Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Factors Affecting the Adoption of Peer Instruction in Computing Courses

Peer Instruction (PI) as defined by Mazur, and variations on this pedagogic technique, have been in use in computing courses for about a decade. Despite dozens of educational research publications documenting positive learning effects, improved retention, student acceptance, and effectiveness for large classes; PI does not appear to be widely adopted for computing courses. This paper reports on a three-way investigation into this apparent contradiction. First, the authors reflect on their own adoption, practice, experience, and abandonment of the use of PI in computing courses. Second, we surveyed the literature regarding the use of PI in computing courses and present a summary of the research findings, variations, and extensions to PI used in computing courses. Third, a survey of computing instructors was conducted to gauge the attitude toward PI in computing courses. To add context, this report considers publications documenting usage of PI in STEM courses, and the adoption of other pedagogic techniques in computing. Particular effort was made to identify the reasons computing instructors don’t adopt PI. This report also includes advice to instructors considering adopting PI in computing courses

An International Pilot Study of K-12 Teachers’Computer Science Self-Esteem

Computer Science (CS) is a new subject area for many K-12 teachersaround the world, requiring new disciplinary knowledge and skills.Teacher social-behavioral factors (e.g. self-esteem) have been foundto impact learning and teaching, and a key part of CS curriculumimplementation will need to ensure teachers feel confident to de-liver CS. However, studies about CS teacher self-esteem are lacking.This paper presents an analysis of publicly available data (n=219)from a pilot study using a Teacher CS Self-Esteem scale. Analy-sis revealed significant differences, including 1) females reportedsignificantly lower CS self-esteem than males, 2) primary teachersreported lower levels of CS self-esteem than secondary teachers, 3)those with no CS teaching experience reported significantly lowerCS self-esteem, 4) teachers with 0-3 years experience had a neg-ative CS self-esteem, but after four years, teachers had a positiveCS self-esteem, and 5) teachers who lived further from metropol-itan areas and in some countries reported lower CS self-esteem.These initial findings suggest a pressing need for future researchto look further into teacher CS self-esteem to inform teacher CSprofessional development

The Design and Evaluation of an Educational Software Development Process for First Year Computing Undergraduates

First year, undergraduate computing students experience a series of well-known challenges when learning how to design and develop software solutions. These challenges, which include a failure to engage effectively with planning solutions prior to implementation ultimately impact upon the students’ competency and their retention beyond the first year of their studies. In the software industry, software development processes systematically guide the development of software solutions through iterations of analysis, design, implementation and testing. Industry-standard processes are, however, unsuitable for novice programmers as they require prior programming knowledge. This study investigates how a researcher-designed educational software development process could be created for novice undergraduate learners, and the impact of this process on their competence in learning how to develop software solutions. Based on an Action Research methodology that ran over three cycles, this research demonstrates how an educational software development methodology (termed FRESH) and its operationalised process (termed CADET which is a concrete implementation of the FRESH methodology), was designed and implemented as an educational tool for enhancing student engagement and competency in software development. Through CADET, students were reframed as software developers who understand the value in planning and developing software solutions, and not as programmers who prematurely try to implement solutions. While there remain opportunities to further enhance the technical sophistication of the process as it is implemented in practice, CADET enabled the software development steps of analysis and design to be explicit elements of developing software solutions, rather than their more typically implicit inclusion in introductory CS courses. The research contributes to the field of computing education by exploring the possibilities of – and by concretely generating – an appropriate scaffolded methodology and process; by illustrating the use of computational thinking and threshold concepts in software development; and by providing a novel evaluation framework (termed AKM-SOLO) to aid in the continuous improvement of educational processes and courses by measuring student learning experiences and competencies

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

Effect of Self-efficacy and Emotional Engagement on Introductory Programming Students

Today organisations both in the private and public sectors rely on Information Technology (IT) solutions and continue to make significant investments enabling business via IT. The increase in investment in IT is due to the demand for more efficient and cost-effective delivery of products and services. The dependency on IT and the increased level of investment in IT have both motivated a wider accountability focus on strategic technology initiatives, and a complex mix of political, organisational, technical and cultural shifts requiring far-sighted management and governance of IT. Throughout the last decade, systems, processes, standards and best practice frameworks have been developed to facilitate effective IT governance. However, a large number of IT initiatives fail to deliver. Getting value from technology deployment via effective IT governance remains a key concern of management. This paper presents the outcome of the analysis of four IT deployment cases studies. The analysis of the four case studies demonstrated a strong connection between project failures and inadequate governance practices

Transformative education: culture-based pedagogies in urban, predominately Black American elementary schools

Problems facing Black American students in underfunded urban schools continue to threaten the viability of public education for this and other nontraditional student populations. These problems, along with the growing diversity of American children, prompt a reexamination of extant data on the practices associated with the effective teaching of Black American students. This meta-ethnography illuminates the findings of four case studies and then explores their implications for teaching. By looking through the lens of critical race theory, this study explores how effective teachers use culture-based instruction to better engage Black American students in the process of learning. Findings identify core teacher behaviors and instructional practices that support the successful teaching of urban Black American primary school children

An Exploration of Traditional and Data Driven Predictors of Programming Performance

This thesis investigates factors that can be used to predict the success or failure of students taking an introductory programming course. Four studies were performed to explore how aspects of the teaching context, static factors based upon traditional learning theories, and data-driven metrics derived from aspects of programming behaviour were related to programming performance. In the first study, a systematic review into the worldwide outcomes of programming courses revealed an average pass rate of 67.7\%. This was found to have not significantly changed over time, or to have differed based upon aspects of the teaching context, such as the programming language taught to students. The second study showed that many of the factors based upon traditional learning theories, such as learning styles, are context dependent, and fail to consistently predict programming performance when they are applied across different teaching contexts. The third study explored data-driven metrics derived from the programming behaviour of students. Analysing data logged from students using the BlueJ IDE, 10 new data-driven metrics were identified and validated on three independently gathered datasets. Weaker students were found to make a greater percentage of successive errors, and spend a greater percentage of their lab time resolving errors than stronger students. The Robust Relative algorithm was developed to hybridize four of the strongest data-driven metrics into a performance predictor. The novel relative scoring of students based upon how their resolve times for different types of errors compared to the resolve times of their peers, resulted in a predictor which could explain a large proportion of the variance in the performance of three independent cohorts, $R^2$ = 42.19\%, 43.65\% and 44.17\% - almost double the variance which could be explained by Jadud's Error Quotient metric. The fourth study situated the findings of this thesis within the wider literature, by applying meta-analysis techniques to statistically synthesise fifty years of conflicting research, such that the most important factors for learning programming could be identified. 482 results describing the effects of 116 factors on programming performance were synthesised and consolidated to form a six class theoretical framework. The results showed that the strongest predictors identified over the past fifty years are data-driven metrics based upon programming behaviour. Several of the traditional predictors were also found to be influential, suggesting that both a certain level of scientific maturity and self-concept are necessary for programming. Two thirds of the weakest predictors were based upon demographic and psychological factors, suggesting that age, gender, self-perceived abilities, learning styles, and personality traits have no relevance for programming performance. This thesis argues that factors based upon traditional learning theories struggle to consistently predict programming performance across different teaching contexts because they were not intended to be applied for this purpose. In contrast, the main advantage of using data-driven approaches to derive metrics based upon students' programming processes, is that these metrics are directly based upon the programming behaviours of students, and therefore can encapsulate such changes in their programming knowledge over time. Researchers should continue to explore data-driven predictors in the future

Impact of Scratch on the achievements of first-year computer science students in programming in some Nigerian polytechnics

To support the advancement of modern civilisation, our institutions of higher learning must produce the right pool of professionals, who can develop innovative software. However, the teaching and learning of the first programming language (CS1) remains a great challenge for most educators and novice computer students. Indicators such as failure and attrition rates, and CS1 student engagement, continue to show that conventional pedagogy does not adequately meet the needs of some beginning CS students. For its ease in introducing novices to programming, Scratch—a visual programming environment following the constructionism philosophy of Seymour Papert—is now employed even in some higher education CS1 classes with mixed evidence of its impact. Scratch captures the constructionist agenda by its slogan: “Imagine, Program, Share.” Therefore, this study explored the impart of using a constructionist Scratch programming pedagogy on higher education CS1 students’ achievements. This study also sought to compare the impacts of the two CS1 modes: the conventional class - involving textual programming language, lectures and labs, and the constructionist Scratch inquiry-based programming class. It further aims to discover if gender, academic level, age, prior programming, and visual artistic abilities moderate the effects of programming pedagogy on students’ achievements. To realize the study’s aims, the study employed a quasi-experimental pretest-posttest nonequivalent groups design, involving four intact CS1 classes of polytechnic students (N = 418) in north-central Nigeria. The investigation was conducted in phases: a pilot (n = 236) and main (n=182) studies lasting two academic sessions, with each study comprising one experimental and one control group. In each session, learning in both modes lasted for six weeks. In both studies, purposive sampling was employed to select institutions, and selected institutions were randomly assigned to treatment groups. Instruments employed included CS1 Student Profile Questionnaire (CSPROQ) and Introductory Programming Achievement Test (IPAT). To strengthen the research design, I employed Coarsened Exact Matching (CEM) algorithm—after conducting a priori power analysis—to generate matched random samples of cases from both studies. Thus, research data employed in the analysis include: from the pilot, 41 cases in each treatment group; from the main study, 42 cases in each treatment group. Descriptive and inferential statistics were employed to find answers to research questions and test the research hypothesis. Data from both studies satisfied the requirements for statistical tests employed, i.e., t-test and ANCOVA. The alpha level used in testing hypotheses was p = 0.05. The dependent variable is the IPAT post-test score, while the independent variables are treatment, gender, age, academic achievement level, prior programming, and prior visual art. The covariate was the IPAT pretest score. Statistical analyses were conducted using SPSS version 23. The t-test results from both pilot and main studies indicated that, both programming pedagogies had significant effects on student IPAT scores, although the effect of the constructionist Scratch intervention was higher. Results from the one-way ANCOVA analysis of both pilot and main study data—while controlling for students’ IPAT pretest scores—yielded the same outcome: There was significant main effect of treatment on students’ IPAT posttest scores, although the impact was moderate. Controlling for pre test scores, analysis of the main studies data yielded no significant main effects of: gender, age, academic level, prior programming and prior visual artistic ability. The result from the main study also reveals no interaction effect of treatment, gender, academic level, age, prior programming, and prior artistic ability. While the quality of CS1 students’ performance in each session varies as their IPAT achievements show, yet the results of this research revealed a consistent pattern: Students in the constructionist Scratch class outperformed those in the conventional class, although the impart was moderate. This finding implies college students without prior programming experience can perform better in a class following a constructionist Scratch programming pedagogy. The study recommends the use of Scratch, following a constructionist pedagogy with first-year students in colleges, especially those without prior background in programmingSchool of ComputingPh. D. (Computing Education

The impact of peer instruction on ninth grade students’ trigonometry knowledge

In this study, we conducted peer instruction (PI) activities to promote student participation in the learning process and test the hypothesis that PI improves student achievement. Two ninth-grade classes were randomly assigned as treatment and control groups. Pre-test and post-test data were obtained for measuring mathematics achievement in trigonometry. Data were analyzed using analysis of covariance procedures with an alpha significance level of 0.05. Results indicated no significant effects of peer instruction on achievement. This study implies that more robust studies are needed to reveal the real effect of PI