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

Review of Measurements Used in Computing Education Research and Suggestions for Increasing Standardization

The variables that researchers measure and how they measure them are central in any area of research. Which research questions can be asked and how they are answered depends on measurement. This paper describes a systematic review of the literature in computing education research to summarize the commonly used variables and measurements in 197 papers and to compare them to best practices in measurement for human-subjects research. Characteristics of the literature that are examined in the review include variables measured (including learner characteristics), measurements used, and type of data analysis. The review illuminates common practices related to each of these characteristics and their interactions with other characteristics. The paper lists standardized measurements that were used in the literature and highlights commonly used variables for which no standardized measures exist. To conclude, this review compares common practice in computing education to best practices in human-subjects research to make recommendations for increasing rigor

Educational Research Abstracts

Editors\u27 Note: As noted in previous issues of the Journal of Mathematics and Science: Collaborative Explorations, the purpose of this Educational Research Abstract section is to present current published research on issues relevant to math and science teaching at both the K-12 and college levels. Because educational research articles are published in so many different academic journals, it is a rare public school teacher or college professor who reads all the recent published reports on a particular instructional technique or curricular advancement. Indeed, the uniqueness of various pedagogical strategies has been tacitly acknowledged by the creation of individual journals dedicated to teaching in a specific discipline. Yet many of the insights gained in teaching certain physics concepts, biological principles, or computer science algorithms can have generalizability and value for those teaching in other fields or with different types of students. In this review, the focus is on background knowledge. Abstracts are presented according to a question examined in the published articles. Hopefully, such a format will trigger your reflections about the influence of students’ entering mathematical and scientific conceptions (and misconceptions,) as well as generate ideas about your own teaching situation. The abstracts presented here are not intended to be exhaustive, but rather a representative sampling of recent journal articles. Please feel free to identify other useful research articles on a particular theme or to suggest future teaching themes to be examined. You may send your comments and ideas via email to [email protected] or by regular mail to The College of William and Mary, P. O. Box 8795, Williamsburg, VA 23185-8795

Statistical and Machine Learning Models to Predict Programming Performance

This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant factors, that can predict whether students will be ‘weak’ or ‘strong’ programmers with approximately 80% accuracy after only three weeks of programming experience. This thesis makes three fundamental contributions. The first contribution is a longitudinal study identifying factors that influence introductory programming success, investigating 25 factors at four different institutions. Evidence of the importance of mathematics, comfort-level and computer game-playing as predictors of programming performance is provided. A number of new instruments were developed by the author and a programming self-esteem measure was shown to out-perform other previous comparable comfort-level measures in predicting programming performance. The second contribution of the thesis is an analysis of the use of machine learning (ML) algorithms to predict performance and is a first attempt to investigate the effectiveness of a variety of ML algorithms to predict introductory programming performance. The ML models built as part of this research are the most effective models so far developed. The models are effective even when students have just commenced a programming module. Consequently, timely interventions can be put in place to prevent struggling students from failing. The third contribution of the thesis is the recommendation of an algorithm, based on detailed statistical analysis that should be used by the computer science education community to predict the likely performance of incoming students. Optimisations were carried out to investigate if prediction accuracy could be further increased and an ensemble algorithm, StackingC, was shown to improve prediction performance. The factors identified in this thesis and the associated machine learning models provide a means to predict accurately programming performance when students have only completed preliminary programming concepts. This has not previously been possible

The Impact of Teacher Self-Efficacy on Methodology and the Use of Graphing Technology in Teaching Factoring Quadratic Functions: Perspectives of International Introductory Algebra Teachers

The primary purpose of the study was to determine whether there is a relationship between self-efficacy of international algebra teachers and their level of incorporating technology in teaching factoring quadratic functions to introductory algebra students. The secondary purpose of the study was to examine the influence of self-efficacy on the perspective of international teachers with respect to the methods they use to teach factoring quadratic functions to introductory algebra students. The participants, 54 mathematics educators form 15 countries on five continents, replied to the UVGIA survey instrument. Quantitative analysis of data brought two results. There is a strong positive relationship between the level of self-efficacy of teachers and their level of implementations of technology regardless of country of origin. The second result shows that the level of self-efficacy of math teachers is statistically different in individualistic countries versus collectivistic countries, revealing higher self-efficacy in collectivistic countries. However, their level of implementation of technology is not statistically different. Qualitative analysis of open-ended questions showed teachers’ perspectives on teaching and learning factoring quadratic functions to introductory algebra students. Teachers identify students’ lack of basic mathematical skills, their lack of understanding graphs, difficulties with identifying the purpose, and difficulties factoring when the leading coefficient is different than 1. Teachers recommend incorporating meaningful applications into mathematical methods with real-life contexts, graphs and visualizations, and systematic reviews of background knowledge. They suggest removing automatic procedures in favor of conceptual understanding and eliminating some methods of factoring

Atitudes em relação à aprendizagem estatística: o caso dos estudantes de pós-graduação em linguística aplicada

Researchers, students, and practitioners in the area of English language studies often avoid statistics which may be due to their fear of its numeric nature and high technicality. This paper presents the findings of a study that investigated a small group of postgraduate students’ attitudes toward learning statistics. The respondents were 20 postgraduate students from the language faculty of a public university in Malaysia. Schau et al.’s (1995) Survey of Attitudes toward Statistics was used to elicit the data. The quantitative findings indicated the majority of students had moderately positive attitudes toward learning statistics. These findings also indicated how a two-day workshop could significantly improve these postgraduate students' attitude toward learning statistics. The qualitative results revealed that the students regarded statistics workshops as highly necessary and could make their results more presentable and credited. The results highlight the necessity of more and better statistics courses and workshops for students in similar areas.Investigadores, estudiantes y profesionales en el área de estudios del idioma inglés a menudo evitan las estadísticas que pueden deberse a su temor a su naturaleza numérica y su alto nivel técnico. Este artículo presenta los hallazgos de un estudio que investigó las actitudes de un pequeño grupo de estudiantes de postgrado hacia las estadísticas de aprendizaje. Los encuestados fueron 20 estudiantes de postgrado de la facultad de idiomas de una universidad pública en Malasia. Se utilizó la Encuesta de actitudes hacia la estadística de Schau et al. (1995) para obtener los datos. Los hallazgos cuantitativos indicaron que la mayoría de los estudiantes tenían actitudes moderadamente positivas hacia las estadísticas de aprendizaje. Estos hallazgos también indicaron cómo un taller de dos días podría mejorar significativamente la actitud de estos estudiantes de posgrado hacia las estadísticas de aprendizaje. Los resultados cualitativos revelaron que los estudiantes consideraron los talleres de estadísticas como altamente necesarios y que podrían hacer que sus resultados sean más presentables y acreditados. Los resultados resaltan la necesidad de más y mejores cursos de estadística y talleres para estudiantes en áreas similares.Pesquisadores, estudantes e profissionais da área de estudos da língua inglesa freqüentemente evitam estatísticas que podem ser devidas ao medo de sua natureza numérica e alta tecnicalidade. Este artigo apresenta as conclusões de um estudo que investigou um pequeno grupo de atitudes de estudantes de pós- graduação em relação às estatísticas de aprendizagem. Os respondentes foram 20 estudantes de pós- graduação da faculdade de língua de uma universidade pública na Malásia. A Pesquisa de Atitudes em Relação à Estatística de Schau et al. (1995) foi usada para elucidar os dados. Os resultados quantitativos indicaram que a maioria dos estudantes tinha atitudes moderadamente positivas em relação às estatísticas de aprendizagem. Essas descobertas também indicaram como um workshop de dois dias poderia melhorar significativamente a atitude desses estudantes de pós-graduação em relação às estatísticas de aprendizagem. Os resultados qualitativos revelaram que os estudantes consideravam as oficinas de estatística como altamente necessárias e poderiam tornar seus resultados mais apresentáveis e creditados. Os resultados destacam a necessidade de mais e melhores cursos de estatística e oficinas para alunos de áreas afins

Predicting and Improving Performance on Introductory Programming Courses (CS1)

This thesis describes a longitudinal study on factors which predict academic success in introductory programming at undergraduate level, including the development of these factors into a fully automated web based system (which predicts students who are at risk of not succeeding early in the introductory programming module) and interventions to address attrition rates on introductory programming courses (CS1). Numerous studies have developed models for predicting success in CS1, however there is little evidence on their ability to generalise or on their use beyond early investigations. In addition, they are seldom followed up with interventions, after struggling students have been identified. The approach overcomes this by providing a web-based real time system, with a prediction model at its core that has been longitudinally developed and revalidated, with recommendations for interventions which educators could implement to support struggling students that have been identified. This thesis makes five fundamental contributions. The first is a revalidation of a prediction model named PreSS. The second contribution is the development of a web-based, real time implementation of the PreSS model, named PreSS#. The third contribution is a large longitudinal, multi-variate, multi-institutional study identifying predictors of performance and analysing machine learning techniques (including deep learning and convolutional neural networks) to further develop the PreSS model. This resulted in a prediction model with approximately 71% accuracy, and over 80% sensitivity, using data from 11 institutions with a sample size of 692 students. The fourth contribution is a study on insights on gender differences in CS1; identifying psychological, background, and performance differences between male and female students to better inform the prediction model and the interventions. The final, fifth contribution, is the development of two interventions that can be implemented early in CS1, once identified by PreSS# to potentially improve student outcomes. The work described in this thesis builds substantially on earlier work, providing valid and reliable insights on gender differences, potential interventions to improve performance and an unsurpassed, generalizable prediction model, developed into a real time web-based system

Exploring student perceptions about the use of visual programming environments, their relation to student learning styles and their impact on student motivation in undergraduate introductory programming modules

My research aims to explore how students perceive the usability and enjoyment of visual/block-based programming environments (VPEs), to what extent their learning styles relate to these perceptions and finally to what extent these tools facilitate student understanding of basic programming constructs and impact their motivation to learn programming