learning objectives? (2) How did the proposed abstractions, examples, and tools function in student experimentation and reasoning? (3) What motivated students in these courses to study machine learning? And what types of creative work were enabled by successful attainment of the learning objectives? (4) What were significant challenges and misconceptions for these students? To answer these questions, we employ methods including quantitative analysis of software log data and assignment submissions, qualitative coding of student written work and forum posts, and examination of students' final creative projects. This work informs further practical work and research around ML education for creative practitioners. It also has broader implications for ML education for other populations, including contributing to development of pedagogical content knowledge for ML, illustrating how ML may be integrated earlier into CS curricula, and providing approaches and tools that can be adopted in education of the broader public.
We investigate conditions in which novices make reference errors when programming. We asked students from introductory programming courses to perform a simple code-writing task that required constructing references to computer objects and their attributes. By experimentally manipulating the nature of the attributes in the tasks, from identifying attributes (e.g. name or label) to descriptive attributes (e.g. calories, texture), the study revealed the relative frequencies with which students mistakenly omit an identifying attribute name while attempting to reference the attribute value. We explain how this behavior is consistent with the use of metonymy, a form of figurative expression in human communication. Our analysis also reveals how the presentation of examples can affect the construction of the reference in the student's solution. We discuss plausible accounts of the reference-point errors and how they may inform a working cognitive model.
It is important for both academics and students of practically every discipline to clearly comprehend the differences between academic and professional perspectives in terms of assessing a deliverable. It is especially interesting to determine whether the aspects (both quantitative and qualitative) deemed important to evaluate by an expert are the same as those established by academics and students. Such potential discrepancies are indicative of the unexpected challenges students may encounter once they graduate and begin working. In this work, we propose a learning activity in which computer engineering students made a video about their future profession after hearing an expert in the field who discussed about the characteristics and difficulties of his or her work. Academics, professional experts and students assessed the videos by means of a questionnaire. This article reports a quantitative and qualitative study of the results of this experience, which was conducted for three academic years. The study involved 63 students, 6 academics and 4 computer professionals with extensive experience; and 14 videos were evaluated. Professional experts proved to be the most demanding in the assessment, followed by academics. The least demanding group was the students. These differences are more salient if more substantial issues are examined. The experts focused more on aspects of content, whereas the student preferred to concentrate on format. The academics focus falls between these two extremes. Understanding how experts value knowledge can guide educators in their search for effective learning environments in computer education.
Computer science education efforts are expanding across the globe to equip students with the necessary computing skills for today's digital world. However, preparing students to become literate in computing activities requires the training of tens of thousands of teachers in computer science. The discrepancy between student needs and teacher preparation in computer science has raised questions of quality teachers, particularly for teachers who do not possess adequate content or pedagogical knowledge to teach computer science efficiently. To address this issue, we designed an instrument to measure knowledge needed to teach computer science (i.e., computer science pedagogical content knowledge). Results exhibited that our instrument measured aspects of teachers' computer science pedagogical content knowledge; however, teachers' prior background in teaching did not influence their performance. We discuss implications for future research and practice.