Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

A review of Australasian investigations into problem solving and the novice programmer

This Australasian focused review compares a number of recent studies that have identified difficulties encountered by novices while learning programming and problem solving. These studies have shown that novices are not performing at expected levels and many novices have only a fragile knowledge of programming, which may prevent them from learning and applying problem solving strategies. The review goes on to explore proposals for explicitly incorporating problem solving strategy instruction into introductory programming curricula and assessment, in an attempt to produce improved learning outcomes for novices. Finally, directions suggested by the reviewed studies are gathered and some unanswered questions are raised

A cognitive study of learning to program in introductory programming courses.

Programming is notoriously hard for novices to learn and a substantial number of learners fail in introduction to programming courses. It is not just a UK problem: a number of multi-institutional and multi-national studies reveal that the problem is well-known and is widespread. There is no general agreement about the causes or the remedies. The major factors which can be hypothesised as a cause of this phenomenon are: learners' psychology; teaching methods; complexity of programming. In this study, learners' common mistakes, bugs, misconceptions, frequencies and type of errors (syntactic and semantic) in the early stages of learning programming were studied. Noticing the patterns of rationales behind novices' mistakes swayed the study toward investigating novices' mental ability which was found to have a great effect on their learning performance. It was observed that novices reported a recognisable set of models of program execution each of which was logically acceptable as a possible answer and it appeared that some students even used these models systematically. It was suspected that the intellectual strategies behind their reasoning could have been built up from their programming background knowledge and it was surprising when it was found that some of those novices had not even seen a program before. A diagnostic questionnaire was designed that apparently examined a student's understanding of assignments and sequence but in fact was capturing the reasoning strategy behind their interpretation of each question, regardless of a correct or wrong answer. The questionnaire was administered in the first week of an introductory programming course, without giving any explanation of what the questions were about. A full response from most participants was received, despite the fact that the questions were unexplained. Confronted with a simple program, about half of novices seem to spontaneously invent and consistently apply a mental model of program execution. They were called the consistent subgroup. The other half are either unable to build a model or to apply one consistently. They were called the inconsistent subgroup. The first group perform very much better in their end-of-course examination than the rest. Meta-analysis of the results of six experiments in UK and Australia confirmed a strong effect of consistency on success which is highly significant (p < 0:001). A strong eect persisted in every group of candidates, sliced by background factors of programming experience (with/ without), relevant programming experience(with/without), and prior programming course (with/without) which might be thought to have had an effect on success. This result confirms that consistency is not simply provided by prior programming background. Despite the tendency in institutions to rely on students' prior programming background as a positive predictor for success, this study revealed that prior programming education did not have a noticeable eect on novices' success. A weak positive eect of prior programming experience was observed overall which appeared to be driven by one experiment with a programming-skilful population. This study shows that students in the consistent subgroup have the ability to build a mental model, something that follows rules like a mechanical construct. It also seems that when programming skill is measured by a weak assessment mechanism, the eect of consistency on success is reduced