The Team Multiple Errands Test: A Platform to Evaluate Distributed Teams

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

Analysis of ligation and DNA binding by Escherichia coli DNA ligase (LigA).

NAD+-dependent DNA ligases are essential enzymes in bacteria, with the most widely studied of this class of enzymes being LigA from Escherichia coli. NAD+-dependent DNA ligases comprise several discrete structural domains, including a BRCT domain at the C-terminus that is highly-conserved in this group of proteins. The over-expression and purification of various fragments of E. coli LigA allowed the investigation of the different domains in DNA-binding and ligation by this enzyme. Compared to the full-length protein, the deletion of the BRCT domain from LigA reduced in vitro ligation activity by 3-fold and also reduced DNA binding. Using an E. coli strain harbouring a temperature-sensitive mutation of ligA, the over-expression of protein with its BRCT domain deleted enabled growth at the non-permissive temperature. In gel-mobility shift experiments, the isolated BRCT domain bound DNA in a stable manner and to a wider range of DNA molecules compared to full LigA. Thus, the BRCT domain of E. coli LigA can bind DNA, but it is not essential for DNA nick-joining activity in vitro or in vivo

Gender, generation and the experiences of farm dwellers resettled in the Ciskei Bantustan, South Africa, ca 1960–1976

This paper examines the experiences of farm dwellers resettled in rural townships in the Ciskei Bantustan during the decades of the 1960s and 1970s. Drawing on the oral testimonies of elderly residents of Sada and Ilinge townships, the paper shows how gendered and generational inequalities within households were crucial factors shaping individuals' experiences of resettlement from the farms. The paper engages with an older literature that regarded the abolition of labour tenancy and linked resettlement programmes as the final stage of farm tenants' proletarianization. It highlights the problems of this linear narrative, and argues that men and women experienced and understood this process in radically different ways. Male labour migration and the remnants of farm paternalism meant that while resettlement cemented the status of migrant men, for women and non-migrant men this process was characterized by contradiction: on the one hand, escape from the spatial hegemonies of farm paternalism and, on the other, heightened economic exposure

The Development of a Testbed to Assess an Intelligent Tutoring System for Teams

Work has been ongoing to develop an Intelligent Tutoring System (ITS) for teams. As part of this work, we are developing a flexible, scalable, military-based set of collaborative team tasks that can serve as a “testbed” to exercise various aspects of a team ITS architecture. Warfighting teams are a core part of any operation as individual soldiers combine their skill sets and plan, coordinate and act as one entity to accomplish assigned objectives. The team ITS test bed presented in this paper uses simple team tasks to train soldiers on basic functions including observation, target detection, target identification, communication within the team and decision making under stress. The testbed allows for manipulation of various dimensions of tutor feedback, learner work-load, and team size. The testbed enables researchers to systematically evaluate the effectiveness of different types of feedback on militarily-relevant training tasks

Evaluating Distributed Teams with the Team Multiple Errands Test

Modern day teams, whether in the military or civilian workplace, have the ability to achieve goals that are otherwise unobtainable by individuals. The timing and characteristics of feedback that teams receive during training are critical. Though there is a solid foundation of research on optimal feedback, there is limited exploration of what constitutes ideal team feedback including addressing the individual team member versus the whole team and whether that feedback is public (visible to the entire team) or private (visible only to one member of the team). Previous research that studied the effect of feedback on team performance has yielded slightly different conclusions. For example, research focused on the privacy of feedback suggests that public feedback can have a motivational effect that improves performance. The aim of this work is to discover the most effective combination of the target and privacy of feedback. To accomplish this goal a modified version of the Multiple Errands Test (MET) was developed to evaluate the performance of three-member teams, the Team MET (TMET). The MET, normally used for evaluating cognitive processing, requires that specific rules be followed while completing multiple tasks within a time constraint. Participants performed the TMET while coordinating purchases in a virtual mall. In each of four timed shopping sessions, participants received feedback on their performance as an individual and team. Feedback was given in one of four conditions: individual private, team private, individual public, and team public. Task performance and rule errors were measured as dependent variables. Results did not yield a broadly significant effect of feedback condition on team or individual performance. However, the study did demonstrate the validity of the TMET as a platform for assessing a team\u27s ability to perform under heavy cognitive load

Modality and Timing of Team Feedback: Implications for GIFT

This paper discusses considerations relevant to the design of team feedback in intelligent tutoring systems (ITSs). While team tutoring is a goal for the Generalized Intelligent Framework for Tutoring (GIFT), further research must be done to explore the focus, modalities, and timing of feedback for teams. Alt-hough there have been a number of studies on feedback, there are a limited number of studies on feedback for teams. This theoretical paper leverages previous research on ITSs, training, individual feedback, and teamwork models to inform appropriate decisions about the most effective feedback mechanisms for teams. Finally, the implications of team feedback on the design of GIFT are discussed. Teams have the ability to achieve goals that are unobtainable by individuals alone. It is important to implement effective training for teams to support performance effectiveness. An important element of training is feedback. Feedback has the function of guiding or motivating individuals based on their past performance. The purpose of guiding feedback is to direct an individual to a desired behavior. The purpose of motivational feedback is to motivate the individual by mentioning future rewards (Ilgen, Fisher & Taylor, 1979). Although there have been a number of studies on feedback, there are a limited number of studies on feedback for teams. A common theme among these studies is determining whether feedback should be given at an individual or team level (Tindale, 1989). Some studies for teams suggest that team performance is influenced by feedback on an individual level (Berkowitz & Levy, 1956) and some studies suggest that groups outperform individuals when feedback is given to the entire team after each decision is made (Tindale, 1989). The purpose of the current paper is to characterize the range of modalities of feedback, timing of feedback, focus level of feedback, and who should receive feedback (i.e., individual vs. feedback) for teams to assist in the design of feedback for ITSs for teams. Finally, the implications of team feedback on the design of GIFT is discussed

Taxonomy of Teams, Team Tasks, and Tutors

While significant research has been done on teams and teaming (Salas, et al. 2004), less work has been done to characterize teams and team tasks in terms of the feasibility for them to benefit from intelligent tutoring. This theoretical paper begins to describe how the parameters of team structures addressed may affect the ways in which a team can accommodate external guidance. In addition, parameters of team tasks and resulting team tutors are also described. Examples of both team structures and team tasks are provided so that the resulting theoretical framework offers guidance for design decisions during the construction of intelligent tutoring systems (ITSs) for teams and the Generalized Intelligent Framework for Tutoring’s (GIFT) supporting team architecture. ITSs have been successful at improving performance in a wide variety of domains ranging from academic topics such as math (e.g., Koedinger, Anderson, Hadley & Mark, 1997) to work-based tasks such as management of power plants (Faria, Silva, Vale & Marques, 2009). However, there have been few ITSs designed for educating or training teams (Sottilare, Holden, Brawner & Goldberg, 2011). Despite much research on teaming since the 1970s, team performance is widely variable and difficult to predict (Sims & Salas, 2007), and there is a significant need for team-based ITSs. A taxonomy of team tutoring is present-ed (see Figure 29 for top level key elements). This paper describes three taxonomies: teams, team tasks, and relevant tutoring factors. The taxonomies are based on reviewing the teaming literature with a particular focus on the characteristics of each that would influence the design of a team-based intelligent tutoring system. This work leverages the extensive literature review of teaming by Burke et al. (in progress) as well as recent work that has sought to identify those major factors which impact team performance Salas, Shuffler, Thayer, Bedwell & Lazzara (in press). The taxonomies provided below are designed to help guide the design of software architecture to support team ITSs within GIFT. GIFT is a powerful software architecture designed to support a wide spectrum of intelligent tutoring. It supports the traditional components of most ITSs: the learner model, the domain model, the pedagogical model, and the learner interface, but does so generically (Sottilare, Brawner, Goldberg & Holden, 2012; Sottilare, Graesser, Hu & Holden, 2013). Thus, a multitude of learners might manipulate a wide range of user interfaces as they engage with various domains while being taught using 190 a variety of pedagogies. However, the GIFT architecture does not naturally support teams. Team compo-nents are necessary if GIFT is to support team tutoring, but they are not present in the current release. In their 2011 paper, Sottilare et al., the creators of GIFT, describe the challenges of creating team tutors in detail

The Hidden Challenges of Team Tutor Development

This paper describes the unexpected challenges of team tutor development such as the task and logistics. Previously, a research team from Iowa State University (ISU) working with the U.S. Army Research Laboratory (ARL) developed the reconnaissance (Recon) task for simple team tutoring with the Generalized Intelligent Framework for Tutoring (GIFT) (Bonner et al., 2015; Gilbert et al., 2015). Considerations were included for the testing environment such as audio-based team interactions, initialization of the scenario simultaneously, and the inclusion of eyetracking and screen capture technology. Throughout the process of tutor development, several computational challenges have been encountered such as the implementation of team rules, determination of the appropriate amount of feedback, and the use of participants’ behavior history as input to the tutor. Our descriptions of these challenges should forewarn future developers of team tutors. We also suggest enhancements to GIFT to aid this process

The Challenges of Building Intelligent Tutoring Systems for Teams

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

Creating a Team Tutor Using GIFT

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