The Effects of Objects-First and Objects-Late Methods on Achievements of OOP Learners

Our research explored the effects of objects-first and objects-late methods on achievements of object-oriented programming (OOP) learners during a graduate course. The course’s scope was virtually identical for two groups, but the structure of the contents differed in sequence. The objects-first method emphasized the design and discussion of the object-oriented concepts from the very beginning while the objects-late deferred these concepts to the late lectures. The objects-first learners used all visual functionalities of BlueJ IDE. However, the objects-late learners started with only the text-based interfaces of BlueJ and they benefited its visual support in the last lectures. At the end of the study, we found that there was a statistically significant difference between OOP learner groups

Student success model in programming course: A case study in UUM

The complexity and difficulty ascribed to computer programming has been asserted to be the causes of its high rate of failure record and attrition. It is opined that programming either to novice, middle learner, and the self-branded geeks is always a course to be apprehensive of different studies with varying findings. Studies on factors leading to the success of programming course in higher institution have been carried out. The record at Universiti Utara Malaysia (UUM) shows that 38% of semester one undergraduate students failed the programming course in 2013. This really motivates this study, which aims at investigating the practical factors affecting the success of programming courses, and to position its’ theoretically findings to complement the existing findings. Data were gathered using a quantitative approach, in which a set of questionnaire were distributed to 282 sampled respondents, who are undergraduate and postgraduate students of Information Technology (IT) and Information and Communication Technology (ICT). Having screened and cleaned the data, which led to the deletion of four outlier records, independent T-test, correlation, and regression were run to test the hypotheses. The results of Pearson correlation test reveal that teaching tools, OOP concepts, motivation, course evaluation, and mathematical aptitude are positively related to academic success in programming course, while fear is found to be negatively related. In addition, the regression analysis explains that all the elicited independent variables except fear are strongly related. Besides, the independent T-test also discovers no deference between groups with and without previous programming experience

The Design and Implementation of a Notional Machine for teaching Introductory Programming

Comprehension of both programming and programs is a difficult task for novices to master, with many university courses that feature a programming component demonstrating significant failure and drop out rates. Many theories exist that attempt to explain why this is the case. One such theory, originally postulated by du Boulay, is that students do not understand the properties of the machine; they do not understand what they are or how they are controlling them by writing code. This idea formed the development of the notional machine, which exists solely as an abstraction of the physical machine to aid with its understanding and comprehension. This work contributes a design for a new notional machine and a graphical notation for its representation. The notional machine is designed to work with object-oriented languages (in particular Java). It provides several novel contributions over pre-existing models -- while existing similar models are generally constrained to line by line operation, the notional machine presented here can scale effectively across many program sizes, from few objects and lines to many. In addition, it is able to be used in a variety of formats (in both electronic and unplugged form). It also melds together three traditionally separate diagrams that had to be understood simultaneously (the stack trace, class diagram and object heap.) Novis, an implemented version of the notional machine, is also presented and evaluated. It is able to create automatic and animated versions of notional machine diagrams, and has been integrated into BlueJ's main interface. Novis can present static notional machine diagrams at selected stages of program execution, or animate ongoing execution in real time. The evaluation of Novis is presented in two parts. It is first tested alongside a selection of methodically chosen textbook examples to ensure it can visualise a range of useful programs, and it then undergoes usability testing with a group of first year computer science students

Approaches to Support Student Learning in Introductory Programming Laboratory Classes

Objectives: This thesis will explore some innovative solutions to communication difficulties that exist in higher education teaching of introductory programming. Communication between a teacher and student is important, as it is the main opportunity where a student can ask a teacher questions about a particular problem they have, and a teacher can give feedback to direct them towards a solution. It is expected that through utilising technology in laboratory practical classes, communication between teachers and student can be improved. Methods: This thesis primarily explores the possibilities of using student compiler and method invocation data, collected during a practical class and sent directly to a teacher. This data maybe beneficial as a method of allowing teachers to see if a student requires help. This thesis utilises a variety of research methods including questionnaires, observations of classroom interactions and collection of data recorded from student and teachers interactions with the technology. The approaches are used during an investigation into the current approaches of laboratory practical teaching, before progressing onto investigations using the technology developed that accompanies this thesis. Results: The results identified that the majority of the students and teachers who used the technology felt that it improved their ability to communicate within laboratory practical classes. The teachers felt that they could use the data collected by the technology to view activity from the students and see a student’s progress. The teachers could interpret the data collected from the technology and students who needed help could be identified. Conclusions: This thesis has demonstrated that technology has the potential to improve communication in laboratory classes, and enable teachers to support students more effectively. However, the technology developed in this thesis, does not eliminate the requirement for a teacher to interact with a student face-to-face, but rather its role is to act as an indicator of students who may need assistance

Teaching programming with computational and informational thinking

Computers are the dominant technology of the early 21st century: pretty well all aspects of economic, social and personal life are now unthinkable without them. In turn, computer hardware is controlled by software, that is, codes written in programming languages. Programming, the construction of software, is thus a fundamental activity, in which millions of people are engaged worldwide, and the teaching of programming is long established in international secondary and higher education. Yet, going on 70 years after the first computers were built, there is no well-established pedagogy for teaching programming. There has certainly been no shortage of approaches. However, these have often been driven by fashion, an enthusiastic amateurism or a wish to follow best industrial practice, which, while appropriate for mature professionals, is poorly suited to novice programmers. Much of the difficulty lies in the very close relationship between problem solving and programming. Once a problem is well characterised it is relatively straightforward to realise a solution in software. However, teaching problem solving is, if anything, less well understood than teaching programming. Problem solving seems to be a creative, holistic, dialectical, multi-dimensional, iterative process. While there are well established techniques for analysing problems, arbitrary problems cannot be solved by rote, by mechanically applying techniques in some prescribed linear order. Furthermore, historically, approaches to teaching programming have failed to account for this complexity in problem solving, focusing strongly on programming itself and, if at all, only partially and superficially exploring problem solving. Recently, an integrated approach to problem solving and programming called Computational Thinking (CT) (Wing, 2006) has gained considerable currency. CT has the enormous advantage over prior approaches of strongly emphasising problem solving and of making explicit core techniques. Nonetheless, there is still a tendency to view CT as prescriptive rather than creative, engendering scholastic arguments about the nature and status of CT techniques. Programming at heart is concerned with processing information but many accounts of CT emphasise processing over information rather than seeing then as intimately related. In this paper, while acknowledging and building on the strengths of CT, I argue that understanding the form and structure of information should be primary in any pedagogy of programming

Proceedings of the Second Program Visualization Workshop, 2002

The Program Visualization Workshops aim to bring together researchers who design and construct program visualizations and, above all, educators who use and evaluate visualizations in their teaching. The first workshop took place in July 2000 at Porvoo, Finland. The second workshop was held in cooperation with ACM SIGCSE and took place at HornstrupCentret, Denmark in June 2002, immediately following the ITiCSE 2002 Conference in Aarhus, Denmark

An Exploration of Traditional and Data Driven Predictors of Programming Performance

This thesis investigates factors that can be used to predict the success or failure of students taking an introductory programming course. Four studies were performed to explore how aspects of the teaching context, static factors based upon traditional learning theories, and data-driven metrics derived from aspects of programming behaviour were related to programming performance. In the first study, a systematic review into the worldwide outcomes of programming courses revealed an average pass rate of 67.7\%. This was found to have not significantly changed over time, or to have differed based upon aspects of the teaching context, such as the programming language taught to students. The second study showed that many of the factors based upon traditional learning theories, such as learning styles, are context dependent, and fail to consistently predict programming performance when they are applied across different teaching contexts. The third study explored data-driven metrics derived from the programming behaviour of students. Analysing data logged from students using the BlueJ IDE, 10 new data-driven metrics were identified and validated on three independently gathered datasets. Weaker students were found to make a greater percentage of successive errors, and spend a greater percentage of their lab time resolving errors than stronger students. The Robust Relative algorithm was developed to hybridize four of the strongest data-driven metrics into a performance predictor. The novel relative scoring of students based upon how their resolve times for different types of errors compared to the resolve times of their peers, resulted in a predictor which could explain a large proportion of the variance in the performance of three independent cohorts, $R^2$ = 42.19\%, 43.65\% and 44.17\% - almost double the variance which could be explained by Jadud's Error Quotient metric. The fourth study situated the findings of this thesis within the wider literature, by applying meta-analysis techniques to statistically synthesise fifty years of conflicting research, such that the most important factors for learning programming could be identified. 482 results describing the effects of 116 factors on programming performance were synthesised and consolidated to form a six class theoretical framework. The results showed that the strongest predictors identified over the past fifty years are data-driven metrics based upon programming behaviour. Several of the traditional predictors were also found to be influential, suggesting that both a certain level of scientific maturity and self-concept are necessary for programming. Two thirds of the weakest predictors were based upon demographic and psychological factors, suggesting that age, gender, self-perceived abilities, learning styles, and personality traits have no relevance for programming performance. This thesis argues that factors based upon traditional learning theories struggle to consistently predict programming performance across different teaching contexts because they were not intended to be applied for this purpose. In contrast, the main advantage of using data-driven approaches to derive metrics based upon students' programming processes, is that these metrics are directly based upon the programming behaviours of students, and therefore can encapsulate such changes in their programming knowledge over time. Researchers should continue to explore data-driven predictors in the future