89 resultados para novice programming

em Queensland University of Technology - ePrints Archive


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In this research paper, we study a simple programming problem that only requires knowledge of variables and assignment statements, and yet we found that some early novice programmers had difficulty solving the problem. We also present data from think aloud studies which demonstrate the nature of those difficulties. We interpret our data within a neo-Piagetian framework which describes cognitive developmental stages through which students pass as they learn to program. We describe in detail think aloud sessions with novices who reason at the neo-Piagetian preoperational level. Those students exhibit two problems. First, they focus on very small parts of the code and lose sight of the "big picture". Second, they are prone to focus on superficial aspects of the task that are not functionally central to the solution. It is not until the transition into the concrete operational stage that decentration of focus occurs, and they have the cognitive ability to reason about abstract quantities that are conserved, and are equipped to adapt skills to closely related tasks. Our results, and the neo-Piagetian framework on which they are based, suggest that changes are necessary in teaching practice to better support novices who have not reached the concrete operational stage.

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This PhD project studied the genetic epistemology of novice programmers, and provides empirical evidence that the development of programming skills can be described using the neo-Piagetian cognitive development framework. The thesis identifies the manifestations of each of the early neo-Piagetian stages of development in the programming domain – that is: sensorimotor, preoperational and concrete operational. This research informs not only tertiary pedagogy, but teaching and learning of computer programming in any setting. It will enable educators to (a) identify the developmental stage of their students, (b) provide stage-appropriate learning resources and (c) assist students in transitioning to the next more mature stage of reasoning.

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Novice programmers have difficulty developing an algorithmic solution while simultaneously obeying the syntactic constraints of the target programming language. To see how students fare in algorithmic problem solving when not burdened by syntax, we conducted an experiment in which a large class of beginning programmers were required to write a solution to a computational problem in structured English, as if instructing a child, without reference to program code at all. The students produced an unexpectedly wide range of correct, and attempted, solutions, some of which had not occurred to their teachers. We also found that many common programming errors were evident in the natural language algorithms, including failure to ensure loop termination, hardwiring of solutions, failure to properly initialise the computation, and use of unnecessary temporary variables, suggesting that these mistakes are caused by inexperience at thinking algorithmically, rather than difficulties in expressing solutions as program code.

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We report on a longitudinal research study of the development of novice programmers in their first semester of programming. In the third week, almost half of our sample of students could not answer an explain-in-plain-English question, for code consisting of just three assignment statements, which swapped the values in two variables. We regard code that swaps the values of two variables as the simplest case of where a programming student can manifest a SOLO relational response. Our results demonstrate that the problems many students face with understanding code can begin very early, on relatively trivial code. However, using traditional programming exercises, these problems often go undetected until late in the semester. New approaches are required to detect and fix these problems earlier.

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Recent research on novice programmers has suggested that they pass through neo-Piagetian stages: sensorimotor, preoperational, and concrete operational stages, before eventually reaching programming competence at the formal operational stage. This paper presents empirical results in support of this neo-Piagetian perspective. The major novel contributions of this paper are empirical results for some exam questions aimed at testing novices for the concrete operational abilities to reason with quantities that are conserved, processes that are reversible, and properties that hold under transitive inference. While the questions we used had been proposed earlier by Lister, he did not present any data for how students performed on these questions. Our empirical results demonstrate that many students struggle to answer these problems, despite the apparent simplicity of these problems. We then compare student performance on these questions with their performance on six explain in plain English questions.

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Recent research indicates that some of the difficulties faced by novice programmers are manifested very early in their learning. In this paper, we present data from think aloud studies that demonstrate the nature of those difficulties. In the think alouds, novices were required to complete short programming tasks which involved either hand executing ("tracing") a short piece of code, or writing a single sentence describing the purpose of the code. We interpret our think aloud data within a neo-Piagetian framework, demonstrating that some novices reason at the sensorimotor and preoperational stages, not at the higher concrete operational stage at which most instruction is implicitly targeted.

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Recent research has proposed Neo-Piagetian theory as a useful way of describing the cognitive development of novice programmers. Neo-Piagetian theory may also be a useful way to classify materials used in learning and assessment. If Neo-Piagetian coding of learning resources is to be useful then it is important that practitioners can learn it and apply it reliably. We describe the design of an interactive web-based tutorial for Neo-Piagetian categorization of assessment tasks. We also report an evaluation of the tutorial's effectiveness, in which twenty computer science educators participated. The average classification accuracy of the participants on each of the three Neo-Piagetian stages were 85%, 71% and 78%. Participants also rated their agreement with the expert classifications, and indicated high agreement (91%, 83% and 91% across the three Neo-Piagetian stages). Self-rated confidence in applying Neo-Piagetian theory to classifying programming questions before and after the tutorial were 29% and 75% respectively. Our key contribution is the demonstration of the feasibility of the Neo-Piagetian approach to classifying assessment materials, by demonstrating that it is learnable and can be applied reliably by a group of educators. Our tutorial is freely available as a community resource.

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Recent research from within a neo-Piagetian perspective proposes that novice programmers pass through the sensorimotor and preoperational stages before being able to reason at the concrete operational stage. However, academics traditionally teach and assess introductory programming as if students commence at the concrete operational stage. In this paper, we present results from a series of think aloud sessions with a single student, known by the pseudonym “Donald”. We conducted the sessions mainly over one semester, with an additional session three semesters later. Donald first manifested predominately sensorimotor reasoning, followed by preoperational reasoning, and finally concrete operational reasoning. This longitudinal think aloud study of Donald is the first direct observational evidence of a novice programmer progressing through the neo-Piagetian stages.

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In this paper, we look at the concept of reversibility, that is, negating opposites, counterbalances, and actions that can be reversed. Piaget identified reversibility as an indicator of the ability to reason at a concrete operational level. We investigate to what degree novice programmers manifest the ability to work with this concept of reversibility by providing them with a small piece of code and then asking them to write code that undoes the effect of that code. On testing entire cohorts of students in their first year of learning to program, we found an overwhelming majority of them could not cope with such a concept. We then conducted think aloud studies of novices where we observed them working on this task and analyzed their contrasting abilities to deal with it. The results of this study demonstrate the need for better understanding our students' reasoning abilities, and a teaching model aimed at that level of reality.

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Timely feedback is a vital component in the learning process. It is especially important for beginner students in Information Technology since many have not yet formed an effective internal model of a computer that they can use to construct viable knowledge. Research has shown that learning efficiency is increased if immediate feedback is provided for students. Automatic analysis of student programs has the potential to provide immediate feedback for students and to assist teaching staff in the marking process. This paper describes a “fill in the gap” programming analysis framework which tests students’ solutions and gives feedback on their correctness, detects logic errors and provides hints on how to fix these errors. Currently, the framework is being used with the Environment for Learning to Programming (ELP) system at Queensland University of Technology (QUT); however, the framework can be integrated into any existing online learning environment or programming Integrated Development Environment (IDE)

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This paper reports on a replication of earlier studies into a possible hierarchy of programming skills. In this study, the students from whom data was collected were at a university that had not provided data for earlier studies. Also, the students were taught the programming language Python, which had not been used in earlier studies. Thus this study serves as a test of whether the findings in the earlier studies were specific to certain institutions, student cohorts, and programming languages. Also, we used a non–parametric approach to the analysis, rather than the linear approach of earlier studies. Our results are consistent with the earlier studies. We found that students who cannot trace code usually cannot explain code, and also that students who tend to perform reasonably well at code writing tasks have also usually acquired the ability to both trace code and explain code.