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

Computing education theories : what are they and how are they used?

In order to mature as a research field, computing education research (CER) seeks to build a better theoretical understanding of how students learn computing concepts and processes. Progress in this area depends on the development of computing-specific theories of learning to complement the general theoretical understanding of learning processes. In this paper we analyze the CER literature in three central publication venues -- ICER, ACM Transactions of Computing Education, and Computer Science Education -- over the period 2005--2015. Our findings identify new theoretical constructs of learning computing that have been published, and the research approaches that have been used in formulating these constructs. We identify 65 novel theoretical constructs in areas such as learning/understanding, learning behaviour/strategies, study choice/orientation, and performance/progression/retention. The most common research methods used to devise new constructs include grounded theory, phenomenography, and various statistical models. We further analyze how a number of these constructs, which arose in computing education, have been used in subsequent research, and present several examples to illustrate how theoretical constructs can guide and enrich further research. We discuss the implications for the whole field

Defining the Competencies, Programming Languages, and Assessments for an Introductory Computer Science Course

The purpose of this study was to define the competencies, programming languages, and assessments for an introductory computer science course at a small private liberal arts university. Three research questions were addressed that involved identifying the competencies, programming languages, and assessments that academic and industry experts in California’s Central Valley felt most important and appropriate for an introduction to computer science course. The Delphi methodology was used to collect data from the two groups of experts with various backgrounds related to computing. The goal was to find consensus among the individual groups to best define aspects that would best comprise an introductory CS0 course for majors and non-majors. The output would be valuable information to be considered by curriculum designers who are developing a new program in software engineering at the institution. The process outlined would also be useful to curriculum designers in other fields and geographic regions who attempt to address their local education needs. Four rounds of surveys were conducted. The groups of experts were combined in the first round to rate the items in the straw models determined from the literature and add additional components when necessary. The academic and industry groupings were separated for the remainder of the study so that a curriculum designer could determine not only the items deemed most important, but also their relative importance among the two distinct groups. The experts selected items in each of the three categories in the second round to reduce the possibilities for subsequent rounds. The groups were then asked to rank the items in each of the three categories for the third round. A fourth round was held as consensus was not reached by either of the groups for any of the categories as determined by Kendall’s W. The academic experts reached consensus on a list of ranked competencies in the final round and showed a high degree of agreement on lists of ranked programming languages and assessments. Kendall’s W, values, however, were just short of the required 0.7 threshold for consensus on these final two items. The industry experts did not reach consensus and showed low agreement on their recommendations for competencies, programming languages, and assessments