Early identification of novice programmers' challenges in coding using machine learning techniques

It is well known that many first year undergraduate university students struggle with learning to program. Educational Data Mining (EDM) applies machine learning and statistics to information generated from educational settings. In this PhD project, EDM is used to study first semester novice programmers, using data collected from students as they work on computers to complete their normal weekly laboratory exercises. Analysis of the generated snapshots has shown the potential for early identification of students who later struggle in the course. The aim of this study is to propose a method for early identification of "at risk" students while providing suggestions on how they can improve their coding style. This PhD project is within its final year

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 and assessment of novice learning tools for problem solving and program development

There is a great demand for the development of novice learning tools to supplement classroom instruction in the areas of problem solving and program development. Research in the area of pedagogy, the psychology of programming, human-computer interaction, and cognition have provided valuable input to the development of new methodologies, paradigms, programming languages, and novice learning tools to answer this demand. Based on the cognitive needs of novices, it is possible to postulate a set of characteristics that should comprise the components an effective novice-learning tool. This thesis will discover these characteristics and provide recommendations for the development of new learning tools. This will be accomplished with a review of the challenges that novices face, an in-depth discussion on modem learning tools and the challenges that they address, and the identification and discussion of the vital characteristics that constitute an effective learning tool based on these tools and personal ideas

A study of novice programmer performance and programming pedagogy.

Identifying and mitigating the difficulties experienced by novice programmers is an active area of research that has embraced a number of research areas. The aim of this research was to perform a holistic study into the causes of poor performance in novice programmers and to develop teaching approaches to mitigate them. A grounded action methodology was adopted to enable the primary concepts of programming cognitive psychology and their relationships to be established, in a systematic and formal manner. To further investigate novice programmer behaviour, two sub-studies were conducted into programming performance and ability. The first sub-study was a novel application of the FP-Tree algorithm to determine if novice programmers demonstrated predictable patterns of behaviour. This was the first study to data mine programming behavioural characteristics rather than the learner’s background information such as age and gender. Using the algorithm, patterns of behaviour were generated and associated with the students’ ability. No patterns of behaviour were identified and it was not possible to predict student results using this method. This suggests that novice programmers demonstrate no set patterns of programming behaviour that can be used determine their ability, although problem solving was found to be an important characteristic. Therefore, there was no evidence that performance could be improved by adopting pedagogies to promote simple changes in programming behaviour beyond the provision of specific problem solving instruction. A second sub-study was conducted using Raven’s Matrices which determined that cognitive psychology, specifically working memory, played an important role in novice programmer ability. The implication was that programming pedagogies must take into consideration the cognitive psychology of programming and the cognitive load imposed on learners. Abstracted Construct Instruction was developed based on these findings and forms a new pedagogy for teaching programming that promotes the recall of abstract patterns while reducing the cognitive demands associated with developing code. Cognitive load is determined by the student’s ability to ignore irrelevant surface features of the written problem and to cross-reference between the problem domain and their mental program model. The former is dealt with by producing tersely written exercises to eliminate distractors, while for the latter the teaching of problem solving should be delayed until the student’s program model is formed. While this does delay the development of problem solving skills, the problem solving abilities of students taught using this pedagogy were found to be comparable with students taught using a more traditional approach. Furthermore, monitoring students’ understanding of these patterns enabled micromanagement of the learning process, and hence explanations were provided for novice behaviour such as difficulties using arrays, inert knowledge and “code thrashing”. For teaching more complex problem solving, scaffolding of practice was investigated through a program framework that could be developed in stages by the students. However, personalising the level of scaffolding required was complicated and found to be difficult to achieve in practice. In both cases, these new teaching approaches evolved as part of a grounded theory study and a clear progression of teaching practice was demonstrated with appropriate evaluation at each stage in accordance with action researc

A Systematic Mapping Study of Code Quality in Education -- with Complete Bibliography

While functionality and correctness of code has traditionally been the main focus of computing educators, quality aspects of code are getting increasingly more attention. High-quality code contributes to the maintainability of software systems, and should therefore be a central aspect of computing education. We have conducted a systematic mapping study to give a broad overview of the research conducted in the field of code quality in an educational context. The study investigates paper characteristics, topics, research methods, and the targeted programming languages. We found 195 publications (1976-2022) on the topic in multiple databases, which we systematically coded to answer the research questions. This paper reports on the results and identifies developments, trends, and new opportunities for research in the field of code quality in computing education

Fifty years of the Psychology of Programming

This paper reflects on the evolution (past, present and future) of the ‘psychology of programming' over the 50 year period of this anniversary issue. The International Journal of Human-Computer Studies (IJHCS) has been a key venue for much seminal work in this field, including its first foundations, and we review the changing research concerns seen in publications over these five decades. We relate this thematic evolution to research taking place over the same period within more specialist communities, especially the Psychology of Programming Interest Group (PPIG), the Empirical Studies of Programming series (ESP), and the ongoing community in Visual Languages and Human-Centric Computing (VL/HCC). Many other communities have interacted with psychology of programming, both influenced by research published within the specialist groups, and in turn influencing research priorities. We end with an overview of the core theories that have been developed over this period, as an introductory resource for new researchers, and also with the authors’ own analysis of key priorities for future research

How can the teaching of programming be used to enhance computational thinking skills?

The use of the term computational thinking, introduced in 2006 by Jeanette Wing, is having repercussions in the field of education. The term brings into sharp focus the concept of thinking about problems in a way that can lead to solutions that may be implemented in a computing device. Implementation of these solutions may involve the use of programming languages.This study explores ways in which programming can be employed as a tool to teach computational thinking and problem solving. Data is collected from teachers, academics, and professionals, purposively selected because of their knowledge of the topics of problem solving, computational thinking, or the teaching of programming. This data is analysed following a grounded theory approach. A Computational Thinking Taxonomy is developed. The relationships between cognitive processes, the pedagogy of programming, and the perceived levels of difficulty of computational thinking skills are illustrated by a model.Specifically, a definition for computational thinking is presented. The skills identified are mapped to Bloom’s Taxonomy: Cognitive Domain. This mapping concentrates computational skills at the application, analysis, synthesis, and evaluation levels. Analysis of the data indicates that the less difficult computational thinking skills for beginner programmers are generalisation, evaluation, and algorithm design. Abstraction of functionality is less difficult than abstraction of data, but both are perceived as difficult. The most difficult computational thinking skill is reported as decomposition. This ordering of difficulty for learners is a reversal of the cognitive complexity predicted by Bloom’s model. The plausibility of this inconsistency is explored.The taxonomy, model, and the other results of this study may be used by educators to focus learning onto the computational thinking skills acquired by the learners, while using programming as a tool. They may also be employed in the design of curriculum subjects, such as ICT, computing, or computer science