Arto Vihavainen

Extreme apprenticeship method in teaching programming for beginners

Learning a craft like programming is efficient when novices learn from people who already master the craft. In this paper we define Extreme Apprenticeship, an extension to the cognitive apprenticeship model. Our model is based on a set of values and practices that emphasize learning by doing together with continuous feedback as the most efficient means for learning. We show how the method was applied to a CS I programming course. Application of the method resulted in a significant decrease in the dropout rates in comparison with the previous traditionally conducted course instances.

A systematic review of approaches for teaching introductory programming and their influence on success

Decades of effort has been put into decreasing the high failure rates of introductory programming courses. Whilst numerous studies suggest approaches that provide effective means of teaching programming, to date, no study has attempted to quantitatively compare the impact that different approaches have had on the pass rates of programming courses. In this article, we report the results of a systematic review on articles describing introductory programming teaching approaches, and provide an analysis of the effect that various interventions can have on the pass rates of introductory programming courses. A total of 60 pre-intervention and post-intervention pass rates, describing thirteen different teaching approaches were extracted from relevant articles and analyzed. The results showed that on average, teaching interventions can improve programming pass rates by nearly one third when compared to a traditional lecture and lab based approach.

Management, structures and tools to scale up personal advising in large programming courses

We see programming in higher education as a craft that benefits from a direct contact, support and feedback from people who already master it. We have used a method called Extreme Apprenticeship (XA) to support our CS1 education. XA is based on a set of values that emphasize actual programming along with current best practices, coupled tightly with continuous feedback between the advisor and the student. As such, XA means one-on-one advising which requires resources. However, we have not used abundant resources even when scaling up the XA model. Our experiments show that even in relatively large courses (n = 192 and 147), intensive personal advising in CS1 does not necessarily lead to more expensive course organization, even though the number of advisor-evaluated student exercises in a course grew from 252 to 17420. A thorough comparison of learning results and organizational costs between our traditional lecture/exercise-based course model and XA-based course model is presented.

Increasing Adoption of Smart Learning Content for Computer Science Education

Computer science educators are increasingly using interactive learning content to enrich and enhance the pedagogy of their courses. A plethora of such learning content, specifically designed for computer science education, such as visualization, simulation, and web-based environments for learning programming, are now available for various courses. We call such content smart learning content. However, such learning content is seldom used outside its host site despite the benefits it could offer to learners everywhere. In this paper, we investigate the factors that impede dissemination of such content among the wider computer science education community. To accomplish this we surveyed educators, existing tools and recent research literature to identify the current state of the art and analyzed the characteristics of a large number of smart learning content examples along canonical dimensions. In our analysis we focused on examining the technical issues that must be resolved to support finding, integrating and customizing smart learning content in computer science courses. Finally, we propose a new architecture for hosting, integrating and disseminating smart learning content and discuss how it could be implemented based on existing protocols and standards.

Extreme apprenticeship method: key practices and upward scalability

Programming is a craft that can be efficiently learned from people who already master it. Our previous work introduced a teaching method we call Extreme Apprenticeship (XA), an extension to the cognitive apprenticeship model. XA is based on a set of values that emphasize doing and best programming practices, together with continuous feedback between the master and the apprentice. Most importantly, XA is individual instruction that can be applied even in large courses. Our initial experiments (n = 67 and 44) resulted in a significant increase in student achievement level compared to previous courses. In this paper, we reinforce the validity of XA by larger samples (n = 192 and 147) and a different lecturer. The results were similarly successful and show that the application of XA can easily suffer if the core values are not fully adhered to.

Students' Syntactic Mistakes in Writing Seven Different Types of SQL Queries and its Application to Predicting Students' Success

The computing education community has studied extensively the errors of novice programmers. In contrast, little attention has been given to students mistake in writing SQL statements. This paper represents the first large scale quantitative analysis of the students syntactic mistakes in writing different types of SQL queries. Over 160 thousand snapshots of SQL queries were collected from over 2000 students across eight years. We describe the most common types of syntactic errors that students make. We also describe our development of an automatic classifier with an overall accuracy of 0.78 for predicting student performance in writing SQL queries.

How novices tackle their first lines of code in an IDE: analysis of programming session traces

While computing educators have put plenty of effort into researching and developing programming environments that make it easier for students to create their first programs, these tools often have only little resemblance with the tools used in the industry. We report on a study, where students with no previous programming experience started to program directly using an industry strength programming environment. The programming environment was augmented with logging capability that recorded every keystroke and event within the system, which provided a view on how the novices tackle their first lines of code. Our results show that while at first, the students struggle with syntax -- as is typical with learning a new language -- no evidence can be found that suggests that learning to use the programming environment is hard. In a two-week period, the students learned to use the basic features of the programming environment such as specific shortcuts. Although we observed students using copy-paste-programming relatively often, most of the pasted code is from their own previous work. Finally, when considering the compilation errors and error distributions, we hypothesize that the errors are a product of three factors; the exercises, the environment, and the data logging granularity.

Three years of design-based research to reform a software engineering curriculum

Most of the research-oriented computer science departments provide software engineering education. Providing up-to-date software engineering education can be problematic, as practises used in modern software development companies have been developed in the industry and as such do not often reach teachers in university contexts. The danger, and often the unfortunate reality, is that institutions giving education in software engineering end up teaching the subject using outdated practices with technologies no longer in use. In this article we describe a three-year design-based research where the goal has been to design and reform a software engineering subtrack within our bachelor curriculum that would make it possible for the students to have strong up-to-date theoretical and practical skills in software engineering without a need to remove any of the existing theoretical aspects.

Identification of programmers from typing patterns

Being able to identify the user of a computer solely based on their typing patterns can lead to improvements in plagiarism detection, provide new opportunities for authentication, and enable novel guidance methods in tutoring systems. However, at the same time, if such identification is possible, new privacy and ethical concerns arise. In our work, we explore methods for identifying individuals from typing data captured by a programming environment as these individuals are learning to program. We compare the identification accuracy of automatically generated user profiles, ranging from the average amount of time that a user needs between keystrokes to the amount of time that it takes for the user to press specific pairs of keys, digraphs. We also explore the effect of data quantity and different acceptance thresholds on the identification accuracy, and analyze how the accuracy changes when identifying individuals across courses. Our results show that, while the identification accuracy varies depending on data quantity and the method, identification of users based on their programming data is possible. These results indicate that there is potential in using this method, for example, in identification of students taking exams, and that such data has privacy concerns that should be addressed.

Massive increase in eager TAs: experiences from extreme apprenticeship-based CS1

Incorporating students to participate as teaching assistants in our CS1 as early as during their second semester has started a snowball effect, in which more and more students want to be a part of the experience. We allow students to contribute and take responsibility in a context they see as meaningful for teaching. The students-as-teachers approach means that they are mentored by senior teachers in the actual teaching context, which guarantees enough peer and faculty support for students undertaking the task. A significant percentage of our students (ca. 20%) participate as teachers. This has brought us several benefits: (1) new students are welcomed to the learning community by other students as representatives of the institution, not just student organizations, (2) students understand and undertake the responsibility of being a teacher early, and (3) a massive number of eager TAs.