Welcome to a new volume of Computer Science Education! As you peruse the articles in this issue, you may notice a significant change to how their abstracts are formatted. Beginning in volume 29, we are pleased to introduce structured abstracts for all articles published in the journal. The uniform format is meant to emphasize the key elements that are common to empirical articles and review articles alike and to ensure that authors consistently and succinctly communicate about their paper to prospective readers.
For those who may not be familiar with a structured abstract format Computer Science Education provides the following guidance to authors about how to interpret each of these headers:
Background and Context: Describe the problem space you are working in and why the problem you are addressing is relevant and important for the CS Education community. It is helpful to capture details about the unit of analysis under study: who the intended learner is, what content is being learned, and where the learning is taking place.
Objective: Plainly state what you trying to achieve or find out. For an empirical study, this may be formulated somewhat like a hypothesis; for a review article, you will want to capture the main goal of the review.
Method: Introduce the study design and methods you used for this work. For empirical studies, readers should have a good idea of the particular data collection and analysis techniques to be applied in the article. Use specific names for the methods you employ and avoid general descriptors like “statistical” or “qualitative”. Review articles should describe how literature was identified and synthesized. (However, a formal meta-analysis procedure is not necessarily required for a high-quality review article.)
Findings: Briefly state what you found, especially as it pertains to the objective stated earlier.
Implications: Identify 1–2 implications or contributions of this work for the CS Education research community within and/or beyond your specific study context. What do your findings above have to say about work in this field?
We are also excited to introduce two new members of our Editorial Board. Their combined expertise in Computing Education and related fields demonstrates the breadth of topics that have become part of the scope of Computer Science Education.
Erik Barendsen is a Professor of Science Education at Radboud University and a Professor of Computing Education at Open University in the Netherlands. His interests include design-based and context-based teaching and learning in computer science, computational thinking, and its integration into the school curriculum, teachers’ practical knowledge (in particular PCK), STEM education, assessment in computing education, and qualitative methodologies. Erik is involved in design and implementation of computing and digital literacy curricula for primary and secondary education in the Netherlands. He is active in practice-oriented research projects involving schools as project partners and teachers as (PhD) researchers.
Mark Guzdial is a Professor in Computer Science & Engineering and Engineering Education Research at the University of Michigan. He studies how people come to understand computing and how to make that more effective. He was one of the founders of the International Computing Education Research conference. He was one of the leads on the NSF alliance “Expanding Computing Education Pathways” which helped US states improve and broaden their computing education. He invented and has written several books on the “Media Computation” contextualized approach to computing education. With his wife and colleague, Barbara Ericson, he received the 2010 ACM Karl V. Karlstrom Outstanding Educator award. He is an ACM Distinguished Educator and a Fellow of the ACM. His most recent book is Learner-Centered Design of Computing Education: Research on Computing for Everyone (Morgan & Claypool, 2015). He is the recipient of the 2019 ACM SIGCSE Outstanding Contributions to Education award.
The current issue presents four research papers whose common focus is the modeling and assessment of learners’ characteristics.
The first article, Interpersonal Process Recall: A Novel Approach to Illuminating Students’ Software Development Processes, by Moskal and Wass presents a fresh view on software development processes. The approach taken, Interpersonal Process Recall (IPR), was originally developed in the context of counselor training and is applied to novice programmers by Moskal and Wass. In their study, IPR elicits students’ recall of intentions, motivations, and values related to the software development processes and, supported by facilitators, develops a reflective view of these processes. Moskal and Wass’ study analyzes data taken from screen captures and transcripts from individual as well as collaborative recall sessions. Through these means, the authors discover several themes, such as incongruences between planning and actual practice, code commenting being an individual activity, and strategies for debugging. Follow-up reflections reveal that participants not only discovered aspects of their practice they had not been consciously aware of so far but also developed ways to improve these practices.
Chen et al. present a study on the influence of the type of the first programming language on attitude and achievement. Their paper The Effects of First Programming Language on College Students’ Computing Attitude and Achievement: A Comparison of Graphical and Textual Languages examines data from over 10,000 undergraduate students in introductory computer science courses at a large number of U.S. institutions. The study aims at understanding whether or not significant effects arise from the type of the first programming language students had been exposed to. The findings from this study indicate that previous programming experience, irrespective of the type of first programming language, has beneficial effects on both attitude and achievement. While acknowledging that the treatment in the study was only the first programming language and not longitudinal developments, the authors also report on findings that suggest that graphical programming languages offer a better foundation than textual programming languages for a very specific age group, children of age 10 or younger; such effects could not be shown for children whose first contact with programming languages occurred when they were over 10 years old. This study thus serves as an important starting point for more detailed investigations taking into account age at the time of first exposure and longitudinal effects regarding the transition between graphical and textual languages.
In their article Review of Measurements Used in Computing Education Research and Suggestions for Increasing Standardization, Margulieux, Ketenci, and Decker review instruments and common practices used to measure variables in Computing Education. By presenting these findings along with a literature review of papers using the instruments described and a summary of data analysis methods, this paper serves as a valuable reference for researchers who wish to put their instruments and methods used into context. Also, this paper presents a unique source for understanding and adopting common practices in qualitative and quantitative research.
The final article in this issue, Modelling Achievement in Advanced Computer Science: The Role of Learner Characteristics and Perceived Learning Environment by Nasser-Abu Alhija and Levi-Eliyahu, examine a hypothesized structural model for achievement in advanced computer science classes. The authors evaluate questionnaires administered to over 300 Israeli high-school students in Grades 11 and 12. From the questionnaire data, they derive a structural model which then is analyzed using Structural Equation Modeling. Through a reduced version of this model in which only significant effects are incorporated, the authors determine Mathematics achievement, self-efficacy, and perceived classroom learning environment as the most influential variables. This study adds to the body of knowledge on modeling achievement in introductory computer science course by extending the scope to senior high-school students. It also guides those who design interventions by shedding light on the relative importance of factors that influence students’ achievement. Finally, the results from this study reconfirm the influence of learning environment factors such as effective teaching strategies, high teacher expectations, and supportive learning climate.