Low retention rates in higher education Information Technology (IT) studies have led to an unmet demand for IT specialists. Therefore, universities need to apply interventions to increase retention rates and provide the labor market with more IT graduates. However, students with different characteristics may need different types of interventions. The current study applies a person-oriented approach and identifies the profiles of first-year IT students in order to design group-specific support. Tintos (1975, 1993) integration model was used as a framework to analyze questionnaire data from 509 first-year IT students in Estonia. The students response profiles were distinguished through latent profile analysis, and the students were divided into four classes based on their responses to questions about academic integration, professional integration and graduation-related self-efficacy. The difference in academic integration was smaller between the classes than the difference in professional integration. Based on the results it was suggested that one class of students need extra courses to increase their academic integration. Two classes need more professional integration (e.g., work practice), which can be achieved in collaboration with IT companies. In addition, one class of IT students seems to need no additional interventions applied by the university to be retained.
The introduction of Computing to the National Curriculum in England has led to a situation where in-service teachers need to develop subject knowledge and pedagogical expertise in computer science, which presents a significant challenge. Professional learning opportunities can support this; these may be most effective when situated in the teachers' own working practices. This paper describes a project to support Computing teachers in developing pedagogical skills by carrying out classroom-based research in their schools. A group of 22 primary (Grades K-5) and secondary (Grades 6-10) teachers from schools across England planned, designed and implemented research projects either individually or in small groups, supported by a team of university colleagues. Inter and intra group progress was shared online and face-to-face within a distributed community of inquiry. Data collection included surveys, video data, and the projects completed by the teachers. The findings from the project are analysed using Clarke and Hollingsworth's Interconnected Model of Teacher Professional Growth (IMTPG), which enables an identification and exploration of teacher change. Results of the analysis demonstrate that the approach can foster ``growth networks" - the construct used within IMTPG to indicate teacher change which is likely to be sustained and fundamental to teachers' understanding. The individual nature of this change indicates that the approach supports personal change related to each teacher's specific situation. Although there is a huge literature on action research as part of teacher professional learning, we believe this to be the first time this has been carried out in the context of computer science education. We conclude by critically reflecting on the lessons that we have learned in leading this project.
We present a methodological improvement to the ``Grade Point Penalty'' technique of detecting discrimination in large data sets by using a logistic model to account for heterogeneity in grading between courses. We then use this improved method to examine the grades of undergraduates for evidence of gender bias in Computer Science (CS) courses at a large university. We find that using a logistic model to simulate student performance across all courses removes course selection effects inherent in recorded Grade Point Averages (GPAs), and creates a much more accurate measure of student academic ability. This new measure greatly reduces the observed gender bias in academic achievement in CS and other STEM courses. We find that gender is not an important factor in academic performance in CS at this institution, and that the impact of gender on course performance for CS students is at most very small (less than 1/20th of a GPA point) and of uncertain sign.