With the movement in education towards collaborative learning, it is becoming more important that learners be able to work together in groups and teams. Intelligent tutoring systems (ITSs) have been used successfully to teach individuals, but so far only a few ITSs have been used for the purpose of training teams. This is due to the difficulty of creating such systems. An ITS for teams must be able to assess complex interactions between team members (team skills) as well as the way they interact with the system itself (task skills). Assessing team skills can be difficult because they contain social components such as communication and coordination that are not readily quantifiable. This article addresses these difficulties by developing a framework to guide the authoring process for team tutors. The framework is demonstrated using a case study about a particular team tutor that was developed using a military surveillance scenario for teams of two. The Generalized Intelligent Framework for Tutoring (GIFT) software provided the team tutoring infrastructure for this task. A new software architecture required to support the team tutor is described. This theoretical framework and the lessons learned from its implementation offer conceptual scaffolding for future authors of ITSs.

This paper describes the development and evaluation of an Intelligent Team Tutoring System (ITTS) for pairs of learners working collaboratively to monitor an area. In the Surveillance Team Tutor (STT), learners performed a surveillance task in a virtual environment, communicating to track hostile moving soldiers. This collaborative problem solving task required significant communication to achieve the common goal of perfect surveillance. In a pilot evaluation, 16 twoperson teams performed the task within one of three feedback conditions (Individual, Team, or None) across four trials each. The STT used a unique approach to filtering feedback so that teams in both individual and team conditions received a similar amount of feedback. In one performance measure, Team condition participants made fewer errors in one task than those in other conditions, though at a potential cost of mental workload. Feedback condition also significantly affected participants' subjective rating of both their own performance and their teammate's. This ITTS is one of the first automated team tutoring systems that provided real-time feedback during task execution. Recommendations are offered for the design of the optimal team task for future ITTSs that offer tutoring for small teams performing collaborative problem solving.

This paper presents work on the development of a Game-Based Learning (GBL) application's requirements for female middle school students which teaches fundamental concepts of programming. Currently, there are not enough students who desire to pursue Science, Technology, Engineering, and Mathematic (STEM) career fields. Additionally, female are underrepresented in STEM fields, and increased female participation may help partially address this gap. GBL was used to encourage middle school student interest in STEM by allowing them to practice computer science concepts in engaging contexts outside the classroom. The game Sorceress of Seasons was built to teach fundamental programming concepts, and was based on six requirements specifically targeted at female middle school students. The game was tested with 15 middle school-aged students. Playing the game had a positive effect on students' attitudes towards programming, with female students reporting a larger increase in computer science interest than males when compared with their previous attitudes. The results suggest that the game may be successful in increasing interest in STEM in these students. The requirements developed to guide the design of the game played a role in the game's effectiveness, and may be useful when developing an educational tool targeting female STEM interest.

Intelligent Tutoring Systems have been useful for individual instruction and training, but have not been widely created for teams, despite the widespread use of team training and learning in groups. This paper reviews two projects that developed team tutors: the Team Multiple Errands Task (TMET) and the Recon Task developed using the Generalized Intelligent Framework for Tutoring (GIFT). Specifically, this paper 1) analyzes why team tasks have significantly more complexity than an individual task, 2) describes the two team-based platforms for team research, and 3) explores the complexities of team tutor authoring. Results include a recommended process for authoring a team intelligent tutoring system based on our lessons learned that highlights the differences between tutors for individuals and team tutors.

Teams have the ability to achieve goals that are unobtainable by individuals alone. However, there is little agreement on a standard model for researching the performance of distributed teams. Initial pilot results suggest that the Multiple Errands Test (MET), when adapted to a team in a virtual environment, is a platform for evaluating the impact of feedback characteristics. To demonstrate the potential of the Team MET as a platform for future team research in the broader CSCW community, an example study is described in which team members are given feedback in one of four conditions: individual private, team private, individual public, and team public.