Perceived Usefulness, Perceived Ease of Use, and User

Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions of these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited hgih convergent, discriminant, and factorial validity. Perceived usefulness was significnatly correlated with both self-reported current usage r = .63, Study 1) and self-predicted future usage r = .85, Study 2). Perceived ease of use was also significantly correlated with current usage r = .45, Study 1) and future usage r = .59, Study 2). In both studies, usefulness had a signficnatly greater correaltion with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.

Perceived usefulness, perceived ease of use, and user acceptance of information technology

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

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Reinforcement Learning: An Introduction

This article addresses the centrality of the self-efficacy mechanism in human agency. Self-per- cepts of efficacy influence thought patterns, actions, and emotional arousal. In causal tests the higher the level of induced self-efficacy, the higher the perfor- mance accomplishments and the lower the emotional arousal. Different lines of research are reviewed, show- ing that the self-efficacy mechanism may have wide explanatory power. Perceived self-efficacy helps to ac- count for such diverse phenomena as changes in coping behavior produced by different modes of influence, level of physiological stress reactions, self-regulation of refractory behavior, resignation and despondency to failure experiences, self-debilitating effects of proxy control and illusory inefficaciousness, achievement strivings, growth of intrinsic interest, and career pur- suits. The influential role of perceived collective effi- cacy in social change is analyzed, as are the social con- ditions conducive to development of collective inefficacy. Psychological theorizing and research tend to cen- ter on issues concerning either acquisition of knowledge or execution of response patterns. As a result the processes governing the interrelation- ship between knowledge and action have been largely neglected (Newell, 1978). Some of the re- cent efforts to bridge this gap have been directed at the biomechanics problem—how efferent com- mands of action plans guide the production of ap- propriate response patterns (Stelmach, 1976,1978). Others have approached the matter in terms of algorithmic knowledge, which furnishes guides for executing action sequences (Greeno, 1973; Newell, 1973). ,

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Self-efficacy mechanism in human agency

New developments in the science of learning science of learning overview mind and brain how experts differ from novices how children learn learning and transfer the learning environment curriculum, instruction and commnity effective teaching - examples in history, mathematics and science teacher learning technology to support learning conclusions from new developments in the science of learning.

How people learn: Brain, mind, experience, and school.

: The aim of the book is to advance the understanding of how humans think. It seeks to do so by putting forth a theory of human problem solving, along with a body of empirical evidence that permits assessment of the theory. (Author)

Human Problem Solving

Human Problem Solving.

Contents: Preface. An Applied Information-Processing Psychology. Part I: Science Base. The Human Information-Processor. Part II: Text-Editing. System and User Variability. An Exercise in Task Analysis. The GOMS Model of Manuscript Editing. Extensions of the GOMS Analysis. Models of Devices for Text Selection. Part III: Engineering Models. The Keystroke-Level Model. The Unit-Task Level of Analysis. Part IV: Extensions and Generalizations. An Exploration into Circuit Design. Cognitive Skill. Applying Psychology to Design Reprise.

The Psychology of Human-Computer Interaction

Introduction The Nature of Theories What Are Unified Theories of Cognition? Is Psychology Ready for Unified Theories? The Task of the Book Foundations of Cognitive Science Behaving Systems Knowledge Systems Representation Machines and Computation Symbols Architectures Intelligence Search and Problem Spaces Preparation and Deliberation Summary Human Cognitive Architecture The Human Is a Symbol System System Levels The Time Scale of Human Action The Biological Band The Neural Circuit Level The Real-Time Constraint on Cognition The Cognitive Band The Level of Simple Operations The First Level of Composed Operations The Intendedly Rational Band Higher Bands: Social, Historical, and Evolutionary Summary Symbolic Processing for Intelligence The Central Architecture for Performance Chunking The Total Cognitive System RI-Soar: Knowledge-Intensive and Knowledge-Lean Operation Designer-Soar: Difficult Intellectual Tasks Soar as an Intelligent System Mapping Soar onto Human Cognition Soar and the Shape of Human Cognition Summary Immediate Behavior The Scientific Role of Immediate-Response Data Methodological Preliminaries Functional Analysis of Immediate Responses The Simplest Response Task (SRI) The Two-Choice Response Task (2CRT) Stimulus-Response Compatibility (SRC) Discussion of the Three Analyses Item Recognition Typing Summary Memory, Learning, and Skill The Memory and Learning Hypothesis of Soar The Soar Qualitative Theory of Learning The Distinction between Episodic and Semantic Memory Data Chunking Skill Acquisition Short-Term Memory (STM) Summary Intendedly Rational Behavior Ciyptarithmetic Syllogisms Sentence Verification Summary Along the Frontiers Language Development The Biological Band The Social Band The Role of Applications How to Move toward Unified Theories of Cognition References Name Index Subject Index

Unified Theories of Cognition

: This paper focuses on a basic substantive problem: the nature of knowledge and representation. There are ample indications that artificial intelligence is in need of substantial work in this area. The paper proposes a theory of the nature of knowledge, namely, that there is another computer system level immediately above the symbol (or program) level. The nature of computer system levels is reviewed, the new level proposed, and its definition is treated in detail. Knowledge itself is the processing medium at this level and the principle of rationality plays a central role. Some consequences of the existence of the knowledge level and some relations to other fields are discussed.

Allen Newell

Papers

Human Problem Solving

Human Problem Solving.

The Psychology of Human-Computer Interaction

Unified Theories of Cognition

The knowledge level