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This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

The first official test of the AutoTutor teaching machine in this country has been completed by the RAF. Results show that trainees taught by Auto‐Tutors learned as much as those taught in classrooms by human instructors, in just 39 per cent of the time.

Viewing teaching as an industry, one might say that educational television and teaching machines are means of raising the teacher's productivity. Television widens greatly the range of the very able teacher, but it can make no real allowance for variations in pace between fust and slow learners. In the teaching machine, on the other hand, the pace of the machine is the pace of the student using it. It gives individual tuition in a real sense, taking much of the mechanical routine work of teaching from the teacher's hands so that he can spend his valuable time on the more essential aspects of his work. The first machine described here provides a useful and simple means of evaluating the principle of the teaching machine — if not the full possibilities of such a machine as the Autotutor II described overleaf

Programmed learning and teaching machines have been classified traditionally as linear or branching. In the light of recent developments this division is no longer adequate, but it still forms a useful starting point. Techniques will be classified here as linear, branching, and ‘later’, but before considering them separately their common factors should be emphasised.

This chapter examines some of the challenges and emerging strategies for authoring, distributing, managing, and evaluating Intelligent Tutoring Systems (ITSs) to support computer-based adaptive instruction for teams of learners. Several concepts related to team tutoring are defined along with team processes, and fundamental tutoring concepts are provided including a description of the learning effect model (LEM), an exemplar describing interaction between learners and ITSs with the goal of realizing optimal tutor decisions. The challenges noted herein are closely related to the LEM and range from acquisition of learner data, synthesis of individual learner and team state models based on available data, and tutor decisions which center on optimizing strategies (recommendations) and tactics (actions) given the state of the learner, the team, and the conditions under which they are being instructed, the environment. Finally, we end this chapter with recommendations on how to use this book to understand and design effective ITSs for teams.

The aim of this paper is to link two sides of knowledge transfer (obtaining and providing knowledge), represented by the interplay between experts and novices, possibilities of technical support, and individual and organizational outcomes. An heuristic is developed to link up these different aspects and focus on practical application of some of them; the authors seek to answer the following research question: how can the organization support activities that would encourage knowledge transfer between novices and experts?

The authors used interviews, document collection, and observations on‐site to gain insights into knowledge management and e‐learning activities at Lufthansa, a German airline company, beginning in 2004, with the first qualitative investigation, in the form of telephone interviews. Over the following six years, the authors followed up with archival analysis and in 2010 conducted interviews with four experts who are responsible for knowledge management and e‐learning at the group level at Lufthansa. All interviews were recorded, transcribed and coded, then a qualitative content analysis was conducted. The interviews were complemented by several demonstrations of the system during a visit on‐site.

Every person can be simultaneously a novice and an expert in different fields of knowledge. Novices and experts need organizational leeway which allows time for creating “knowledge nuggets” (providing knowledge) and for learning (obtaining knowledge). The Lufthansa example shows that organizational leeway, the convergence of e‐learning and knowledge management in the form of rapid e‐learning, and introduction of knowledge transfer methods that provide opportunities for employees to obtain and provide knowledge, i.e. practice knowledge transfer on the job.

The contribution of this paper is that the authors develop an heuristic, which explains technically supported knowledge transfer processes among novices and experts, and their individual and organizational outcomes. The heuristic helps to classify knowledge transfer processes and their outcomes.

This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs.

Peter Vernon, editor of Visual Education, takes a personal look at the forthcoming INTERNAVEX 70

Educational technology is no more than the application of mechanical devices to the learning process. It has been over‐written and over‐rated; but it still applies that the machinery and software involved are an adjunct to the central educational process and not a place to or a panacea for pedagogical ills.