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Intelligent learning environments can be designed to support the development of learners’ cognitive skills, strategies, and metacognitive processes as they work on complex decision-making and problem-solving tasks. However, the complexity of the tasks may impede the progress of novice learners. Providing adaptive feedback to learners who face difficulties requires learner modeling approaches that can identify learners’ proficiencies and the difficulties they face in executing required skills, strategies, and metacognitive processes. This paper discusses a multilevel hierarchical learner modeling scheme that analyzes and captures learners’ cognitive processes and problem-solving strategies along with their performance on assigned tasks in a game-based environment called UrbanSim that requires complex decision making for dealing with counterinsurgency scenarios. As the scenario evolves in a turn-by-turn fashion, UrbanSim evaluates the learners’ moves using a number of performance measures. Our learner modeling scheme interprets the reported performance values by analyzing the learners’ activities captured in log files to derive learners’ proficiencies in associated cognitive skills and strategies, and updates the learner model. We discuss the details of the learner modeling algorithms in this paper, and then demonstrate the effectiveness of our approach by presenting results from a study we conducted at Vanderbilt University.

This paper examines the relationships of various functional elements within a class of instructional technologies called adaptive instructional systems (AISs) which include intelligent tutoring systems (ITSs), intelligent mentors or recommender systems, and intelligent instructional media. AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, and preferences of each individual learner or team in the context of domain learning objectives. Under Project 2247.1, The Institute for Electrical and Electronic Engineers (IEEE) is developing standards and guidance for the modeling of AIS to characterize what is and is not an AIS. This paper was composed to document recommendations and generate discussion about the four models that have been proposed as core to the concept of AISs: learner models, adaptive models, domain models and interface models.

The Generalized Intelligent Framework for Tutoring (GIFT) is a research prototype with three general goals associated with its functions and components: 1) lower the skills and time required to author Intelligent Tutoring Systems (ITSs) in a variety of task domains; 2) provide effective adaptive instruction tailored to the needs of each individual learner or team of learners; and 3) provide tools and methods to evaluate the effectiveness of ITSs and support research to continuously improve instructional best practices. This special issue focuses primarily on the third goal, GIFT as a research testbed. A discussion thread covers each article within this special issue and discusses its actual and potential impact on GIFT as a research tool for AIED. Our primary motivation was to introduce the AIED community to GIFT not just as a research tool, but as an extension of familiar challenges taken on previously by AIED scientists and practitioners. This preface provides a high level overview of the GIFT functions (authoring, instructional delivery and management, and experimentation) and presents its primary design principles. To learn more about GIFT, freely access the software, documentation, and associated technical papers visit www.GIFTtutoring.org .

The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams.

Teams and teamwork are ubiquitous in military and civilian organizations. Their importance to organizational success cannot be overstated. This article describes the relationship and effect of three concepts: Intelligent Tutoring Systems (ITSs), shared mental models, and teamwork. The nexus between these concepts is examined to determine its capability to support adaptive instruction of teams, defined here as collectives of interdependent individuals who must communicate and interact with each other in order to perform assigned tasks and missions. An assumption underlying this examination is that augmenting the mental modeling processes of ITS with the mental models shared by members of interdependent teams will allow the considerable and increasingly research-established capabilities of intelligent tutoring of individuals to be applied in training teams. Specifically, we reviewed the learning and performance literature to identify how shared mental models of cognition could be used to enhance the adaptive instruction of teams. Our goal is to develop a methodology to enhance training and educational options for institutions that provide adaptive team instruction at the point-of-need. Toward this end, we discuss the adaptation of the Generalized Intelligent Framework for Tutoring (GIFT), an open source tutoring architecture, to accommodate team models and states. While this article makes a first step toward defining a process for team tutoring, challenges remain. Team tutors must have the ability to manage uncertainty and the dynamic nature of team interaction and communication in order to make effective and timely decisions that optimize team and team member performance.

This paper examines the components, functions, and interactions of adaptive instructional systems (AISs) as a method to construct a conceptual model for use in the development of IEEE standards. AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, and preferences of each individual learner or team in the context of domain learning objectives. IEEE is exploring standards and best practices for AIS modeling, interoperability, and evaluation under its Project 2247 and affiliated working group. This paper was composed to document the interaction of learners with AISs in the context of a domain of instruction. The goal is to identify key interactions within AISs that drive instructional decisions, and to identify the data and methods required to support those machine-based instructions. In other words, we seek to identify methods to assess learner/team progress toward instructional objectives (e.g., knowledge, acquisition, skill development, performance, retention, and transfer of skills from instruction to operational/working environments. As part of the examination of AIS elements, we review a set of popular AIS architectures as a method of identifying what makes AISs unique from other instructional technologies. We conclude with recommendations for future AIS research and standards development.

This work presents a study that explores the differences between authoring Scenario-Based Training (SBT) simulation content using Augmented Reality (AR) and a Desktop interface. Through an iterative design process two interface conditions were developed and then evaluated qualitatively and quantitatively. Our conceptual model is a graph based visualization that is presented to help designers understand the scenario learning artifacts and relationships. Our major contribution relies on the comparison made between the two authoring tools (AR, Desktop) with the same capabilities. Results show that no significant difference was found in time taken to complete tasks nor on the perceived usability of the systems. However, as expected the Desktop interface was perceived as more efficient. Based on these findings, insights on future directions for building AR immersive authoring tools are provided. Refereed/Peer-reviewed

This paper explores design principles and methods to promote interoperability and reuse in a class of instructional technologies called adaptive instructional systems (AISs) which include intelligent tutoring systems (ITSs), intelligent mentors or recommender systems, and intelligent instructional media. AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, and preferences of each individual learner or team in the context of domain learning objectives. IEEE is exploring standards and best practices for AIS modeling, interoperability, and evaluation under its Project 2247 and affiliated working group. This paper was composed to document recommendations related to interoperability standards for AISs. The after school market for AISs is large and on the rise in China, the US, and in Europe. The desired level of interoperability for AISs is at the lowest possible level to allow component reuse without impinging upon the intellectual property of the vendor.

GIFT Cloud is the recently released web-based application version of GIFT, an open-source computer-based tutoring architecture that supports authoring, deployment, and evaluation of intelligent tutoring system technologies. This paper presents the GIFT Cloud Authoring Tools, through the lens of usability. Each major element within the authoring tools is described, along with usability design considerations that were made in order to reduce occurrence of error, to organize information, and to support end-user goals. The initial release of GIFT Cloud supports an iterative design approach, informed by user data and feedback, with an overall goal of making tutor authoring practical for subject matter experts without computer programming or instructional design knowledge. As such, lessons learned from this release, as well as plans for future research and usability improvements, are discussed.

An advantage of computer-based instruction is that student entered information can be saved and used throughout learning. Self-reference (tying information to the self) has been shown to have a positive impact on memory and learning. This study evaluates the impact of including self-reference and familiar popular culture names during the assessment phase of adaptive instruction. Participants engaged with a computer-based tutorial about solving logic grid puzzles and were assessed by completing additional puzzles. The assessment puzzles included the participant’s and friends’ names (self-reference), popular culture names, or generic names. Participants in the popular culture condition spent significantly less time solving the standard puzzle than those in the generic condition, with no difference in percentage correct. The inclusion of popular culture names may have facilitated more efficient task performance while maintaining quality of performance. It is envisioned that this strategy can be implemented in computer-based adaptive instruction to improve task efficiency.

This paper discusses the use of adaptive instruction to guide learning by stimulating non-adaptive virtual training environments. Adaptive instruction is sometimes referred to as differentiated instruction and is any learning experience tailored to meet the needs and preferences of an individual learner or team. An intelligent tutoring system (ITS) is the technology which delivers adaptive instruction. Adaptive instructional systems (AISs) use human variability and other learner/team attributes along with instructional conditions to develop/select appropriate strategies (domain-independent policies) and tactics (actions). The goal of adaptive instruction is to optimize learning, performance, retention, and the transfer of skills between the training environment and the work or operational environment where the skills learned during training are to be applied.

Instruction that adapts to individual learner characteristics is often more effective than instruction that treats all learners as the same. A practical approach to making MOOCs adapt to learners may be by integrating frameworks for intelligent tutoring systems (ITSs). Using the Learning Tools Interoperability standard (LTI), we integrated two intelligent tutoring frameworks (GIFT and CTAT) into edX. We describe our initial explorations of four adaptive instructional patterns in the PennX MOOC "Big Data and Education." The work illustrates one route to adaptivity at scale.

This paper discusses the need and methods to develop community-based persona (learner models) to tie together key learner attributes and learning outcomes (e.g., knowledge acquisition) with the goal of facilitating the validation of adaptive instructional strategies and tactics. Adaptive instruction, sometimes referred to as differentiated instruction, is a learning experience tailored to the needs and preferences of each individual learner or team in which strategies (recommendations and plans for action) and tactics (actions by the tutor) are selected with the aim of optimizing learning, performance, retention, and the transfer of skills between the instructional environment (usually provided by an Intelligent Tutoring System or ITS) and the work or operational environment where the skills learned will be applied. Adaptive instructional systems (AISs) use human variability and other learner attributes along with instructional conditions to select appropriate strategies and tactics. This is usually accomplished through the use of machine learning techniques, but large amounts of data are needed to reinforce the learning of these algorithms over time. We propose a method to develop community models more quickly by enabling diverse groups to contribute the results of their experiments and training data in a common instructional domain to a cloud-based model that could be shared by various instructional applications.

This paper describes methods for enhancing the experience application program interface (xAPI) to improve the assessment of domain competency modeling for adaptive instruction. xAPI is an e-learning software specification which allows individual learning experiences and achievements to be amassed in a Learning Record Store (LRS). Adaptive instruction includes tailored training or educational experiences usually delivered and guided by Intelligent Tutoring Systems (ITSs). ITSs can more effectively tailor or adapt instruction when they have more accurate models of the learner's prior knowledge or competency. This paper examines the potential effect of methods to more accurately model learner experiences and domain competency in an LRS. Specifically, we recommend five methods to improve xAPI statements by documenting: 1) achievement types; 2) experience duration; 3) experience source information; 4) domain learning and forgetting; and 5) assessment within learning experiences.

The current paper and presentation provide background on the different uses of intelligent tutoring systems (ITSs) in context of course instruction, discusses specific instructor considerations that are associated with their use, and ways to use ITSs for educational research. Instructor considerations include the time necessary to plan prior to constructing an ITS, the process of constructing ITS lessons for use by students, the method in which students will interact with the ITS, approaches to incorporating ITS use into classes, and the information that instructors would find useful to be output from the ITS. Specifically, the Generalized Intelligent Framework for Tutoring (GIFT), an open-source, domain independent ITS framework will be discussed as an approach to creating adaptive tutoring content for classroom use. GIFT includes straightforward authoring tools for instructors and Subject Matter Experts (SMEs). These authoring tools are powerful, do not require a background in computer science to use, and result in fully adaptive computer-based lessons. Additionally, GIFT provides the flexibility for instructors to bring their pre-generated and already existing instructional material to the system and use it to create ITS lessons. The authoring tools allow the instructor to determine the path of their lesson and the components that their students will experience (i.e. surveys, quizzes, lesson materials, videos). The paper includes details about the development of an instructor dashboard in GIFT, ways for an instructor to use GIFT for educational research, and a discussion of general output information from ITSs that would be relevant to instructors.

Adaptive instruction is computer-based training or education that is tailored to match the difficulty of the content to the states and traits of the learner. Since the individual differences of learners vary widely and contribute greatly to the adaptation decisions by the tutor, adaptive instructional systems (e.g., Intelligent Tutoring Systems – ITSs) need much more content and make many more instructional decisions than non-adaptive instructional systems that instruct all learners only based on their performance level (e.g., low, moderate, high) using identical instructional strategies. Since the authoring of adaptive instruction varies with the complexity of its content and instructional decisions, it is difficult to compare the efficiency of the adaptive instructional authoring tools and methods, and the effort and skill required to use them in the construction of ITSs. This paper puts forth a methodology to assess ITS complexity and operationalize it in an index to enable adaptive instructional scientists to compare authoring tools and methods. The baseline for this initial comparative index is the Generalized Intelligent Framework for Tutoring (GIFT) authoring tools.

The role of affect in learning has received increasing attention from AIED researchers seeking to understand how emotion and cognition interact in learning contexts. The dynamics of affect over time have been explored in a variety of research environments, allowing researchers to determine the extent to which common patterns are captured by hypothesized models. This paper present an analysis of affect dynamics among learners using vMedic, which teaches combat medicine protocols as part of the military training at West Point, the United States Military Academy. In doing so, we seek both to broaden the variety of learning contexts being explored in order better understand differences in these patterns and to test the theoretical predictions on the development of affect over time.