Search for: All records

Elementary grade teachers are often not fully prepared to teach a computing-rich curriculum, and the demand of the digital age to integrate Computational Thinking (CT) into their classrooms has put them at a challenge. Under the larger umbrella, abstraction lies at the heart of CT. Abstraction allows moving between various information levels while targeting complex problems and creating rich design solutions. This study focuses on how one pair of elementary-grade teachers collaborated, using abstraction to solve a maze challenge, helping each other move between different layers of information. The videotaped data of one day of teachers' professional development was analyzed through three dimensions of Community of Practice (CoP). Results suggest that through mutual engagement in pursuing a joint enterprise and their shared repertoire, elementary-grade teachers moved their focus between different levels of abstraction simultaneously and effectively. 

This exploratory research analyzes the video-recorded data of four elementary-grade teachers debugging a school tour activity while utilizing a programable robot, Photon. This summer’s professional development session on computational thinking (CT) integration was four hours long and was focused on debugging as a key CT component. The results indicate that teachers worked collaboratively to debug their way through errors using different strategies, such as step-by-step execution or incremental code development. 

Modern vehicle is considered as a system vulnerable to attacks because it is connected to the outside world via a wireless interface. Although, connectivity provides more convenience and features to the passengers, however, it also becomes a pathway for the attackers targeting in-vehicle networks. Research in vehicle security is getting attention as in-vehicle attacks can impact human life safety as modern vehicle is connected to the outside world. Controller area network (CAN) is used as a legacy protocol for in-vehicle communication, However, CAN suffers from vulnerabilities due to lack of authentication, as the information about sender is missing in CAN message. In this paper, a new CAN intrusion detection system (IDS) is proposed, the CAN messages are converted to temporal graphs and CAN intrusion is detected using machine learning algorithms. Seven graph-based properties are extracted and used as features for detecting intrusions utilizing two machine learning algorithms which are support vector machine (SVM) & k-nearest neighbors (KNN). The performance of the IDS was evaluated over three CAN bus attacks are denial of service (DoS), fuzzy & spoofing attacks on real vehicular CAN bus dataset. The experimental results showed that using graph-based features, an accuracy of 97.92% & 97.99% was achieved using SVM & KNN algorithms respectively, which is better than using traditional machine learning CAN bus features. 

The purpose of this poster is to report on findings from our development efforts. In prior papers, we have reflected on some of the challenges in writing items to measure teachers’ specialized content knowledge (e.g., Orrill et al, 2015). In this paper, we reflect on our analysis of think-aloud interviews to identify what we have learned about the development of PCK items for proportional reasoning.