Understanding and Addressing Misconceptions in Introductory Programming: A Data-Driven Approach

With the expansion of computer science (CS) education, CS teachers in K-12 schools should be cognizant of student misconceptions and be prepared to help students establish accurate understanding of computer science and programming. This exploratory design-based research (DBR) study implemented a data-driven approach to identify secondary school students’ misconceptions using both their compilation and test errors and provide targeted feedback to promote students’ conceptual change in introductory programming. Research subjects were two groups of high school students enrolled in two sections of a Java-based programming course in a 2017 summer residential program for gifted and talented students. This study consisted of two stages. In the first stage, students of group 1 took the introductory programming class and used an automated learning system, Mulberry, which collected data on student problem-solving attempts. Data analysis was conducted to identify common programming errors students demonstrated in their programs and relevant misconceptions. In the second stage, targeted feedback to address these misconceptions was designed using principles from conceptual change and feedback theories and added to Mulberry. When students of group 2 took the same introductory programming class and solved programming problems in Mulberry, they received the targeted feedback to address their misconceptions. Data analysis was conducted to assess how the feedback affected the evolution of students’ (mis)conceptions. Using students’ erroneous solutions, 55 distinct compilation errors were identified, and 15 of them were categorized as common ones. The 15 common compilation errors accounted for 92% of all compilation errors. Based on the 15 common compilation errors, three underlying student misconceptions were identified, including deficient knowledge of fundamental Java program structure, misunderstandings of Java expressions, and confusion about Java variables. In addition, 10 common test errors were identified based on nine difficult problems. The results showed that 54% of all test errors were related to the difficult problems, and the 10 common test errors accounted for 39% of all test errors of the difficult problems. Four common student misconceptions were identified based on the 10 common test errors, including misunderstandings of Java input, misunderstandings of Java output, confusion about Java operators, and forgetting to consider special cases. Both quantitative and qualitative data analysis were conducted to see whether and how the targeted feedback affected students’ solutions. Quantitative analysis indicated that targeted feedback messages enhanced students’ rates of improving erroneous solutions. Group 2 students showed significantly higher improvement rates in all erroneous solutions and solutions with common errors compared to group 1 students. Within group 2, solutions with targeted feedback messages resulted in significantly higher improvement rates compared to solutions without targeted feedback messages. Results suggest that with targeted feedback messages students were more likely to correct errors in their code. Qualitative analysis of students’ solutions of four selected cases determined that students of group 2, when improving their code, made fewer intermediate incorrect solutions than students in group 1. The targeted feedback messages appear to have helped to promote conceptual change. The results of this study suggest that a data-driven approach to understanding and addressing student misconceptions, which is using student data in automated assessment systems, has the potential to improve students’ learning of programming and may help teachers build better understanding of their students’ common misconceptions and develop their pedagogical content knowledge (PCK). The use of automated assessment systems with misconception identification components may be helpful in pre-college introductory programming courses and so is encouraged as K-12 CS education expands. Researchers and developers of automated assessment systems should develop components that support identifying common student misconceptions using both compilation and non-compilation errors. Future research should continue to investigate the use of targeted feedback in automated assessment systems to address students’ misconceptions and promote conceptual change in computer science education

Proceedings of the Resolve Workshop 2006

The aim of the RESOLVE Workshop 2006 was to bring together researchers and educators interested in: Refining formal approaches to software engineering, especially component-based systems, and introducing them into the classroom. The workshop served as a forum for participants to present and discuss recent advances, trends, and concerns in these areas, as well as formulate a common understanding of emerging research issues and possible solution paths

Top-down Automated Theorem Proving (Notes for Sir Timothy)

We describe a "top down" approach for automated theorem proving (ATP). Researchers might usefully investigate the forms of the theorems mathematicians use in practice, carefully examine how they differ and are proved in practice, and code all relevant domain concepts. These concepts encode a large portion of the knowledge in any domain. Furthermore, researchers should write programs that produce proofs of the kind that human mathematicians write (and publish); this means proofs that might sometimes have mistakes; and this means making inferences that are sometimes invalid. This approach is meant to contrast with the historically dominant "bottom up" approach: coding fundamental types (typically sets), axioms and rules for (valid) inference, and building up from this foundation to the theorems of mathematical practice and to their outstanding questions. It is an important fact that the actual proofs that mathematicians publish in math journals do not look like the formalized proofs of Russell & Whitehead's Principia Mathematica (or modern computer systems like Lean that automate some of this formalization). We believe some "lack of rigor" (in mathematical practice) is human-like, and can and should be leveraged for ATP.Comment: Cross list with cs.A

Atlanta University Center Faculty Publications: A Selective Bibliography, March 21, 2017

On February 28, 2006, the Robert W. Woodruff Library and the Library Advisory Council hosted an exhibit and reception celebrating the published works of Atlanta University Center faculty. As a complement to that event, the Robert W. Woodruff Library presents this publication, Atlanta University Center Faculty Publications: A Brief Bibliography, which highlights selected scholarly and research contributions of the Atlanta University Center faculty. The celebration has become an annual event and the bibliography illustrates the richness of faculty contributions within each institution and across the Atlanta University Center community. It should be viewed as a fluid document to be updated as new contributions emerge and will be preserved on the Librarys website for future reference and use

A comparison of code similarity analysers

Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code

An initial evaluation of MathPad(2): A tool for creating dynamic mathematical illustrations

MathPad(2) is a pen-based application prototype for creating mathematical sketches. Using a modeless gestural interface, it lets users make dynamic illustrations by associating handwritten mathematics with free-form drawings and provides a set of tools for graphing and evaluating mathematical expressions and solving equations. In this paper, we present the results of an initial evaluation of the MathPad(2) prototype, examining the user interface\u27s intuitiveness and the application\u27s perceived usefulness. Our evaluations are based on both performance and questionnaire results including first attempt gesture performance, interface recall tests, and surveys of user interface satisfaction and perceived usefulness. The results of our evaluation suggest that, although some test subjects had difficulty with our mathematical expression recognizer, they found the interface, in general, intuitive and easy to remember. More importantly, these results suggest the prototype has the potential to assist beginning physics and mathematics students in problem solving and understanding scientific concepts. (c) 2007 Elsevier Ltd. All rights reserved

The Stained Glass of Knowledge: On Understanding Novice Mental Models of Computing

Learning to program can be a novel experience. The rigidity of programming can be at odds with beginning programmer\u27s existing perceptions, and the concepts can feel entirely unfamiliar. These observations motivated this research, which explores two major questions: What factors influence how novices learn programming? and How can analogy by more appropriately leveraged in programming education? This dissertation investigates the factors influencing novice programming through multiple methods. The CS1 classroom is observed as a whole system , with consideration to the factors present in it that can influence the learning process. Learning\u27s cognitive processes are elaborated to ground exploration into specifically learning programming. This includes extensive literature review spanning multiple disciplines. This allows positioning to guide the investigation. The literature survey also contributes to greater understanding of learning cognition within computing education research through its disciplinary depth. The focus on analogy with the second question is motivated through the factors observed in the first question. Analogy\u27s role in cognition and in education is observed, and the analogical inclinations of technology as a field are showcased. Stigma surrounds the use of analogy in computer science education in spite of these indications. This motivated investigation on how the use of analogy could be better addressed in programming education in order to utilize its value. This research presents a tool for the design of well-formed analogy in programming to answer this question. It also investigates additional forms analogy can take in the classroom setting, proposing relevant cultural forms such as memes can be analogical vehicles that promote learner engagement. This research presents a strong case for the value of analogy use in the CS1 classroom, and provides a tool to facilitate the design of well-formed analogies. In identifying ways to better leverage analogy in the programming classroom, presenting this research will hopefully contribute to dispelling analogy\u27s bad reputation in computing education. By exploring factors that contribute to the learning process in CS1, this research frames education design as experience design. This motivates methods and considerations from user experience design, and investigates aspects of the whole system that can promote or deter a learner\u27s experience. This dissertation presents findings on understanding the learner\u27s experience in the programming classroom, and how analogy can be used to benefit their learning process