Investigating Pre-Service Teachers’ Perceptions of the Virginia Computer Science Standards of Learning: A Qualitative Multiple Case Study

Computer science education is being recognized globally as necessary to better prepare students in all grade levels, K-12, for future success. As a result of this focus on computer science education in the United States and around the world, there is an increased demand for highly qualified teachers with content and pedagogical knowledge to successfully support student learning. As a result, there is a call to include and improve the computer science training offered to pre-service teachers in their educator preparation programs from methods courses to practicum and student teaching experiences. Thus, it is important to understand how pre-service teachers view content, classroom practices, and teaching and learning methodologies and theories to inform teacher educators about best practices for integrating computer science. This multi-case study investigated pre-service teachers’ perceived abilities and intent to integrate the Virginia Computer Science Standards of Learning into future content area instruction, as well as any shifts that occurred in these pre-service teachers’ perceptions as a result of their student teaching experience. Five elementary pre-service teachers enrolled in a teacher preparation program at a large, public research university in the Mid-Atlantic region of the United States comprised the cases in this research study. Data were collected during the participants’ student teaching experience and final semester in their respective programs and was comprised of the following: pre-, mid-, and post-questionnaires, meeting transcriptions (2), semi-structured individual phone interview transcriptions (2), and written/posted exchanges on an online discussion board. Data representing each case were analyzed using a qualitative general inductive approach as outlined by Thomas. A within-case analysis was performed to develop main categories and identify central themes for each case, and a cross-case analysis was then conducted using the NVivo Qualitative Data Analysis Software. The findings revealed similarities and differences across the cases, as well as perceived challenges and benefits to incorporating computer science and the Virginia Computer Science Standards of Learning into future content area lessons as determined by elementary pre-service teachers. Findings from this study can be used to inform and improve pre-service teacher education as well as provide insight to school administrators

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