ACM Transactions on Computing Education最新文献

The overall aim of this article is to stimulate discussion about the activities within CER, and to develop a more thoughtful and explicit perspective on the different types of research activity within CER, and their relationships with each other. While theories may be the most valuable outputs of research to those wishing to apply them, for researchers themselves there are other kinds of contributions important to progress in the field. This is what relates it to the immediate subject of this special journal issue on theory in CER. We adopt as our criterion for value “contribution to knowledge”.

This article’s main contributions are

These are followed by some suggested uses for the categories, and a discussion assessing how the success of the article might be judged.

In this editorial, we introduce the second set of papers for the special issue “Conceptualizing and Using Theory in CER”. These papers focus on meta level discussion on theories in CER, addressing the definition of theories, what theoretical contributions have been developed for CER, how theories have been used, and what other type of contributions there are in the field. The issue also includes guest editors’ own reflections on theory.

Modeling is an integral part of many computing-related disciplines and thus also represents a curricular core component in computing education in tertiary education. Competence models in which modeling is integrated at least to some extent already exist in some of these disciplines. However, for the core component of graphical modeling, a competence model that illuminates the relevant competences in detail is still lacking. Therefore, we develop a competence model for graphical modeling with the aim to make teaching and especially assessments in the field more competence-oriented. This article reports on the first two studies conducted to develop and validate the competence model for graphical modeling. In the first study, the structure of the competence model was developed based on theories and approaches of educational science. Competences relevant for graphical modeling were deductively derived from literature and existing university course descriptions using techniques of qualitative content analysis. The result of the first study is a preliminary competence model. In the second study, the preliminary competence model was reviewed by means of an expert rating in the modeling community. The competence model was revised and refined based on these findings and subsequent expert discussions. The main result of the investigation represents the competence model for graphical modeling, which includes a total of 74 competence facets at different cognitive process levels in the five content areas of “model understanding and interpreting,” “model building and modifying,” “values, attitudes, and beliefs,” “metacognitive knowledge and skills,” and “social-communicative skills.”

Use of theory within a field of research provides the foundation for designing effective research programs and establishing a deeper understanding of the results obtained. This, together with the emergence of domain-specific theory, is often taken as an indicator of the maturity of any research area. This article explores the development and subsequent usage of domain-specific theories and theoretical constructs (TCs) in computing education research (CER). All TCs found in 878 papers published in three major CER publication venues over the period 2005–2020 were identified and assessed to determine the nature and purpose of the constructs found. We focused more closely on areas related to learning, studying, and progression, where our analysis found 80 new TCs that had been developed, based on multiple epistemological perspectives. Several existing frameworks were used to categorize the areas of CER focus in which TCs were found, the methodology by which they were developed, and the nature and purpose of the TCs. A citation analysis was undertaken, with 1,727 citing papers accessed to determine to what extent and in what ways TCs had been used and developed to inform subsequent work, also considering whether these aspects vary according to different focus areas within computing education. We noted which TCs were used most often and least often, and we present several brief case studies that demonstrate progressive development of domain-specific theory. The exploration provides insights into trends in theory development and suggests areas in which further work might be called for. Our findings indicate a general interest in the development of TCs during the period studied, and we show examples of how different approaches to theory development have been used. We present a framework suggesting how strategies for developing new TCs in CER might be structured and discuss the nature of theory development in relation to the field of CER.

The use of established and discipline-specific theories within research and practice is an indication of the maturity of a discipline. With computing education research as a relatively young discipline, there has been recent interest in investigating theories that may prove foundational to work in this area, with discipline-specific theories and many theories from other disciplines emerging as relevant. A challenge for the researcher is to identify and select the theories that provide the best foundation for their work. Learning is a complex and multi-faceted process and, as such, a plethora of theories are potentially applicable to a research problem. Knowing the possible candidate theories and understanding their relationships and potential applicability, both individually or as a community of theories, is important to provide a comprehensive grounding for researchers and practitioners alike.

In this work, we investigate the fundamental connections between learning theories foundational to research and practice in computing education. We build a comprehensive list of 84 learning theories and their source and influential papers, which are the papers that introduce or propagate specific theories within the research community. Using Scopus, ACM Digital Library, and Google Scholar, we identify the papers that cite these learning theories. We subsequently consider all possible pairs of these theories and build the set of papers that cite each pair. On this weighted graph of learning theory connections, we perform a community analysis to identify groups of closely linked learning theories. We find that most of the computing education learning theories are closely linked with a number of broader learning theories, forming a separate cluster of 17 learning theories. We build a taxonomy of theory relationships to identify the depth of connections between learning theories. Of the 294 analysed links, we find deep connections in 32 links. This indicates that while the computing education research community is aware of a large number of learning theories, there is still a need to better understand how learning theories are connected and how they can be used together to benefit computing education research and practice.

In this article, I interrogate the relation between a researcher and the theories that the researcher gets involved with. I use my own trajectory as a computing education researcher as a way to make visible how different conceptions of this relation are shaped through prior encounters with different theories in the human sciences, particularly theories of mind, language, and knowledge. While modernist theories stemming from the Enlightenment that presuppose a disengaged researcher have predominated in CS and CER, theories of mind, language, and knowledge associated with pragmatist and phenomenological philosophical perspectives from the 20th Century challenge these modernist views. Under these newer theoretical perspectives, the researcher is always already involved with theory, even if such theory has withdrawn into the unnoticed background, a background that gives every research study its intelligibility. Recognizing that all researchers are caught in the grip of theory may help in both abandoning theories that no longer serve and staying open to adopt new and emergent theories.

Prior studies in the Computer Science education literature have illustrated that novices make many mistakes in composing SQL queries. Query formulation proves to be difficult for students. Only recently, some headway was made towards understanding why SQL leads to so many mistakes, by uncovering student misconceptions. In this article, we shed new light on SQL misconceptions by analyzing the hypotheses of SQL experts on the causes of student errors. By examining the experts’ perceptions, we draw on their understanding of students’ misconceptions and on their experiences with studying and teaching SQL. For our analysis, we chose the Policy Delphi, a questionnaire instrument specifically designed for gathering opinions and evidence. Through a two-round process, our nineteen participants proposed and voted on underlying causes for SQL errors which resulted in a set of hypotheses per error. Our main contribution to this article is this new set of possible misconceptions. With them, we can design more complete educational approaches to address misconceptions underlying SQL errors made by students, leading to more effective SQL education.

A rich body of empirically grounded results and a solid theory base have often been viewed as signs of a mature discipline. Many disciplines have frequently debated what they should accept as legitimate kinds of theories, the proper roles of theory, and appropriate reference disciplines. Computing education research (CER) in particular has seen a growing number of calls for the development of domain-specific theories for CER, an adaptation of theories from other fields, and engagement with theory-based experimental and predictive research in CER. Many of those calls share the same concerns and aims, yet they use very different vocabulary and lack a consensus over an essential concept: theory.

This article presents sticking points and trouble spots in CER’s theory debates and presents a number of suggestions and ways forward. Firstly, by slightly shifting towards a model-based view of science, CER can avoid centuries of conceptual baggage related to the concept of theory. Secondly, insofar as fields like design, engineering, and social science are considered to be legitimate parts of CER, the role of theory in many CER studies needs to be judged by the criteria of the philosophy of engineering, technology, and social science, not the philosophy of (natural) science. Thirdly, instead of force-fitting elements of ill-suited research paradigms from other disciplines, the philosophy of CER should focus on building a consensus on CER’s own paradigm and describing the field’s relationship with theory in CER’s own terms.