Academic journal article Human Factors

Observation versus Hands-On Practice of Complex Skills in Dyadic, Triadic, and Tetradic Training-Teams

Academic journal article Human Factors

Observation versus Hands-On Practice of Complex Skills in Dyadic, Triadic, and Tetradic Training-Teams

Article excerpt


This study investigated training-team protocols, which Shebilske, Goettl, and Regian (in press) defined as protocols in which trainees practice a task in teams of two or more in order to achieve the goal of performing the task individually. The purpose of having teammates during practice is so that they can help each other achieve that goal. Shebilske, Regian, Arthur, and Jordan (1992) developed a dyadic training-team protocol that increases efficiency (defined as decreased trainer time and resources) without reducing effectiveness (defined as increased task performance). The present investigation compared efficiency and effectiveness for dyadic, triadic, and tetradic training-team protocols. Bandura's (1986) multiprocess theory of observational learning was employed to predict results and to guide the generalization of our laboratory research to automated instruction in applied settings. Specifically, a boundary condition was considered with respect to motivation, attention, retention, and behavioral production, which, according to the theory, are the four processes that determine observational learning.

The task employed by Shebilske et al. (1992) was a complex video research task called Space Fortress (Donchin, 1989; Gopher, Weil, & Bareket, 1994; Mane & Donchin, 1989). The protocol developed by Shebilske et al. was an active interlocked modeling (AIM) dyad protocol. It requires a trainee to perform each half of a task alternately while cooperating with a teammate who performs the other half. Despite having only half as much hands-on experience during practice sessions, the AIM-dyad trainees performed as well as individual trainees on tests that were performed by each trainee alone under identical conditions. The cut in hands-on practice experience yielded a reduction in machine and trainer time.

The individual control protocol required 120 h of machine and trainer practice time (3 min/trial x 12 trials/lesson x 10 lessons/trainee x 20 individuals) as compared with 60 h for the AIM-dyad protocol (3 min/trial x 12 trials/lesson x 10 lessons/trainee x 10 dyads). Meta-analyses of our laboratory's multiple replications of this 100% increase in training efficiency have yielded high confidence in accepting no difference in training effectiveness (Arthur, Day, Bennett, McNelly, & Jordan, 1997), even though separate studies have not had enough power to accept the null hypothesis of equivalent performance.

The AIM-dyad protocol's efficiency has prompted innovative training-team protocols for Israeli Air Force pilots, U.S. navigators, and Aer Lingus airline pilots. After Gopher et al. (1994) demonstrated transfer of skill from Space Fortress to flight, the Israeli Air Force incorporated the PC-based computer trainer into their fighter pilot training program, and they are considering the possibility of reducing equipment requirements by utilizing an AIM-dyad protocol. The U.S. Air Force navigator-training program also developed a dyadic protocol for PC-based intelligent tutor systems to reduce equipment requirements and to improve training. Similarly, Johnston, Regian, and Shebilske (1995) explained that trainers and trainees at the Irish national airline, Aer Lingus, valued a low-fidelity PC simulator more after Nell Johnston's expansion and implementation of ideas in Shebilske and his colleagues' (1992) article.

These applications stemming from the Space Fortress paradigm and the paradigm itself are forms of automated instruction. By automated instruction we mean any instruction that is delivered on a microprocessor-based system. The term used in this way includes computer-assisted instruction, computer-based training, simulator-based training, computerized part-task training, and intelligent tutoring. The applications in automated instruction create a dialogue resulting in a rapid transition of ideas from researchers to practitioners and from practitioners to researchers (Shebilske, Goettl, et al. …

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