This full paper presents results of a model developed using early assessment tasks as predictors to identify at-risk students. To date several studies have been conducted to identify and retain at-risk students in computer science courses. However, both researchers and teachers have long sought to understand early warning signs for identifying at-risk students. While coursework-based predictive models have been developed, they need further investigation, due to inconsistencies in a range of identified factors and techniques employed. This paper presents a classification tree analysis (manually created) and a Random forest classification-based predictive model that uses two variables to predict student performance in introductory programming. Visualisation of the decision tree results is employed as early warning signs for instructors to assist students who identified as at-risk. Data for the formative assessment tasks in the first two weeks of the semester was used for model development, validation and testing. The overall prediction accuracy of the model was 60%. The results of this study showed that it is possible to predict 77% of students that need support, as early as Week 3, based on student performance in continuous formative assessment tasks in a 12-week introductory programming course. Moreover, our classification tree analysis revealed that students who secured less than or equal to 25% in formative assessment tasks in the first two weeks are unlikely to attend or indeed fail the final exam. Additionally, the results provide useful insights for early interventions, to prevent attrition and failure and to increase student retention and student success.

Learning to program is a cognitively demanding activity. Students need to combine mental models of various concepts and constructs to solve problems. Many students new to IT and CS programs have little or no prior experience with abstract reasoning and problem-solving. Instructors attempt to present the core concepts early to allow adequate time for students to complete their programming assignments. However, misconceptions of basic concepts formed in the early stages often get propagated blocking any further progress. Such students often begin to form poor opinions about their capability leading to low self-esteem and performance.

The programming education literature includes many observations that pass rates are low in introductory programming courses, but few or no comparisons of student performance across courses. This paper addresses that shortcoming. Having included a small set of identical questions in the final examinations of a number of introductory programming courses, we illustrate the use of these questions to examine the relative performance of the students both across multiple institutions and within some institutions. We also use the questions to quantify the size and overall difficulty of each exam. We find substantial differences across the courses, and venture some possible explanations of the differences. We conclude by explaining the potential benefits to instructors of using the same questions in their own exams.

Ten selected questions have been included in 13 introductory programming exams at seven institutions in five countries. The students' results on these questions, and on the exams as a whole, lead to the development of a benchmark against which the exams in other introductory programming courses can be assessed. We illustrate some potential benefits of comparing exam performance against this benchmark, and show other uses to which it can be put, for example to assess the size and the overall difficulty of an exam. We invite others to apply the benchmark to their own courses and to share the results with us.

Previous studies of assessment of programming via written examination have focused on analysis of the examination papers and the questions they contain. This paper reports the results of a study that investigated how these final exam papers are developed, how students are prepared for these exams, and what pedagogical foundations underlie the exams. The study involved interviews of 11 programming lecturers. From our analysis of the interviews, we find that most exams are based on existing formulas that are believed to work; that the lecturers tend to trust in the validity of their exams for summative assessment; and that while there is variation in the approaches taken to writing the exams, all of the exam writers take a fairly standard approach to preparing their students to sit the exam. We found little evidence of explicit references to learning theories or models, indicating that the process is based largely on intuition and experience.

In this paper we report programming novices' perceptions and experiences of a Mobile Social Learning Environment (MSLE) designed to support the learning of programming through enabling increased novice-to-novice interactions. By capturing and analysing such interactions early it is hoped that instructors may identify misunderstandings and misconceptions of novice programmers, and provide timely feedback to alleviate novices' misunderstandings. The MSLE incorporates the use of mobile devices as well as the access to dedicated social networking sites. Novices from three different universities, each with different modes of delivery, participated in this research. Novices' perceived advantages of the MSLE include among other things instant access to discussions, increased interactions, instant feedback in a student-student context, and the ability to share ideas and views anytime, anywhere. Novices have experienced increased interactions among themselves and improved engagement with learning in the MSLE. Novices were able to form learning communities and able to share knowledge and ideas about programming language with each other. Some novices were able to provide assistance to others, hence indirectly, strengthening their own knowledge of programming. The cost of mobile devices and Internet charges on the other hand were perceived as disadvantages of a MSLE. Small screens, lack of programming applications for smart phones, laptops' battery life and distractions from other internet media were some of negative experiences observed by novices who used the MSLE.

This paper describes the development of a classification scheme that can be used to investigate the characteristics of introductory programming examinations. We describe the process of developing the scheme, explain its categories, and present a taste of the results of a pilot analysis of a set of CS1 exam papers. This study is part of a project that aims to investigate the nature and composition of formal examination instruments used in the summative assessment of introductory programming students, and the pedagogical intentions of the educators who construct these instruments.

Learning to program is known to be problematic for a signif- icant number of students as evidenced by high failure rates reported by Computer Science schools. Students either fail to comprehend a range of fundamental programming con- cepts or carry misunderstandings and misconceptions about programming well into the semester, leading to summative assessment failures. Multiple choice questions in summative assessments are a popular choice of instrument to test novice learners of programming, yet during their formative stages such questions are typically used in the traditional\\practice' or \\rote learning' contexts, leaving gaps in understanding of programming concepts. In this paper we report the use of multiple choice exercises in a guided learning approach, within the learning context of novice programmers who are typically rst-time university students of a Computer Sci- ence program. In addition to the use of multiple choice questions for practice, we propose a guided learning tool to identify cognitive lapses in learning programming. We re- port results of a pilot study that uses a partially-completed guided learning tool, to prevent students at the outset from falling into the cognitive traps that often ensnare the novice programmer.

We describe the implementation of an art installation that generates animated photomosaics of the viewer. Photomosaics are target images composed of smaller images known as tiles. When a photomosaic is viewed from afar the detail of the tiles is lost and the target image is evident. Up close, the opposite occurs: the detail of the tiles is evident and the target image is lost. Our system uses a photo of the viewer as the target and miniatures of the viewer's face as tiles. Evolutionary search is used to find the best selection and arrangement of tiles. Each newly found best image is then used as the frame of a movie. The resulting animations start from a random arrangement of tiles and gradually the viewer's face emerges and is clearly visible, and then gradually de-materialises into a random pattern.