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that these features would play similar roles when text is spoken, although no direct
comparisons were located. Furthermore, a personalized voice style produces better
comprehension than a formal style (Mayer et al., 2004) and voice quality plays a
role in that an unaccented human voice produces better comprehension than either
accented or synthesized voices (Atkinson et al., 2005).
In focusing on specific manipulations in vicarious multimedia environments that
support learning processes, we first considered research that isolated specific fea-
tures and then considered related research that required learners to engage in a
variety of overt activities that support those processes. Cox et al. (1999) reported
that observing either written tutorial dialog or written instructions that are combined
with dynamically constructed diagrams increased learning gains when contrasted
with viewing written discourse alone or just watching diagrams being constructed
(see also, Mayer, 2001; Sweller, 1999).
Deep (reasoning) questions (Graesser & Person, 1994) that precede statements
containing course content presented by voice engines were shown to produce learn-
ing gains that significantly exceeded those produced by several other conditions
(Craig et al., 2000). These include combining course content with surface-level
questions, combining it with declaratives or presenting the course content alone
(Driscoll et al., 2003). Deep questions conditions were also shown, surprisingly,
to produce greater learning gains than an intelligent tutoring system (AutoTutor)
that produces consistent learning gains of 1.0-2.1 standard deviation units (Craig
et al., 2006; Gholson & Craig, 2006; Gholson et al., 2009). Finally, recent pre-
liminary research indicates that providing vicarious explanations, analogous to
“self-explanations,” that link current content to previously presented materials
shows some promise of supporting learning gains similar to those produced by deep
questions (Craig, Brittingham et al., 2009).
Several overt activities have been shown to support vicarious learning processes:
these include responding to prompts to self-explain, collaborate, and/or to ask or
answer questions (Chi et al., 2008; Rummel & Spada 2005), as well as permitting
learners to self-pace the flow of input information (Mayer & Chandler, 2001). Both
Rummel and Spada (2005) and Chi et al. (2008) provided support for an important
role played by prompting while observing. Rummel and Spada prompted learners
to self-explain and to collaborate with each other while observing models solving
problems. Chi et al. (2008) prompted learners who observed video tapes of tutoring
sessions to discuss the session and also to self-pace the tapes. The roles played
by some of these overt activities in supporting vicarious learning cannot presently
be specified (cf. Mayer & Chandler, 2001), but vicarious analogs of them may be
readily implemented in multimedia environments.
It does seem clear, though, that both directly incorporated cognitive activities
and overt activities have considerable promise for improving vicarious learn-
ing processes. These vicarious learning processes are fairly easily implemented
into computerized multimedia environments with current off the shelf technol-
ogy. This makes these environments easily incorporated into web-based distance
learning environments and blended classroom environments to provide efficient,
cost-effective, and highly reusable learning alternatives.
