Interest in online education is surging, as dramatized bythe success of Khan Academy and recent Stanford online courses, but the technology for online education isin its infancy. Crowdsourcing mechanisms will likelybe essential in order to reach the full potential of thismedium. This paper sketches some of the challengesand directions we hope HCOMP researchers will address.

Introductory engineering courses within large universities often have annual enrollments which can reach up to a thousand students. It is very challenging to achieve differentiated instruction in classrooms with class sizes and student diversity of such great magnitude. Professors can only assess whether students have mastered a concept by using multiple choice questions, while detailed homework assignments, such as planar truss diagrams, are rarely assigned because professors and teaching assistants would be too overburdened with grading to return assignments with valuable feedback in a timely manner. In this paper, we introduce Mechanix, a sketch-based deployed tutoring system for engineering students enrolled in statics courses. Our system not only allows students to enter planar truss and free body diagrams into the system just as they would with pencil and paper, but our system checks the student's work against a hand-drawn answer entered by the instructor, and then returns immediate and detailed feedback to the student. Students are allowed to correct any errors in their work and resubmit until the entire content is correct and thus all of the objectives are learned. Since Mechanix facilitates the grading and feedback processes, instructors are now able to assign free response questions, increasing teacher's knowledge of student comprehension. Furthermore, the iterative correction process allows students to learn during a test, rather than simply displaying memorized information.

The aims of this workshop are to (1) Draw the attention of the AI community to the research challenges and opportunities in semantic cities. (2) Draw the attention on the multidisciplinary dimension and its impact on semantic cities such as transportation, energy, water management. (3) Identify unique issues of this domain and what new techniques may be needed. As example, since governments and citizens are involved data security and privacy are first-class concerns (4) Promoting more cities to become semantic cities (5) Elaborating a (semantic data) benchmark for testing AI techniques on semantic cities. (6) Provide a platform for sharing best-practices and discussion.

Recently, there has been a growing interest in multiagent path planning (MAPF). Applications include vehicle fleet coordination, computer games, robotics, and various military scenarios. Some researchers have worked at a theoretical level, while others implemented solvers to specific applications. Consequently, similar concepts were developed in different subcommunities, using varying terminology.

Thee technical program of this workshop consists of presentations of recent, high-quality research contributions, which were selected by the workshop's international program committee in a peer review process. Five long papers and three short papers were accepted for presentation. The papers address a variety of topics in the context of personalization and recommender systems such as new techniques for group recommendation; user modeling and recommendation on the social web; automated content analysis for personalization and recommendation and mobile advertising.

Human computation is a relatively new research area that studies how to build intelligent systems that involve human computers, with each of them performing computation (for example, image classification, translation, and protein folding) that leverages human intelligence, but challenges even the most sophisticated AI algorithms that exist today. With the immense growth of the Web, human computation systems can now leverage the abilities of an unprecedented number of Internet users to perform complex computation. Various genres of human computation applications are available today, including games with a purpose (for example, the ESP Game) that generate useful data through gameplay, crowdsourcing marketplaces (for example, Amazon Mechanical Turk) that coordinate workers to perform tasks for monetary rewards, and identity verification systems (for example, reCAPTCHA) that generate useful data through users performing computation for access to online content. Despite the variety of human computation applications, there exist many common core research issues. How can we design mechanisms for querying human computers in such a way that incentivizes or encourages truthful responses?

We have explored with three notions: conceptualization, and contextualization from situated cognition, and psychic reflection from activity theory for identifying activities into a method called the activity states framework (ASF). The purpose of our work is to build an AI system based on ASF for the identification of collaborators activities during situated context, e.g., collaborators are engaged in a tutorial activity. In this paper, we will introduce and propose how Web-mediated collaborative activities can be identified from collaborators communication exchanges by applying the ASF.

Experience using computational perception within the context of art and performance is reported. Four different types of pedagogical projects are presented: a new non-majors introductory computing course, an upper-level course covering computer vision and graphics inan integrated manner, an interactive dance piece, and a peer-led tele-workshop outreach series.

This paper suggests some teaching strategies for Algorithms and AI courses. These courses can have a common goal of teaching complex problem-solving techniques. Based on my experience teaching undergraduates in a small liberal-arts college, the paper offers concrete ideas for working toward this goal. These ideas are supported by relevant studies in cognitive science and education. Together, they provide a plan for structuring lessons and assignments to help student become better problem-solvers.

In this study, we propose a method for learning interactions among different types of objects to devise new plans using these objects. Learning is accomplished by observing a given sequence of events with their timestamps and using spatial information on the initial state of the objects in the environment. We assume that no intermediate state information is available about the states of objects. We have used the Incredible Machine game as a suitable domain for analyzing and learning object interactions. When a knowledge base about relations among objects is provided, interactions to devise new plans are learned to a desired extent. Moreover, using spatial information of objects or temporal information of events makes it feasible to learn the conditional effects of objects on each other. Our analyses show that, integrating spatial and temporal data in a spatio-temporal learning approach gives closer results to that of the knowledge-based approach by providing applicable event models for planning. This is promising because gathering spatio-temporal information does not require great amount of knowledge.