Towards using a physio-cognitive model in tutoring for psychomotor tasks.

We report our exploratory research of psychomotor task training in intelligent tutoring systems (ITSs) that are generally limited to tutoring in the desktop learning environment where the learner acquires cognitively oriented knowledge and skills. It is necessary to support computer-guided training in a psychomotor task domain that is beyond the desktop environment. In this study, we seek to extend the current capability of GIFT (Generalized Intelligent Frame-work for Tutoring) to address these psychomotor task training needs. Our ap-proach is to utilize heterogeneous sensor data to identify physical motions through acceleration data from a smartphone and to monitor respiratory activity through a BioHarness, while interacting with GIFT simultaneously. We also uti-lize a computational model to better understand the learner and domain. We focus on a precision-required psychomotor task (i.e., golf putting) and create a series of courses in GIFT that instruct how to do putting with tactical breathing. We report our implementation of a physio-cognitive model that can account for the process of psychomotor skill development, the GIFT extension, and a pilot study that uses the extension. The physio-cognitive model is based on the ACT-R/Φ architecture to model and predict the process of learning, and how it can be used for improving the fundamental understanding of the domain and learner model. Our study contributes to the use of cognitive modeling with physiological con-straints to support adaptive training of psychomotor tasks in ITSs

Content wizard: Concept-based recommender system for instructors of programming courses

Authoring an adaptive educational system is a complex process that involves allocating a large range of educational content within a fixed sequence of units. In this paper, we describe Content Wizard, a concept-based recommender system for recommending learning materials that meet the instructor's pedagogical goals during the creation of an online programming course. Here, the instructors are asked to provide a set of code examples that jointly re.ect the learning goals that are associated with each course unit. The Wizard is built on top of our course-authoring tool, and it helps to decrease the time instructors spend on the task and to maintain the coherence of the sequential structure of the course. It also provides instructors with additional information to identify content that might be not appropriate for the unit they are creating. We conducted an o.- line study with data collected from an introductory Java course previously taught at the University of Pittsburgh in order to evaluate both the practicality and effectiveness of the system. We found that the proposed recommendation's performance is relatively close to the teacher's expectation in creating a computer-based adaptive course

Designing Adaptive Instruction for Teams: a Meta-Analysis

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

Passively Classifying Student Mood And Performance Within Intelligent Tutors

It has been long recognized that successful human tutors are capable of adapting instruction to mitigate barriers (e.g., withdrawal or frustration) to learning during the one-to-one tutoring process. A significant part of the success of human tutors is based on their perception of student affect (e.g., mood or emotions). To at least match the capabilities of human tutors, computer-based intelligent tutoring system (ITS) will need to perceive student affect and improve performance by selecting more effective instructional strategies (e.g., feedback). To date, ITS have fallen short in realizing this capability. Much of the existing research models the emotions of virtual characters rather than assessing the affective state of the student. Our goal was to determine the context and importance of student mood in an adaptable ITS model. To enhance our existing model, we evaluated procedural reasoning systems used in virtual characters, and reviewed behavioral and physiological sensing methods and predictive models of affect. Our experiment focused on passive capture of behaviors (e.g., mouse movement) during training to predict the student\u27s mood. The idea of mood as a constant during training and predictors of performance are also discussed. © International Forum of Educational Technology & Society (IFETS)

The Effects Of Self-Reference And Context Personalization On Task Performance During Adaptive Instruction

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 computerbased tutorial about solving logic grid puzzles and were assessed by completing additional puzzles. The assessment puzzles included the participant\u27s and friends\u27 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