There has been a growing attention to engineering graduates' generic skills competency. Generic skills attainment is included as essential graduate attributes by accreditation bodies such as Accreditation Board for Engineering and Technology (ABET). Generic skills competency is often assessed at program level using indirect measures such as graduate surveys and student interviews. In this work-in-progress, we present the design, collection, and analysis of direct and indirect evidences of engineering students' generic skills learning at course level. In particular, we report an on-going implementation case study in a course for engineering undergraduates. Our study is rooted in the theoretical framework of social epistemic cognition which explains knowledge acquisition in social contexts. We also discuss our preliminary results collected from the online learning community participated by all students in the course (direct evidence) and a survey on the participants' beliefs associated to social epistemic cognition and collaborative learning (indirect evidence).

To stay competitive, plenty of data mining techniques have been introduced to help stores better understand consumers' behaviors. However, these studies are generally confined within the customer transaction data. Actually, another kind of `deep shopping data', e.g. which and why goods receiving much attention are not purchased, offers much more valuable information to boost the product design. Unfortunately, these data are totally ignored in legacy systems. This paper introduces an innovative system, called TagBooth, to detect commodities' motion and further discover customers' behaviors, using COTS RFID devices. We first exploit the motion of tagged commodities by leveraging physical-layer information, like phase and RSS, and then design a comprehensive solution to recognize customers' actions. The system has been tested extensively in the lab environment and used for half a year in real retail store. As a result, TagBooth generally performs well to acquire deep shopping data with high accuracy.