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

An Exploration of Student Reasoning about Undergraduate Computer Science Concepts: An Active Learning Technique to Address Misconceptions

Computer science (CS) is a popular but often challenging major for undergraduates. As the importance of computing in the US and world economies continues to grow, the demand for successful CS majors grows accordingly. However, retention rates are low, particularly for under-represented groups such as women and racial minorities. Computing education researchers have begun to investigate causes and explore interventions to improve the success of CS students, from K-12 through higher education. In the undergraduate CS context, for example; student difficulties with pointers, functions, loops, and control flow have been observed. We and others have utilized student responses to multiple choice questions aimed at determining misconceptions, engaged in retroactive examination of code samples and design artifacts, and conducted interviews in an attempt to understand the nature of these problems. Interventions to address these problems often apply evidenced-based active learning techniques in CS classrooms as a way to engage students and improve learning.In this work, I employ a human-centered approach, one in which the focus of data collection is on the student thought processes as evidenced in their speech and writing. I seek to determine what students are thinking not only through what can be surmised in retrospect from the artifacts they create, but also to gain insight into their thoughts as they engage in the design, implementation,and analysis of those artifacts and as they reflect on those processes and artifacts shortly after. For my dissertation work, I have conducted four studies: 1. a conceptual assessment survey asking students to “Please explain your reasoning” after each answer to code tracing/execution questions followed by task-based interviews with a smaller, different group of students 2. a “coding in the wild” think aloud study that recorded the screen and audio of students as they implemented a simple program and explained their thought process 3. interview analyses of student design diagrams/documentation in a software engineering course, tasking students to explain their designs and comparing what they believed they had designed with what is actually shown from their submitted documentation. These first three studies were formative, leading to some key insights including the benefits students can gain from feedback, students’ tendencies to avoid complexity when programming or encountering concepts they do not fully grasp, the nature of student struggles with the planning stages of problem solving, and insight into the fragile understanding of some key CS concepts that students form. I leverage the benefits of feedback with guided prompts using the misconceptions uncovered in my formative studies to conduct a final, evaluative study. This study seeks to evaluate the benefits that can be gained from a guided feedback intervention for learning introductory programming concepts and compare those benefits and the effort and resource costs associated with each variation, comparing the costs and benefits associated with two forms of feedback. The first is an active learning technique I developed and deem misconception-based feedback (MBF), which has peers working in pairs use prompts based on misconceptions to guide their discussion of a recently completed coding assignment. The second is a human autograder (HAG) group acting as a control. HAG simulates typical autograders, supplying test cases and correct solutions, but utilizes a human stand-in for a computer. In both conditions, one student uses provided prompts to guide the discussion. The other student responds/interacts with their code based on the prompts. I captured screen and audio recordings of these discussions. Participants completed conceptual pre-tests and post-tests that asked them to explain their reasoning. I hypothesized that the MBF intervention will offer avaluable way to increase learning, address misconceptions, and get students more engaged that will be feasible in CS courses of any size and have benefits over the HAG intervention. Results show that for questions involving parameter passing with regards to pass by reference versus pass by value semantics, particularly with pointers, there were significant improvements in learning outcomes for the MBF group but not the HAG group

Development and Application of a Rasch Model Measure of Student Competency in University Introductory Computer Programming

University computer programming instruction nomenclature commonly uses the term Computer Science 1 (CS1) to describe introductory units of study. Success in CS1 is important as a pre-requisite for further study in programming and related disciplines. It is important to measure student progress and the antecedent influences. This study applied the Rasch Model and Messick’s Unified Theory of Validity to construct an interval level measure of CS1 competency with demonstrable suitability for this purpose

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

Evaluating and Securing Text-Based Java Code through Static Code Analysis

As the cyber security landscape dynamically evolves and security professionals work to keep apace, modern-day educators face the issue of equipping a new generation for this dynamic landscape. With cyber-attacks and vulnerabilities substantially increased over the past years in frequency and severity, it is important to design and build secure software applications from the group up. Therefore, defensive secure coding techniques covering security concepts must be taught from beginning computer science programming courses to exercise building secure applications. Using static analysis, this study thoroughly analyzed Java source code in two textbooks used at a collegiate level, with the goal of guiding educators to make a reference of the resources in teaching programming concepts from a security perspective. The resources include the methods of source code analysis and relevant tools, categorized bugs detected in the code, and compliant code examples with fixing the bugs. Overall, the first text revealed a relatively moderate bug rate of approximately 44% of files analyzed contained either regular or security bugs. About 13% of the total bugs found were security bugs and the most common security bug was related to the Pseudo Random security vulnerability. The second text produced a slightly larger bug rate of 53.80% with approximately 8% of security bugs. After combining the texts for an average rate, the total number of security bugs that were likely to appear was roughly 10% percent. This encompasses security bugs such as malicious code vulnerabilities and security vulnerabilities related to exposing or manipulating data in these programs

A Risk Assessment Methodology for Divesting Military Capabilities to Allied Nations

The United States spent over $400 billion dollars on national defense in 2005. Even with support for the war on terrorism still strong, it is doubtful that the U.S. can sustain such a level of defense investment. One strategy to offset the increasing burden of defense spending is to divest the procurement and/or sustainment of individual defense capabilities to allied nations. The decision to divest any capability, however, introduces risk. This thesis presents a methodology to quantify the risk of the decision to divest a military capability to an allied nation, where risk is defined as the set of risk scenarios, likelihoods and consequences possible under each decision alternative. Risk scenarios are composed of combinations of contingencies that require the capability considered for divestiture. The likelihood of each risk scenario is calculated as the product of the likelihoods of its constituent contingency events. The consequence of each risk scenario is calculated as the sum of the consequences of its constituent contingency events. Once the risk of each decision alternative is quantified this information can be used to rank alternatives and identify the scenarios that contribute most to the risk of each alternative

Specification of Software Architecture Reconfiguration

In the past years, Software Architecture has attracted increased attention by academia and industry as the unifying concept to structure the design of complex systems. One particular research area deals with the possibility of reconfiguring architectures to adapt the systems they describe to new requirements. Reconfiguration amounts to adding and removing components and connections, and may have to occur without stopping the execution of the system being reconfigured. This work contributes to the formal description of such a process. Taking as a premise that a single formalism hardly ever satisfies all requirements in every situation, we present three approaches, each one with its own assumptions about the systems it can be applied to and with different advantages and disadvantages. Each approach is based on work of other researchers and has the aesthetic concern of changing as little as possible the original formalism, keeping its spirit. The first approach shows how a given reconfiguration can be specified in the same manner as the system it is applied to and in a way to be efficiently executed. The second approach explores the Chemical Abstract Machine, a formalism for rewriting multisets of terms, to describe architectures, computations, and reconfigurations in a uniform way. The last approach uses a UNITY-like parallel programming design language to describe computations, represents architectures by diagrams in the sense of Category Theory, and specifies reconfigurations by graph transformation rules