Ray S. Perez

Glial Biology in Learning and Cognition

Neurons are exquisitely specialized for rapid electrical transmission of signals, but some properties of glial cells, which do not communicate with electrical impulses, are well suited for participating in complex cognitive functions requiring broad spatial integration and long-term temporal regulation. Astrocytes, microglia, and oligodendrocytes all have biological properties that could influence learning and cognition. Myelination by oligodendrocytes increases conduction velocity, affecting spike timing and oscillations in neuronal activity. Astrocytes can modulate synaptic transmission and may couple multiple neurons and synapses into functional assemblies. Microglia can remove synapses in an activity-dependent manner altering neural networks. Incorporating glia into a bicellular mechanism of nervous system function may help answer long-standing questions concerning the cellular mechanisms of learning and cognition.

Searching for the two sigma advantage: Evaluating algebra intelligent tutors

Abstract This study evaluated 2 off-the-shelf, computer-based, mathematics intelligent-tutoring systems that provide instruction in algebra during a remedial mathematics summer program. The majority of the enrolled high school students failed to pass algebra in the previous semester. Students were randomly assigned in approximately equal proportions to work with the Carnegie Learning Algebra Cognitive Tutor or the ALEKS Algebra Course. Using the tutoring system exclusively, the students completed a 4-h-a-day, 14-day summer school high school algebra class for credit. The results revealed that both tutoring systems produced statistically and practically meaningful learning gains on measures of arithmetic and algebra knowledge.

Prevention of Surgical Skill Decay

The U.S. military medical community spends a great deal of time and resources training its personnel to provide them with the knowledge and skills necessary to perform life-saving tasks, both on the battlefield and at home. However, personnel may fail to retain specialized knowledge and skills if they are not applied during the typical periods of nonuse within the military deployment cycle, and retention of critical knowledge and skills is crucial to the successful care of warfighters. For example, we researched the skill and knowledge loss associated with specialized surgical skills such as those required to perform laparoscopic surgery (LS) procedures. These skills are subject to decay when military surgeons perform combat casualty care during their deployment instead of LS. This article describes our preliminary research identifying critical LS skills, as well as their acquisition and decay rates. It introduces models that identify critical skills related to laparoscopy, and proposes objective metrics for measuring these critical skills. This research will provide insight into best practices for (1) training skills that are durable and resistant to skill decay, (2) assessing these skills over time, and (3) introducing effective refresher training at appropriate intervals to maintain skill proficiency.

The use of knowledge acquisition in instructional design

Abstract Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.

Expertise of ICAI: Development requirements

Abstract The purpose of this paper is to (a) examine the functional characteristics of intelligent computer-assisted instruction (ICAI), (b) propose a structural model that defines expertise requirements for the development of an ICAI system and suggests procedures for integrating the required expertise into a system, and (c) discuss the requirements of a multidisciplinary cooperative effort for its development. After examining four functional components of ICAI systems (expertise module, student-model module, tutorial module, and interface module) and their interrelationships in a typical ICAI model, six intelligent characteristics of ICAI systems are discussed. They are: (a) the generation of instructional presentations, (b) a mixed initiative between the system and student, (c) the ability to model the students learning process, (d) a qualitative decision making function in instructional process, (e) an inferencing function in instructional diagnostic and prescriptive process, and (f) a self-improving function. In the proposed expertise requirement model, we discuss three different kinds of expertise (domain expertise, domain engineering expertise, and instructional expertise) and technical issues required to integrate the different kinds of expertise into a system. Finally, a multidisciplinary cooperative effort for the development of an ICAI system is recommended in a schematic form.

Development and Application of a Multi-Modal Task Analysis to Support Intelligent Tutoring of Complex Skills.

BackgroundContemporary work in the design and development of intelligent training systems employs task analysis (TA) methods for gathering knowledge that is subsequently encoded into task models. These task models form the basis of intelligent interpretation of student performance within education and training systems. Also referred to as expert models, they represent the optimal way(s) of performing a training task. Within Intelligent Tutoring Systems (ITSs), real-time comparison of trainee task performance against the task model drives automated assessment and interactive support (such as immediate feedback) functionality. However, previous task analysis (TA) methods, including various forms of cognitive task analysis (CTA), may not be sufficient to support identification of the detailed design specifications required for the development of an ITS for a complex training task incorporating multiple underlying skill components, as well as multi-modal information presentation, assessment, and feedback modalities. Our current work seeks to develop an ITS for training Robotic Assisted Laparoscopic Surgery (RALS), a complex task domain that requires a coordinated utilization of integrated cognitive, psychomotor, and perceptual skills.ResultsIn this paper, we describe a methodological extension to CTA, referred to as multi-modal task analysis (MMTA) that elicits and captures the nuances of integrated and isolated cognitive, psychomotor, and perceptual skill modalities as they apply to training and performing complex operational tasks. In the current case, we illustrate the application of the MMTA method described here to RALS training tasks. The products of the analysis are quantitatively summarized, and observations from a preliminary qualitative validation are reported.ConclusionsWe find that iterative use of the described MMTA method leads to sufficiently complete and robust task models to support encoding of cognitive, psychomotor, and perceptual skills requisite to training and performance of complex skills within ITS task models.

Advances from the Office of Naval Research STEM Grand Challenge: expanding the boundaries of intelligent tutoring systems

This special issue presents evaluations of four intelligent tutoring systems. These systems were funded under the Office of Naval Research’s STEM Grand Challenge for intelligent tutoring systems. The systems each represent aspects of how ITS can address STEM education or how aspects of multiple systems can be integrated to support STEM education. The selected papers also provide empirical evidence for the effectiveness of each system. The current paper provides an overview of the Office of Naval Research STEM Grand Challenge program, the systems funded under the program, and summaries of the articles within this special issue.

What Is Cognitive Readiness

In this book there are multiple views of cognitive readiness. The purpose of this chapter is to review the various definitions of cognitive readiness. The construct of cognitive readiness will also be briefly compared and contrasted with twenty-first century skills. Cognitive readiness denotes the mental preparation for effective changes in response to altered or unpredictable situations in this fast-changing world. The key constructs in our model are knowledge, skills, and attributes. Knowledge includes domain-specific knowledge for developing cognitive readiness in specific content/process domains as well as prerequisite skills. There are five skills—adaptability, adaptive problem solving, communication, decision making, and situation awareness—and four attributes, i.e., adaptive expertise, creative thinking, metacognition, and teamwork. This chapter will also offer the reader a window into the recent literature regarding these constructs. Several training strategies are proposed to teach cognitive readiness, as well as several strategies to assess cognitive readiness. A feasibility study is presented to show how we investigated the goal of teaching and assessing selected cognitive readiness skills in the context of a Navy simulation. The chapter closes with implications for future research.