About the CRLT The CRLT has as its mission to promote and support a community of scholars dedicated to research on the design, use, and implementation of technology to improve learning. Three primary themes underlie the work at the Center: • research that contributes to the development of new pedagogical models for continuing professional development in the 21st century; • research on and evaluation of interactive distance learning environments that inform our understanding of student learning; and • research on teaching strategies for using current and emerging technologies to support student interaction, collaboration, and engagement in the issues being studied. This report is one of a series from our on-going research on learning and technology. If you have any questions or comments on this report, or if you would like to find out more about the activities of the CRLT, contact:

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

We explore the effects of interfaces to take notes on problem solving and learning in a scientific discovery domain. In 2 experiments (1 correlational, 1 experimental), participants solved a series of 5 scientific reasoning problems in a computer environment. We provided some participants with access to an online notepad and found 3 main results: (a) Using the notepad helped participants solve the problems more accurately; (b) the benefits of using the notepad persisted after participants had stopped using it; and (c) participants who used the notepad for problem solving and self-explanation learned more, regardless of the type of notepad interface that was provided. Implications for learning systems with online notepads are discussed.

The opinions and positions expressed in this practice guide are the authors ’ and do not necessarily represent the opinions and positions of the Institute of Education Sciences or the U.S. Department of Education. This practice guide should be reviewed and applied according to the specific needs of the educators and education agencies using it and with full realization that it represents only one approach that might be taken, based on the research that was available at the time of publication. This practice guide should be used as a tool to assist in decision-making rather than as a “cookbook.” Any references within the document to specific education products are illustrative and do not imply endorsement of these products to the exclusion of other products that are not referenced. U.S. Department of Education

The goals of this study are to evaluate a relatively novel learning environment, as well as to seek greater understanding of why human tutoring is so effective. This alternative learning environment consists of pairs of students collaboratively observing a videotape of another student being tutored. Comparing this collaboratively observing environment to four other instructional methods – one-on-one human tutoring, observing tutoring individually, collaborating without observing, and studying alone – the results showed that students learned to solve physics problems just as effectively from observing tutoring collaboratively as the tutees who were being tutored individually. We explain the effectiveness of this learning environment by postulating that such a situation encourages learners to become active and constructive observers through interactions with a peer. In essence, collaboratively observing combines the benefit of tutoring with the benefit of collaborating. The learning outcomes of the tutees and the collaborative observers, along with the tutoring dialogues, were used to further evaluate three hypotheses explaining why human tutoring is an effective learning method. Detailed analyses of the protocols at several grain sizes suggest that tutoring is effective when tutees are independently or jointly constructing knowledge with the tutor, but not when the tutor independently conveys knowledge.

Prior research has established that peer tutors can benefit academically from their tutoring experiences. However, although tutor learning has been observed across diverse settings, the magnitude of these gains is often underwhelming. In this review, the authors consider how analyses of tutors ’ actual behaviors may help to account for variation in learning outcomes and how typical tutoring behaviors may create or undermine opportunities for learning. The authors examine two tutoring activities that are commonly hypothesized to support tutor learning: explaining and questioning. These activities are hypothesized to support peer tutors ’ learning via reflective knowledge-building, which includes self-monitoring of comprehension, integration of new and prior knowledge, and elaboration and construction of knowledge. The review supports these hypotheses but also finds that peer tutors tend to exhibit a pervasive knowledge-telling bias. Peer tutors, even when trained, focus more on delivering knowledge rather than developing it. As a result, the true potential for tutor learning may rarely be achieved. The review concludes by offering recommendations for how future research can utilize tutoring process data to understand how tutors learn and perhaps develop new training methods.

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.

journal homepage: www.elsevier.com/locate/cedpsych Clickers in college classrooms: Fostering learning with questioning methods

The opinions and positions expressed in this practice guide are the authors ’ and do not necessarily represent the opinions and positions of the Institute of Education Sciences or the U.S. Department of Education. This practice guide should be reviewed and applied according to the specific needs of the educators and education agencies using it and with full realization that it represents only one approach that might be taken, based on the research that was available at the time of publication. This practice guide should be used as a tool to assist in decision-making rather than as a “cookbook.” Any references within the document to specific education products are illustrative and do not imply endorsement of these products to the exclusion of other products that are not referenced. U.S. Department of Education