Opportunities for Adaptive Experiments to Enable Continuous Improvement that Trades-off Instructor and Researcher Incentives

Randomized experimental comparisons of alternative pedagogical strategies could provide useful empirical evidence in instructors' decision-making. However, traditional experiments do not have a clear and simple pathway to using data rapidly to try to increase the chances that students in an experiment get the best conditions. Drawing inspiration from the use of machine learning and experimentation in product development at leading technology companies, we explore how adaptive experimentation might help in continuous course improvement. In adaptive experiments, as different arms/conditions are deployed to students, data is analyzed and used to change the experience for future students. This can be done using machine learning algorithms to identify which actions are more promising for improving student experience or outcomes. This algorithm can then dynamically deploy the most effective conditions to future students, resulting in better support for students' needs. We illustrate the approach with a case study providing a side-by-side comparison of traditional and adaptive experimentation of self-explanation prompts in online homework problems in a CS1 course. This provides a first step in exploring the future of how this methodology can be useful in bridging research and practice in doing continuous improvement

Computing Education Research Compiled: Keyword Trends, Building Blocks, Creators, and Dissemination

The need for organized computing education efforts dates back to the 1950s. Since then, computing education research (CER) has evolved and matured from its early initiatives and separation from mathematics education into a respectable research specialization of its own. In recent years, a number of meta-research papers, reviews, and scientometric studies have built overviews of CER from various perspectives. This paper continues that approach by offering new perspectives on the past and present state of CER: analyses of influential papers throughout the years, of the theoretical backgrounds of CER, of the institutions and authors who create CER, and finally of the top publication venues and their citation practices. The results reveal influential contributions from early curriculum guidelines to rigorous empirical research of today, the prominence of computer programming as a topic of research, evolving patterns of learning-theory usage, the dominance of high-income countries and a cluster of 52 elite institutions, and issues regarding citation practices within the central venues of dissemination.</p

Κατηγοριοποίηση και περιληπτικές αποδόσεις εργασιών συνεδρίων της ACM SIGCSE

Η παρούσα εργασία αφορά στη μελέτη εργασιών οι οποίες παρουσιάστηκαν στο συνέδριο ACM SIGCSE τις χρονιές 2016, 2017 και 2018. Αρχικά, γίνεται μια κατηγοριοποίηση, με βάση τον κύριο τομέα της Εκπαίδευσης της Πληροφορικής τον οποίο αφορά η κάθε εργασία που παρουσιάστηκε στα προαναφερθέντα συνέδρια. Οι κατηγορίες στις οποίες κατατάχθηκαν τα άρθρα είναι οι εξής: • Αξιολόγηση σπουδαστών • Ασφάλεια και προστασία της ιδιωτικής ζωής • Διαδραστικά περιβάλλοντα μάθησης • Διαφορετικότητα των φύλων/ Πολυπολιτισμικότητα • Εκπαίδευση της Μηχανικής Λογισμικού • Εισαγωγή στην Πληροφορική • Εκπαίδευση της Πληροφορικής • Ενσωμάτωση Πληροφορίας • Ηλεκτρονική μάθηση • Οπτικοποίηση • Πρότυπα αναλυτικά προγράμματα • Πρωτοβάθμια και Δευτεροβάθμια Εκπαίδευση • Συνεργατική Μάθηση • Συστήματα διαχείρισης μάθησης • Υπολογιστική Σκέψη • Υπολογιστικός Αλφαβητισμός Στη συνέχεια, δίνονται περιληπτικές αποδόσεις των εργασιών της χρονιάς 2017 που εμπίπτουν στις παρακάτω επιλεγμένες κατηγορίες: • Αξιολόγηση φοιτητών/μαθητών • Εισαγωγή στην Πληροφορική • Εκπαίδευση της Πληροφορικής • Πρωτοβάθμια και Δευτεροβάθμια Εκπαίδευση • Συνεργατική Μάθηση • Υπολογιστική ΣκέψηThis thesis focuses on the study of papers presented at the ACM SIGCSE conference in the years 2016, 2017 and 2018. Initially, a categorization is defined, based on the main areas of IT education that are included in the aforementioned conferences. The categories in which the articles were classified are: • Student evaluation • Security and Privacy • Interactive learning environments • Gender Diversity / Multiculturalism • Software engineering education • CS1 • Computer Science Education • Integration of Information • E-learning • Visualization • Model curricula • K-12 • Collaborative learning • Computational Thinking • Computing Literacy Afterwards, reviews of the papers of the year 2017 are presented concerning the following categories: • Student evaluation • CS1 • Computer Science Education • K-12 • Collaborative learning • Computational Thinkin

The Design and Evaluation of an Educational Software Development Process for First Year Computing Undergraduates

First year, undergraduate computing students experience a series of well-known challenges when learning how to design and develop software solutions. These challenges, which include a failure to engage effectively with planning solutions prior to implementation ultimately impact upon the students’ competency and their retention beyond the first year of their studies. In the software industry, software development processes systematically guide the development of software solutions through iterations of analysis, design, implementation and testing. Industry-standard processes are, however, unsuitable for novice programmers as they require prior programming knowledge. This study investigates how a researcher-designed educational software development process could be created for novice undergraduate learners, and the impact of this process on their competence in learning how to develop software solutions. Based on an Action Research methodology that ran over three cycles, this research demonstrates how an educational software development methodology (termed FRESH) and its operationalised process (termed CADET which is a concrete implementation of the FRESH methodology), was designed and implemented as an educational tool for enhancing student engagement and competency in software development. Through CADET, students were reframed as software developers who understand the value in planning and developing software solutions, and not as programmers who prematurely try to implement solutions. While there remain opportunities to further enhance the technical sophistication of the process as it is implemented in practice, CADET enabled the software development steps of analysis and design to be explicit elements of developing software solutions, rather than their more typically implicit inclusion in introductory CS courses. The research contributes to the field of computing education by exploring the possibilities of – and by concretely generating – an appropriate scaffolded methodology and process; by illustrating the use of computational thinking and threshold concepts in software development; and by providing a novel evaluation framework (termed AKM-SOLO) to aid in the continuous improvement of educational processes and courses by measuring student learning experiences and competencies