Teaching programming to beginners is a complex task. In this paper, the effects of three factors -choice of programming language, problem-solving training and the use of formative assessment -on learning to program were investigated. The study adopted an iterative methodological approach carried out across four consecutive years. To evaluate the effects of each factor (implemented as a single change in each iteration) on students' learning performance, the study used quantitative, objective metrics. The findings revealed that using a syntactically-simple language (Python) instead of a more complex one (Java) facilitated students' learning of programming concepts. Moreover, teaching problem-solving before programming yielded significant improvements in students' performance. These two factors were found to have variable effects on the acquisition of basic programming concepts. Finally, it was observed that effective formative feedback in the context of introductory programming depends on multiple parameters. The paper discusses the implications of these findings, identifies avenues for further research and argues for the importance of studies in computer science education anchored on sound research methodologies to produce generalizable results.
Computer programming is notoriously difficult to learn. To this end, regular practice in the form of application and reflection is an important enabler of student learning. However, educators often find that first-year B.Sc. students do not readily engage in such activities. Providing each student with a programmable robot, however, could be used to facilitate application and reflection since, potentially, robots facilitate engaging learning experiences whilst providing immediate and intuitive feedback. This paper explores whether an introductory course centred upon programming personal robots in preparation for an end-of-course event day-a Robot Olympics-can help students to firstly, engage in programming practice more frequently and secondly, improve the quality of their code. A survey was conducted to examine the students' programming practice behaviour and students' final coursework submissions were also reviewed for aspects of program quality. The findings from this cohort were compared to a reference-group from a previous cohort that shared similar learning objectives and entry requirements, yet had focused on web programming as opposed to using robots. The results reveal statistically significant increases in programming practice compared to the reference-group. Furthermore, being enrolled on the course culminating in the Robot Olympics was a significant predictor of two aspects of program quality: functional coherence and sophistication. This suggests that robot-centred courses can promote engagement with, and enhance some aspects of, programming practice.
The work presented in this thesis is part of a project in instruction based learning (IBL) for mobile robots were a robot is designed that can be instructed by its users through unconstrained natural language. The robot uses vision guidance to follow route instructions in a miniature town model.The aim of the work presented here was to detenn.ine the functional vocabulary of the robot in d1e form of "primitive procedures". In contrast to previous work in the field of instructable robots this was done following a "user-centred" approach were the main concern was to create primitive procedures that can be directly associated with natural language instructions. To achieve this, a corpus of human-to-human natural language instructions was collected and analysed. A set of primitive actions was found with which the collected corpus could be represented. These primitive actions were then implemented as robot-executable procedures.Natural language instructions are under-specified when destined to be executed by a robot. This is because instructors omit information tl1at they consider as "commonsense" and rely on the listener's sensory-motor capabilities to determine the details of the task execution. In this thesis the underspecification problem is solved by determining the missing information, either during the learning of new routes or during their execution by the robot. During learning, the missing information is determined by imitating the commonsense approach human listeners take to achieve the same purpose. During execution, missing information, such as the location of road layout features mentioned in route instructions, is determined from the robot's view by using image template matching. The original contribution of this thesis, in both these methods, lies in the fact that they are driven by the natural language examples found in the corpus collected for the IDL project. 3During the testing phase a high success rate of primitive calls, when these were considered individually, showed that the under-specification problem has overall been solved. A novel method for testing the primitive procedures, as part of complete route descriptions, is also proposed in this thesis. This was done by comparing the performance of human subjects when driving the robot, following route descriptions, with the performance of the robot when executing the same route descriptions. The results obtained from this comparison clearly indicated where errors occur from the time when a human speaker gives a route description to the time when the task is executed by a human listener or by the robot.Finally, a software speed controller is proposed in this thesis in order to control the wheel speeds of the robot used in this project. The controller employs PI (Proportional and Integral) and PID (Proportional, Integral and Differential) control and provides a good alternative to expensive hardware.
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