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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.

Technology for training military teams has evolved through a convergence of advances in simulation technology for individual and collective training, methods for analyzing teamwork and designing training solutions, and intelligent tutoring technologies that adapt training to the student, to accelerate learning. A number of factors have slowed this evolution toward intelligent team tutoring systems (ITTS), including the challenges of processing communications data, which are the currency of teamwork, and the paucity of automated and generalizable measures of team work. Several systems fulfill a subset of the features required of an ITTS, namely the use of team training objectives, teamwork models, measures of teamwork, diagnostic capability, instructional strategies, and adaptation of training to team needs. We describe these systems: the Advanced Embedded Training System (AETS), Synthetic Cognition for Operational Team Training (SCOTT), the AWO Trainer, the Benchmarked Experiential System for Training (BEST), and the Cross-Platform Mission Visualization Tool. We close this chapter with recommendations for future research.

This chapter considers the essential elements and processes in designing and building a computer-based tutor to instruct teams. In this chapter, the choices of authoring tools, the instructional context, the goal of the instruction, and the characteristics of the domain were evaluated in terms of their influence on the Intelligent Tutoring System (ITS) design in support of team learning and performance. While each team tutor may be unique in terms of its learning objectives, measures, selections of learning strategies and tutor interventions, there are some identified design decisions that need to be made. Considering the best decision for the specific tutor's design is intended to ease the authoring burden and make computer-based team tutoring more ubiquitous.

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.

This study explores the development trajectory of private tutoring in Bangladesh. Specifically, it illustrates how private tutoring has emerged and expanded in the country's education system and examines how governments have responded on this issue.

The authors adopted a case study methodology for this study. Data were gathered from different secondary sources such as books, official documents, scholarly articles, and newspaper reports. The data analysis process consists of three-phases of activities: data reduction, data display, and conclusion drawing/verification.

Private tutoring historically existed in Bangladesh. It is rapidly expanded in the country's education system – from primary to higher secondary level. Currently, it has reached an industrialization stage, where commercial coaching has become a major type of tutoring. The expansion of private tutoring is the inevitable result of the continued privatization and marketization of education. To bring the private tutoring under a framework, the government has taken some regulatory measures that can be characterized as “laissez-faire.” The existing measures are largely ineffective, as the policy makers followed an “top-down” approach when framing the policies.

This study, to the best of the authors' knowledge, is the first to target the historical aspects of private tutoring in Bangladesh. It has potential to fill out the literature gap on private tutoring by analyzing its policy aspect.

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.

Education can be classified into formal and informal sectors—the first category as a regular schooling system and the latter category as private tutoring. After completing secondary education, students in many countries receive education from private tutoring to get admission into the university. This study examines the effect of private tutoring on university admission and subsequent students' academic achievement at the university level.

Using survey data from Bangladesh as a case study, this study employs a two-stage least squares (2SLS) methodology.

Considering that coaching centers offer services such as private tutoring, this study finds that an informal education for admission greatly helps academic achievement. Students who benefit from informal schooling are more likely to achieve higher grades in subsequent programs.

This study strongly suggests that formal education at the secondary school level is unable to meet the academic expectations that are demanded at the tertiary level. This forces the development of private tutoring, which supports the students from more financially well-off families to perform well at the cost of educational disparity.

The purpose of this paper is to discuss the development and assessment of learning support environments for academic writing that utilize ICT, such as e-learning and online tutoring, in Japanese higher education. First, the authors introduce the design of an e-learning writing program for the Japanese language and assess whether the program is an effective learning support tool for undergraduates. Second, the authors analyze and assess online tutoring support for academic writing and clarify the merits and disadvantages of online and offline tutoring at writing centers, then suggest instructional strategies by analyzing the writing tutoring process.

The authors adopted e-learning goals to assess its effectiveness. The authors asked the participants questions they could answer from five-point scales, conducted a paired t-test, and included a free description-type questionnaire. Then, to assess online tutoring, the 12 students took pre- and post-test questionnaires, after which the authors conducted a Wilcoxon signed rank test. In addition, the authors carried out a Kruskal–Wallis rank sum test in order to confirm differences in satisfaction level and the effectiveness of face-to-face tutoring and online tutoring.

By analyzing the pre- and post-test results, the t-test confirmed that the students found the e-learning system to be effective for nurturing academic literacy. This means the system is appropriate as a support tool for nurturing academic writing, especially writing knowledge and rules, and university must provide a comprehensive learning support environment including e-learning. Next, the authors found no statistically significant difference between face-to-face and online tutoring, although some problems with the writing process remained. So online tutoring has opportunity to promote autonomous learning. The research results make it clear that because of writing centers’ potential and their effectiveness in utilizing ICT tools.

Research findings about academic writing are to improve the tutoring process and writing strategies, such as the use of ICT for academic writing support like e-learning, online tutoring, do not exist. To provide learning opportunity to learners and promote autonomous learning, e-learning and online tutoring are important. For the reasons noted above, it is necessary to provide an alternative writing support environment to students in Japan. Therefore, the authors report on and assess the development of learning support environments for e-learning programs and online tutoring for academic writing at the undergraduate level in Japan.

The purpose of this study is to improve the utilization of walk-in tutors at the University of Dayton.

This paper discusses the application of mixed methods participatory action research with the use of the lean six sigma (LSS) define-measure-analyze-improve-control (DMAIC) methodology combined with discrete event simulation to improve the tutoring processes at the University of Dayton.

The impact of these improvements provided a great balance between tutor utilization and student wait time, and as a result, an increase in pay was negotiated for tutors. Additionally, two schools within the University, Engineering and Business, have seen the value of the tutoring for their students and have provided additional financial support for tutoring services. Specifically, these schools now have dedicated by-appointment tutors for their students.

Incorporating simulation within the LSS methodology provides a “prototype” simulation of the potential solutions prior to changing the process in the real world. This approach can minimize the risk of implementing solutions that are costly or ineffective. The improvement of increased tutor pay helped to demonstrate the university’s commitment to tutors and tutoring. Through continuous improvement efforts, the tutoring department has tripled the number of courses that they support through these enhanced relationships, improved utilization and the overall culture and work environment. The LSS methodology and tools combined with discrete event simulation can be used as a guide for improving other repetitive processes within the university.

Education is increasingly using Intelligent Tutoring Systems (ITS), both for modelling instructional and teaching strategies and for enhancing educational programs. The first part of the paper introduces the basic structure of an ITS as well as common problems being experienced within the ITS community. The second part describes WITNeSS ‐ an original hybrid intelligent system using Fuzzy‐Neural‐GA techniques for optimising the presentation of learning material to a student. The original work in this paper is related to the concept of a “virtual student”. This student model, modelled using fuzzy technologies, will be useful for any ITS, providing it with an optimal learning strategy for fitting the ITS itself to the unique needs of each individual student. In the third part, experiments focus on problems developing a “virtual student” model, which simulates, in a rudimentary way, human learning behaviour. Part four finishes with concluding remarks.