The Evolution of an Automated Reading Strategy Tutor: From the Classroom to a Game-Enhanced Automated System - New Science of Learning - page 294

Table 14.1 Comparisons of training components across self-explanation training techniques

Introduction Demonstration Practice

SERT:one-on-
one

Live direct
lecture

Continuous video and
worksheet

Short-term one-on-one with
expert human tutor

SERT: Group Live direct

lecture

Partitioned video and group
discussion

Short-term peer-to-peer
practice with
summarization

iSTART Animated

vicarious
lecture

Animated vicarious
discussion with
interactive identification

Long-term one-on-one with
animated coach

iSTART-ME Animated

vicarious
lecture

Animated vicarious
discussion with
interactive identification

Long-term one-on-one with
animated coach and
game, and peer-to-peer
game

Table 14.1 shows a summary of the different pedagogical implementations between
versions.
Of course, there are other variations between the versions which are not listed in
Table 14.1 (e.g., see Levinstein, Boonthum, et al., 2007), but the primary pedagogi-
cal changes involved computerization and availability of collaboration. For example,
both the SERT transition from one-on-one to group and the iSTART transition from
original to iSTART-ME consisted of a change from completely individualized train-
ing into a more hybrid environment that included some one-on-one interactions
as well as collaborative learning. The other major transition, from SERT group to
original iSTART, required automating each of the components. The original SERT
materials had to be converted into electronic materials, most of which had to be
re-conceptualized to fit within an animated script while maintaining the effective-
ness of instruction.
This chapter has focused on the evolutionary changes of a training program as
it has transitioned from human-based to computer-based and from individualized to
collaborative. Each of these transitions helped to improve upon previous versions by
addressing specific training limitations found during testing and evaluations. This is
likely to be one of the many development trends in the future as our community
continues to evolve into an era of electronic education.

Acknowledgments This research was supported in part by the Institute for Educational Sciences
(IES R305G020018-02; R305G040046; R305A080589) and National Science Foundation (NSF
REC0241144; IIS-0735682). Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the authors and do not necessarily reflect the views of the
IES or NSF.

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