It is well-documented that most students do not have adequate proficiencies in inquiry and metacognition, particularly at deeper levels of comprehension that require explanatory reasoning. The proficiencies are not routinely provided by teachers and normal tutors so it is worthwhile to turn to computer-based learning environments. This article describes some of our recent computer systems that were designed to facilitate explanation-centered learning through strategies of inquiry and metacognition while students learn science and technology content. Point&Query augments hypertext, hypermedia, and other learning environments with question–answer facilities that are under the learner control. AutoTutor and iSTART use animated conversational agents to scaffold strategies of inquiry, metacognition, and explanation construction. AutoTutor coaches students in generating answers to questions that require explanations (e.g., why, what-if, how) by holding a mixed-initiative dialogue in natural language. iSTART models and coaches students in constructing self-explanations and in applying other metacomprehension strategies while reading text. These systems have shown promising results in tests of learning gains and learning strategies. Imagine an active, curious, self-regulated learner who asks

Users of educational hypertext are faced with the challenge of creating meaning both within and between texts. Cohesion is an important factor contributing to whether a reader is able to capture meaning and comprehend text. When readers are required to fill in conceptual gaps in text, comprehension can fail if they do not have sufficient knowledge. Cohesion helps low-knowledge readers to create a more coherent mental representation of the text. However, text that is too cohesive can inhibit active processing, and thus reduce coherence for more knowledgeable readers. Similar patterns have been found for hypertext, which requires readers to create coherence between multiple electronic texts. Domain novices are in greater need of explicit pointers to important links between documents and gain from having less control over system navigation. Domain experts are in less need of scaffolding within the system. We discuss the use of a multimedia reading strategy training program to help low-knowledge readers better understand less cohesive text. Finally, we discuss four principles to guide hypertext development geared toward improving coherence and metacognitive engagement.

It is well documented that most students do not have adequate proficiencies in inquiry and metacognition, particularly at deeper levels of comprehension that require explanatory reasoning. The proficiencies are not routinely provided by teachers and normal tutors so it is worthwhile to turn to computer-based learning environments. This article describes some of our recent computer systems that were designed to facilitate explanation-centered learning through strategies of inquiry and metacognition while students learn science and technology content. Point&Query augments hypertext, hypermedia, and other learning environments with question-answer facilities that are under the learner control. AutoTutor and iSTART use animated conversational agents to scaffold strategies of inquiry, metacognition, and explanation construction. AutoTutor coaches students in generating answers to questions that require explanations (e.g., why, what-if, how) by holding a mixed-initiative dialogue in natural language. iSTART models and coaches students in constructing self-explanations and in applying other metacomprehension strategies while reading text. These systems have shown promising results in tests of learning gains and learning strategies.