Automated Feedback for 'Fill in the Gap' Programming Exercises

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)

Robust Controller Design of Networked Control Systems with Nonlinear Uncertainties

We address robust stabilization problem for networked control systems with nonlinear uncertainties and packet losses by modelling such systems as a class of uncertain switched systems. Based on theories on switched Lyapunov functions, we derive the robustly stabilizing conditions for state feedback stabilization and design packet-loss dependent controllers by solving some matrix inequalities. A numerical example and some simulations are worked out to demonstrate the effectiveness of the proposed design method.

Raman spectroscopic study of the mixed anion mineral Yecoraite Bi5Fe3O9(Te4+O3)(Te6+O4)2•9H2O

Tellurates are rare minerals as the tellurate anion is readily reduced to the tellurite ion. Often minerals with both tellurate and tellurite anions in the mineral are found. An example of such a mineral containing tellurate and tellurite is yecoraite. Raman spectroscopy has been used to study this mineral, the exact structure of which is unknown. Two Raman bands at 796 and 808 cm-1 are assigned to the ν1 (TeO4)2- symmetric and ν3 (TeO3)2- antisymmetric stretching modes and Raman bands at 699 cm-1 are attributed to the the ν3 (TeO4)2- antisymmetric stretching mode and the band at 690 cm-1 to the ν1 (TeO3)2- symmetric stretching mode. The intense band at 465 cm-1 with a shoulder at 470 cm-1 is assigned the (TeO4)2- and (TeO3)2- bending modes. Prominent Raman bands are observed at 2878, 2936, 3180 and 3400 cm-1. The band at 3936 cm-1 appears quite distinct and the observation of multiple bands indicates the water molecules in the yecoraite structure are not equivalent. The values for the OH stretching vibrations listed provide hydrogen bond distances of 2.625 Å (2878 cm-1), 2.636 Å (2936 cm-1), 2.697 Å (3180 cm-1) and 2.798 Å (3400 cm-1). This range of hydrogen bonding contributes to the stability of the mineral. A comparison of the Raman spectra of yecoraite with that of tellurate containing minerals kuranakhite, tlapallite and xocomecatlite is made.

Losing their marbles : syntax-free programming for assessing problem-solving skills

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.

Further evidence of a relationship between explaining, tracing and writing skills in introductory programming

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.