9,386 research outputs found

    Feedback Generation for Performance Problems in Introductory Programming Assignments

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    Providing feedback on programming assignments manually is a tedious, error prone, and time-consuming task. In this paper, we motivate and address the problem of generating feedback on performance aspects in introductory programming assignments. We studied a large number of functionally correct student solutions to introductory programming assignments and observed: (1) There are different algorithmic strategies, with varying levels of efficiency, for solving a given problem. These different strategies merit different feedback. (2) The same algorithmic strategy can be implemented in countless different ways, which are not relevant for reporting feedback on the student program. We propose a light-weight programming language extension that allows a teacher to define an algorithmic strategy by specifying certain key values that should occur during the execution of an implementation. We describe a dynamic analysis based approach to test whether a student's program matches a teacher's specification. Our experimental results illustrate the effectiveness of both our specification language and our dynamic analysis. On one of our benchmarks consisting of 2316 functionally correct implementations to 3 programming problems, we identified 16 strategies that we were able to describe using our specification language (in 95 minutes after inspecting 66, i.e., around 3%, implementations). Our dynamic analysis correctly matched each implementation with its corresponding specification, thereby automatically producing the intended feedback.Comment: Tech report/extended version of FSE 2014 pape

    Improving Introductory Computer Science Education with DRaCO

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    Today, many introductory computer science courses rely heavily on a specific programming language to convey fundamental programming concepts. For beginning students, the cognitive capacity required to operate with the syntactic forms of this language may overwhelm their ability to formulate a solution to a program. We recognize that the introductory computer science courses can be more effective if they convey fundamental concepts without requiring the students to focus on the syntax of a programming language. To achieve this, we propose a new teaching method based on the Design Recipe and Code Outlining (DRaCO) processes. Our new pedagogy capitalizes on the algorithmic intuitions of novice students and provides a tool for students to externalize their intuitions using techniques they are already familiar with, rather than with the syntax of a specific programming language. We validate the effectiveness of our new pedagogy by integrating it into an existing CS1 course at California Polytechnic State University, San Luis Obispo. We find that the our newly proposed pedagogy shows strong potential to improve students’ ability to program

    Relationships: computational thinking, pedagogy of programming, and Bloom’s Taxonomy

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    This study explores the relationship between computational thinking, teaching programming, and Bloom’s Taxonomy. Data is collected from teachers, academics, and professionals, purposively selected because of their knowledge of the topics of problem solving, computational thinking, or the teaching of programming. This data is analysed following a grounded theory approach. A computational thinking taxonomy is developed. The relationships between cognitive processes, the pedagogy of programming, and the perceived levels of difficulty of computational thinking skills are illustrated by a model. Specifically, a definition for computational thinking is presented. The skills identified are mapped to Bloom’s Taxonomy: Cognitive Domain. This mapping concentrates computational skills at the application, analysis, synthesis, and evaluation levels. Analysis of the data indicates that abstraction of functionality is less difficult than abstraction of data, but both are perceived as difficult. The most difficult computational thinking skill is reported as decomposition. This ordering of difficulty for learners is a reversal of the cognitive complexity predicted by Bloom’s model. The plausibility of this inconsistency is explored. The taxonomy, model, and the other results of this study may be used by educators to focus learning onto the computational thinking skills acquired by the learners, while using programming as a tool. They may also be employed in the design of curriculum subjects, such as ICT, computing, or computer science. <br/

    Computer Programming Effects in Elementary: Perceptions and Career Aspirations in STEM

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    The development of elementary-aged students’ STEM and computer science (CS) literacy is critical in this evolving technological landscape, thus, promoting success for college, career, and STEM/CS professional paths. Research has suggested that elementary- aged students need developmentally appropriate STEM integrated opportunities in the classroom; however, little is known about the potential impact of CS programming and how these opportunities engender positive perceptions, foster confidence, and promote perseverance to nurture students’ early career aspirations related to STEM/CS. The main purpose of this mixed-method study was to examine elementary-aged students’ (N = 132) perceptions of STEM, career choices, and effects from pre- to post-test intervention of CS lessons (N = 183) over a three-month period. Findings included positive and significant changes from students’ pre- to post-tests as well as augmented themes from 52 student interviews to represent increased enjoyment of CS lessons, early exposure, and its benefits for learning to future careers

    How can the teaching of programming be used to enhance computational thinking skills?

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    The use of the term computational thinking, introduced in 2006 by Jeanette Wing, is having repercussions in the field of education. The term brings into sharp focus the concept of thinking about problems in a way that can lead to solutions that may be implemented in a computing device. Implementation of these solutions may involve the use of programming languages.This study explores ways in which programming can be employed as a tool to teach computational thinking and problem solving. Data is collected from teachers, academics, and professionals, purposively selected because of their knowledge of the topics of problem solving, computational thinking, or the teaching of programming. This data is analysed following a grounded theory approach. A Computational Thinking Taxonomy is developed. The relationships between cognitive processes, the pedagogy of programming, and the perceived levels of difficulty of computational thinking skills are illustrated by a model.Specifically, a definition for computational thinking is presented. The skills identified are mapped to Bloom’s Taxonomy: Cognitive Domain. This mapping concentrates computational skills at the application, analysis, synthesis, and evaluation levels. Analysis of the data indicates that the less difficult computational thinking skills for beginner programmers are generalisation, evaluation, and algorithm design. Abstraction of functionality is less difficult than abstraction of data, but both are perceived as difficult. The most difficult computational thinking skill is reported as decomposition. This ordering of difficulty for learners is a reversal of the cognitive complexity predicted by Bloom’s model. The plausibility of this inconsistency is explored.The taxonomy, model, and the other results of this study may be used by educators to focus learning onto the computational thinking skills acquired by the learners, while using programming as a tool. They may also be employed in the design of curriculum subjects, such as ICT, computing, or computer science

    The AutoProof Verifier: Usability by Non-Experts and on Standard Code

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    Formal verification tools are often developed by experts for experts; as a result, their usability by programmers with little formal methods experience may be severely limited. In this paper, we discuss this general phenomenon with reference to AutoProof: a tool that can verify the full functional correctness of object-oriented software. In particular, we present our experiences of using AutoProof in two contrasting contexts representative of non-expert usage. First, we discuss its usability by students in a graduate course on software verification, who were tasked with verifying implementations of various sorting algorithms. Second, we evaluate its usability in verifying code developed for programming assignments of an undergraduate course. The first scenario represents usability by serious non-experts; the second represents usability on "standard code", developed without full functional verification in mind. We report our experiences and lessons learnt, from which we derive some general suggestions for furthering the development of verification tools with respect to improving their usability.Comment: In Proceedings F-IDE 2015, arXiv:1508.0338

    An integrated environment for problem solving and program development

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    A framework for an integrated problem solving and program development environment that addresses the needs of students learning programming is proposed. Several objectives have been accomplished: defining the tasks required for program development and a literature review to determine the actual difficulties involved in learning those tasks. A comprehensive Study of environments and tools developed to support the learning of problem solving and programming was then performed, covering programming environments, debugging aids, intelligent tutoring systems, and intelligent programming environments. This was followed by a careful analysis and critique of these systems, which uncovered the limitations that have prevented them from accomplishing their goals. Next, an extensive study of problem solving methodologies developed in this century was carried out and a common model for problem solving was produced. The tasks of program development were then integrated with the common model for problem solving. Then, the cognitive activities required for problem solving and program development were identified and also integrated with the common model to form a Dual Common Model for problem Solving and Program Development. This dual common model was then used to define the functional specifications for a problem solving and program development environment which was designed, implemented, tested, and integrated into the curriculum. The development of the new environment for learning problem solving and programming was followed by the planning of a cognitively oriented assessment method and the development of related instruments to evaluate the process and the product of problem solving. A detailed statistical experiment to study the effect of this environment on students\u27 problem solving and program development skills, including system testing by protocol analysis, and performance evaluation of students based on research hypotheses and questions, was also designed, implemented and the result reported
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