  Providing adaptive scaffolds to help learners develop self-regulated learning
(SRL) processes has been an important goal for intelligent learning
environments. Adaptive scaffolding is especially important in open-ended
learning environments (OELE), where novice learners often face difficulties in
completing their learning tasks. This paper presents a systematic framework for
adaptive scaffolding in Betty's Brain, a learning-by-teaching OELE for middle
school science, where students construct a causal model to teach a virtual
agent, generically named Betty. We evaluate the adaptive scaffolding framework
and discuss its implications on the development of more effective scaffolds for
SRL in OELEs. We detect key cognitive/metacognitive inflection points, i.e.,
instances where students' behaviors and performance change as they work on
their learning tasks. At such inflection points, Mr. Davis (a mentor agent) or
Betty (the teachable agent) provide conversational feedback, focused on
strategies to help students become productive learners. We conduct a classroom
study with 98 middle schoolers to analyze the impact of adaptive scaffolds on
students' learning behaviors and performance. Adaptive scaffolding produced
mixed results, with some scaffolds (viz., strategic hints that supported
debugging and assessment of causal models) being generally more useful to
students than others (viz., encouragement prompts). We also note differences in
learning behaviors of High and Low performers after receiving scaffolds.
Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's
Brain can be further improved to narrow the gap between High and Low
performers.
