Agent Technologies Designed to Facilitate Interactive Knowledge Construction

Collaborative Problem Solving: Processing Actions, Time, and Performance

This study is based on one collaborative problem solving task from an international assessment: the Xandar task. It was developed and delivered by the Organization for Economic Co-operation and Development Program for International Student Assessment (OECD PISA) 2015. We have investigated the relationship of problem solving performance with invested time and number of actions in collaborative episodes for the four parts of the Xandar task. The parts require the respondent to collaboratively plan a process for problem solving, implement the process, reach a solution, and evaluate the solution (For a full description, see the Materials and Methods section, "Parts of the Xandar Task.") Examples of an action include posting to a chat log, accessing a shared resource, or conducting a search on a map tool. Actions taken in each part of the task were identified by PISA and recorded in the data set numerically. A confirmatory factor analysis (CFA) model looks at two types of relationship: at the level of latent variables (the factors) and at extra dependencies, which here are direct effects and correlated residuals (independent of the factors). The model, which is well-fitting, has three latent variables: actions (A), times (T), and level of performance (P). Evidence for the uni-dimensionality of performance level is also found in a separate analysis of the binary items. On the whole for the entire task, participants with more activities are less successful and faster, based on the United States data set employed in the analysis. By contrast, successful participants take more time. By task part, the model also investigates relationships between activities, time, and performance level within the parts. This was done because one can expect dependencies within parts of such a complex task. Results indicate some general and some specific relationships within the parts, see the full manuscript for more detail. We conclude with a discussion of what the investigated relationships may reveal. We also describe why such investigations may be important to consider when preparing students for improved skills in collaborative problem solving, considered a key aspect of successful 21st century skills in the workplace and in everyday life in many countries.

Learning by Communicating in Natural Language With Conversational Agents

Learning is facilitated by conversational interactions both with human tutors and with computer agents that simulate human tutoring and ideal pedagogical strategies. In this article, we describe some intelligent tutoring systems (e.g., AutoTutor) in which agents interact with students in natural language while being sensitive to their cognitive and emotional states. These systems include one-on-one tutorial dialogues, conversational trialogues in which two agents (a tutor and a “peer”) interact with a human student, and other conversational ensembles in which agents take on different roles. Tutorial conversations with agents have also been incorporated into educational games. These learning environments have been developed for different populations (elementary through high school students, college students, adults with reading difficulties) and different subjects spanning science, technology, engineering, mathematics, reading, writing, and reasoning. This article identifies some of the conversation patterns that are implemented in the dialogues and trialogues.

Does cognitive remediation for schizophrenia improve functional outcomes?

PURPOSE OF REVIEW

Cognitive deficits are recognized as key determinants of functional outcome in schizophrenia. Cognitive remediation for schizophrenia, which is intended to improve both cognition and functional outcome, has been shown to impact cognition regardless of quality of trial methodology used. However, the impact of cognitive remediation on functional outcomes is more variable. A number of recently published articles specifically address the issues impacting the effectiveness of cognitive remediation at improving psychosocial outcomes.

RECENT FINDINGS

In this review, studies published since 2011 have been summarized, with a particular focus on psychosocial outcomes. Cognitive remediation may include a focus on neurocognition and/or social cognition, but is increasingly integrative, targeting a range of cognitive skills. Psychosocial outcomes include quality of life, employment outcomes, academic functioning, and social functioning.

SUMMARY

The reviewed literature indicates that cognitive remediation is most likely to impact functional outcome when individuals are given opportunities to practice the cognitive skills in real-world settings. By integrating a cognitive remediation program with psychosocial rehabilitation programs, functional outcomes are enhanced. Cognitive remediation programs that do not solely rely on drill and practice, but instead incorporate strategy teaching and methods to address beliefs and motivation, are associated with better psychosocial outcomes.