Making, grounded in theories of constructionism, offers learners the opportunity to construct their own knowledge through constructing personally meaningful artifacts, thus making them interested and committed to the process of their own learning. However, making-based learning is challenging to implement owing to the limitations of materials and well-qualified teachers. Online learning is a way for marginalized learners, who often do not have access to quality educational resources such as well-qualified teachers and learning materials, to access well-qualified teachers. With the growth of synchronous e-learning, learners now have opportunities to interact with their teachers and obtain immediate feedback, a process known to be productive for learning. In this paper, we report on a large-scale making-based online intervention whose design leverages the potential of online learning and cheap, easily accessible materials to create a science, technology, engineering and math (STEM) learning pathway for girls from marginalized communities by engaging them in STEM practices. Using evidence from observations and interviews we evaluate the effectiveness of the intervention in creating these learning pathways. We find that while the intervention increases learner interest in STEM practices, thus initiating a pathway to deepening participation, the language used in the intervention, technology, material and teacher factors can become barriers that have the potential to constrain this pathway and further marginalize the students. We discuss implications and potential solutions to these challenges.

In an Internet of Things (IoT) enabled e-health system, smart health devices sense the health data continuously and share the collected data with the neighboring controller device (i.e., personal server) via some wireless communication mechanism (i.e., bluetooth and zigbee), and finally the data is stored on some health server (i.e., a server over the cloud). The health data is then accessible to healthcare service providers (for example, doctors, nursing staff, relatives of patient) for tracking and monitoring of health conditions of the patients for their better treatment at the earliest. In an IoT enabled health system, the smart healthcare devices communicate over public channel, which causes various types of threats and attacks on the ongoing communication. Therefore, we need a powerful privacy-preserving security mechanism to secure the communication happens in an IoT enabled e-health system as the health data is strictly private and confidential. In this paper, we propose a new privacy-preserving access control and key management scheme for the secure communication of IoT enabled e-health system (SPCS-IoTEH). We also conduct informal security analysis of the proposed SPCS-IoTEH to show its robustness against various types of active and passive attacks. The performance of SPCS-IoTEH is also shown to be better than other existing competing schemes.

Unit test cases have become an essential tool to test application code. Several applications make use of SQL queries in order to retrieve or update information from a database. Database queries for these applications are written natively in SQL using JDBC or using ORM frameworks like Hibernate. Unit testing these applications is typically done by loading a fixed dataset and running unit tests. However with fixed datasets, errors in queries may be missed. In this demonstration, we present a system that takes as input a database application program, and generates datasets and unit tests using the datasets to test the correctness of function with queries in the application. Our techniques are based on static program analysis and mutation testing. We consider database applications written in Java using JDBC or Hibernate APIs. The front-end of our system is a plugin to the IntelliJ IDEA IDE. We believe that such a system would be of great value to application developers and testers.